Report on Footprint of Passive Control ... - iSCAPE Project · Report on Footprint of Passive Control Systems ––––––––– –––– D3.3 September 2017 This project

Report on Footprint of Passive Control Systems ––––––––– –––– D3.3 September 2017

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 689954.

Ref. Ares(2017)4379004 - 07/09/2017

D3.3 Report on footprint of PCSs

- 1 -

Project Acronym and Name

iSCAPE - Improving the Smart Control of Air Pollution in Europe

Grant Agreement Number

689954

Document Type Report

Document version & WP No.

V. 06 WP3

Document Title Report on Footprint of Passive Control Systems

Main authors Salem S. Gharbia, Abhijith K. V., Aakash C. Rai, Andreas N. Skouloudis, Erika Brattich, Athanasios Votsis, Väinö Nurmi, Carl Fortelius, Kirsti Jylhä, Achim Drebs, Gaia Papini, Francesco Matacchiera, Arianna Valmassoi, Alessio Brunetti, Francesco Barbano, Francesco Pilla, Silvana Di Sabatino, Beatrice Pulvirenti, Prashant Kumar and Guillem Camprodon

Partner in charge University College Dublin (UCD)

Contributing partners University College Dublin (UCD), University of Surrey (UoS), European Commission Joint Research Centre (JRC), University of Bologna (UNIBU), Finnish Meteorological Institute (FMI), Emilia-Romana Protection and Environmental Regional Agency (APRA-ER), Institute for Advanced Architecture of Catalonia (IAAC)

Release date 8/9/2017

The publication reflects the author’s views. The European Commission is not liable for any use that may be made of the information contained therein.


- 2 -

Document Control Page

Short Description This report is the results of the work described in Task 3.2 of the iSCAPE project as an output Deliverable D3.3. The report aims to summarize the iSCAPE intervention evaluation methods, sites description, instruments setup and experimental protocols for the potential of using physical passive controls (low boundary walls), green infrastructure (trees, hedges, green walls and/or roofs) and the utilization of photo-catalytic coatings (in road tiles or walls).

Review status Action Person Date

Quality Check Coordination Team 06/09/2017

Internal Review John Gallagher (TCD) Beatrice Pulvirenti (UNIBU)

31/8/2017

Distribution Public


- 3 -

Revision history

Version Date Modified by Comments

V0.1

20/8/2017

Salem S. Gharbia, Abhijith K. V., Aakash C. Rai, Andreas N. Skouloudis, Erika Brattich, Athanasios Votsis, Väinö Nurmi, Carl Fortelius, Kirsti Jylhä, Achim Drebs, Gaia Papini, Francesco Matacchiera, Arianna Valmassoi, Alessio Brunetti, Francesco Barbano, Francesco Pilla, Silvana Di Sabatino, Beatrice Pulvirenti, Prashant Kumar and Guillem Camprodon

The first draft.

V0.2 30/8/2017 Salem S. Gharbia

Task leader’s edits.

Draft for internal review process.

V0.3 31/8/2017 John Gallagher Received the internal reviewers’ comments.

V0.4 31/8/2017 Salem S. Gharbia Draft after addressing the reviewers’ comments.

V0.5 1/9/2017 Beatrice Pulvirenti Received the internal reviewers’ comments.

V0.6 4/9/2017 Salem S. Gharbia Draft after addressing the reviewers’ comments.

Statement of originality: This deliverable contains original unpublished work except where clearly indicated otherwise. Acknowledgement of previously published material and of the work of others has been made through appropriate citation, quotation or both.


- 4 -

Table of Contents Table of Contents .......................................................................................................................... - 4 - List of Tables ................................................................................................................................. - 6 - List of Figures ............................................................................................................................... - 7 - List of abbreviations ..................................................................................................................... - 9 -

1 Executive Summary ................................................................................................... - 11 - 2 Introduction ................................................................................................................ - 12 - 3 Physical infrastructure .............................................................................................. - 13 -

Recap of WP1 recommendations on LBW ..................................................................... - 13 - Methodology of LBW evaluation (Dublin) ...................................................................... - 14 -

LBW intervention assessment and evaluation ............................................................ - 14 - Site selection criteria for the LBW intervention ........................................................... - 15 - LBW Site description .................................................................................................. - 18 - Instrumentation, data setup and collection ................................................................. - 21 - Experimental protocol ................................................................................................. - 22 -

SWOT analysis of LBW intervention .............................................................................. - 22 -

4 Photocatalytic coatings ............................................................................................. - 25 - Expected efficiency from tests with individual pollutants ........................................... - 25 - Identification of street locations for real applications .................................................. - 26 - Annual climatic characterizations at street level .......................................................... - 27 - Details of local monitoring campaigns .......................................................................... - 30 - Expected efficiency at neighborhood and street level ................................................. - 31 -

5 Green infrastructure design ..................................................................................... - 33 - State of the art for green infrastructure (GI) evaluation at city-scale ......................... - 33 - Methodology for GI evaluation at the city-scale assessment of air pollution reduction

in Guildford .................................................................................................................................. - 35 - Modelling approach .................................................................................................... - 35 -

Modelled domain .............................................................................................................. - 35 - Model inputs and validation ........................................................................................ - 36 - Modelled scenarios for "what if" analysis .................................................................... - 37 -

SWOT analysis for Guildford .......................................................................................... - 38 - GI evaluation at city-scale - Bologna (IT) ....................................................................... - 39 -

Methodology for GI evaluation at city-scale assessment for Bologna (IT) .................. - 43 - SWOT analysis for Bologna ............................................................................................ - 45 - Methodology for GI evaluation at city-scale assessment for Vantaa ......................... - 47 - SWOT analysis for Vantaa .............................................................................................. - 48 -

6 Green infrastructure evaluation at neightbourhood scale ..................................... - 49 - State of the art for GI evaluation at neighbourhood-scale ........................................... - 49 -

Near-road environment ............................................................................................... - 49 - Methodology for GI evaluation for neighborhood-scale assessment for Guildford .. - 51 -

Site description ........................................................................................................... - 51 -


- 5 -

Instrument setup ......................................................................................................... - 53 - Experimental protocol ................................................................................................. - 54 -

Methodology for GI evaluation for neighbourhood-scale assessment for Bologna . - 55 - GI evaluation at neighborhood-scale - Bologna .......................................................... - 55 - Site description ........................................................................................................... - 56 -

1.1.2 Instrument setup ......................................................................................................... - 58 - Experimental protocol ................................................................................................. - 71 -

Methodology for GI evaluation at neighbourhood-scale assessment for Vantaa ..... - 75 - SWOT analysis ................................................................................................................. - 75 -

Guildford ..................................................................................................................... - 75 - Bologna ....................................................................................................................... - 76 - Vantaa ........................................................................................................................ - 77 -

7 References / Bibliography ........................................................................................ - 79 - Appendix (A) Low Boundary Walls Location Selection ................................................ - 87 - Appendix (B) Technical specifications for the instruments ......................................... - 99 -


- 6 -

List of Tables TABLE 1: KEY STUDIES RELATED TO THE USE OF LBWS AS A PASSIVE CONTROL SYSTEM. ........................................ - 17 - TABLE 2: DETAILS OF TWO MONITORING LOCATIONS. .............................................................................................. - 18 - TABLE 3: SWOT ANALYSIS OF THE LBW INTERVENTIONS FOR OBTAINING LOCAL-SCALE AIR QUALITY BENEFITS. ........ - 24 - TABLE 4: SWOT ANALYSIS OF GREEN INFRASTRUCTURAL (GI) INTERVENTIONS FOR OBTAINING CITY-SCALE AIR QUALITY

BENEFITS. .................................................................................................................................................... - 39 - TABLE 5: AREAS OCCUPIED BY GI IN THE AREA OF BOLOGNA METROPOLITAN CITY. .................................................. - 43 - TABLE 6: SWOT ANALYSIS OF THE BOLOGNA INTERVENTION. ................................................................................. - 46 - TABLE 7: SWOT ANALYSIS OF THE VANTAA INTERVENTION. ................................................................................... - 48 - TABLE 8: DETAILS OF SIX MONITORING LOCATIONS. ................................................................................................ - 52 - TABLE 9: DESCRIPTION OF VARIABLES IN CEPTOMETER ACCUPAR LP-80. ............................................................. - 71 - TABLE 10: SWOT ANALYSIS FOR GUILDFORD NEIGHBORHOOD SCALE EVALUATION. ................................................ - 76 - TABLE 11: SWOT ANALYSIS FOR BOLOGNA NEIGHBORHOOD SCALE EVALUATION. ................................................... - 77 - TABLE 12: SWOT ANALYSIS FOR VANTAA NEIGHBORHOOD SCALE EVALUATION. ...................................................... - 78 - TABLE 13: TECHNICAL SPECIFICATION OF GILL R3-50 SONIC ANEMOMETER. ........................................................ - 101 - TABLE 14: TECHNICAL SPECIFICATIONS OF HCS2S3 THERMOHYGROMETER. ........................................................ - 102 - TABLE 15: TECHNICAL SPECIFICATIONS OF NET RADIOMETER CNR4. .................................................................... - 105 - TABLE 16: TECHNICAL SPECIFICATIONS OF VAISALA BAROMETER PTB110. .......................................................... - 107 - TABLE 17: TECHNICAL SPECIFICATION OF LI-COR LI-7500A CO2/H2O ANALYSER. .............................................. - 108 - TABLE 18: TECHNICAL SPECIFICATIONS OF THE T200 NO/NO2/NOX ANALYSER. .................................................. - 109 - TABLE 19: TECHNICAL SPECIFICATIONS OF THE T300 CO ANALYSER. ................................................................... - 110 - TABLE 20: TECHNICAL SPECIFICATIONS OF THE T100 SO2 ANALYSER. ................................................................. - 111 - TABLE 21: TECHNICAL SPECIFICATIONS OF THE THERMO SCIENTIFIC MODEL 49I O3 ANALYSER. ............................ - 113 - TABLE 22: TECHNICAL SPECIFICATIONS OF THE AIRTOXIC CHROMATOTECH BTEX ANALYSER. .............................. - 114 - TABLE 23: TECHNICAL SPECIFICATIONS OF THE FAI SWAM 5A PM10 AND PM2.5 SAMPLER. .................................. - 117 - TABLE 24: TECHNICAL SPECIFICATIONS OF CEPTOMETER MODEL ACCUPAR LP-80. .............................................. - 117 -


- 7 -

List of Figures FIGURE 1:SCHEMATIC REPRESENTATIONS OF THE TWO MONITORING LOCATIONS WITH THE TWO LBW CONFIGURATIONS.

THE ORANGE CIRCLE AND BLACK RING DENOTE MEASUREMENT POINT BEHIND AND IN FRONT OF THE LBW, RESPECTIVELY. ............................................................................................................................................ - 19 -

FIGURE 2: : DETAILS OF THE NASSAU STREET LBW INTERVENTION LOCATION. ........................................................ - 20 - FIGURE 3:DETAILS OF THE PEARSE STREET LBW INTERVENTION LOCATION. ........................................................... - 21 - FIGURE 4: SCHEMATIC DIAGRAM OF THE HETEROGENEOUS PHOTOCATALYSIS PRODUCED BY AN ANATASE TIO2 LAYER.

(BOONEN & BEELDENS, 2014). ..................................................................................................................... - 25 - FIGURE 5: EXPERIMENT TO COMPARE THE PERFORMANCES OF ALL CERAMIC SAMPLES WITH PURETI COATINGS. ..... - 26 - FIGURE 6: AERIAL VIEW OF LAZZARETTO CAMPUS SITE (CREDIT: BING). .................................................................. - 27 - FIGURE 7: THE METEOROLOGICAL SITES IN EMIGLIA ROMAGNA AND THE LOCATION OF PORTA CASTIGLIONE WEATHER STATION WITH RESPECT TO THE LAZZARETTO UNIVERSITY CAMPUS WHERE EXPERIMENTS WILL BE CARRIED OUT. ..... - 28 - FIGURE 8: THREE NEARLY COMPLETE CYCLES OF SOLAR RADIATION NEAR THE LAZZARETTO UNIVERSITY CAMPUS. THE

BLACK LINE REPRESENTS THE ROLLING DAILY TREND LINE. .............................................................................. - 29 - FIGURE 9: THREE NEARLY COMPLETE CYCLES OF ACCUMULATED RAIN. THE BLACK LINE REPRESENTS THE ROLLING DAILY

TREND LINE. ................................................................................................................................................ - 30 - FIGURE 10: THE CORRESPONDING WIND SPEED AND DIRECTION. THE BLACK LINE REPRESENTS THE ROLLING DAILY TREND

LINE. ........................................................................................................................................................... - 30 - FIGURE 11: GRAPHICAL REPRESENTATION OF THE EXPERIMENTAL AREA WHERE THE PHOTOCATALYTIC PAINTS WILL BE

UTILISED. ..................................................................................................................................................... - 31 - FIGURE 12: THE ATTRIBUTION OF CORINAIR EMISSIONS SOURCES FOR THE PROVINCE BOLOGNA FROM 1990 TO 2010

FOR NOX (IN MG). ROAD TRANSPORT IS THE MAIN EMISSION SOURCE FOR THIS POLLUTANT. ............................ - 32 - FIGURE 13: THE ATTRIBUTION OF CORINAIR EMISSIONS SOURCES FOR THE PROVINCE BOLOGNA FROM 1990 TO 2010

FOR NON-METHANE VOCS (IN MG). ROAD TRANSPORT IS NOT THE MAIN SOURCE OF POLLUTION FOR THIS POLLUTANT. ................................................................................................................................................. - 33 -

FIGURE 14: MODELLED DOMAIN OF GUILDFORD BOROUGH ALONG WITH THE MAJOR ROADS AND BUILDINGS. ............. - 36 - FIGURE 15: (A) COMPARISON WITH THE NATIONAL AVERAGE FOR URBAN GREEN AREAS PER CAPITA (M2) IN MAJOR ITALIAN

CITIES; (B) DENSITY OF URBAN GREEN AREAS IN THE MACRO-ZONES OF ITALY; (C) AVAILABILITY (GREEN) AND DENSITY OF URBAN GREEN AREAS (GREY) IN CITIES WITH MORE 200.000 INHABITANT OR METROPOLITAN AREAS (ISTAT, 2011). ........................................................................................................................................... - 40 -

FIGURE 16: BOLOGNA URBAN ECOSYSTEM. MAP OBTAINED BY QUICKBIRD SATELLITE .............................................. - 42 - FIGURE 17: BOLOGNA URBAN ECOSYSTEM. RESULTS OBTAINED BY THE GI CLASSIFICATION. .................................... - 43 - FIGURE 19: SCHEMATIC REPRESENTATIONS OF SIX MONITORING LOCATIONS WITH THE TYPE OF VEGETATION AND ROAD

DETAILS. THE ORANGE CIRCLE AND BLACK RING DENOTE MEASUREMENT POINT BEHIND AND IN FRONT OF THE VEGETATION BARRIER, RESPECTIVELY. .......................................................................................................... - 53 -

FIGURE 20: INSTRUMENTS ARE MOUNTED ON TRIPOD AND KEPT CLOSE TO EACH OTHER DURING INTER-CALIBRATION. IN THE FIGURE, 1) GRIMM AEROSOL SPECTROMETER, 2) PTRAK 8525, 3) QTRAK 7575, 4) MICROAETH AE51, 5) WEATHER STATION KESTREL 4500. ............................................................................................................... - 54 -

FIGURE 21: A) POSITION OF BOLOGNA (YELLOW DOT; 44°29’37’’N, 11°20’19’’E) IN NORTH-EAST ITALY; B) POSITION OF THE TWO STREET CANYONS IN BOLOGNA; C) STREET VIEW OF LAURA BASSI VERATTI STREET WITH TREES; D) STREET VIEW OF MARCONI STREET WITHOUT TREES. ..................................................................................... - 57 -

FIGURE 22: A) LEAF SPECIMEN OF PLATANUS A. B) TRUNK DETAIL OF PLATANUS A. ................................................. - 58 - FIGURE 23: THE THERMALCAM FLIR T620 (A) REAR SECTION (B) FRONT SECTION ................................................. - 59 - FIGURE 24: SCHEMATIC ILLUSTRATION OF THE STEPS USED TO MEASURE A COMPONENT OF WIND VELOCITY THROUGH AN

ULTRASONIC ANEMOMETER. .......................................................................................................................... - 60 - FIGURE 25: GILL R3-50 SONIC ANEMOMETER. ...................................................................................................... - 61 - FIGURE 26: HCS2S3 THERMOHYGROMETER. ........................................................................................................ - 62 - FIGURE 27: CNR4 NET RADIOMETER. ................................................................................................................... - 62 -


- 8 -

FIGURE 28: VAISALA BAROMETER PTB110. .......................................................................................................... - 63 - FIGURE 29: LI-COR LI-7500A CO2/H2O ANALYSER. ........................................................................................... - 64 - FIGURE 30: EXAMPLE OF ARPA-ER MOBILE LABORATORIES. ................................................................................. - 64 - FIGURE 31: (A) PARTS OF THE CEPTOMETER; (B) FRONT VIEW OF THE CEPTOMETER. ............................................... - 69 - FIGURE 32: ARPA-ER MOBILE LABORATORIES IN LAURA BASSI VERATTI ST. (44°29’00.52’’N, 11°22’03.11’’E) AND

MARCONI ST. (44°29’56.21’’N, 11°20’18.56’’E). .......................................................................................... - 72 - FIGURE 32: POSITIONING OF THE SONIC ANEMOMETERS IN LAURA BASSI VERATTI ST. ............................................. - 73 - FIGURE 33: SECOND ANEMOMETER POSITIONED ON BANISTERS OF A BALCONY AT THE SECOND FLOOR OF A 15M BUILDING

IN LAURA BASSI VERATTI ST. AND MARCONI ST. ............................................................................................ - 73 - FIGURE 34: THIRD ANEMOMETER POSITIONED ON THE ROOF OF A 15M BUILDING ABOVE THE STREET CANYON. .......... - 74 -


- 9 -

List of abbreviations ADMS Atmospheric Dispersion Modeling System AROME Applications de la Recherche à l’Opérationnel à Méso-Echelle =

mesoscale applications of research for operational use BTEX benzene, toluene, ethylbenzene, and xylenes CFD Computational Fluid Dynamics CO carbon monoxide CO2 carbon dioxide EC elemental carbon ES Ecosystem Services FIR Far Infrared Radiation GI Green Infrastructure GIS Geographic Information System H2SO4 sulphuric acid HARMONIE Hirlam Aladin Regional/Meso-scale Operational NWP In Europe HIRLAM High Resolution Limited Area Modelling HNO3 nitric acid HO2 peroxy radical ISTAT National Institute of Statistics LAD Leaf Area Density LAI Leaf Area Index LBW Low Boundary Wall NDIR Non-Dispersive InfraRed NO nitric oxide NO2 nitrogen dioxide NOx nitrogen oxides NWP Numerical Weather Prediction O3 ozone OH hydroxyl radical


- 10 -

ONA Optimised Noise-reduction Averaging algorithm- PAN peroxyacyl nitrate PAR Photosynthetically Active Radiation PBL Planetary Boundary Layer PCS Passive Control Systems PM particulate matter PMx all particles with aerodynamic diameter less or equal to x μm PNC Particle number concentration RAD Root Area Density RCP Representative Carbon Pathway SAU Utilized Agricultural Surface SO2 sulphur dioxide SURFEX Surface Externalisée SWOT strengths, weaknesses, opportunities, threats TCI Thermal Comfort Index UFP ultra-fine particles UHI Urban Heat Island VOCs Volatile Organic Compounds WP Work Package WTP Willingness to Pay


- 11 -

1 Executive Summary Due to the serious impacts on public health, it is essential to control air pollution, especially in and around cities where a majority of the world’s population lives and pollution concentrations are typically much higher than in rural areas. Passive control systems (PCSs) are interventions for reducing air pollution, which include low boundary walls, green infrastructure (GI), and photocatalytic coating. In this report, we analyse the footprint and the benefits of implementing PCSs as interventions to reduce personal exposure to air pollution in the built environment, with a specific focus on their application in iSCAPE cities. This report, in addition to discussing the available literature, provides the methodologies for the assessment and evaluation of PCSs interventions. This report summarises the iSCAPE intervention evaluation methods, sites description, instruments setup and experimental protocols for the potential of using physical passive controls (low boundary walls) and green infrastructure (trees, hedges, green walls and/or roofs), and the utilisation of photo-catalytic coatings (in road tiles or walls). This report considers a SWOT (strengths – weaknesses – opportunities – threats) analysis for each type of PCS intervention.


- 12 -

2 Introduction The majority of the population lives in urban areas in both the European Union (72%; European Environment Agency 2015) and indeed globally (54%; United Nations 2014). Air pollution level in many European cities are above permissible limits (Guerreiro et al. 2016; European Environment Agency 2013) and thus is one of the primary environmental health risk ( European Environment Agency 2015). It is important to control air pollution, especially in and around cities where a majority of the world’s population lives (UN, 2015) and the pollution levels are also typically much higher than those in rural areas. Road traffic is the dominant source of air pollutants including particulate matter (PM), nitrogen oxides (NOx) carbon monoxide (CO) and volatile organic compounds (VOCs) (HEI 2010). Air pollutants from traffic are emitted close to a ground level causing elevated pollutant concentrations near highways than urban background concentrations (Karner et al. 2010). These traffic generated emissions contribute to increase air pollution exposure in “on the road”, “near field/road” and “far field” micro environments (Batterman et al. 2014; Batterman 2013). In on-road micro environments, drivers, commuters, pedestrians, and cyclists are exposed to higher level of air pollution. The significant population lives in near-road environments. For example, 45 million people live or work within 100 m from massive traffic ways in the US (EPA 2016) while about 40% of Toronto population lives within 500 m of an expressway or 100 m of a major road (HEI 2010). The majority of these people are low-income or minority residents (Tian et al. 2013; Carrier et al. 2014b). Also, exposure to traffic-related air pollutants to vulnerable children at school escalates concerns over air quality in the near-road region (Carrier et al. 2014a; Kim et al. 2004). Numerous studies have demonstrated the association of adverse health impacts on people living near-road conditions with proximity to the highways. The range of health implications includes exacerbation of asthma (Volk et al. 2011; Evans et al. 2014; Clark et al. 2010), impaired lung function (Laumbach & Kipen 2012), cardiovascular morbidity and mortality (Cahill et al. 2011; Brook et al. 2010; Wilker et al. 2013), adverse birth outcomes (Michelle et al. 2012), and cognitive declines (Volk et al. 2011; HEI 2010). Passive control systems (PCSs) are interventions for reducing air pollution, which include low boundary walls, green infrastructure (GI), and photocatalytic coating (Gallagher et al., 2015; Mo et al., 2009). With GI interventions, several solutions exist such as trees and hedges (Abhijith et al., 2017). This report is the output of task 3.2 of the iSCAPE project which addresses the footprint of PCSs that are discussed in WP1 and their benefits on each iSCAPE city of intervention.


- 13 -

3 Physical infrastructure This section presents the Low Boundary Wall (LBW) intervention to reduce personal exposure from air pollution on a local scale (street scale). In iSCAPE, Dublin city in Ireland has been chosen as the location for examining LBWs in the built environment. LBWs are a type of the physical Passive Control Systems (PCS) and has been shown to effectively impact on air flow and pollutant dispersion in low-rise street canyons. Therefore, Dublin provides multiple locations to examine the implications of the LBW as a type of PCS. This report presents the LBW site location options and selection criteria.

Recap of WP1 recommendations on LBW The LBW intervention has been discussed in detail in deliverable report 1.2 as part of iSCAPE Work Package (WP) 1. The recommendations and conclusions drawn from this report are summarised as follows. LBWs can provide a solution to enhance localized dispersion and improving air quality in distinct street canyons settings. LBWs have many potential drawbacks, as pollutants concentrations can increase in front of the LBWs (similar to noise barriers). Depending on the wind direction, street geometry and position of the LBW, may cause air pollutant concentrations to increase behind the barrier, having the opposite effect to its intention. Since wind direction is variable, an LBW may have a positive effect today and adverse effect tomorrow, which makes the designing process very hard regarding city planning, as a result of this we must be cautious in where these are placed. The summarised important points, recommendations and some guidelines regarding the use of LBWs as physical control systems are presented as following:

• LBWs act as a baffle at street level and increase the distance between the pollutant source and human receptor.

• Both measurements and modelling studies show LBWs as an active physical passive control method.

• Reductions in pollutant concentrations have been reported on the footpaths in most wind conditions when LBWs exist.

• Low wind speeds, wall and canyon geometry, impact the effectiveness of the LBWs to promote dispersion and the development of vortices in street canyons, which transport pollutants to roof level and escape the street canyon.

• Adverse effects on air quality were measured on the leeward footpath from model simulations for perpendicular wind conditions, where the LBWs exist.


- 14 -

• More research is needed to develop guidelines to provide practical instructions for implementing LBWs in a street canyon environment.

• An increase in pollutants concentrations on the road may occur when LBWs are located in a street.

Methodology of LBW evaluation (Dublin)

LBW intervention assessment and evaluation The assessment and evaluation plan of the Dublin LBW intervention is built on the following evaluation methods, which will be implemented as part of iSCAPE WP6:

• Measuring study for the real-world LBW application in Dublin, • CFD modelling study of the street canyon before and after the LWB intervention

supported by a wind tunnel experiment, mainly to calibrate the CFD models. Five studies have been implemented in Dublin to study the effectiveness of using the LBWs as intervention in the built-in environment to reduce the personal exposure of the air pollution, see Table 1 (Gallagher et al., 2012, Gallagher et al., 2013, King et al., 2009, McNabola et al., 2008, McNabola et al., 2009). Table 1 shows that mainly two evaluation methods have been used to assess the LBW intervention, the real-world measuring studies and the CFD modelling. In general, benzene, CO, PM2.5 or NOx are used by the mentioned studies as single pollutants to quantify the impact of LBWs on air quality in urban street canyons. Using LBWs has been investigated first by initial studies, which are implemented along a boardwalk in Dublin, Ireland (McNabola et al., 2008, King et al., 2009). Those studies investigated the influence of a boundary wall constructed between a boardwalk and an adjacent road with three lanes of one-directional traffic in Dublin city centre. McNabola et al. (2008) real-world measuring study concluded that a LBW acted as a baffle, that when located on the outer edge of footpaths or in the centre of the street canyon, altered pollutant dispersion within the street canyon. A follow-on study by King et al. (2009), which was a CFD modelling study based on the McNabola et al. (2008), reported that the effect of the boardwalk on air and noise pollution is that the segregation of human and vehicular traffic increased the distance between the source and the receptor. McNabola et al. (2008) performed an air quality sampling study, which measured reductions of between 35% and 57% in personal pollutant exposure for pedestrians walking along the boardwalk as opposed to the adjacent footpath. Following the field sampling study McNabola et al. (2009) performed a general computational modelling study to model the case and again reductions in personal pollutant exposure of up to 40% and 75% in perpendicular and parallel wind conditions, respectively have been calculated. Gallagher et al. (2012) in a later study found that footpath LBWs models ranged from a 19% to 30% reduction on the leeward footpath and reductions of 26% to 50% on the windward footpath.


- 15 -

Gallagher et al. (2013) took the same case forward and assessed LBWs in a real study and reported reductions in pollutant concentrations of up to 35% to a maximum increase of 25% on the footpaths in varying wind conditions. Regarding the effects of the LBW physical characteristics, King et al. (2009) concluded that the height of the LBW impacted the effectiveness of the barrier on air flow and pollutant concentrations on the footpath based on McNabola et al. (2009) CFD modeling study. Also, McNabola et al. (2009) reported that the location of the LBWs impacted the results for pollutant concentrations. The street layout, limited wind conditions and omission of vehicular turbulence are noted to provide inaccuracies in the results compared to real case conditions (McNabola et al., 2009). The simplification in the emissions models generated errors, which were accounted to be more influential in model results for low wind speeds in the street canyon (Gallagher et al., 2012, Gallagher et al., 2015). A study by Gallagher et al. (2013) adopted a semi-empirical equation for a real LBW case study to calibrate the models and account for factors such as vehicular turbulence, in addition to the fleet composition in the street canyon. The study reported that the omission of vehicular turbulence decreases the street level dispersion. The turbulence effects of LBWs is dependent on site-specific characteristics: street geometry, wind conditions and vehicular turbulence (Gallagher et al., 2013, McNabola et al., 2008).

Site selection criteria for the LBW intervention University College Dublin (UCD), Dublin City Council (DCC) and Trinity College Dublin (TCD) have setup a site selection criteria for implementing the LBW intervention. These selection criteria are as follows:

• A minimum of one location should be a typical street canyon. • The location should be in the street with several traffic lanes i.e. localised source of

pollution. • The road should have heavy traffic patterns and with a footpath. • The location should have the potential to reduce personal exposure to air pollution for

a vulnerable population group. • The already existing barriers (LBWs) should have an advantage on building new

LBWs. • The intervention campaign should be implemented with minimal or no traffic

disruptions. • The location should be safe to step up the instruments. • The existing LBW can be a small barrier, continuous street furniture or continues road

steel railing. • The intervention location should be in Dublin city centre.


- 16 -

• The intervention location should have minimum large vegetation (trees), which may affect the airflow.

• The selected street canyon locations should have narrow (1:1) or regular (2:1) aspect ratio.

• The existing LBW should have an enough length (at least 8 m), which will allow for an effective campaign.

• The location should have easy access to the power supply. • The location should have a safe and easy access to implement the experiments.

After undertaking the Dublin LBW location selection campaign based on the above criteria, a number of locations around the Dublin city have been shortlisted, which can be seen in Appendix (A). Regarding the specific locations available, only a few solid barriers can be considered as barriers for improving localized air quality for pedestrians (no place to walk in most of the existing barriers). However, only three options of urban furniture are continuous enough in this category to examine as LBW. Although all railings seem feasible, some of them are not on a busy street which is not worth exploring.


- 17 -

Reference Evaluation method Location & methods description Findings (McNabola et al., 2008)

Real-world measuring study

Air quality samples were taken along the length of a boardwalk in Dublin city to study whether pedestrians using the boardwalk would have a lower air pollution exposure than those using the adjoining footpath along the road. The same case has been modelled using CFD to provide more understanding.

The results of the study show significant reductions in pedestrian exposure to both traffic derived particulates and hydrocarbons along the boardwalk as opposed to the footpath.

(King et al., 2009)

CFD modelling This study offers a combined analysis of pedestrian exposure to noise and air pollution within a specific urban setting in Dublin, Ireland.

The results show that the boardwalk has reduced pedestrian exposure to air and noise pollution and that further reductions may be achieved by more strict segregation of pedestrian and road traffic in urban areas.

(McNabola et al., 2009)

CFD modelling The impact of low boundary walls on the dispersion of air pollutants in street canyons has been brought forward in this investigation study using CFD modelling, again in Dublin, Ireland.

The results of this study show that a low boundary wall located in the central median of the street canyon creates a significant reduction in pedestrian exposure on the footpath. Reductions of up to 40% were found for perpendicular wind directions and up to 75% for parallel wind directions, relative to the same canyon with no wall.

(Gallagher et al., 2012)

CFD modelling This numerical modelling study assessed the spatial distribution of concentrations of a tracer pollutant in a street canyon as a result of introducing of passive controls in asymmetrical street canyons to reduce personal exposure to air pollutants on footpaths.

The percentage difference in concentrations induced by the presence of footpath LBWs ranged from an increase of up to 19% to a reduction of 30% on the leeward footpath, with reductions between 26% and 50% on the windward footpath with varying H1/H2 ratios.

(Gallagher et al., 2013)

Real-world measuring study & CFD modelling

This study investigates the potential real-world application of passive control systems to reduce personal pollutant exposure in an urban street canyon in Dublin, using both modelling and measurement approaches.

The results indicate that lane distribution, fleet composition and vehicular turbulence all affect pollutant dispersion, in addition to the canyon geometry and local meteorological conditions. The paper suggests that the use of passive controls displayed mixed results for improvements in air quality on the footpaths for different wind and traffic conditions.

Table 1: Key studies related to the use of LBWs as a passive control system.


- 18 -

Therefore, two LBW intervention locations have been found to follow the setup criteria. These locations are described in section 3.2.3.

LBW Site description For the iSCAPE LBW intervention in Dublin, two locations have been considered with two different configurations. The study will evaluate air pollution reduction potentials of these tow settings. Monitoring sites are selected based on the local conditions of the two locations. The two locations are identified near major roads of Dublin city centre and detailed in Table 2 and Figures 2&3. For assessing the impact of LBW distance from the road, we have selected two sites. One of them is close to the traffic (≤1m) and other is away from the road (≥2m). Figure shows a schematic diagram of monitoring sites. These sites are situated in a busy area with more than 2-3 storey buildings on either side of a two lane road. The two sites are located in the heart of the Dublin city center around Trinity College Dublin. The two locations for implementing the LBW interventions can be used to reduce the personal exposure of air pollution of vulnerable school children who pass the city center in their way to the school near the dockland. Also, the two locations are located around Trinity College Dublin (TCD), each of them located near one of the main entrances for TCD so the intervention can help reduce personal exposure to air pollution for pedestrians in and around TCD. Full traffic volume history and direction of roads at each site were secured and available to be used in the simulation study. Dimensions of LBWs, the distance from the edge of the road to monitoring locations, and width of lanes are illustrated in Figure 1. The plan is to quantify the pollutant reduction potentials of the two different LBW configurations by comparing pollutant concentrations in the clear area and behind the LBW. Also, statistical analysis of the data collected during a campaign can give some insight on the impact of meteorology and LBW characteristics on pollutant removal.

Site Name with type of vegetation Name of the road, number of lanes, width of the road and direction

Distance from road

LBW attributes L: Length W: Width H: Height

A. Pearse St-LBW Pearse St 4 lanes- One direction ~ 16m N-S

0.7m L: ~7m W:0.1m H:1.2m

B. Nassau St-LBW Nassau St 4 lanes – One direction ~16m N-S

2m L:~36m W:~0.2m H:~2m

Table 2: Details of two monitoring locations.


- 19 -

Figure 1:Schematic representations of the two monitoring locations with the two LBW configurations. The orange

circle and black ring denote measurement point behind and in front of the LBW, respectively.


- 20 -

Figure 2: : Details of the Nassau street LBW intervention location.

Symmetrical


- 21 -

Figure 3:Details of the Pearse street LBW intervention location.

Instrumentation, data setup and collection As part of the measurement study on the LBW interventions in Dublin, the plan is to monitor PM2.5, PM10, NOx, CO2 and CO. The instrument should provide particulate matter concentrations on 1-min time resolution.


- 22 -

CO and CO2 are monitored in ppm having time base of 1-min. Local meteorological conditions (air temperature, relative humidity and wind speed and direction) are logged by the portable weather station at 1-min resolution. All instrument data is averaged to 1 minute to match with the wind data. Traffic counting is collected from Dublin City Council (DCC) to be used in this study.

Experimental protocol A set of instruments are mounted on a tripod stand at 1.5m height. The Pearse street monitoring site, namely A, has a bright area (without any disturbance to air flow) adjacent to the LBW. However, B (Nassau street) the LBW has a steel fence on top of the LBW which might affect the airflow to the monitoring instruments. The CFD evaluation study for the intervention will investigate and quantify the consequences of the steel fence on top of the LBW in addition to studying the effect of having a perforated LBW. The portable weather stations are always attached to the tripod in the clear area or front of the LBW. The campaign is designed to conduct 15 days of monitoring per site, making a total of 30 days. The field measurement is started and ended around 08.00 h and 18.00 h (local time), respectively. This enables to collect 8 to 10 hours of data every day. Inter calibration between each set of instruments is achieved by running instruments side by side for 20 to 30 min prior and finishing the measurements.

SWOT analysis of LBW intervention SWOT (strengths – weaknesses – opportunities – threats) analysis is a powerful tool for planning the future directions of a business/non-profit venture by assessing its strengths and weaknesses together with the foreseeable opportunities and treats (Helms and Nixon, 2010). This tool has also been used by a few researchers to study various city planning activities such as environmental planning (Marcucci and Jordan, 2013) and storm-water management (Mguni et al., 2016). By using this technique, deploying LBW intervention in Dublin is analysed, and the impact on the air quality is given in Table 3. The SWOT analysis shows that LBWs act as a baffle and alter air flow patterns at street level. Currently, limited research projects have been addressed LBWs as a passive control system, so iSCAPE deployment of LBWs is essential to improve the knowledge. The review process for the available literature shows that LBWs have the potential of enhancing local dispersion in the built environment. The height of the LBW, its location in the street and whether spaces exist in the barrier was found to influence air flow in street canyons. The confined street canyon study needs to be expanded to a city-scale, as the frequency and variation of road characteristics and intersections are not considered in the LBW studies to


- 23 -

date. There is some evidence that LBWs could cause deteriorations in air quality for vehicular users and, in particular, pedestrians and cyclists.


- 24 -

Strength Weakness

• LBWs act as a baffle and alter air flow patterns at street level.

• Both measurements and modelling studies show LBWs as an active physical passive control method.

• LBWs have the potential of enhancing local dispersion in the built environment.

• Reductions in pollutant concentrations have been reported on the footpaths in most wind conditions when LBWs exist.

• Modeling studies show that LBWs decrease the pollutants levels by 26% to 50% on the windward footpath.

• There is some evidence that LBWs could cause deteriorations in air quality for vehicular users and, in particular, pedestrians and cyclists.

• The pollutants concentrations increase in front of the LBWs.

• Depending on wind direction, street geometry and position of the LBW, it may cause air pollutant concentration to increase behind it, having the opposite effect to its intention.

• Adverse effects on air quality were measured on the leeward footpath from model simulations for perpendicular wind conditions, where the LBWs exist.

• More research is needed to develop guidelines to provide practical instructions for implementing LBWs in a street canyon environment.

Opportunities Threats • iSCAPE project can provide detailed

studies which can provide LBWs practical implementation recommendations regarding the size, height, length and direction on the LBWs.

• iSCAPE project with the Dublin City Council can provide policy recommendations on the implementation of LBWs in the built environment.

• Since wind direction is variable, an LBW may have a positive effect today and adverse effect tomorrow, which makes the designing process very hard regarding city planning, as a result of this we must be very careful in where these are placed.

• LBWs can act as obstacles on the footpath which prevent the easy access to shops, especially for loading purposes.

• LBWs are hard to be integrated and accepted in city planning practices.

• LBWs are very hard to introduce to the public community as important urban planning item.

Table 3: SWOT analysis of the LBW interventions for obtaining local-scale air quality benefits.


- 25 -

4 Photocatalytic coatings Although photocatalysis it is well known for many years now its use in real atmosphere is still at the experimental stage and the scientific literature on the subject has not yet reached a full consensus. The physical mechanism acting is similar to that of photosynthesis, as outlined in Figure 4, air purification through heterogeneous photocatalysis consists of different steps. Under the influence of UV-light, the photoactive TiO2 at the surface of the material is activated; subsequently, the pollutants are oxidized due to the presence of the photocatalyst and precipitated on the surface of the material. Finally, the products of the reaction can be removed from the surface by the rain or cleaning/washing with water. The scheme of the photocatalysis reaction is has been described in detail by the work carried out in WP1 at deliverable D1.2.

Figure 4: Schematic diagram of the heterogeneous photocatalysis produced by an anatase TiO2 layer. (Boonen

& Beeldens, 2014).

Expected efficiency from tests with individual pollutants

The photocatalytic oxidation of NO is usually assumed to be a surface reaction between NO and an oxidizing species formed upon the adsorption of a photon by the photocatalyst, e.g., a hydroxyl radical, both adsorbed at the surface of the photocatalyst. It has been shown that the final product of the photocatalytic oxidation of NO in the presence of TiO2 is nitric acid (HNO3) while HNO2 and NO2 have been identified as intermediate products in the gas phase over the photocatalyst. The resulting reaction pathway of the photocatalytic oxidation of NO has been proposed as photocatalytic conversion of NO via HNO2 to yield NO2, which is subsequently oxidized by the attack of a hydroxyl radical to the final product HNO3:

NOads + OHads → HNO2ads


- 26 -

HNO2ads + OHads → NO2ads + H2Oads

NO2ads + OHads → HNO3ads

In this study, the approach has been to monitor directly NO2 after the ceramic sample was illuminated by a source of UV light. Thereby, the gas carrier was wet air (RH ~ 10%), while 1 ppm of NO2 was mixed to the gas carrier to measure the abatement of it. In the following figure 5, examples of the performed measurements are shown. It can be observed the very wide reaction of the sensor underwent to 1 ppm of NO2 in wet air. Moreover, for each experiment, a clear difference between the behaviour of various samples is evident.

Figure 5: Experiment to compare the performances of all ceramic samples with PURETI coatings.

Identification of street locations for real applications The objective of iSCAPE is to verify the effectiveness of photocatalytic paints applied in real weather conditions. This latter aspect will be studied in the area of the new University Campus of Engineering Faculty of the University of Bologna, located in via Terracini. It is an area of 3500 square meters, located on the first north-western suburbs of the city (figure 3), consisting of about ten buildings, among which some exterior walls will be chosen, forming a canyon. The paints will be supplied by PURETI (www.pureti.com), partner of the project. Lazzaretto Campus is about 3 km far from city centre, in the north-west direction. It is located northern of train station railroad junction; the area is surrounded by deeply urbanized area to the north and to the east. Lazzaretto area is placed within the Navile district, an area subjected by the Municipality to the urbanistic plan “University buildings in Navile neighbourhood” until the end of 2018. The aim of


- 27 -

the plan is the construction of a residential area with more than 2000 new buildings and an extension of the Engineering Faculty with new classrooms and student accommodations up to an area of 25000 m2. However, the area nowadays is made up of several buildings, some commercial depots and the Engineering Faculty.

Figure 6: Aerial view of Lazzaretto Campus site (credit: Bing).

It is hard to identify the population of this area due to the high presence of commuters, both students and workers. In 2016, Municipality census reported about 60000 people living in the entire Navile district, of which Lazzaretto area is part. In 2015, students enrolled in Engineering Faculty were about 3500 with 2 annual premature deaths if the WHO figures are correct as was mentioned in the introduction of this report.

Annual climatic characterizations at street level The climatological condition that should be taken into account are: the solar radiation at the location where the photocatalytic tests will be carried out, the amount of accumulated rain in the site because this will help in revisiting the catalyst and the wind direction and speed because this will indicate the upstream origins of the anthropogenic pollutants. Although at the area of Emilia-Romagna are operating a lot of public and private meteorological stations as shown at Figure 7, the site with the closest proximity at the Lazzaretto campus is the station at measured in Porta Castiglione (Castiglione Gate). Porta Castiglione is one of the ten gates of ancient part of the city and it is located in the south-west sector. Since the distance is only 3.8 km from Lazzaretto, it can be assumed that this behaviour is representative also for the University site.


- 28 -

Figure 8 shows the amount of solar radiation since 1 Jan 2015. The figure shows the seasonal distribution of the irradiation: it oscillates from 400 W/m2 during winter to 800-900 W/m2 during summer. Peaks are present as well, especially in the period from April to August, when differences between the maximum and the minimum can reach up to 200 W/m2. These spikes can be associated to summer heatwaves occurred during August 2015 and 2016, when temperature in the same station reached 35 °C.

Figure 7: The meteorological sites in Emiglia Romagna and the location of Porta Castiglione weather station with respect to the Lazzaretto University Campus where experiments will be carried out.


- 29 -

Figure 8: Three nearly complete cycles of solar radiation near the Lazzaretto University Campus. The black line

represents the rolling daily trend line.

Figure 9 and 10 shows the corresponding accumulated rain in mm and the wind speed and direction from 1 Jan 2015 till the 20 July 2017.

0

200

400

600

800

1000

1200

01/01/2015

01/04/2015

30/06/2015

28/09/2015

27/12/2015

26/03/2016

24/06/2016

22/09/2016

21/12/2016

21/03/2017

19/06/2017

SolarR

adiatio

n(W

m-2)


- 30 -

Figure 9: Three nearly complete cycles of accumulated rain. The black line represents the rolling daily trend line.

Figure 10: The corresponding wind speed and direction. The black line represents the rolling daily trend line.

Details of local monitoring campaigns During summer 2018, the Lazzaretto area will host some open-air experiments to investigate the role of photocatalytic coats in pollution removal. An approach similar to those followed for the experimental campaign in the city centre will be adopted. In particular, two street canyons between the university buildings will be identified. One area will be painted by PURETI and the other will be left untouched. The graphical representation area of this domain is indicated at Figure 11. Two levels of monitoring will be placed to monitor the situation and to find differences between them. In order to have a complete picture of the environmental situation, at the lower point an ultrasonic anemometer, a barometer and a thermo-hygrometer will be placed, respectively


- 31 -

designed to measure wind horizontal and vertical components at high frequency, atmospheric pressure, relative humidity and temperature. At the top of the street of each canyon an anemometer, a radiometer to measure the solar radiation and a LICOR, which inspects CO2 and water vapour concentration, will be installed. This last instrument allows to identify fluxes, if coupled with anemometer. Measurements will start before the painting of walls. This first period of measurement will enable to understand if there are some differences among the two selected street canyons and to take them into account in the following comparison campaign. At both canyons, measurements of air quality gaseous pollutants (NOx, O3, CO concentrations) and meteorological parameters (temperature, relative humidity, pressure and wind speed/direction) will be performed. The comparison between the measurements at the two sites will give information about the capacity of Pureti photocatalytic coatings to capture pollutants in a real open field campaign. The campaigns will be conducted with 5 local sites of measuring local air-quality and with one local meteorological station providing measurements with at least four values registered per hour. The corresponding reference data from at least two conventional stations located nearby will be also utilised.

Figure 11: Graphical representation of the experimental area where the photocatalytic paints will be utilised.

Expected efficiency at neighborhood and street level The aim was to estimate the rate of the photocatalytic tests for NO2 abatement with PURETI coatings. Although we still need to test different RH percentages and different UV irradiation for defining the best abatement conditions we already know that the ratio between the signal in NO2 and the signal in air has been taken as given promising comparisons even only for NO2. Summarizing, the following percentages of NO2 were obtained: Sample A, 17%; Sample B, 29%; Sample C, 39%; and Sample D, 26%.


- 32 -

Finally, it could be important to test other gases (particularly VOCs) with the aim to better define the performances of PURETI photocatalytic coatings. Taking into account that the domain of Bologna is characterised by the emissions shown at Figures 12 and 13 as they evolved since 1990 we estimate that the application of photocatalysis at Lazzaretto will improve the annual mean concentrations by at least between 20 and 30%. Of course, this is a positive result of abatement that will be able to remain active for several months due to the regular rain frequencies at this domain.

Figure 12: The attribution of CORINAIR emissions sources for the province Bologna from 1990 to 2010 for NOx

(in Mg). Road transport is the main emission source for this pollutant.

0

5000

10000

15000

20000

25000

30000

1990 1995 2000 2005 2010

MgNOx 1-Energyproductionand

fueltransformation2-Nonindustrialcombustion

3-Industrialcombustion

4-Productiveprocesses

5-Extractionanddistributionoffuels6-Solventuse

7-Roadtransport

8-Othermobilesources

9-Waste

10-Agriculture

11-Other


- 33 -

Figure 13: The attribution of CORINAIR emissions sources for the province Bologna from 1990 to 2010 for non-

methane VOCs (in Mg). Road transport is not the main source of pollution for this pollutant.

5 Green infrastructure design State of the art for green infrastructure (GI)

evaluation at city-scale Vegetation surfaces such as leaves, stems, and bark serve as effective deposition sites for particulate matter (PM), and uptake gaseous air pollutants through their leaf stomata, leading to an overall reduction of pollutant concentration in the air, and improvement in the air quality. To quantify this air quality benefit due to vegetation, pollutant deposition models (such as UFORE and iTree) can be used, which calculate the pollutant deposition/uptake by vegetation based on the airflow conditions, vegetation characteristics, and pollutant concentration (Hirabayashi et al., 2011, Hirabayashi et al., 2012, Hirabayashi et al., 2015). Such models have been extensively used for assessing the air quality and health benefits of urban vegetation in the US. It has been reported that urban trees and shrubs can help to reduce CO by 0.009%, NO2 by 2.7%, O3 by 4.4%, PM10 by 3.5%, and SO2 by 4.3% in 11 different cities (Nowak et al., 2006); and PM2.5 by 0.24% in 10 different US cities (Nowak et al., 2013). For the contiguous US, Nowak et al. (2014) estimated that trees help to reduce NO2 by 0.296%, O3 by 0.514%, PM2.5 by 0.199%, and SO2 by 0.483%; thereby providing health benefits valued between $1.5–13.0 billion annually. A similar pollutant deposition modelling study was performed by Tallis et al. (2011) for assessing the effect of urban tree canopy on the removal of PM10 for Greater London Authority (GLA). It was estimated that the current urban tree canopy (20% area of the domain) led to PM10 reduction by 1.4%, and an increase in the tree canopy (from 20% area to 30% area) would lead to a reduction of PM10 by 2.6 % in GLA. It was also suggested

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1990 1995 2000 2005 2010

MgVOCs 1-Energyproductionand

fueltransformation2-Nonindustrialcombustion

3-Industrialcombustion

4-Productiveprocesses

5-Extractionanddistributionoffuels6-Solventuse

7-Roadtransport

8-Othermobilesources

9-Waste

10-Agriculture

11-Other


- 34 -

that tree plantation, with a large proportion of coniferous to broad-leaved, should be targeted in high pollution zones such as busy streets to maximize their PM10 reduction potential (Tallis et al., 2011). In addition to pollutant uptake/deposition on vegetation that provides air quality benefits, vegetation can also change the airflow and pollutant dispersion characteristics in the urban environment, which can lead to an improvement or deterioration in the air quality. To access this effect of vegetation on local-scale air quality, sophisticated techniques such as wind-tunnel and CFD modelling of pollutant dispersion have been undertaken. Overall, it has been found that under trafficked street canyons, the dispersion effect due to trees can have a negative impact on air quality, whereas low-vegetation such as hedges lead to local-scale air quality benefits (Janhäll, 2015, Abhijith et al., 2017). However, it is extremely difficult to conduct such studies at larger scales (such as city and regional scales) due to the high amount of resources required to build large scale wind-tunnel or CFD models. Therefore, there exist only a handful of such studies. Jeanjean and co-workers have studied the effectiveness of trees to disperse traffic emissions by using a wind-tunnel validated CFD model to simulate a 4 km2 area in Leicester city centre in the UK (Jeanjean et al., 2015, Jeanjean et al., 2016). They reported that the concentrations of traffic-generated air pollutants reduced by 7–9% at the pedestrian height owing to enhanced pollutant dilution due to an increase in the air turbulence levels caused by trees. Barnes et al. (2014) studied the effect of varying the surface roughness of the urban environment on the pollutant dispersion characteristics, which is an indirect method to simulate the effect of vegetation (increasing the vegetation cover leads to an increase in the surface roughness). They simulated pollution dispersion in a 6.5 km2 area of central Birmingham, UK by using a Gaussian plume dispersion model ADMS-Urban. Their model results showed that an increase in the surface roughness (or increasing the vegetation cover) would lead to a reduction in the ground-level pollutant concentrations, both locally in the area of increased roughness and downwind of that area. As evident from the above discussion, urban vegetation can provide air quality benefit through a combination of the deposition and dispersion effects on air pollutants. To maximize this benefit, it has been proposed that vegetation must be (i) near the air pollution source (e.g. roadsides) since high concentration of air pollutants would lead to higher deposition rates, and (ii) close to the ground-level since it enhances the pollutant dispersion, while allowing air from aloft to dilute the ground-level pollutants (Janhäll, 2015). However, there is hardly any study that has quantified the potential air quality benefits that can be obtained from such a targeted vegetation planting at the city-scale, which forms the motivation for this study. The present study is primarily targeted at accessing the potential of roadside vegetation in helping cities and boroughs in the UK to comply with the air quality standard for nitrogen dioxide (NO2) concentration around roads - the only standard that UK is currently failing to meet (DEFRA, 2017). For example, in the borough of Guildford, an estimated 52 roads will exceed the annual mean limit (40 µg/m3) for NO2 in 2017. In order to comply with the current and future air quality standards, the UK government is undertaking several nationwide programs, which


- 35 -

include fleet modernisation and promoting public transportation. Despite those efforts, it is estimated that 45 and 27 roads in Guildford will exceed the annual mean limit for NO2 in 2020 and 2030, respectively (DEFRA, 2017). Since high NO2 concentrations occur in certain places due to highly localised reasons, the onus has been put on local authorities to tackle this issue through a host of innovative approaches and technologies. As discussed above, deploying roadside vegetation can be an effective way of improving air quality, and can assist the local city planners in meeting their air quality targets. Therefore, through this investigation, we will evaluate the potential of roadside vegetation in controlling the above-mentioned exceedances of NO2 beyond the regulatory limit for Guildford, which can serve as a case study for future replication amongst other cities and borough across the UK and Europe.

Methodology for GI evaluation at the city-scale assessment of air pollution reduction in Guildford

Modelling approach To assess the city-scale benefits of new vegetation planting, we will use the integrated modelling approach suggested by Tiwary et al. (2009) that combines (i) pollutant dispersion modelling by using a Gaussian plume model (ADMS-Urban) and (ii) deposition modelling of air pollutants on vegetation by using an appropriate deposition model (e.g., UFORE or i-Tree). In this approach, we will use the following steps:

1. Develop spatio-temporal maps of the deposition velocities on the vegetation surfaces in Guildford by using the vegetation characteristics and the meteorological conditions as inputs in the UFORE/i-Tree model. These maps would be developed for the different “what-if” scenarios described in Section 5.2.4.

2. Develop high-resolution air pollution maps for the different scenarios by running ADMS-Urban, while accounting for the pollutant deposition on the vegetation surfaces through the spatio-temporal maps of deposition velocities developed in Step (1).

3. Compare the pollutant concentrations in Guildford with and without roadside vegetation under the different scenarios to assess the effects of a proposed vegetation planting strategy on air quality.

Modelled domain The integrated modelling approach described above will be used to study the air quality benefits of the existing and proposed vegetation cover for a 19 km × 26 km area that encompasses the complete Guildford borough in the UK (borough area = 270.9 km2) as shown in Figure 14. The land use in Guildford is predominantly residential, and about half of the city’s population


- 36 -

(population estimated at around 130,000) lives within the urban area of Guildford town, located in the centre of Guildford borough (GBC, 2016).

Figure 14: Modelled domain of Guildford borough along with the major roads and buildings.

Model inputs and validation

Model inputs Road traffic is the major source of air pollution in Guildford (GBC, 2016), and the major roads (M roads, A roads, and B roads) in Guildford are shown in Figure 1. In the modelled domain, there are 16.6 km of M roads (motorways), 232.9 km of A roads, and 99.6 km of B roads; henceforth referred to as “major roads”, and 1379.0 km of local/minor roads; henceforth referred to as “minor roads”. A majority of the traffic volume passes through the major roads; whereas minor roads have relatively much lower traffic volumes. In order to estimate the pollutant emissions from the roads, ADMS-Urban utilizes the EFT v7.0 developed by DEFRA (2016), which requires (i) vehicle counts, fleet composition, and traffic speed as inputs. We obtained the data for the traffic counts and fleet composition in Guildford from the Department for Transport (DfT), UK which operates ~130 traffic counters for “major roads”, and ~30 traffic counters for “minor roads”. The traffic speed on the roads was assumed to be constant, and taken to be the average traffic speed in UK: 59.2 miles/hour for M roads (DfT, 2016), 37 miles/hour for urban A roads (DfT, 2017), and 18.9 miles/hour for rural A roads (DfT, 2017).


- 37 -

The traffic speed at B roads was assumed to be equal to that for the A roads, and for the “minor roads”, the traffic speed was assumed to be 20 miles/hour. Meteorological variables such as wind velocity and direction, temperature, relative humidity, and cloud cover are required to solve the transport equations in the ADMS-Urban model. The hourly data for those variables were obtained from a weather station located in South Farnborough (latitude = 51:28N longitude = 00:77W, and altitude = 65 metres), which is at distance of 14.5 km from the centre of the modelled domain. The land-cover data for the modelled domain was obtained from the 2015 land cover map, which is produced by the centre for ecology and hydrology (CEH), UK based on satellite imagery and digital cartography at a resolution of 25 m (Rowland et al., 2017).

1.1.1.1 Model validation Model validation will be performed by comparing the model results for the annual mean NO2 concentration in Guildford in 2015 (2015-BASE scenario described) with the corresponding concentrations at 17 different sites in Guildford, as measured by the Guildford borough council (GBC, 2016). Those measurements include roadside, urban background, and rural background concentrations of NO2.

Modelled scenarios for "what if" analysis In order to evaluate the benefits of planting roadside vegetation in Guildford vis-à-vis reducing the roadside NO2 concentration and complying with the relevant standards, as discussed in Section 5.1. We will investigate different scenarios with and without roadside vegetation for the years 2015 and 2039 as described below. The year 2015 has been chosen to represent the current situation in Guildford since data for the model inputs is freely available for this year. The year 2039 has been chosen since 2040 is the year when the strategic road network (SRN) of UK aspires to have zero breaches of road-side air quality (DfT, 2015) and the UK government will end the sale of new conventional petrol and diesel cars and vans (DEFRA, 2017). This means that the end of year 2039 would mark a radical shift towards zero-emission vehicles, and therefore year 2039 is an ideal year for studying the impact of planting road-side vegetation on avoiding the ongoing breaches in air quality standards near roads. 2015-BASE: This is the baseline case for the year 2015 with the currently estimated vegetation cover on the major roads. 2015-BASE-NoRV: This is a hypothetical scenario for the year 2015, which assumes that there does not exist any roadside vegetation. By comparing this scenario with the 2015-BASE, we will be able to estimate the air quality benefits provided by the existing road-side vegetation in Guildford.


- 38 -

2039-BAU: This is the business as usual scenario for the year 2039, which assumes that the traffic and fleet composition have changed, while the roadside vegetation remains at the same level as a 2015-BASE scenario. By comparing this scenario with the 2015-BASE, we will be able to estimate the air quality benefits provided by the existing road-side vegetation in Guildford for the year 2039. 2039-MaxRT-Con: This is an alternative scenario for the year 2039 with the maximum possible coniferous tree cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting coniferous trees along all major roads. 2039-MaxRT-Dec: This is an alternative scenario for the year 2039 with the maximum possible deciduous tree cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting deciduous trees along all major roads. 2039-MaxRH-Con: This is an alternative scenario for the year 2039 with the maximum possible coniferous hedge cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting coniferous hedges along all major roads. 2039-MaxRH-Dec: This is an alternative scenario for the year 2039 with the maximum possible deciduous hedge cover on all major roads. By comparing this scenario with the 2039-BAU, we will be able to estimate the maximum air quality benefits that can be achieved by planting deciduous hedges along all major roads. Thus, through systematically studying the seven scenarios outlined above, we will estimate the air quality benefits of planting trees or hedges along major roads in Guildford, and estimate the potential for reductions in the exceedances of the NO2 limit value in the year 2039.

SWOT analysis for Guildford From our SWOT analysis, Table 4, it becomes clear that GI interventions have a positive effect on the city-scale air quality and provide other benefits as well including energy savings in buildings, avoiding a storm-water runoff, urban heat island mitigation, and carbon sequestration. However, integrating GI practices into city planning is a complex task, and can often compete with other high-priority development activities such as housing and road construction. There also seems to be a lack of understanding about the benefits of GI interventions in the public, which forms the main weakness and poses considerable threats to the widespread adoption of GI practices. Despite this lack of understanding, GI practices are generally perceived positively by the public, and opportunities exist for city planners to retrofit existing built areas or design newly built area while adopting GI design practices.


- 39 -

Strengths Weaknesses

• Sufficient evidence exists showing that GI interventions have a positive effect on air quality.

• Air quality benefits obtained are for a long-term and sustainable.

• GI interventions are aesthetically pleasing.

• GI interventions provide other benefits such as energy savings in buildings, avoiding storm-water runoff, urban heat island mitigation, and carbon sequestration.

• Design guidelines for deploying GI interventions are complex or unavailable.

• Air quality benefits obtained from deploying GI interventions are difficult to quantify, and obtained under long time durations.

• Lack of public knowledge about the benefits.

• Green roofs and walls have high deployment and maintenance cost.

Opportunities Threats

• Tree plantation is being increasingly recognised as an important measure to combat air pollution and climate change.

• Retrofitting GI interventions in existing built areas are possible.

• Green infrastructure is perceived positively by the general public.

• Increased adoption of green roofs and walls in building design will likely reduce their cost.

• Poor design of GI interventions can lead to air quality deterioration in certain situations.

• Not well integrated into city planning practices.

• GI interventions often compete with other developmental activities such as housing and transportation.

• Often viewed as less important than other development activities.

Table 4: SWOT analysis of green infrastructural (GI) interventions for obtaining city-scale air quality benefits.

As evident from the SWOT analysis, deploying GI practices have a multitude of benefits for a city; however, quantifying them poses a challenge. Through this investigation, we plan to quantify the air quality benefits of deploying road-side GI interventions by simulating the different model scenarios discussed in Section 5.2.4 by using Guildford as a case study. From the modelling results, we will demonstrate the potential of deploying roadside vegetation in curtailing NO2 exceedances for the year 2039, thus providing the necessary impetus for city planners in Guildford towards adopting such GI practices.

GI evaluation at city-scale - Bologna (IT) The frequency and types of urban green areas in major Italian cities can be inferred by data provided from ISTAT (National Institute of Statistics). In particular, for the year 2011, urban


- 40 -

green areas represent 2.7% of territory of the provincial capitals (more than 550 million m2). Protected areas represent 14.8% of municipal area, while the Utilized Agricultural Surface (SAU) is equal to 45.5% of the territory. According to the National Institute of Statistics, every resident has an average of 30.3m2 of urban green spaces (Figure 15).

Figure 15: (a) Comparison with the national average for urban green areas per capita (m2) in major Italian cities; (b) density of urban green areas in the macro-zones of Italy; (c) Availability (green) and density of urban green

areas (grey) in cities with more 200.000 inhabitant or metropolitan areas (ISTAT, 2011).

ISTAT defines 43 provincial capitals with a "green profile" which are characterized by a: significant endowment of urban green spaces (19 cities), natural protected areas (11 cities), or agricultural use areas (11 cities), while only two cities have all the three above characteristics. Only in 15% of the provincial capitals, urban green areas are equal to or greater than 50 m2 per capita, while in 17.7% they do not exceed the threshold of 9 m2 per capita. Focusing on relations with national average, about a fifth of the cities has higher density and availability values than national average such as Sondrio, Trento, Potenza and Matera. Instead, half of major Italian cities are characterized by having both indicators lower (more than 70% located in the South). Specifically, Bologna, together with 24 other Italian cities, are characterized by both values below the national average values of green areas (ISTAT, 2011). Over 60% of the cities has a lower average value of urban green areas density, while in 25.8% of cities urban green areas shows an impact on the overall surface of the town more than 4%, and in nine cities green areas cover more than 10% of the territory. Figure. 15.b shows square meters of green areas per capita in Italian cities divided by macro-regions. The lowest value is detected for cities in the Centre of Italy (23 m2 per inhabitant) and in North-West (24.3 m2). In


- 41 -

Northeast cities, average value is almost double compared to previous one (45.4 m2 per inhabitant). Finally, value is comparatively high in the South (37.1 m2) and slightly in the Islands (26.7 m2). Continuing to consider the division by macro-areas, more than half North-west capitals are characterized by higher ‘green areas density’ value, more than 4%, while Northeast capitals shows very higher value 36%. In the Centre and in the Islands, more than 80%of the city shows lower ‘green areas density’. Only 13.6% and 9.5% of cities detected a value greater than 4%. Finally, South capitals are placed in an intermediate position, and the share of city greater allocation is 15.4%. The examination about different types of urban green areas shows specific characterizations in different urban contexts. Specifically, historical green areas, such parks, villas and private gardens, represent a third of all urban green areas, ‘public service green areas’ represent 15.9%, lastly street furniture are 9.4%. These types listed above account for 60% of all the green areas in Italians capitals. Other types affect so much less significant, accounting for only 10% of urban green detected specifically: sports areas (3.8%), school gardens/courtyards (3.4%), ‘urban forestry areas’ (2.4%) and urban gardens (0.2%). The remaining urban green spaces (about 30% of the total) namely as "other" includes botanical gardens, zoos, cemeteries, green fallow and other green surfaces that do not coincide with the classes mentioned above. A type of green growing spread in cities are the "urban gardens", activated in less than 44 administrations. In 58 municipalities in urban green areas, include "botanical gardens" (Verde Urbano, ISTAT, 2011). To deepen the analysis of green areas in the city of Bologna, focusing on types and frequency in urban and suburban areas, one needs to take into account that Bologna city, together with the neighbors, is a metropolitan city. This analysis is carried out within the area covered by Bologna city, Sasso Marconi, Casalecchio di Reno, Zola Predosa, Castelmaggiore, Calderara di Reno, Granarolo dell'Emilia, Castenaso, San Lazzaro di Savena, Ozzano dell'Emilia and Pianoro, considering continuous urban residential and commercial patterns. This area is called ‘urban ecosystem’. (www.arpae.it/cms3/documenti/_cerca_doc/meteo/laboratorio_telerilevamento/labt00012_lt.bov_ecosistemaurbano&urbanizzazioni_20070328.pdf) The boundaries of the urban ecosystem are shown in Figure. 16. The area runs along the cardinal directions (Emilia, San Donato, San Vitale, Futa and Porrettana), by including neighboring municipalities, for a total surface of 279 km2. Within this urbanized area there are some agricultural areas cultivated with arable crops and fruit trees. To these, are added, to the south, forest areas dominated by downy oak (Quercus pubescens) and artificial plants of coniferous. In the urban area, the green areas are represented by parks, trees, bushes, private gardens. The urban ecosystem of Bologna has an area of approximately 279 km2, for a total of 521,840 inhabitants and includes areas of Bologna city, Sasso Marconi, Casalecchio di Reno, Zola Predosa, Castelmaggiore, Calderara di Reno, Granarolo dell'Emilia, Castenaso, San Lazzaro di Savena, Ozzano dell'Emilia and Pianoro. The district of Bologna has been called "metropolitan city" through Law 142/1990, which sets out the principles of ordinance of


- 42 -

municipalities and provinces and determines its functions. From elaborated data the net surface to green, excluding green road traffic, is 185 km2, or 66% of the total area. But nearly two-thirds of these are agricultural lands, situates mostly in the plain, and therefore cannot be placed at the same level as public parks, which by definition are accessible to all. Eliminating the count, the agricultural area shows that greenery covers 24% of the territory of the urban ecosystem and on average every inhabitant has 131 m2 of green (Table 5).

Figure 16: Bologna urban ecosystem. Map obtained by Quickbird satellite

Type of GI Surface (m2)

Tree crowns and bushes 38,624

Meadows, flowerbeds and courtyards 29,691

Agricultural green 116,233


- 43 -

Total 184,548

Table 5: Areas occupied by GI in the area of Bologna metropolitan city.

Figure 17: Bologna urban ecosystem. Results obtained by the GI classification.

Methodology for GI evaluation at city-scale assessment for Bologna (IT)

Two methodologies are described here, one experimental, based on satellite imaging, and one theoretical, based on city-scale numerical codes. The first methodology has been used by ARPA


- 44 -

(www.arpae.it/cms3/documenti/_cerca_doc/meteo/laboratorio_telerilevamento/labt00012_lt.bov_ecosistemaurbano&urbanizzazioni_20070328.pdf). A geographic information system was created containing all vector and raster information, such as road mapping, buildings, public green cards, hydrography, the map of Bologna with fake colors and the green map in three classes obtained, the DEM used to calculate slopes. The multispectral image data obtained by the Quickbird satellite, used for the green classification, has been compared with the pancromatic Emilia-Romagna image data obtained in 2002-2003 by Emilia-Romagna Regional Authority. The satellite collected panchromatic (black and white) imagery at 61 centimeter resolution and multispectral imagery at blue range (450-520 nm), green (520-600 nm), red (630-690 nm), near-IR (760-890 nm). Given the high resolution of the images, at the methodological level, by area green means any surface on which vegetation is insisted, both herbaceous or woody, public or private. This kind of generalization makes it possible to take full advantage of the potential of classification algorithms, even on highly fragmented surfaces of small size. As agricultural areas are heterogeneous, for their presence at the same time of the various classes (arable land / cultivated meadows, plowed land and land with Stubble) and given the objective difficulty of discriminating plowed soil or turpentine at the multispectral level, it was decided to mask these variables with the vector coverage of regional soil use in 2003 and re-insert them into classification. The use of soil 2003 was achieved with photo-interpretation of pan-chromatic Quickbird images, acquired at the same time as multispectral data. This expectation, with a minimum cartographic unit of 1.56 ha and a working scale of 1: 25,000, does not detail the small size green areas that are embedded in the urban fabric. Two classes of GI have been considered:

• tree crowns and bushes, forest areas and sports areas • meadows, flowerbeds and courtyards

On these areas were grounded truths, on which to calculate the rating thresholds. These two classes represent quite well the reality of greenery in Bologna, which is characterized by the presence of a large tree-lined network and by parks and yards with woody vegetation pools and meadows on compacted clay soils and heavily cracked in the summer season. It was not possible to discriminate more classes, such as hardwood conifers, because with the four bands available, it is not possible to obtain a separability between the various species. For the purposes of classification, the SpectralAngleMapper algorithm was chosen. The first result obtained with 0.1 rad thresholds has undergone the high ability to discriminate against green. It was interesting to note that in some areas of the mosaic image, it was possible to


- 45 -

classify the green roof. At the same time, however, there are false classifications on the roofs due to very hybrid spectral responses. Finally, it was decided to mask the areas affected by buildings, using the vector coverage provided by the SIT Office of the City of Bologna. With this operation, about 54 hectares of greenery have been eliminated, consisting of hanging gardens, building blocks and false classifications in proportion not to be reckoned but with a prevalence of false classification in the historic center and building blocks in suburban areas. Subsequently, to improve the preliminary result obtained, they were applied two different thresholds:

1. 0.2 rad threshold for GI class 1. 2. 0.1 rad threshold for GI class 2.

In the two classes identified, information on previously masked agricultural areas was added. The classification was subsequently vectorized and uploaded in a GIS environment for overlapping with the area boundaries and population data. The second methodology is based on the simulations by means of CFD model ENVI-met, together with a non-dimensional model ADMS-Urban. ENVI-met is a prognostic non-hydrostatic model composed by a three-dimensional main model and one-dimensional Atmospheric Boundary Layer model. It uses Reynolds-Averaged Navier-Stokes equations combined with the advection-diffusion equation using the standard k-εmodel. The model solves those basic equations forward in time by simulating wind field modification due to buildings, roads and vegetation. The inlet profile and top model conditions are obtained from ADMS-Urban one-dimensional model and zero-gradient condition is used for the output profile. Vegetation model is treated as a 1D column and each plant is distinguished for its LAD (Leaf Area Density) and RAD (Root Area Density). Vegetation is an active element of the model in terms of evapo-transpiration processes, shadow and drag effects. Temperature of the ground surfaces is calculated from an energy balance of the net radiative energy fluxes, turbulent fluxes of heat and vapour and soil heat flux, while the temperature of building facades is computed by taking into account the heat transmission through walls and roofs. The differential equations are solved on a staggered grid system using the finite difference method. It is worth noticing that ENVI-met cannot capture meteorological variations of wind temperature induced by large scale variation of meteorological conditions. This is because the diurnal evolution of temperature and wind field is based on initial conditions only. This feature implicitly limits the comparison with field measurements to those days characterized by weak synoptic forcing and stationary weather conditions.

SWOT analysis for Bologna The following table 6 presents the SWOT analysis for the Bologna intervention.

Strength Weaknesses


- 46 -

• ADMS family gives results for urban pollutant concentrations and temperature distribution with a good accuracy

• From ADMS results it is possible to obtain distributions that can be input values for numerical codes working at neighborhood scale (ENVI-met, Fluent)

• Urban air temperature is sensitive to urban morphology, whose influence is not captured by models.

• Misinterpreted buildings albedo may interfere with regular energy propagation

Opportunities Threats

• It will be possible to test reliability of this approach by comparing numerical results with data from local measurements

• Measured local values of energy balance terms are needed to ADMS

Table 6: SWOT analysis of the Bologna intervention.


- 47 -

Methodology for GI evaluation at city-scale assessment for Vantaa

In the socioeconomic impact assessment part conducted in WP5, we will assess the benefits of green infrastructure (GI) based on the modelling results from SURFEX simulations and the thermal comfort index (TCI) calculations. Our approach in Vantaa for the socioeconomic assessment is to study the total value that people attach to all the ecosystem services (ES) provided by the simulated GI. In this case, the benefits are not categorized based on ES but rather the value of total package is being analysed, also controlling for other spatial characteristics of the urban economy and the built environment. Our chosen method for this is the hedonic pricing method, in which the observed prices in residential real estate markets are used to analyse the willingness to pay for the proximity of properties to a given ecosystem (see Votsis 2017 for an application to Helsinki’s municipality). The willingness to pay for different GI types is an indicator of the economic impacts of different GI interventions. We have an existing database of real estate transactions in Vantaa for years 1970-2011 which provides a detailed picture of the housing market in Vantaa and the Finnish Capital Region, and years 2000-2011 are coupled with a GIS-database of the existing GI and other urban characteristics. We will analyse the value of the current ecosystems and simulate the changes resulting from the simulated GI. The hedonic pricing method will be complemented with 1) the impact pathway approach to assess the air quality benefits of GI, 2) energy saving calculations to assess the energy saving potential of GI, 3) aesthetic value calculations to assess the aesthetic value of GI (already partly contained in the hedonic calculations), and 4) earlier green roof benefit simulations to assess the value of proposed green roof implementation (see Nurmi et al. 2016 for an application to Helsinki’s centre). The urban climate of Vantaa and the effect of interventions under current and altered conditions are to be simulated using the SURFEX atmosphere surface interaction model (Masson et al. 2013) at scales ranging from the neighbourhood to the city scale, yielding estimates of the urban climate itself, as well as the anthropogenic usage of energy for heating and cooling of building space. While the most straightforward and reliable method of simulation would be to conduct extended integrations of a high-resolution climate model, representing the present and altered climates, the computational cost involved would be far too high for the current project. Instead, an alternative procedure, based on the concept of climatically representative periods, will be followed. The starting point will be a set of 12 monthly periods, chosen so as to represent a typical year of the period 1980-2009 in terms of observed temperature, humidity, insolation and wind speed (Jylhä et al., 2011). High-resolution hind casts will be produced for these months using the Harmonie-AROME NWP system (Bengtsson et al., 2017), and data necessary to apply SURFEX will be extracted from the hind casts. By use of these forcing data, the effects of various interventions on the urban climate will be established by configuring SUREFX accordingly. Altered climatic conditions will be taken into account by modifying the


- 48 -

atmospheric forcing data extracted from Harmonie-AROME according to chosen representative carbon pathways (RCPs).

SWOT analysis for Vantaa The following table 7 presents the SWOT analysis for the Vantaa intervention.

Strengths Weaknesses

• Able to estimate evidence-based real-world changes (in urban economic behavior and socioeconomic impacts) resulting from GI rather than hypothetical changes (stated, for instance, in interviews)

• As people see GI as a bundle of goods rather than a list of ecosystem services, the hedonic pricing method is the right way to approach the socioeconomic value of GI, especially in a spatially explicit urban planning context.

• Complementary analysis of the value of thermal comfort index, air quality changes, aesthetic value and energy saving is able to break down the value of GI into components.

• Some market imperfections, relating notably to imperfect and asymmetrical information in the housing market, may result in the estimated value being lower than the sum of all the benefit components.

• We are not able to distinguish between which specific ecosystem service the value is based upon.

• In cases where the GI intervention (or its impact) is extensive, the equilibrium is different after the simulation, so in principle the marginal values could change as well.

Opportunities Threats • Compare the values of alternative GI

options or interventions and provide recommendations on how the GI should be implemented optimally; for instance, what kind of GI is optimal at a given location and how much of it, as well as what kind of fiscal policies are needed to make a GI investment economically feasible and sustainable.

• Simulation results may not describe the real changes after such GI implementation. For this reason, we will perform the aforementioned complement approaches as well, and compare the results obtained with each approach.

Table 7: SWOT analysis of the Vantaa intervention.


- 49 -

6 Green infrastructure evaluation at neightbourhood scale State of the art for GI evaluation at neighbourhood-scale

Near-road environment Numerous exposure assessment investigations analysed pollutant concentration distribution in the near-road environment and finding from these studies are summarised in some reviews (Karner et al. 2010; Pasquier and André 2017). The near-road pollutant concentration levels are affected by distance to the road, road configuration, meteorology, and adjacent infrastructure geometries such as noise barriers, vegetation. In general, pollutants concentrations in near road environments decrease with distance from the road. Decay in concentration of pollutants with distance from highway can be rapid, gradual or no trend irrespective of their reactiveness. For example, inert pollutant CO and reactive ultrafine particles (UFP) shows rapid decline in their concentration. Whereas gradual decrease with distance is observed elemental carbon (EC, inert) and PM10 (reactive) and no trend is noticed in PM2.5 concentration (Karner et al. 2010; Pasquier & André 2017). Depending up on the type of pollutant, concentration reaches close to background levels by about 80m to 600m from the road (Karner et al. 2010; Pasquier & André 2017; Patton et al. 2017). Apart from a distance to the road, specific road ways characteristics such as at elevated, at-grade, depressed roads can also influence the pollutant concentration distribution near highways (Patton et al. 2014; Baldauf et al. 2013; Steffens et al. 2014). Moreover, meteorological conditions affects near-road pollutant concentrations (Pasquier and André 2017). When wind direction is perpendicular to the road, i.e. the wind flows from the road to the nearby areas, pollutants reach longer distance in downwind compared to winds parallel or inclined to the road (Karner et al. 2010). Lower concentrations are observed with high wind speeds and opposite with low wind speeds (He and Dhaniyala 2012; Zhang et al. 2015). In addition, stable atmospheric conditions in winter seasons induced higher pollutant concentrations as opposed to a decrease under relatively unstable summer periods (Padró-Martínez et al. 2012; Barros et al. 2013; Pasquier and André 2017). Apart from above-discussed elements modifying near-road air quality, the noise barriers and vegetation along the highways, strongly alter the pollutant concentration profile immediate region (<50m) from the traffic emissions. Noise barriers reduced pollutant concentration behind the barrier by 50% compared to without barrier (Hagler, Tang, et al. 2011; Hagler et al. 2012; Finn et al. 2010). Whereas, vegetation near highways reduced pollutants concentration in the adjacent downwind region by 15% to 60% than a clear area without vegetation (Abhijith et al. 2017). These infrastructural solutions are effective in reducing pollutant concentration in the


- 50 -

most important exposure zone which is 50m from the road. In this zone, pollutants concentrations remains more than 50% of the on-road levels (Pasquier and André 2017; Cahill et al. 2016; Karner et al. 2010; Baldauf et al. 2013; Patton et al. 2017). Vegetation along the highways consisting trees as well as other vegetation types such as hedges, shrubs and bushes forms a barrier to the pollutants reaching nearby areas and these are referred to studies as ‘vegetation barriers’ or green belts (Brantley et al. 2014; Islam et al. 2012; Morakinyo & Lam 2016) .Green infrastructure act as a barrier for pollutants released from the roads to reach adjacent areas. This creates a high pollutant concentration zone between road and vegetation, forcing pollutants to loft over vegetation. Thus, vegetation barriers help in vertical dispersion and dilution of pollutants before reaching downwind region. The barrier effect of green belts depends upon meteorological parameters, and physical characteristics of the green belt such as position, thickness, height and porosity (Abhijith et al. 2017; Baldauf 2017). Several studies have investigated pollutant reduction by green infrastructures along highways and they adopted modelling (Morakinyo & Lam 2016; Neft et al. 2016), experimental (Fantozzi et al. 2015; Al-Dabbous & Kumar 2014; Brantley et al. 2014; Hagler et al. 2012) or combined modelling and experimental (Morakinyo et al. 2016) methodology for assessing pollutant reduction potential of various types of vegetation. Overall, most of the studies reported a positive effect of vegetation on reducing air pollution. Although some investigation observed mixed and negative effects in pollution abatement by green infrastructure (Abhijith et al. 2017). Among physical characteristics of the green belt, thickness and density are main factors determining in lowering near-road pollution exposure. The increase in thickness as well as the density of vegetation results in decrease in pollutant concentration (Tong et al. 2016; Chen et al. 2016; Shan et al. 2007). Studies identified the thickness of 10m for 50% reduction of pollutant concentration (Neft et al. 2016; Shan et al. 2007). However, other vegetation parameters such as height and spacing of vegetation, leaf thickness, and the presence of hairs or wax on leaf surface also influence pollutant reduction by green infrastructure. Wind speed and direction, humidity and temperature affects neighbourhood air quality in near road environment. For example, the highest reduction is observed in perpendicular wind direction (Brantley et al. 2014). Evergreen trees are preferred to provide pollutant reduction feature in all seasons along roads (Baldauf et al. 2013). Baldauf (2017) listed general recommendation for planting vegetation barriers along highways for improving air quality in near road environments. Although previous investigations have quantified pollutant reduction by various green infrastructures, they have considered either one or two pollutants or vegetation types. Similarly, the recommendations by Baldauf (2017) requires further refining for better practical implementation. For example, the 10m thick vegetation barrier may not be feasible in many near-road situations. In addition, earlier studies are inadequate in understanding the dilution/dispersion and deposition components of pollution reduction by vegetation. Overall, detailed quantifications of individual green interventions are required to enrich existing knowledge to overcome above-discussed limitations. This report proposes methodology to: (i) quantify air pollution reduction potential of different vegetation barriers such as hedges, trees, and mixed vegetation, (ii) study effects of vegetation characteristics on air pollution removal,


- 51 -

(iii) evaluate and compare the effect of vegetation on reducing various regulated (PM2.5, PM10, CO) and unregulated pollutants (UFP, black carbon, BC), (iv) study the impact of meteorology on near road environment, and (v) provide insight on dilution and dispersion component of vegetation induced pollutant reduction. We focus on the following pollutants as a part of this work: UFP, PM1, PM2.5, PM10, CO, and BC. These pollutants have different decay characteristics with distance from the road. CO and UFPs have rapid concentration decay with distance, while BC, PM10 have gradual decay. PM2.5 has no trend in concentration variation with distance from the road. These pollutants represent each classification based on pollutant concentration decay with distance, and reactiveness in near road environment (Pasquier and André 2017; Karner et al. 2010).

Methodology for GI evaluation for neighborhood-scale assessment for Guildford

Site description In this study, we have considered three vegetation configurations: (i) trees, (ii) hedges and (iii) mixed vegetation barrier with trees and shrubs. Study evaluates air pollution reduction potentials of these three types of vegetation. Monitoring locations are selected based on type of green infrastructure present near highways. Six sites are identified near major roads of Guildford town and detailed in Table 8. Guildford town is a highly populated area in Guildford borough, which is a part of Surrey County (Surrey-i 2015). Guildford Borough has a population of 137,183 (Surrey-i 2015). Most popular mode of transportation is car that includes about 72% of trips to work and 42% of commute to school (Al-Dabbous & Kumar 2014). For assessing the impact of green infrastructure distance from the road, we have selected two sites for each vegetation type. One of them is close to the traffic (≤1m) and other is away from road (≥2m). Figure shows schematic diagram of monitoring sites. In particular, these includes Aldershot-Hedge and Aldershot-Tree sites that are along same road; they are approximately 200m away from each other (Figs. 1a, c). Green infrastructure on Aldershot sites are close to the traffic emission. These sites are situated in a residential area with double story houses on either side of two lane road. Similarly, Sutherland-Tree site and Sutherland-vegetation barrier site are 100m apart from each other and these are next to a recreational park near four lane road (Figs. 1d, e). Vegetation in Sutherland sites is away from traffic emission. Stoke road-Hedge site is near to a children’s play area adjacent to a two lane street and hedge is away from the traffic emission (Fig 18b). Vegetation barrier site at Shalford is next to a public park and a busy two lane traffic is close to the barrier. Average traffic volume and direction of roads at each site were counted that are provided in Table 8. Dimensions of green infrastructure, distance from edge of road to monitoring point, and width of lanes are depicted in Figure 18. We are aiming to quantify the pollutant reduction potentials of different vegetation by comparing the concentration levels of clear area and behind vegetation. Moreover, statistical analysis of data


- 52 -

collected during campaign can give some insight on the impact of meteorology and vegetation characteristics on pollutant removal.

Site Name with type of vegetation

Name of the road, number of lanes, width of the road and direction

Average hourly Traffic volume per hour

Vegetation type

Distance from road

Vegetation barrier attributes L: Length W: Width H: Height

B. Aldershot-Hedge A323 2 lanes ~ 7m E-W

750 Hedge 1m L:36m W:1m H:1.2m

B. Stoke park-hedge A320 2 lanes ~7m

1200 Hedge 2m L:~36m W:~1.2m H:~2m

C. Aldershot-Tree A323 2 lanes ~ 7m E-W

750 Tree 1m L: ~40m W:~6m H:~ 10m

D. Sutherland-Tree A3100 4 lanes ~13m NW-SE

1650 Tree

3m L: ~50m W: ~9m H: ~7m

E. Sutherland- vegetation barrier

A3100 4 lanes ~13m NW-SE

1650 Trees and hedge

3m L: ~40m W:~7m H: ~5m

F. Shalford-vegetation barrier

A281 2 lanes ~ 7m N-S

1200 Trees and hedge

1m L: ~66 m W:~3.5 m H: ~4 m

Table 8: Details of six monitoring locations.


- 53 -

Figure 18: Schematic representations of six monitoring locations with the type of vegetation and road details.

The orange circle and black ring denote measurement point behind and in front of the vegetation barrier, respectively.

Instrument setup In this work, we have monitored PM1, PM2.5, PM10, PNC, BC and CO. Two GRIMM aerosol monitors, model EDM 107 and 11-C measured PM1, PM2.5, and PM10. The instrument provides particulate matter concentrations on 31 different channels at 6 seconds time resolution. In addition, particles collected on PTFE filter in the GRIMM allows for chemical and morphological exploration. Two PTRAK 8525 (TSI Inc.) are employed to measure PNC in the size range of 0.2 to 1 μm. In this study, we set PTRAK to record PNC values at every 6 seconds. BC concentrations are collected using a couple of MicroAeth AE51 (Aeth Labs) with time averaging period of 10 seconds. Attenuation generated due to instrumental optical and electronic noise is rectified by post processing the data with Optimised Noise-reduction Averaging algorithm (ONA; Hagler, et al. 2011). CO and CO2 are monitored in ppm with two QTRAK 7575 (TSI Inc.) having time base of 6 seconds. Local meteorological conditions (air temperature, relative humidity and wind speed and direction) are logged by portable weather station Kestrel 4500 at 1-min resolution. All instrument data is averaged to 1 minute to match with the wind data. Traffic counting is performed by using SMART Traffic Counter App developed by University of Wollongong, Australia.


- 54 -

Experimental protocol One set of instruments (includes GRIMM, PTRAK, QTRAK, and MicroAeth) are mounted on a tripod stand at 1.5m height. Three monitoring sites, namely A, C, and F, have a clear area (without any disturbance to air flow) adjacent to the green infrastructure. On other sites, B, D, and E the green intervention is continues leaving no clear area (without any vegetation) along the same road. Due to this reason, one of the instrument set up is placed in the clear area and the remaining one is positioned behind the green infrastructure on sites A, C, and F. Both tripods are located equidistant from the road. Whereas, on sites B, D, and E tripods are held in front and behind the vegetation. The portable weather station is always attached to the tripod in the clear area or in front of the vegetation. The campaign is designed to conduct 5 days of monitoring per site, making a total of 30 days. The field measurement is started and ended around 08.00 h and 18.00 h (local time), respectively. This enables to collect 8 to 10 hours of data every day. Inter calibration between each set of instruments is achieved by running instruments side by side for 20 to 30 min prior and finishing the measurements as shown in Fig 19. No field campaigns are carried out on rainy days. Traffic is counted in the first 20 minutes of an hour.

Figure 19: Instruments are mounted on tripod and kept close to each other during inter-calibration. In the figure,

1) GRIMM aerosol spectrometer, 2) PTRAK 8525, 3) QTRAK 7575, 4) MicroAeth AE51, 5) weather station Kestrel 4500.


- 55 -

Methodology for GI evaluation for neighbourhood-scale assessment for Bologna

GI evaluation at neighborhood-scale - Bologna The characterization of Green Infrastructure (GI) in Bologna takes place with two intensive field campaigns, one in summer and one in winter. In this work, influence of tree-lanes is studied inside two different street canyons of Bologna. Terms like "street canyon", or "urban street canyon", refers to the urban structure where a street is flanked by two opposite rows of buildings. In cities, between the buildings usually there is a lane, travelled by car and pedestrians. Inside an urban street canyon, traffic jam is a source of gases and aerosols negatively affecting human health. Green infrastructures influence the environment inside the canyon both by removing pollution and by trapping it under their crowns. Pollution removal is carried on firstly with photosynthesis, by which the plants can absorb sunlight and carbon dioxide for their metabolism. Another important removing factor is the leaf capture of particulate matter, which is remoted from the proximity of tree crown and can be disposed removing the fallen leaf. On the other hand, trees act also as obstacles to the air circulation. Due to the limited porosity of their crowns, air masses velocity decreases and so does pollutant efficiency removal. The two intensive field campaigns are planned with the precise purpose to correctly evaluate the efficiency of GI in urban street canyons. This was realized identifying two urban street canyons inside the city, sharing similar exposition of vulnerable population (e.g., elderly people and children), similar traffic conditions and emitting sources, but very well differenced in terms of vegetation, i.e. one area (Laura Bassi Veratti St.) includes intensive green spaces, while the other one (Marconi St.) is almost free from vegetation. Following the acquisition and analysis of experimental data collected during the field campaigns, the prognostic three-dimensional and non-hydrostatic CFD-based model ENVI-met 4.2 (M. Bruse and team, http://www.envi-met.com/), one of the few available models capable of simulating airflow interactions with buildings and vegetation (Bruse and Fleer, 1998) will be applied for the evaluation of green structures. This approach was previously validated by Maggiotto et al. (2014), where the model simulations from the temperature perturbation-type model ADMS-TH and the CFD-based ENVI-met were directly compared with field observations: the comparison showed that although both models showed good agreement with experimental observations, ENVI-met is more appropriate to take into account the effect of buildings geometry and vegetation on flow pattern and temperature distribution in different scenarios. In the following, we present firstly the two sites, then the instrumental setup and finally the adopted experimental protocol adopted for the two field campaigns, of which the summer campaign is currently under way.


- 56 -

Site description The study area covers both the historical city centre as well as the residential part outside the walls of Bologna, in the south-eastern part of the city. The study area comprises two main parallel street canyons (namely Laura Bassi Veratti St. and Marconi St.) running north-south with building heights from 5m to 25 m. Laura Bassi Veratti St. is characterized by the presence of a tree lane of deciduous trees at both sides of the street (Figure 20c), while Marconi St. is a tree-free street canyon (Figure 20d). Laura Bassi Veratti St. is a 700m long lane located in a residential area south-east of the city centre. The carriage is thus surrounded by both small private houses (2-3 floors) and higher buildings (4-5 floors), so the height is very variable. Marconi St., about 600m long, is surrounded by buildings with at least 4-5 floors, which means 15m on average. The street is composed by 4 lanes, two for private and two for public transport reserved. Along the carriageway, frequently there are porticos, covered pathways where today sideways are placed. No vegetative element is planted alongside this road, except for the last 50 m approaching one street end, where a single lane of deciduous trees is placed on one side of the road. Besides having the same north-south orientation, the two streets have the same orientation with respect of the prevalent impinging wind too. (iSCAPE D1.4).


- 57 -

Figure 20: a) Position of Bologna (yellow dot; 44°29’37’’N, 11°20’19’’E) in north-east Italy; b) Position of the two street canyons in Bologna; c) street view of Laura Bassi Veratti street with trees; d) street view of Marconi street

without trees.

More than 100 trees (Platanus Acerifolia Mill) are located along both sides of the Laura Bassi Veratti St. The spacing between tree trunks is approximately 8m so that there is a leaf crown interference. Platanus Acerifolia (Mill.) is a hybrid of Platanus Orientalis and Platanus Occidentalis, widespread in European urban habitats, from planes up to 700-800m a.s.l. The average height of the branch-free trunk is about 5m and the crown extends up to 15m in height (Fig. 21a). Platanus A. is a large deciduous tree growing 20-30m in most landscapes and exceptionally over 40m, with a trunk up to 3m or more in circumference. Numerous lateral branches, often with a disorganized structure arise from the trunk. The leaves are thick and stiff-textured, broad, palmately lobed, superficially maple-like, leaves are 15-20cm wide, with 3-5 lobes (Figure 21a). The bark is usually pale grey-green, smooth and exfoliating, or buff-brown and not exfoliating (Figure 21b). The young leaves in spring are coated with minute, fine, stiff hairs at first, but these wear off and by late summer the leaves are hairless or nearly so. Platanus A. is a prolific bloomer, featuring flowers borne in clusters of one to three spheres on a pendulus stem, with male and female flowers on separate stems; its round flowers appear in April-May. The fruit matures in about 6 months, to 2–3 cm diameter, and comprises a dense spherical cluster of achenes with numerous stiff hairs; the cluster breaks up slowly over the winter to release the numerous 2–3 mm seeds.


- 58 -

Figure 21: a) Leaf specimen of Platanus A. b) trunk detail of Platanus A.

Tree grows best in full sun but also thrive in partial shade, grows in almost any soils, acidic or alkaline, loamy, sandy or clay. It grows best in moist, well-drained soil, but tolerate dry soil as well. The plant tolerates poor cultural conditions, including heat, drought, and poor soils. Due to its well-known resilience to urban conditions including air pollution, it has been commonly used in urban areas.

1.1.2 Instrument setup In this section, we describe the equipment used for the experimental field campaign in Bologna.

1.1.2.1 Thermal imaging camera Thermal imaging cameras can be employed in the analysis of environmental temperatures at microclimate scales to estimate the impact of urbanization across the metropolitan area, providing input data for mesoscale models, and enhance the prediction of UHI (Urban Heat Island) expansion through assessing the microclimate impact of growth and neighbourhood design. Many studies previously highlighted temperature variations in urban areas using high-resolution remote sensing imagery with the addition of ground thermography techniques for specific measurements of situ surface (e.g., Di Sabatino et al., 2009; Maggiotto, 2014). The aim of this methodology is to quantify thermal variation in relation to urban land use, further processing remote sensing data, through Geographic Information Systems (GIS). These techniques can be easily used by non-modeling researchers to describe urban thermal environment, getting results readily provided to urban planners and policy makers.


- 59 -

To this aim, two high performance FLIR T620 ThermalCAM (Figure 22), with uncooled microbolometer, 640 x 480 pixels resolution and an image acquisition frequency of 50-603 Hz, will be used in Bologna campaign.

Figure 22: The ThermalCAM FLIR T620 (a) rear section (b) front section

Large bright 4.3-inch LCD screen, presents sharp and bright images also in outdoor environments. ThermalCAM has an integrated high quality visual camera with 5-megapixel resolution that generates crisp visual images in all conditions. The range for temperature collection ranges from -40°C to + 650 °C with a precision ±2°C, or ± 2% of the range. Measurements are acquired by means of a movable pointer, and include automatic identification of the minimum or maximum temperature within an area (round or square), isotherms (and visible alarm acoustic), delta T and automatic indication of the deviation. Atmospheric attenuation correction is automatic and is based on input distance, ambient temperature and relative humidity. ThermalCAM displays simultaneously on the LCD screen these parameters, frizzing with thermal image. Thermal images will be accompanied by measurements of temperature of the surface via a digital USB Thermo-Hygrometer.

Ultrasonic anemometers Sonic anemometers are devices using ultrasonic sound waves to measure the three components of wind velocity (u, v, w) and air temperature. Wind velocity is calculated according to the time of flight of sonic pulses between pair of transducers as schematically shown in Figure 22.


- 60 -

Figure 23: Schematic illustration of the steps used to measure a component of wind velocity through an

ultrasonic anemometer.

The spatial resolution is given by the path length between transducers, typically 10 and 20cm. Sonic anemometers allow high resolution measurements until 50Hz, which makes them well suited for atmospheric turbulence measurements. Due to the absence of moving parts, they are also suitable for long-term use in exposed automated weather stations and weather buoys. As reported in Figure 4, sonic anemometers use the time-of-flight measurements to calculate the speed of sound C along each axis; deriving the mean speed sound from that calculated along each axis, and taking into account the effect of the cross-wind normal to the measurement axes, the sonic temperature (equivalent to a virtual temperature) of the air is derived as , which can be converted into a true temperature assuming atmospheric pressure and humidity as known. It is worth to note, however, that due to the high sensitivity of sonic anemometers to very small errors in the speed of sound measurement, the accuracy of the derived sonic temperature is not generally good enough to be relied as a true temperature measurement. This is not particularly important, though, since the primary purpose of the sonic temperature is a fast response measurement to be combined with the w vertical wind component in order to calculate heat fluxes However, this is not particularly important, because primary purpose of the sonic temperature is as a fast response measurement which can be combined with the W wind


- 61 -

component in order to calculate heat fluxes. A separate sensor for absolute measurements of temperature as done in this field campaign. High frequency measurements of wind velocity components and air temperature at high frequency can be used to derive useful quantities to characterize the atmospheric boundary layer, such as the turbulence and thermal stratification of the atmosphere, from which information on the potential of spreading of pollutants from the measurement site can be inferred. In fact, the use of fast and precise anemological and temperature data has always been considered particularly useful in the field of Planetary Boundary Layer (PBL) dynamics, air pollution monitoring and agrometeorology experimental study (Sozzi et al., 2002). Since 1957, year of the first documented use of a sonic anemometer in micrometeorology, the sonic anemometer continues to be the main instrument for the direct instrument for the direct measurement of momentum and sensible heat turbulent fluxes (eddy correlation method). Consequently, several studies in recent years have been devoted to the analysis flow and turbulence intensity, within and above street canyon (Rotach., 1994; Louka et al., 1998), or yielded airflow patterns, stability conditions, and turbulence properties as a function of the incoming wind direction (Dobre et al., 2005; Zajic et al., 2011). The instrumentation employed in the Bologna field campaign comprises 6 ultrasonic anemometers GILL R3-50 (Figure 24), installed at three different heights at the two sites as will be described in the 6.1.3 Section.

Figure 24: GILL R3-50 sonic anemometer.

Technical specifications of GILL R3-50 and other instrumentation used in the field campaigns is reported in the Appendix.


- 62 -

Thermo-hygrometer HC2S3 Rotronic temperature and relative humidity probe (Figure 25) are installed with anti-radiation screen together with sonic anemometers for accurate temperature and humidity measurements; the probe includes a polyethylene filter that protects its sensor from fine dust and particles and minimizes water absorption and retention.

Figure 25: HCS2S3 thermohygrometer.

Net radiometer The CNR4 net radiometer (Figure 26), manufactured by Kipp & Zonen, measures the energy balance between incoming short-wave and Long Wave Far Infrared (FIR) Radiation versus surface-reflected short-wave and outgoing long-wave radiation.

Figure 26: CNR4 net radiometer.

The CNR4 net radiometer consists of a pyranometer pair, one facing upward, the other facing downward, and a pyrgeometer pair in a similar configuration. The pyranometer pair measures the short-wave radiation. And the pyrgeometer pair measures long-wave radiation. The upper long-wave detector of CNR4 has a meniscus dome, which ensures that water droplets role off easily and improves the field of view to nearly 180°, compared with a 150° for a flat window. All 4 sensors are integrated directly into the instrument body, instead of separate modules mounted onto the housing, but are each calibrated individually for optimal accuracy.


- 63 -

Two temperature sensors, a Pt-100 and Thermistor, are integrated for compatibility with every data logger. The temperature sensor is used to provide information to correct the infrared readings for the temperature of the instrument housing. Care has been taken to place the long-wave sensors close to each other and close to the temperature sensors. This assures that the temperatures of the measurement surfaces are the same and accurately known, which improves the quality of the long-wave measurements. The design is very light in weight and has an integrated sun shield that reduces thermal effects on both long-wave and short-wave measurements. The cables are yellow with waterproof connectors as used with all our new radiometers. The mounting rod can be unscrewed for transport.

Barometer The Vaisala Barometer PTB110 (Figure 27) is designed for accurate barometric pressure measurements at room temperature and for general environmental pressure monitoring over a wide temperature range. It uses the Vaisala BAROCAP® sensor, a silicon capacitive absolute pressure sensor developed by Vaisala for barometric pressure measurement applications.

Figure 27: Vaisala Barometer PTB110.

Open path CO2/H2O Gas Analyzer The LI-COR LI-7500 DS (Figure 28) is a high speed, high precision, non-dispersive infrared (NDIR) gas analyzer that accurately measures densities of carbon dioxide and water vapor in turbulent air structures. With the eddy covariance technique, these data are used in conjunction with sonic anemometer air turbulence data to determine the fluxes of CO2 and water vapor.


- 64 -

Figure 28: LI-COR LI-7500A CO2/H2O analyser.

ARPA-ER Mobile laboratories ARPA-ER mobile laboratories (Figure 29) are vans equipped for air quality and meteorological measurements.

Figure 29: Example of ARPA-ER mobile laboratories.


- 65 -

In particular mobile laboratories are equipped for continuous measurements of atmospheric pollutants such as nitrogen oxides (NOx), carbon monoxide (CO), sulphur dioxide (SO2), ozone (O3), BTEX (benzene, toluene, ethylbenzene, and xylenes), and particulate matter (PM10 and PM2.5). In atmospheric chemistry, NOx is a generic term for the nitrogen oxides mostly relevant for air pollution, namely nitric oxide (NO) and nitrogen dioxide (NO2). These gases contribute to the formation of smog, particulate matter and acid rains, as well as tropospheric ozone. NOx gases are usually produced from the reaction between atmospheric nitrogen and oxygen during combustion of fuels in air at high temperatures, such as that occurring in car engines. In fact, oxygen and nitrogen do not react at ambient temperatures, but at high temperatures they undergo an endothermic reaction producing various oxides of nitrogen. Such high temperatures are normally encountered inside an internal combustion engine or a power station boiler, during the combustion of a mixture of air and a fuel, and naturally in lighting flash. Thermal NOx refers to NOx formed through high temperature oxidation of the diatomic nitrogen found in combustion air. The formation rate is primarily a function of temperature and the residence time of nitrogen at that temperature. At high temperatures (> 1600°C) molecular nitrogen and oxygen in the combustion air disassociate into their atomic states and participate in a series of reactions. Below the three main reactions mechanism producing thermal NOx:

N2+O→NO+N N+O2→NO+O N+OH→NO+H

All three reactions are reversible. In atmospheric chemistry, NOx stands for the total concentration of NO and NO2; the ratio NO/NO2 is determined by the intensity of sunshine (which converts NO2 to NO) and the concentration of ozone (which reacts with NO to form again NO2). In the presence of excess oxygen, NO reacts with it to form NO2, with a time depending on the concentration in air. Carbon monoxide is a colorless, odorless, tasteless gas that is slightly dense than air. It is toxic to hemoglobic animals when encountered in concentrations higher than 35 ppm. In the atmosphere, it is spatially variable and short lived, with a role in the formation of tropospheric ozone. It is produced from the partial oxidation of carbon-containing compounds, when there is not enough oxygen to produce CO2, such as when operating a stove or an internal combustion engine in an enclosed space. Carbon monoxide is present in small amounts in the atmosphere, mainly produced by volcanic activity but also from natural and man-made fires. Fossil-fuel combustion also contributes to its production. Besides from its high biological toxicity due to its combination with hemoglobin to produce carboxyhemoglobin, which usurps the space in


- 66 -

hemoglobin that normally carries oxygen but is ineffective for delivering oxygen to tissues, it is also a short-lived greenhouse gas and has an indirect radiative forcing effect by elevating concentrations of methane and tropospheric ozone through chemical reactions with other atmospheric constituents (e.g., the hydroxyl radical OH) that would otherwise destroy them. Also, it can be eventually oxidized to CO2 through natural reactions in the atmosphere. Together with aldehydes, it can take part to the series of chemical reactions forming photochemical smog. In particular, it reacts with OH to produce a radical intermediate HOCO, which rapidly transfers its radical hydrogen to O2 to form peroxy radical (HO2) and carbon dioxide. HO2 subsequently reacts with NO to form NO2 and OH. Since OH is formed during the formation of NO2, the balance of the sequence of reactions starting with CO and leading to the formation of O3 is

CO+O2+h* → CO2+O3

where refers to the photon of light absorbed by NO2. In urban areas, it is a temporary atmospheric pollutant from the exhaust of internal combustion engines but also from incomplete combustion of various other fuels (wood, coal, charcoal, oil, natural gas, and trash). Sulphur dioxide SO2 is a toxic gas with a pungent, irritating smell. It is the product of burning sulfur or burning materials containing sulfur

S+O2→SO2

It is a noticeable component in the atmosphere, especially produced by volcanic eruptions. It is a major air pollutant and has significant impacts on human health. In addition, the atmospheric concentration of SO2 can affect the habitat suitability for plant communities, as well as animal life. It is a precursor to acid rain and atmospheric particulate matter. Ozone is a gas naturally present in the atmosphere. Most ozone (91%) is found in the stratosphere (from 10-16 km above Earth’s surface up to about 50 km altitude). The stratospheric region with the highest ozone concentration is commonly known as “ozone layer”, extending all over the globe with some variations in altitude and thickness. The remaining O3, about 10%, is found in the troposphere. Although being chemically identical, stratospheric and tropospheric ozone have very different roles in the atmosphere and very different effects on living beings. Stratospheric ozone, in fact, plays a beneficial role by absorbing most of the biologically damaging ultraviolet sunlight (UV-B). The absorption of UV radiation by ozone creates a source of heat, which actually forms the stratosphere itself. Without O3 layer filtering action, more of the Sun’s UV-B radiation would penetrate the atmosphere and would reach the Earth’s surface. On the contrary, at the Earth’s surface, O3 comes into direct contact with life-forms and displays its destructive side, with harmful effects on crop production, forest growth, and human health. Near-surface ozone is a key component of photochemical “smog”. Ozone also acts as a greenhouse gas, absorbing some of the infrared energy emitted by the Earth. The majority of tropospheric ozone formation occurs when NOx, CO and Volatile Organic Compounds (VOCs) react in the atmosphere in the presence of sunlight. Motor vehicle exhaust,


- 67 -

industrial emissions, and chemical solvents are the major anthropogenic sources of these chemicals. Although these precursors often originate in urban areas, winds can carry NOx hundreds of kilometers far apart, causing ozone formation to occur in less populated regions as well. The chemical reactions involved in tropospheric ozone formation are a series of complex cycles in which CO and VOCs are oxidized to form water vapor and CO2.

∙OH+CO→∙HOCO ∙HOCO+O2→HO2+CO2

HOCO then react with NO to give NO2 which is photolyzed to give atomic oxygen and, through reaction with oxygen, O3.

HO2∙+NO→∙OH+NO2 NO2+hν→NO+O(3P) 2(34) + 25 → 23

The balance of this sequence of chemical reactions is CO+2O2+hν→CO2+O3

This cycle involving HOx and NOx is terminated by the reaction of OH with NO2 to form nitric acid or by the reaction of peroxy radicals with each other to form peroxides. BTEX form an important group of aromatic Volatile Organic Compounds (VOCs) because of their role in tropospheric chemistry and the risk posed to human health. Together with PAHs, they are grouped in the list of potential carcinogens. In addition, they undergo complex photochemical reactions giving rise to a number of highly toxic and carcinogenic secondary pollutants, such as tropospheric ozone and peroxyacyl nitrate (PAN), which are injurious not only to human health but also to vegetation (Saxena and Ghosh, 2012). In urban atmosphere, BTEX constitute up to 60% of non-methane VOCs, and are considered an efficient indicator of pollution arising from road traffic (because of increased global consumption of gasoline). In particular, among BTEX, benzene has been chosen as a prime target for assessment of pollution levels in the urban atmosphere as it is considered to be a genotoxic carcinogen with fatal and mutagenic effects. According to Brocco et al. (1997), “the reaction of the BTEX with OH and or nitrate (NO3) radicals serves as the dominant degradation processes for aromatic VOCs in the atmosphere and the resulting products contribute to secondary organic aerosol (SOA) formation by nucleation and condensation”. Odum et al. (1997) reported that “the reaction of toluene with NOx in the presence of a light source formed SOA with a significant aerosol yield and therefore, aromatic VOCs influence gas phase pollutants directly and particle-phase pollutants indirectly”. In the presence of NOx, BTEX react with OH radicals to form ozone thus modifying the oxidizing capacity of the atmosphere (Atkinson et al., 2000). Particulate matter, also known as particle pollution, is a complex mixture of extremely small particles and liquid droplets that get into the air. This complex mixture includes both organic and inorganic particles, such as dust, pollen, soot, smoke, and liquid droplets. These particles very greatly in size, composition, and origin. Particles can be directly emitted (primary particles),


- 68 -

for instance when dust is carried by wind, or indirectly formed (secondary particles), when gaseous precursors previously emitted into the air turn into particulate matter. It is convenient to classify particles by their aerodynamic diameter properties because: 1) these properties govern the transport and removal of particles from the air; 2) they also govern their deposition within the respiratory system; 3) they are associated with the chemical composition and sources of particles. All these properties are conveniently summarized by the aerodynamic diameter, i.e., the size of a unit-density sphere with the same aerodynamic characteristics. Particles are usually sampled and described on the basis of their aerodynamic diameter, usually called simply the particle size. Mass and composition in urban environments tend to be divided into two main groups: coarse and fine particles. The barrier between these two fractions of particles lies between 1 and 2.5 μm, but is generally fixed by convention at 2.5 μm in aerodynamic diameter for measurement purposes. PM stands for particulate matter suspended in air, while PM followed by a number refers to all particles with a certain maximum size (aerodynamic diameter), including all smaller particles. So, PM10(2.5) is particulate matter with an aerodynamic diameter of up to 10(2.5) μm; so, PM10 includes PM2.5 which includes ultrafine particles (PM0.1). The largest particles (coarse) are mechanically produced by the break-up of larger solid particles: for instance, wind-blown dust from agriculture, uncovered soil, unpaved roads, or mining operations. Traffic produces road dust and air turbulence that can stir up road dust. Near coasts, evaporation of sea spray can produce large particles. Pollen grains, spores, plant and insect parts are also in this size range. Fine particles are largely formed from reaction of gaseous precursors. The smallest particles, less than 0.1 μm, are formed by nucleation, i.e., condensation of low-vapor-pressure substances formed by high-temperature vaporization or by chemical reactions in the atmosphere to form new particles. Sub-micrometre sized particles can result from condensation of metals or organic compounds that are vaporized in high-temperature combustion processes or by condensation of gases that have been converted in atmospheric reactions to low-vapor pressure substances. For instance, SO2 is oxidized in the atmosphere to form sulphuric acid (H2SO4) which can be neutralized by NH3 to form ammonium sulfate. NO2 is oxidized to nitric acid (HNO3) which in turn can react to form ammonium nitrate. Secondary sulphate and nitrate particles are usually the dominant components of fine particles. The instrumental setup on the mobile laboratories consists of:

• a chemiluminescence NO/NO2/NOx API 200E analyser; • a gas filter correlation CO API 300E analyser; • an UV fluorescence SO2 API 100E analyser; • a Thermo Scientific UV photometric Model 49i Ozone Analyser; • a GC/PID Chromatotec Air Toxic for automatic monitoring of BTEX; • a FAI SWAM 5a Dual Channel monitor for sampling PM10 and PM2.5.

Meteorology measurements in mobile laboratories include: wind speed measured by cup anemometer and wind direction measured by wind vane; atmospheric pressure measured by


- 69 -

a barometer; temperature and relative humidity measured by a thermohygrometer; rain as measured by a rain gauge.

Ceptometer The AccuPAR model LP-80 (Figure 30) is a menu-driven, battery-operated linear PAR ceptometer, used to measure light interception in plant canopies, and to calculate Leaf Area Index (LAI). It consists of an integrated microprocessor-driven datalogger and one probe. The probe contains 80 independent sensors, spaced 1cm apart. The photosensors measure PAR (Photosynthetically Active Radiation) in the 400-700nm waveband. The AccuPAR displays PAR in units of micromols per meter squared per second (mol m-2s-1). The instrument is capable of hand-held or unattended measurement. The AccuPAR can be operated in environments with temperatures from 0 to 50 °C, and in relative humidities of up to 100%.

Figure 30: (a) Parts of the ceptometer; (b) Front view of the ceptometer.

Within a plant canopy, Accu-PAR measures a PAR which is a combination of radiation transmitted though the canopy and radiation scattered by leaves within the canopy. Since the complete model of transmission and scattering (Norman and Jarvis) is very complex and not suitable for inversion, LP-80 determines LAI from a formula suggested by Norman (1979) suggested a simple light scattering model giving the fraction of transmitted PAR , τ (ratio of PAR measured below the canopy to PAR above the canopy), below a canopy of LAI, L

6 = exp ; <=>.@ABC D

<= EFG BC=<

(2)

where fb is the fraction of incident PAR which is beam, a is the leaf absorptivity in the PAR band (AccuPAR assumes 0.9 in LAI sampling routines), and K is the extinction coefficient for the canopy. Inverting equation 2 gives the following

H = <= E

FG BC=< IJ K

;(<=>.@ABC) (3)


- 70 -

whose result is within a few percent of values derived from the complete Norman-Jarvis model. Ceptometer was used by several authors for LAI estimation in several types of discontinuous canopies (e.g., Brenner et al. 1995; Peper and McPherson 1998; Hyer and Goetz 2004; Serrano and Peñuelas 2005). Experiments conducted in Florida's Gainesville Sun described that thicker tree canopies in Gainesville led to lower consumer power usage per capita than in Ocala (Jensen, 2000). AccuPAR LP-80 measurements can be used to quantify effect of riparian vegetation, removal on stream energy balance that have occurred through management, and can provide inputs to models of stream temperature. Variable Description

PAR (µm-2s-2)

PAR (Photosynthetically Active Radiation) is defined as the radiation in the 400 to 700 nanometer waveband. It represents the portion of the spectrum which plants use for photosynthesis. Under a plant canopy, radiation levels can vary from full sun to almost zero over the space of a few centimetres. Therefore, reliable measurement of PAR requires many samples at different locations under the canopy. Intercepted PAR data can be used for determining important parameters of canopy structure and for the calculation of LAI.

Tau (τ) It is defined as the ratio of below canopy PAR measurements to the most recent above canopy PAR value. It is measured automatically by the instrument, based upon the PAR readings you make.

LAI (m2m-2)

LAI (Leaf Area Index) is defined as the area of leaves per unit area of soil surface. The AccuPAR calculates LAI based on the above and below-canopy PAR measurements along with other variables that relate to the canopy architecture and position of the sun. These variables are the zenith angle, a fractional beam measurement value (automatically calculates), and a leaf area distribution parameter (also known as x) for particular canopy analyzed.

Zenith Angle (z)

Zenith angle can be defined as the angle that sun makes with respect to the zenith, or the point in the sky directly overhead, vertical to where you stand. The zenith is defined as being 0° and the horizon is 90°. The zenith angle of the sun is necessary for calculation of certain canopy structure parameters, such as LAI.

Fraction of Beam Radiation

(Fb)

Fractional beam radiation is the ratio of direct beam radiation coming from the sun to radiation coming from all ambient sources like the atmosphere or reflected from other surfaces. A fractional beam radiation value is necessary for calculation of LAI using PAR data. The AccuPAR obtains this value by comparing the above canopy PAR measurement to the calculated value of potential incoming solar radiation at your location and zenith angle.

Leaf Distributio

Leaf Distribution Parameter (also known as Chi or x) refers to the distribution of leaf angles within a canopy. The parameter x is the ratio of the length of the


- 71 -

n Parameter

(x)

horizontal to the vertical axis of the spheroid described by the leaf angle distribution of a canopy. It can also be measured as the ratio of the projected area of an average canopy element (a leaf, for example) on a horizontal plane to its projection on a vertical plane. The default value for x is 1.0, which assumes the canopy angle distribution to be spherical. For onions, (vertical crop) x would be about 0.7, while on the other extreme, strawberries, would have a x value of about 3 (horizontal crop).

Table 9: Description of variables in Ceptometer AccuPAR LP-80.

Experimental protocol In the following, we describe the intensive field campaign in Bologna.

Thermographic campaign The two thermal cameras will be used to analyse temperature distribution of building façades and ground surfaces in Laura Bassi Veratti and Marconi Sts. Images at the two sites will be simultaneously taken during a 24-hour acquisition with regular intervals of 1 hour (total of 12 acquisitions in 24 hours). The day will be selected according to the weather forecast conditions, as a clear-sky, calm wind, day during the week of 20-25th August. This choice will allow to collect images at 12:00 (close to the maximum surface temperature), 14:00, 16:00 (close to the maximum air temperature), 18:00, 20:00, 22:00 (close to maximum UHI intensity). This will be possible using the two hand-held IR cameras by an operator on foot, allowing us to move quickly through normal working day traffic and narrow spaces. To be able to maintain a similar resolution for all images, several shots of portions of the same building façade at several heights will be taken. Images will be taken using a standard camera set-up, and then the images will be analysed using the software FLIR quick-report 1.2 where all analysis parameters will be settled. Analysed building will be selected on the basis of the homogeneity of construction material and the absence of obstacles (balconies, eave, etc.), metal or glass. Ground measurements in the centre of street crossings will be also carried out.

1.1.2.2 Air quality measurements At both sites, ARPA-ER mobile laboratories are located along the streets (Figure 31) and are collecting high time resolution (1 minute) measurements of air quality gaseous pollutants (NOx, O3, CO concentrations) and meteorological (temperature, relative humidity) parameters; hourly measurements of BTEX (benzene, toluene and xylene), SO2 concentrations and other meteorological parameters (pressure and wind speed/direction); daily measurements of particulate matter (PM10 and PM2.5) concentrations.


- 72 -

Figure 31: ARPA-ER mobile laboratories in Laura Bassi Veratti St. (44°29’00.52’’N, 11°22’03.11’’E) and Marconi

St. (44°29’56.21’’N, 11°20’18.56’’E).

Flow measurement campaign At both sites, three GILL R3-50 ultrasonic anemometers are installed at three different heights (Figure 32). In particular, two sonic anemometers are positioned inside the street canyons: the first (Anemometer 1) is just above the roof of the ARPA-ER mobile laboratories, just below the tree crown in Laura Bassi Veratti St. at z = 4.5 m agl; and the second anemometer (Anemometer 2) is positioned on banisters of a balcony at the second floors of a 15m high building (Figure 33), just above the tree crowns in Laura Bassi Veratti St. Thermo-hygrometers HC2S3 and Vaisala PTB110 are also installed at the first level to complement measurements with high time resolution temperature, relative humidity and atmospheric pressure data.


- 73 -

Figure 32: Positioning of the sonic anemometers in Laura Bassi Veratti St.

Figure 33: Second anemometer positioned on banisters of a balcony at the second floor of a 15m building in

Laura Bassi Veratti St. and Marconi St.

The third anemometer (Figure 34) is positioned together with a CNR4 net radiometer, and a LI-840A LICOR respectively for fast reliable measurements of solar radiation and atmospheric water vapor and CO2 concentrations, on the roof of a 15m building, at 3 m from the floor of the roof building, at a height of 18 m agl. All anemometers are set to collect measurements every 50 ms. The LI-7500A LICOR, when coupled to sonic anemometers, allows to identify CO2 and H2O fluxes.


- 74 -

Figure 34: Third anemometer positioned on the roof of a 15m building above the street canyon.

Simultaneous measurements at the two sites enables for a direct comparison between two urban street canyons, one with trees and one without. All the installation is already functioning and will remain active until the first half of September. The idea is also to compare measurements taken during the month of August, when traffic is hardly decreased in Bologna due to the summer holidays, with those of September, when traffic will be back to normal levels. A further experimental campaign is also planned during winter 2017-2018 in order to study the winter environmental conditions in Bologna, with same experimental setup. Beside the reduction of the vegetative element influence, in that period several phenomena typical of the Po Valley occur, such as thermal inversions, frequent stagnant conditions due to reduced ventilation, fog and reduced incoming solar radiation which deeply affect pollution level inside and outside the city.

LAI/LAD estimation In situ measurements of the PAR photosynthetically active radiation absorbed by the radiation will be carried on. Three intensive measurements will be carried on capturing the evolution of leaf fall (October, November, December). Due to the large number of trees in Laura Bassi Veratti St., we will select a tree representative of two features: tree height and density of the canopy. All measurements are going to be taken parallel to the ground and perpendicularly to the orientation of Laura Bassi Veratti St. Five replicas will be done at the same measurement point just near the crown (where the sensor measures unobstructed PAR) and at its base (where the LAI is supposed to be maximum).


- 75 -

The two points will be chosen at different heights so as to have a qualitative and quantitative analysis of the radiation reaching the lower levels of the vegetation.

Methodology for GI evaluation at neighbourhood-scale assessment for Vantaa

In earlier analysis (Votsis 2017) we have shown that optimal GI interventions, when housing markets are used as indicators, change from one neighbourhood to the other in the Helsinki, notably due to factors relating to density, scarcity of natural land uses, and urban development drivers. Here we will be able to focus this knowledge to GI-induced air quality benefits. The hedonic pricing method described in section 5.4 will be applied in Vantaa in a spatially disaggregate fashion and the results are first derived at the neighbourhood scale. What is described in section 5.4 is therefore applicable here, as well. Additionally, it is possible to derive a spatially variable willingness to pay (described in 5.4) at neighbourhood resolutions, whereas the estimated neighbourhood impacts are controlled for other property-level or neighbourhood-level marginal benefits and costs beyond GI interventions. Although the aggregation of neighbourhood-scale impacts to city-wide impacts uses additional economic information (see section 5.4), the basic blocks are the costs-benefits at the property and neighbourhood scales, since they are derived from property-level microeconomic behaviour. In terms of data, this is possible due to the fact that the available real estate transaction data are point observations at their precise geographical location and that the GI and other built-environmental information overlaid on the economic data comes at a 10-meter spatial resolution. The high spatial resolutions of the SURFEX and TCI simulations are in this case enabling the neighbourhood-scale economic analysis, resulting in a spatially resolved social, economic, ecological, and climate dataset for neighbourhood-level resolution of economic impacts.

SWOT analysis

Guildford Overall, vegetation has a positive effect on air quality in near road environment. Green infrastructure is identified as a sustainable approach to improve air quality and it has potential to combine with other interventions. The main weakness emerged are a lack of space requirement near highways to accommodate thick vegetation barriers. In addition, absence of regulatory recommendations on planting and maintaining green infrastructure in near road environment. Opportunities revealed further research can form generic recommendations. These recommendations can be combined with existing urban design regulations. The proposed field campaign is aiming to quantify the air pollutant reduction by different vegetation and their comparison expects to reveal suitable configuration in near road conditions. Study


- 76 -

also trying to generate a better understanding of pollutant reduction mechanisms of vegetation i.e. dispersion and deposition. The impact of meteorological conditions such as wind speed and direction, temperature and humidity on air quality improvement by vegetation will be clarified. In general, proposed investigations is targeting the opportunities arose during SWOT analysis. Strength Weakness • Green infrastructures are effective in

reducing pollutant concentration up to 50% in near road conditions (< 50m from highways).

• Enhances air pollution dispersion and captures through deposition

• Sustainable approach to improve air quality and mitigate climate change

• Ease in integrating with other infrastructural interventions

• Other benefits like storm water management, biodiversity and aesthetics improvement.

• Large thickness is required to reach certain levels of pollutant reduction.

• Considerable space is required to implement vegetation barrier near major roads.

• Frequent maintenance such as pruning, weeding, manuring and watering are needed

• No guidelines for planting and maintaining vegetation are available.

• Suitability of green infrastructure and its dimensions requirements are site specific

Opportunities Threats • Detailed studies can deliver generic

recommendations for green infrastructure implementation.

• Further investigations can provide better understanding of dispersion and deposition parts of pollutant reduction as well as Optimum dimensions of vegetation in different built environment conditions

• Formation of policy recommendations on green infrastructures and integration of these to the existing city design regulations

• Scientific community and general public are searching for greener alternatives for solving air pollution and climate change

• Difficult to produce generalised solution for all roads

• Improper design can deteriorate air quality

• Lesser importance is given compared while designing highway design

Table 10: SWOT analysis for Guildford neighborhood scale evaluation.

Bologna strength Weaknesses


- 77 -

• It is an experimental analysis, so it is scientifically focused on Bologna as target city;

• Involves collaboration between several authorities (municipality, environmental agency, University)

• Measurement place is crucial. It has to satisfy several requirements:

• The place has to be easy reachable and near to the situation we want to measure;

• If it is public place, probably permissions documents are needed;

• Different approaches are needed in presence or absence of electricity

• Weather and period affect the observed phenomena because of:

• Traffic is different throughout the year and so the pollutant measured;

• Weather conditions can rise or low pollutant levels (rain, fog...);

• Weather can be also a challenge for measurement security;

• Many instruments work in different ways and several operations are needed to make the measures comparable;

Opportunities Threats • Data taken can be useful also beyond the

purpose of the project; • We will be able to better understand

Green Infrastructure reliability: • How to place them; • Under what circumstances are effective in

reducing pollution levels; • Can be an instrument for administrators

for green areas managing; • It can lead to future collaborations

between University and local authorities (municipality, environmental agency…);

• • It is not always possible to have a long

experimental campaign. • A weather element can compromise the

correct data retrieval because: • Extraordinary weather conditions could

make the instruments unreliable • Because of a weather configuration,

measurements could not appreciate any difference between tree-lined streets and trees without trees;

• The finding could not be put into practice by urban city planners if the project communication does not reach the people in charge;

Table 11: SWOT analysis for Bologna neighborhood scale evaluation.

Vantaa The SWOT analysis described in section 5.8 applies here as well, since the analytical methodology of the economic impacts is largely bottom-up (even though the neighborhood analysis is not simply


- 78 -

aggregated to the city-wide scale, but additional fiscal elements and cost-benefit considerations are inserted). Some additional strengths and threats apply here however, and so the SWOT table is as follows: Strengths Weaknesses

• Able to show that a good GI intervention at one neighborhood will not necessarily work at another neighborhood, because a key driver for the economic impacts of GI at a certain area is its location in the network of urban economic activities.

• Able to estimate evidence-based real-world changes (in urban economic behavior and socioeconomic impacts) resulting from GI rather than hypothetical changes (stated, for instance, in interviews)

• As people see GI as a bundle of goods rather than a list of ecosystem services, the hedonic pricing method is the right way to approach the socioeconomic value of GI, especially in a spatially explicit urban planning context.

• Complementary analysis of the value of thermal comfort index, air quality changes, aesthetic value and energy saving is able to break down the value of GI into components.

• Some market imperfections, relating notably to imperfect and asymmetrical information in the housing market, may result in the estimated value being lower than the sum of all the benefit components.

• We are not able to distinguish between which specific ecosystem service the value is based upon.

• In cases where the GI intervention (or its impact) is extensive, the equilibrium is different after the simulation, so in principle the marginal values could change as well.

Opportunities Threats • Compare the values of alternative GI options or

interventions and provide recommendations on how the GI should be implemented optimally; for instance, what kind of GI is optimal at a given location and how much of it, as well as what kind of fiscal policies are needed to make a GI investment economically feasible and sustainable.

• Aggregation: uncertainty for urban policy can be generated when results of a bottom-up neighborhood impact analysis, in which location decisions matter, diverge from result of a city-wide analysis in which only the overall welfare impacts matter.

• Simulation results may not describe the real changes after such GI implementation. For this reason, we will perform the aforementioned complement approaches as well, and compare the results obtained with each approach.

Table 12: SWOT analysis for Vantaa neighborhood scale evaluation.


- 79 -

7 References / Bibliography ABHIJITH, K. V., KUMAR, P., GALLAGHER, J., MCNABOLA, A., BALDAUF, R., PILLA, F., BRODERICK, B., DI SABATINO, S. & PULVIRENTI, B. 2017. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments – A review. Atmospheric Environment, 162, 71-86.

Agency, E.E., 2015. Air quality in Europe — 2015 report, Available at: papers2://publication/uuid/1D25F41B-C673-4FDA-AB71-CC5A2AD97FDD.

Al-Dabbous, A.N. & Kumar, P., 2014. The influence of roadside vegetation barriers on airborne nanoparticles and pedestrians exposure under varying wind conditions. Atmospheric Environment, 90, pp.113–124. Available at: http://dx.doi.org/10.1016/j.atmosenv.2014.03.040.

Atkinson R., 2000. Atmospheric chemistry of VOCs and NOx. Atmos Environ 34, 01-2063.

Baldauf, R. et al., 2013. Integrating Vegetation and Green Infrastructure into Sustainable Transportation Planning. TR News, 288(288), pp.14–18. Available at: https://ezp.sub.su.se/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=92951459&site=ehost-live&scope=site.

Baldauf, R., 2017. Roadside vegetation design characteristics that can improve local, near-road air quality. Transportation Research Part D: Transport and Environment, 52, pp.354–361. Available at: http://linkinghub.elsevier.com/retrieve/pii/S136192091630685X.

Baldauf, R.W. et al., 2013. Air quality variability near a highway in a complex urban environment. Atmospheric Environment, 64, pp.169–178. Available at: http://dx.doi.org/10.1016/j.atmosenv.2012.09.054.

BARNES, M. J., BRADE, T. K., MACKENZIE, A. R., WHYATT, J. D., CARRUTHERS, D. J., STOCKER, J., CAI, X. & HEWITT, C. N. 2014. Spatially-varying surface roughness and ground-level air quality in an operational dispersion model. Environ Pollut, 185, 44-51.

Barros, N. et al., 2013. How wide should be the adjacent area to an urban motorway to prevent potential health impacts from traffic emissions? Transportation Research Part A: Policy and Practice, 50, pp.113–128. Available at: http://dx.doi.org/10.1016/j.tra.2013.01.021.

Batterman, S., 2013. The Near-Road Ambient Monitoring Network and Exposure Estimates for Health Studies. EM (Pittsburgh, Pa.), 2013(7), pp.24–30. Available at: http://www.ncbi.nlm.nih.gov/pubmed/25705106\nhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4335686.

Batterman, S., Chambliss, S. & Isakov, V., 2014. Spatial resolution requirements for traffic-related air pollutant exposure evaluations. Atmospheric Environment, 94, pp.518–528. Available at: http://dx.doi.org/10.1016/j.atmosenv.2014.05.065.

Bengtsson, Lisa, Ulf Andrae, Trygve Aspelien, Yurii Batrak, Javier Calvo, Wim de Rooy, Emily Gleeson, Bent Hansen-Sass, Mariken Homleid, Mariano Hortal, Karl-Ivar Ivarsson, Geert Lenderink, Sami Niemelä, Kristian Pagh Nielsen, Jeanette Onvlee, Laura Rontu, Patrick Samuelsson, Daniel Santos


- 80 -

Muñoz, Alvaro Subias, Sander Tijm, Velle Toll, Xiaohua Yang, and Morten Ødegaard Køltzow, (2017): The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System. Mon. Wea. Rev., 145, 119-1935. DOI: 10.1175/MWR-D-16-0417.

Boonen, E. and Beeldens, A., 2014. Recent photocatalytic applications for air purification in Belgium. Coatings, 4(3), pp.553-573.

Brantley, H.L. et al., 2014. Field assessment of the effects of roadside vegetation on near-road black carbon and particulate matter. Science of the Total Environment, 468–469, pp.120–129. Available at: http://dx.doi.org/10.1016/j.scitotenv.2013.08.001.

Brenner A.J., Cueto Romero M., Garcia Haro J., Gilabert M.A., Incoll L.D., Martinez Fernandez J., Porter E., Pugnaire F.I., Younis M.T., 1995. A comparison of direct and indirect methods for measuring leaf and surface areas of individual bushes, Plant, Cell and Environment 18, 1332-1340.

Brocco D., Fratarcangelli R., Lepore L., Petricca M., Ventrone I., 1997. Determination of aromatic hydrocarbons in urban air of Rome. Atmos Environ 31, 557-566.

Brook, R.D. et al., 2010. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the american heart association. Circulation, 121(21), pp.2331–2378.

Bruse M., Fleer H., 1998. Simulating surface-air-plant interactions inside urban environments with a three dimensional numerical model. Environ Model Softw 13 (3-4), 373-384.

Cahill, T. a. et al., 2011. Very Fine and Ultrafine Metals and Ischemic Heart Disease in the California Central Valley 2: 1974–1991. Aerosol Science and Technology, 45(9), pp.1135–1142.

Cahill, T.A. et al., 2016. Transition metals in coarse, fine, very fine and ultra-fine particles from an interstate highway transect near Detroit. Atmospheric Environment, 145, pp.158–175.

Carrier, M. et al., 2014a. Ambient air pollution concentration in montreal and environmental equity: Are children at risk at school? Case Studies on Transport Policy, 2(2), pp.61–69.

Carrier, M. et al., 2014b. The application of three methods to measure the statistical association between different social groups and the concentration of air pollutants in Montreal: A case of environmental equity. Transportation Research Part D: Transport and Environment, 30, pp.38–52. Available at: http://dx.doi.org/10.1016/j.trd.2014.05.001.

Chen, L. et al., 2016. Experimental examination of effectiveness of vegetation as bio-filter of particulate matters in the urban environment. Environmental Pollution, 208, pp.198–208. Available at: http://dx.doi.org/10.1016/j.envpol.2015.09.006.

Clark, N.A. et al., 2010. Effect of early life exposure to air pollution on development of childhood asthma. Environmental Health Perspectives, 118(2), pp.284–290.

DEFRA 2016. Emissions factors toolkit v7.0 user guide. Department for Environment, Food & Rural Affairs.

DEFRA 2017. Air quality plan for nitrogen dioxide (NO2) in UK (2017). Department for Environment Food and Rural Affairs.


- 81 -

DFT 2015. Road investment strategy: for the 2015/16 – 2019/20 road period. Department for transport.

DFT 2016. Travel time measures for the strategic road network: July 2015 to June 2016 report. Department for Transport.

DFT 2017. Road congestion and travel times. Department for Transport.

Di Sabatino S., Hedquist B.C., Carter W., Leo L.S., Fernando H.J.S., 2009. Phoenix urban heat island experiment: effects of built elements. Proc. Of the Eighth Symposium on the Urban Environment, Phoenix, Arizona

Dobre A., Arnold S.J., Smalley R.J., Boddy J.W.D., Barlow J.F., Tomlin A.S., Belcher S.E., 2005. Flow filed measurements in the proximity of an urban intersection in London, UK. Atmospheric Environment 39, 4647-4657.

EPA, 2016. Health Impacts of Near Roadway Air Pollution and Mitigation Strategies Different types of roadways.

European Environment Agency, 2013. Air quality in Europe—2013 Report: EEA report no 9/2013, Available at: http://www.eea.europa.eu/publications/air-quality-in-europe-2013.

European Environment Agency, 2015. State and Outlook 2015 the European Environment,

Evans, K.A. et al., 2014. Increased ultrafine particles and carbon monoxide concentrations are associated with asthma exacerbation among urban children. Environmental Research, 129, pp.11–19. Available at: http://dx.doi.org/10.1016/j.envres.2013.12.001.

Fantozzi, F. et al., 2015. Spatio-temporal variations of ozone and nitrogen dioxide concentrations under urban trees and in a nearby open area. Urban Climate, 12(2), pp.119–127. Available at: http://dx.doi.org/10.1016/j.uclim.2015.02.001.

Finn, D. et al., 2010. Tracer studies to characterize the effects of roadside noise barriers on near-road pollutant dispersion under varying atmospheric stability conditions. Atmospheric Environment, 44(2), pp.204–214. Available at: http://dx.doi.org/10.1016/j.atmosenv.2009.10.012.

GALLAGHER, J., BALDAUF, R., FULLER, C. H., KUMAR, P., GILL, L. W. & MCNABOLA, A. 2015. Passive methods for improving air quality in the built environment: A review of porous and solid barriers. Atmospheric Environment, 120, 61-70.

GALLAGHER, J., GILL, L. W. & MCNABOLA, A. 2012. Numerical modelling of the passive control of air pollution in asymmetrical urban street canyons using refined mesh discretization schemes. Building and Environment, 56, 232-240.

GALLAGHER, J., GILL, L. W. & MCNABOLA, A. 2013. The passive control of air pollution exposure in Dublin, Ireland: A combined measurement and modelling case study. Science of The Total Environment, 458–460, 331-343.

GBC 2016. 2016 Air quality annual status report (ASR). Guildford Borough Council.

Guerreiro, C. et al., 2016. Air quality in Europe — 2016 report,


- 82 -

Hagler, G.S.W. et al., 2012. Field investigation of roadside vegetative and structural barrier impact on near-road ultrafine particle concentrations under a variety of wind conditions. Science of the Total Environment, 419, pp.7–15. Available at: http://dx.doi.org/10.1016/j.scitotenv.2011.12.002.

Hagler, G.S.W., Tang, W., et al., 2011. Model evaluation of roadside barrier impact on near-road air pollution. Atmospheric Environment, 45(15), pp.2522–2530. Available at: http://dx.doi.org/10.1016/j.atmosenv.2011.02.030.

Hagler, G.S.W., Yelverton, T.L.B., et al., 2011. Post-processing method to reduce noise while preserving high time resolution in aethalometer real-time black carbon data. Aerosol and Air Quality Research, 11(5), pp.539–546.

He, M. & Dhaniyala, S., 2012. Vertical and horizontal concentration distributions of ultrafine particles near a highway. Atmospheric Environment, 46, pp.225–236. Available at: http://dx.doi.org/10.1016/j.atmosenv.2011.09.076.

HEI, 2010. Traffic-related air pollution: a critical review of the literature on emissions, exposure, and health effects. Health Effects Institute, Special Re(January), pp.1–386. Available at: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Traffic-Related+Air+Pollution:+A+Critical+Review+of+the+Literature+on+Emissions,+Exposure,+and+Health+Effects#1.

HELMS, M. M. & NIXON, J. 2010. Exploring SWOT analysis – where are we now? Journal of Strategy and Management, 3, 215-251.

HIRABAYASHI, S., KROLL, C. N. & NOWAK, D. J. 2011. Component-based development and sensitivity analyses of an air pollutant dry deposition model. Environmental Modelling & Software, 26, 804-816.

HIRABAYASHI, S., KROLL, C. N. & NOWAK, D. J. 2012. Development of a distributed air pollutant dry deposition modeling framework. Environ Pollut, 171, 9-17.

HIRABAYASHI, S., KROLL, C. N. & NOWAK, D. J. 2015. i-Tree eco dry deposition model descriptions.

Hyer E.J., Goetz S.J., 2004. Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation: assessments from a boreal forest site. Agric for Meteorol 122, 157-174, doi:10.1016/j.agrformet.2003-09.013

Islam, M.N. et al., 2012. Pollution attenuation by roadside greenbelt in and around urban areas. Urban Forestry and Urban Greening, 11(4), pp.460–464. Available at: http://dx.doi.org/10.1016/j.ufug.2012.06.004.

JANHÄLL, S. 2015. Review on urban vegetation and particle air pollution – Deposition and dispersion. Atmospheric Environment, 105, 130-137.

JEANJEAN, A. P. R., HINCHLIFFE, G., MCMULLAN, W. A., MONKS, P. S. & LEIGH, R. J. 2015. A CFD study on the effectiveness of trees to disperse road traffic emissions at a city scale. Atmospheric Environment, 120, 1-14.


- 83 -

JEANJEAN, A. P. R., MONKS, P. S. & LEIGH, R. J. 2016. Modelling the effectiveness of urban trees and grass on PM2.5 reduction via dispersion and deposition at a city scale. Atmospheric Environment, 147, 1-10.

Jensen R.R., 2000. Measurements, comparison, and use of remotely derived leaf area index predictors. Dissertation, University of Florida, 133 pages.

Jylhä K, Kalamees T, Tietäväinen H, Ruosteenoja K, Jokisalo J, Hyvönen R, Ilomets S, Saku S, Hutila A, (2011), Rakennusten energialaskennan testivuosi TRY2012 ja arviot ilmastonmuutoksen vaikutuksista. (Test reference year 2012 for building energy demand and impacts of climate change), Finnish Meteorological Institute, Reports, 2011:6, 110 p

Karner, A.A., Eisinger, D.S. & Niemeier, D.E.B.A., 2010. Near-Roadway Air Quality : Synthesizing the Findings from Real-World Data. , 44(14), pp.5334–5344.

Kim, J.J. et al., 2004. Traffic-related air pollution near busy roads: The East Bay Children’s Respiratory Health Study. American Journal of Respiratory and Critical Care Medicine, 170(5), pp.520–526.

KING, E. A., MURPHY, E. & MCNABOLA, A. 2009. Reducing pedestrian exposure to environmental pollutants: A combined noise exposure and air quality analysis approach. Transportation Research Part D: Transport and Environment, 14, 309-316.

Laumbach, R.J. & Kipen, H.M., 2012. Respiratory health effects of air pollution: Update on biomass smoke and traffic pollution. Journal of Allergy and Clinical Immunology, 129(1), pp.3–11. Available at: http://dx.doi.org/10.1016/j.jaci.2011.11.021.

Louka P., Belcher S.E., Harrison R.G., 1998. Modified street canyon flow. Journal of Wind Engineering and Industrial Aerodynamics 74-76, 485-493.

Maggiotto G., Santo M., Buccolieri R., Leo L.S., Di Sabatino S., 2014. Study of the urban heat island in Lecce (Italy) by means of ADMS and ENVI-MET. Int J Environ Poll 55(1-4), doi:10.1504/IJEP.2014.065903

MARCUCCI, D. J. & JORDAN, L. M. 2013. Benefits and challenges of linking green infrastructure and highway planning in the United States. Environ Manage, 51, 182-97.

Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A., (2013), The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929-960, doi:10.5194/gmd-6-929-2013

MCNABOLA, A., BRODERICK, B. M. & GILL, L. W. 2008. Reduced exposure to air pollution on the boardwalk in Dublin, Ireland. Measurement and prediction. Environment International, 34, 86-93.


- 84 -

MCNABOLA, A., BRODERICK, B. M. & GILL, L. W. 2009. A numerical investigation of the impact of low boundary walls on pedestrian exposure to air pollutants in urban street canyons. Science of The Total Environment, 407, 760-769.

MGUNI, P., HERSLUND, L. & JENSEN, M. B. 2016. Sustainable urban drainage systems: examining the potential for green infrastructure-based stormwater management for Sub-Saharan cities. Natural Hazards, 82, 241-257.

Michelle Wilhelm, Jo Kay Ghosh, Jason Su, Myles Cockburn, Michael Jerrett, B.R., 2012. Traffic-Related Air Toxics and Term Low Birth Weight in Los Angeles County, California. Environmental Health Perspectives, (1), pp.132–138.

Mo J, Zhang Y, Xu Q, Joaquin Lamson J and Zhao R, Photocatalytic purification of volatile organic compounds in indoor air: a literature review, Atmospheric Envi¬ronment, 2009, 43, 2229-2246

Morakinyo, T.E. & Lam, Y.F., 2016. Simulation study of dispersion and removal of particulate matter from traffic by road-side vegetation barrier. Environmental Science and Pollution Research, 23(7), pp.6709–6722.

Morakinyo, T.E., Lam, Y.F. & Hao, S., 2016. Evaluating the role of green infrastructures on near-road pollutant dispersion and removal: Modelling and measurement. Journal of Environmental Management, 182, pp.595–605. Available at: http://dx.doi.org/10.1016/j.jenvman.2016.07.077.

Neft, I. et al., 2016. Simulations of aerosol filtration by vegetation: Validation of existing models with available lab data and application to near-roadway scenario. Aerosol Science and Technology, 50(9), pp.937–946. Available at: http://www.tandfonline.com/doi/full/10.1080/02786826.2016.1206653.

Nikolaou, I.E. & Evangelinos, K.I., 2010. A SWOT analysis of environmental management practices in Greek Mining and Mineral Industry. Resources Policy, 35(3), pp.226–234. Available at: http://dx.doi.org/10.1016/j.resourpol.2010.02.002.

Norman J.M. (1979). Modelling the complete crop canopy. In Modification of the Aerial Environment of Crops (eds. B.J. Barfield and J. Gerber) American Society of Agricultural Engineers, St. Joseph, MI, 249-277.

Norman J.M., and Jarvis P.G., 1974. Photosynthesis in Sitka Spruce (Picea sitchensis (Bong.) Carr.) III. Measurements of canopy structure and interception of radiation. J Appl Ecol 12, 839-878.

NOWAK, D. J., CRANE, D. E. & STEVENS, J. C. 2006. Air pollution removal by urban trees and shrubs in the United States. Urban Forestry & Urban Greening, 4, 115-123.

NOWAK, D. J., HIRABAYASHI, S., BODINE, A. & GREENFIELD, E. 2014. Tree and forest effects on air quality and human health in the United States. Environ Pollut, 193, 119-29.

NOWAK, D. J., HIRABAYASHI, S., BODINE, A. & HOEHN, R. 2013. Modeled PM2.5 removal by trees in ten U.S. cities and associated health effects. Environ Pollut, 178, 395-402.

Nurmi V, Votsis A, Perrels A, Lehvavirta S (2016), Green roof cost-benefit analysis, Journal of Benefit-Cost Analysis, 7: 488-522, doi:10.1017/bca.2016.18


- 85 -

Odum J.R., Jungkamp T.P.W., Griffin R.J., Forstner H.J.L., Flagan R.C., Seinfeld J.H, 1997. Aromatics, reformulated gasoline, and atmospheric organic aerosol formation. Environ Sci Technol 31, 1890-1897.

Padró-Martínez, L.T. et al., 2012. Mobile monitoring of particle number concentration and other traffic-related air pollutants in a near-highway neighborhood over the course of a year. Atmospheric Environment, 61, pp.253–264. Available at: http://dx.doi.org/10.1016/j.atmosenv.2012.06.088.

Pasquier, A. & André, M., 2017. Considering criteria related to spatial variabilities for the assessment of air pollution from traffic. Transportation Research Procedia, 25, pp.3358–3373. Available at: http://dx.doi.org/10.1016/j.trpro.2017.05.210.

Patton, A.P. et al., 2014. Spatial and temporal differences in traffic-related air pollution in three urban neighborhoods near an interstate highway. Atmospheric Environment, 99, pp.309–321. Available at: http://dx.doi.org/10.1016/j.atmosenv.2014.09.072.

Patton, A.P. et al., 2017. Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles. Environmental Science & Technology, 51(1), pp.384–392. Available at: http://pubs.acs.org/doi/abs/10.1021/acs.est.6b04633.

Peper P.J., McPherson E.G., 1998. Comparison of five methods for estimating leaf area index of open crown deciduous trees. Journal of Arboriculture 24(2) 98-111.

Rotach M.W., 1994. Determination of the zero-plane displacement in an urban area. Atmospheric Environment 29, 1473-1486.

ROWLAND, C. S., MORTON, R. D., CARRASCO, L., MCSHANE, G., O'NEIL, A. W. & WOOD, C. M. 2017. Land Cover Map 2015 (25m raster, GB). NERC Environmental Information Data Centre.

Saxena P., Ghosh C., 2012. A review of assessment of benzene, toluene, ethylbenzene and xylene (BTEXX) concentration in urban atmosphere of Dehli. International Journal of the Physical Sciences 7(6), 850-860,

Serrano L., Peñuelas J., 2005. Assessing forest structure and function from spectral transmittance measurements: a case study in a Mediterranean holm oak forest. Tree Physiology 25, 67-74.

Shan, Y. et al., 2007. Effects of vegetation status in urban green spaces on particle removal in a street canyon atmosphere. Acta Ecologica Sinica, 27(11), pp.4590–4595.

Sozzi R., Georgiadis T., Valentini M., 2002. Introduzione alla turbolenza atmosferica-Concetti, stime, misure. Pitagora Editrice, Bologna.

Steffens, J.T. et al., 2014. Effects of roadway configurations on near-road air quality and the implications on roadway designs. Atmospheric Environment, 94, pp.74–85. Available at: http://dx.doi.org/10.1016/j.atmosenv.2014.05.015.

Surrey-i, 2015. Census key statistics ( Key demographics , age , gender , ethnicity , religion , disability, health and carers ), Guildford Local Authority in Surrey. , (November 2015), pp.25–27.

TALLIS, M., TAYLOR, G., SINNETT, D. & FREER-SMITH, P. 2011. Estimating the removal of atmospheric particulate pollution by the urban tree canopy of London, under current and future environments. Landscape and Urban Planning, 103, 129-138.


- 86 -

Tian, N., Xue, J. & Barzyk, T.M., 2013. Evaluating socioeconomic and racial differences in traffic-related metrics in the United States using a GIS approach. J Expos Sci Environ Epidemiol, 23(2), pp.215–222. Available at: http://dx.doi.org/10.1038/jes.2012.83.

TIWARY, A., SINNETT, D., PEACHEY, C., CHALABI, Z., VARDOULAKIS, S., FLETCHER, T., LEONARDI, G., GRUNDY, C., AZAPAGIC, A. & HUTCHINGS, T. R. 2009. An integrated tool to assess the role of new planting in PM10 capture and the human health benefits: a case study in London. Environ Pollut, 157, 2645-53.

Tong, Z. et al., 2016. Roadside vegetation barrier designs to mitigate near-road air pollution impacts. Science of the Total Environment, 541, pp.920–927. Available at: http://dx.doi.org/10.1016/j.scitotenv.2015.09.067.

UN 2015. World urbanization prospects: the 2014 revision. New York: United Nations.

United Nations, 2014. World Urbanization Prospects 2014. Demographic Research, p.32.

Volk, H.E. et al., 2011. Residential proximity to freeways and autism in the CHARGE study. Environmental Health Perspectives, 119(6), pp.873–877.

Votsis, A. (2017), Planning for green infrastructure: The spatial effects of parks, forests, and fields on Helsinki's apartment prices, Ecological Economics 132: 279-289, doi:10.1016/j.ecolecon.2016.09.029

WHO 2014. Burden of disease from ambient air pollution for 2012. World Health Organization.

Wilker, E.H. et al., 2013. Residential proximity to high-traffic roadways and poststroke mortality. Journal of Stroke and Cerebrovascular Diseases, 22(8), pp.e366–e372. Available at: http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.03.034.

Zajic D., Fernando H.J.S., Calhoun R., Princevac M., Brown M.J., Pardyjak E.R., 2011. Flow and turbulence in an urban canyon. American Meteorological Society 50, 203-2

Zhang, H. et al., 2015. Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environmental Research, 140, pp.242–254. Available at: http://dx.doi.org/10.1016/j.envres.2015.04.004.


- 87 -

Appendix (A) Low Boundary Walls Location Selection


- 88 -

In the following, the Dublin LBW location selection campaign results are reported.


- 89 -


- 90 -


- 91 -


- 92 -


- 93 -


- 94 -


- 95 -


- 96 -


- 97 -


- 98 -


- 99 -

Appendix (B) Technical specifications for the instruments


- 100 -

In the following, we report Tables containing the main technical specifications for instruments adopted in the experimental field campaigns in Bologna

GILL R3-50 Anemometer Wind Speed Specifications

Range 0-45 m/s

Accuracy <1.0% RMS

Resolution 0.01 m/s

Wind Direction Specifications

Range 0-359°

Accuracy ≤ ±1.0°RMS

Resolution 1°

Speed of sound

Range 300-370 m/s

Accuracy 0.01 m/s

Resolution ≤ ±0.5% @20°C

Measurement

Ultrasonic output rate 50Hz

Output formats UVW, Speed of sound

Digital output

Communication RS422 full duplex, 8 data bits, 1 stop bit, no parity

Baud rates 2400-115200

Output rate Selectable 0.4 to 50s

Power requirement

Anemometer 9-30V DC (<150mA @24V DC or 300 mA @12V DC)


- 101 -

Mechanical

Material Aluminium/Carbon fibre

Weight 1.0Kg

Size 750mm x 240mm

Environmental

Protection class IP65

Operating temperature -40ºC to +60ºC

Precipitation 300mm/hr

Table 13: Technical specification of GILL R3-50 sonic anemometer.

HCS2S3 thermohygrometer Specifications

Electronics Operating Limits

-40° to +100°C

Storage Temperature Range

-50° to +100°C

Filter Description Polyethylene (standard) or Teflon (optional, ordered separately)

Current Consumption < 4.3 mA (@ 5 Vdc)

< 2.0 mA (@ 12 Vdc)

Supply Voltage 5 to 24 Vdc

Startup Time 1.5 s (typical)

Maximum Startup Current < 50 mA (for 2 μs)

Analog Outputs ±3 mV (maximum) offset at 0 V.

< ±1 mV (0.1°C, 0.1% R. H.) deviation for digital signal


- 102 -

Diameter 15 mm (0.6 in.)

Length 85 mm (3.3 in.) without connector

183 mm (7.25 in.) with connector

Weight 10 g

Air temperature

Sensor PT100 RTD, IEC 751 1/3 Class B

Measurement Range -40° to +60°C (default)

Output Signal Range 0 to 1 V

Accuracy ±0.1°C with standard configuration settings (at 23°C)

Long-Term Stability < 0.1°C/year

Sensor Time Constant - Standard PE Filter

≤ 22 s (63% step change [1 m/s air flow at sensor])

Sensor Time Constant - Optional Teflon Filter

≤ 30 s (Typical 4 s, 63% of a step change [1 m/s air flow at sensor])

Relative humidity

Sensor ROTRONIC® Hygromer IN-1

Measurement Range 0 to 100% RH (non-condensing)

Output Signal Range 0 to 1 Vdc

Long-Term Stability < 1% RH per year

Accuracy ±0.8% RH with standard configuration settings (at 23°C)

Sensor Time Constant - Standard PE Filter

≤ 22 s (63% of a 35 to 80% RH step change [1 m/s air flow at sensor])

Sensor Time Constant - Optional Teflon Filter

≤ 30 s (Typical 10 s, 63% of a 35 to 80% RH step change [1 m/s air flow at sensor])

Table 14: Technical specifications of HCS2S3 thermohygrometer.


- 103 -


- 104 -

Net radiometer CNR4 Specifications

Pyranometer Spectral Response

305 to 2800 nm

Pyrgeometer Spectral Response

4.5 to 42 μm

Response Time < 18 s

Temperature Dependence of Sensitivity

< 4% (-10° to +40ºC)

Sensitivity Range 5 to 20 μV W-1 m2

Pyranometer Output Range 0 to 15 mV

Pyrgeometer Output Range ±5 mV

Non-Linearity < 1%

Tilt Error < 1%

Pyranometer Uncertainty in Daily Total

< 5% (The uncertainty values are for a 95% confidence level.)

Pyrgeometer Uncertainty in Daily Total

< 10% (The uncertainty values are for a 95% confidence level.)

Directional Error < 20 W m-2 (pyranometer)

Angles up to 80° with 1000 W/m2 beam radiation

Operating Temperature Range -40° to +80°C

Compliance Conforms to the CE guideline 89/336/EEC 73/23/EEC.

Height 6.6 cm dome-to-dome

Width 11.1 cm

Length 23.5 cm


- 105 -

Weight 850 g without cable

Table 15: Technical specifications of net radiometer CNR4. VAISALA PTB110 Operating range

Pressure ranges 500 ... 1100 hPa

600 ... 1100 hPa

800 ... 1100 hPa

800 ... 1060 hPa

600 ... 1060 hPa

Temperature range -40 ... +60 °C

Humidity range non-condensing

General

Supply voltage 10 … 30VDC

Supply voltage control with TTL level trigger

Supply voltage sensitivity negligible

Current consumption < 4mA

in shutown mode < 1μA

Output voltage 0 … 2.5V DC

0 … 5V DC

Output frequency 500 … 1100Hz

Resolution 0.1hPa

Load resistance minimum 10kΩ

Load capacitance maximum 47nF


- 106 -

Settling time 1s to reach full accuracy after power-up

Response time 500ms to reach full accuracy

Acceleration sensitivity negligible

Pressure connector M5 (10-32) internal thread

Pressure fitting barbed fitting for 1/8''

Minimum pressure limit 0hPa abs

Maximum pressure limits 2000hPa abs

Electrical connector removable connector for 5 wires (AWG 28 … 16)

Terminals Pin 1: external triggering

Pin 2: signal ground

Pin 3: supply ground

Pin 4: supply voltage

Pin 5: signal output

Housing material, plastic cover ABS/PC blend

Housing classification IP32

Metal mounting plate Al

Weight 90g

Accuracy

Linearity ±0.25 hPa

Hysteresis ±0.03 hPa

Repeatability ±0.03 hPa

Pressure calibration uncertainty ±0.15 hPa

Voltage calibration uncertainty ± 0.7 mV


- 107 -

Frequency calibration uncertainty

± 0.3 Hz

Accuracy at +20 °C ±0.3 hPa

Total accuracy at

+15 ... +25 °C ±0.3 hPa

0 ... +40 °C ±0.6 hPa

-20 ... +45 °C ±1.0 hPa

40 ... +60 °C ±1.5 hPa

Long-term stability ±0.1 hPa/year

Table 16: Technical specifications of Vaisala Barometer PTB110.

LI-7500A specifications

CO2 H2O

Calibration range 0-3000 ppm 0 - 60 ppt

Accuracy Within 1% of reading

Within 2% of reading

Zero drift (per °C) ±0.1 ppm typical ±0.03 ppt typical

±0.3 ppm max. ±0.05 ppt max.

RMS noise (typical @ 370 ppm CO2 and 10 mmol mol-1 H2O)

5 Hz 0.08 ppm 0.0034 ppt

10 Hz 0.11 ppm 0.0047 ppt

20 Hz 0.16 ppm 0.0067 ppt

Gain drift (% of reading per °C) ±0.02% typical ±0.15% typical

±0.1% max. ±0.30% max.


- 108 -

@ 370 ppm @ 20 ppt

Direct sensitivity to H2O (mol CO2/mol H2O)

±2.00E-05 typical ---

±4.00E-05 max. ---

Direct sensitivity to CO2 (mol H2O/mol CO2)

--- ±0.02 typical

--- ±0.05 max

Table 17: Technical specification of LI-COR LI-7500A CO2/H2O analyser.

T200 Specifications

Ranges Min: 0 - 50 ppb full scale

Max: 0 - 20,000 ppb full scale (selectable, dual-range supported)

Measurement Units ppb, ppm, μg/m3, mg/m3 (selectable)

Zero Noise < 0.2 ppb (RMS)

Span Noise < 0.5% of reading (RMS) above 50 ppb

Lower Detectable Limit 0.4 ppb

Zero Drift < 0.5 ppb/24 hours

Span Drift < 0.5% of full scale/24 hours

Lag Time 20 seconds

Rise/Fall Time < 60 seconds to 95%

Linearity 1% of full scale

Precision 0.5% of reading above 50 ppb

Sample Flow Rate 500 cc/min ±10%

Power Requirements 100V-120V, 220V-240V, 50/60 Hz


- 109 -

Analog Output Ranges 10V, 5V, 1V, 0.1V (selectable)

Recorder Offset ±10%

Operating Temperature Range

5 - 40ºC

Dimensions (HxWxD) 178 x 432 x 597 mm

Weight Analyzer: 18 kg

External pump: 7 kg

Table 18: Technical specifications of the T200 NO/NO2/NOx analyser.


- 110 -

T300 Specifications

Ranges Min: 0 - 1 ppm full scale

Max: 0 - 1,000 ppm full scale (selectable, dual-range supported)


Zero Noise < 0.2 ppm (RMS)

Span Noise < 0.5% of reading (RMS) above 5 ppm

Lower Detectable Limit 0.04 ppm

Zero Drift < 0.1 ppm/24 hours


Lag Time 10 seconds



Precision 0.5% of reading above 5 ppm



Analog Output Ranges 10V, 5V, 1V, 0.1V (selectable)



5 - 40°C operating


Weight 18 kg

Table 19: Technical specifications of the T300 CO analyser.


- 111 -

T100 Specifications

Ranges Min: 0 - 50 ppb full scale

Max: 0 - 20,000 ppb full scale (selectable, dual-range supported)


Zero Noise < 0.2 ppb (RMS)

Span Noise < 0.5% of reading (RMS) above 50 ppb

Lower Detectable Limit

0.4 ppb

Zero Drift < 0.5 ppb/24 hours


Lag Time 20 seconds



Precision 0.5% of reading above 50 ppb



Analog Output Ranges

10V, 5V, 1V, 0.1V (selectable)



5 - 40°C operating


Weight 16.2 kg

Table 20: Technical specifications of the T100 SO2 analyser.


- 112 -


- 113 -

Thermo Scientific 49i

Custom Ranges 0 to 0.05 to 200ppm, 0 to 0.1 to 400mg/m3

Flow Rate 1 to 3L/min.

Height 219mm

Depth 584mm

Linearity ±1% full scale

Precision 1ppb

Inputs 16 Digital Inputs (standard), 8 0 to 10VDC Analog

Preset Measurement Ranges

0 to 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50,100

Response Time 20 seconds (10 second lag time)

Span Drift <1% full scale per month

Temperature 20° to 30°C (Performance) or 0° to 45°C (Operating)

Operating voltage 100 to 115VAC; 220 to 240VAC

Weight 16kg

Width 425mm

Zero drift <1 ppb

Zero noise 0.25 ppb RMS

Table 21: Technical specifications of the Thermo Scientific Model 49i O3 analyser.

airTOXIC Chromatotech

Lower detection limit ≤ 0.01 ppb = 0.0325 μg/m3


- 114 -

Detection range and linearity: BENZENE

3.25 to 3250 μg/m3 = 0-1000 ppb

0.32 to 325 μg/m3 = 0-100 ppb

0.032 to 32.5 μg/m3 =0-10 ppb

Relative standard variation: PRECISION

Better than 0.3 % over 48h (Retention Time)

Better than 2 % over 48 h on 1 ppb (Concentration)

Cycle time 15 or 20 or 30 min

Gas supply Nitrogen: 4 ml/min (inlet 3 bars; 1/8’’ swagelok)

Air or nitrogen for CALIB: 50 ml/min continuously

and 180 ml/min in CALIB method

Detector cleaning : 3 ml/min

15ml/min , Sample inlet (vacuum pump) ¼’’swagelok,

Sample volume 20 to 400 ml or more (programmable)

Operation Temperature Room with air conditioning: 10 to 35°c

Power supply main 230V 50Hz or 115V 60 Hz

Electrical consumption: Average 150 VA, Peak 360 VA

Dimensions Rack 482 mm (19")Height 222 mm (5U), depth 600 mm

Weight 18 kg =analyser ( 37 kg with packaging )

Table 22: Technical specifications of the airTOXIC Chromatotech BTEX analyser.

FAI Swam 5a

Mass measurement operative interval

Mass thickness till 5 mg/cm²


- 115 -

Mass thickness measurement reproducibility

±2 μg/cm²

Mass measurement reproducibility

± 10 μg; ± 15 μg; ± 23 μg respectively with sampling ß spot area 5.20; 7.07;

11.95 cm²

β source 14C with 3.7MBeq (100 μCi) nominal activity

Operating flow rate Programmable in the range 0.8 – 2.5 m³/h

Flow rate measurement reproducibility

1% of the measured value

Flow rate measurement relative uncertainty

2% of the measured value

Flow rate control Automatic, with regulation valve moved by a step motor. Stability in flow rate

control better than the 1% of the required nominal value

Max allowed pressure drop 40 kPa at 2.3 m³/h

Filters Loader/Unloader capacity

No. 36 filter cartridges (72 on demand)

Filter cartridges Standard supply: for Æ 47 mm filter membranes

Service compressed air Operating pressure 200÷300 kPa (supplied by an auxiliary air compressor supplied

with the instrument)

Power supply 230 V (± 10%) 50 Hz single-phase

Absorbed electric power 1200 W (max)

Power supply continuity in direct current

2 12 V 3.5 Ah floating batteries - Autonomy to complete mass measurements and

filters movements


- 116 -

Air compressor unit 12 l/min at 300 kPa

Operating conditions inside the installation

Relative Humidity lower then 85% (with no condensate)

cabinet

Storage conditions Temperature within - 10 and + 55 °C

Relative Humidity lower then 85% (with no condensate)

Dimensions (W x D x H)

Sampling unit 430 x 540 x 370 mm

Vacuum pump unit 200 x 320 x 200 mm

Air compressor unit 180 x 320 x 200 mm

Weights

Sampling unit 36 kg

Vacuum pump unit 10 kg

Air compressor unit 18 kg

Sampling inlets manufactured by FAI

− PM10 sampling inlet (LVS-PM10 model, in compliance with EN 1234-1

Instruments (on customer demand)

standard, working at 2.3 m3/h)

− PM10 sampling inlet LVS-PM10 with 1 m3/h nominal flow rate (equivalent

to the LVS-PM10 EN 1234-1 model)

− PM2.5 sampling inlet (LVS-PM2.5 model, nominal flow rate 2.3 m3/h)

− PM2.5 sampling inlet (LVS-PM2.5 model, nominal flow rate 1 m3/h)


- 117 -

− PM1 sampling inlet (LVS-PM1 model, nominal flow rate 2.3 m3/h)

Table 23: Technical specifications of the FAI Swam 5a PM10 and PM2.5 sampler.

Accupar LP-80

Operating Environment 0° to 50° C (32°-122°)

100% relative humidity

Probe Length 86.5cm

Overall Length 102cm

PAR Range 0 to >2500µmol m-2 s-1

Resolution 1µmol m-2 s-1

Minimum Spatial resolution 1cm

Unattended logging interval User selectable, between 1 and 60 minutes

External PAR sensor connector Locking 5-pin sealed circular connector

Table 24: Technical specifications of ceptometer model AccuPAR LP-80.

Documents

Report on Footprint of Passive Control ... - iSCAPE Project · Report on Footprint of Passive Control Systems ––––––––– –––– D3.3 September 2017 This project