AN AIR POLLUTION CLIMATOLOGY FOR CHRISTCHURCH, NEW ZEALAND

Tim AppelhansDepartment of Geography, University of Canterbury, Private Bag 4800,

Christchurch, New Zealand, PhD candidate, tap26@student.canterbury.ac.nz

Abstract

In order to understand recent trends in air quality and in an attempt to put them into a wider perspective, this study presents a trend analysis of meteorological conditions that are conducive to elevated concentrations of particulate matter in Christchurch, New Zealand. Classification tree analysis is carried out to identify meteorological classes to be used as proxies of expected probability of NES guideline exeedence. A key motivation for this work is to be able to assess historic trends in air pollution potential and to gain a deeper understanding of atmospheric forcings on climatic scales in both time and space.

Key words: Trend Analysis, PM10, Synoptic forcing, CART

1. INTRODUCTION

Elevated concentrations of PM10 have long been a problem in Christchurch, New Zealand as a result of domestic fires used for space heating and exceedences of National Environmental Standards (NES) occur regularly. The NES threshold for PM10 is specified at 50 μg/m3 for a 24-hour average obtained between midnight and midnight, and only one exceedence is allowed per year before significant restrictions are placed on the issuing of resource consents for discharges to air. It is assumed that efforts by the regional council (Environment Canterbury – Ecan) to improve air quality regulation and public awareness have enhanced air quality in Christchurch in recent years. However, reliable air quality records are short, with quality-assured monitoring data only being available since 1999. Emission sources are primarily domestic with industrial and traffic sources together only accounting for approximately 15 – 20% of the total amount of particulates released into the atmosphere. Prevailing westerly synoptic flow is highly modified in the Canterbury region by topographical features of the southern alps and Banks Peninsula as well as the land-sea discontinuity of New Zealands east coast. As a result, low level flow in the Canterbury region is predominantly north-easterly. At night, especially in winter, the Planetary Boundary Layer is regularly decoupled from gradient flow and low level flow becomes north-westerly as a result of katabatic winds from the foothills of the southern alps. Drainage flow is cold and undercutting is a common feature forming a shallow layer of high atmospheric stability close to the surface (Kossmann & Sturman 2004).To investigate emission reduction strategies, an in depth undestanding of atmospheric influences on PM10

concentration variations is essential. An extensive body of research exists that describes meteorological control mechanisms on pollution transport and dispersion in a general manner (eg. Oke 1987, Arya 1999, Zawar-Reza & Spronken-Smith 2005). Furthermore, numerous studies have investigated local/regional atmospheric influences on air quality for a vast range of pollutants and locations around the world, including Christchurch (eg. Kossmann & Sturman 2004, Corsmeier et al. 2006, Elminir 2005, Wise & Comrie 2005, Zawar-Reza et al. 2009). In addition to local analyses, there is a long history of studies that have investigated general synoptic controls on air quality, eg. Sanchez et al. (1990), Yap & Chung (1977), Heidborn & Yap (1986), McGregor & Bamzelis (1995) and more recently Makra et al. (2009), Kuo et al. (2008), Cheng et al. (2007), among others. For New Zealand however, investigation of synoptic forcing with respect to air quality meteorology is sparse. Khan et al. (2007) have studied relationships between synoptic weather situations and night-time O3 and NOx concentrations in Auckland. Owens & Tapper (1977) have investigated meteorological influences (including synoptic influences) on various pollutants in Christchurch. Furthermore, trend assessment of air quality in New Zealand has only been established in recent years (eg. Bluett et al. 2007).This study outlines a statistical investigation of local meteorological controls on air quality in Christchurch to aid understanding of local processes. Findings are used to investigate important synoptic forcings and an extended record of pollution potential is examined.

2. METHODOLOGY

Classification trees are used to investigate meteorological potential to produce elevated concentrations of PM10 in Christchurch. Classification and Regression Trees (CART) describe a statistical procedure that was introduced by Breiman et al. (1984). CART have been applied to a wide variety of environmental studies including air quality problems (eg. Zheng et al. 2009, Hendrikx et al. 2005). Slini et al. (2006) evaluated 4 different statistical techniques to forecast PM10 concentrations for Thessaloniki, Greece and concluded that CART proved satisfactory in capturing concentration trends. Based on a set of predictor variables, this statistical approach splits the response into a set of classes (or nodes) with maximum possible class purity and arranges the final splits into a decision tree diagram. For an in-depth description of the recursive partitioning algorithm used in this study refer to Hothorn et al. (2006).

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In a conservative set up of the algorithm (minimum final class size was set to 100 observations and splits were only allowed at p-levels < 0.01), the binary response whether the national guideline of 50 μg/m3 was breached or not (exceedence yes/no) was regressed against a wealth of predictor variables. Analysis was restricted to winter months May – August as this is the period when most of the yearly exceedences occur. Lagged information of several meteorological variables was taken into account to capture possible autocorrelation processes. Note, that information on vertical atmospheric structures was not available at satisfactory temporal resolution. Based on the outlined inputs, a classification tree with 6 terminal nodes was produced (see Figure 1). Out of the comprehensive set of potential predictor variables, the algorithm retained only 3 to split the set into the outlined classes. The two variables that were found to have most explanatory power are average wind speed between 1200hrs and 2400hrs and minimum temperature of the following day. This is not surprising as temperature is the key driver behind domestic emission release and wind speed governs dispersion of pollutants to a high degree, especially as no information of atmospheric stratification is available. Two of the terminal nodes represent conditions that are likely to result in degraded air quality (nodes 1 & 2). One class of meteorological conditions clearly promotes good air quality (node 6) and the remaining 3 classes can be described as intermediate. Terminal node 6, the class with lowest pollution potential, accounts for approximately 50% of the original data (600 out of 1194 days). The remaining terminal nodes each represent about 10% of the input data. Having a binary response variable enables direct assessment of pollution potential of each terminal node. The probability of an exceedence occurring under meteorological conditions that are represented by terminal node 6 is 0.04. On the other end of the scale, 84% of all days with an atmospheric set up as described by terminal node 1 will see a breach of the national air quality guideline for PM10. To summarise, the classification tree enables an assessment of air quality potential by assigning an exceedence probability to any given day based on its atmospheric set up.

3. RESULTS

To investigate whether the identified meteorological classifcation is indeed representative of different atmospheric conditions, terminal nodes were analysed towards their synoptic forcings. Figure 2 shows the relative increase or decrease in frequency of a set of 12 synoptic types identified by Kidson (2000) for each of the terminal nodes in comparison with their overall distribution. The further a box deviates from the line of expected frequency distribution, the stronger the relationship between the synoptic type and the exceedence probability. Analysis of the presented differences in synoptic type frequencies with focus on expected associated large-scale airflow in the Canterbury region suggests that each of the terminal nodes does indeed represent distinctly different atmospheric conditions. Node 1 shows a marked increase in the occurrence of anticyclonic conditions (H, HSE) and a significant decrease in situations that are likely to promote north-westerly (often gusty) foehn winds in

Figure 1: Classification tree assigning each day into an exceedence probability class (node). Meteorological variables used to apply splits are shown in inner nodes. n denotes the number of observations in each node (inner and terminal).

The seventh International Conference on Urban Climate, 29 June - 3 July 2009, Yokohama, Japan

Christchruch (T, TNW). This is in line with the local conditions identified in the node which can be summarised in broad terms as calm and cold. Node 3 on the other hand, yields an increase in southerly dominated flow (SW, HW, HNW) and local meteorology is classified as windy but very cold which reflects the advection of antarctic air masses over the region. In summary, the split criteria and the resulting classes represent local meteorological conditions that are dinstinct and are in line with the observed dominance of synoptic features within the terminal nodes. For a full overview of synoptic types and their associated flow patterns, refer to Kidson (2000). Based on this meteorological classification, emission reduction startegies can be assessed with higher accuracy as concentration trends can now be analysed seperately for different atmospheric conditions. Figure 3 provides evidence for emission reduction by comparing two years, 2000 and 2007, that exhibit a very similar frequency distribution of atmospheric conditions as identified in the classification tree (see Figure 4). However, the amount of days with concentrations falling into 'alert'

or 'action' categories as defined by New Zealands Ministry for the Environment (MfE 2002) is reduced by 50%. In accordance to the national guidelines for ambient air quality, each day is classified into a category depending on its 24hr mean PM10

concentration. Class thresholds are defined as fractions of the guideline and are as follows for PM10: Good (< 33% of guideline), Acceptable (33% – 66% of guideline), Alert (66% – 100% of guideline) and Action (> 100% of guideline).Finally, in an attempt to assess long-term trends in air pollution potential, a long-term record of probability class frequencies was produced to evaluate year-to-year variability in atmospheric controls. Unfortunately, data availability prohibited creation of a climatological time series spanning a number of decades. However, a 16 year record of probability class frequencies from 1993 – 2008 was produced (see Figure 4). Initial analysis suggests that 2001 was the year with the highest probability of degraded air quality throughout this period. This observation is in line with Ecans monitoring data which identifies 2001 as the year with the highest amount of exceedences (51 out of 123 winter days) in ther (quality assured) monitoring record. In addition to 2001, identified high pollution years as suggested by the probability distribution are 1997, 1999 and 2006. This is again in line with exceedence records which show 49 (although based on data that is not quality assured), 50 and 27 respectively. Note, that the amount of exceedences in 2006 is expected to be low due to successful emission reduction strategies outlined above.

4. Discussion

Classification tree analysis was utilised to investigate both local conditions as well as synoptic forcings

Figure 3: Association plot showing deviation from independence of synoptic types and exceedence probability classes (represented by terminal node number).

Figure 2: Frequency of days with assigned exceedence probability (left) and days classified according to ambient air quality guidelines (right) for 2000 and 2007. A description of air quality guidelines is given in the text.

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leading to degraded air quality in Christchurch, New Zealand. Although strong evidence is found that local conditions are indeed a result of synoptic forcing, further analysis of regional scale processes such as anomalies in the meso-scale pressure field or the like is essential to establish a clear link between larger scale atmospheric processes and their local meteorological responses. If these links can be identified, global climate dynamics and their contribution towards New Zealand climate can be investigated. Furthermore, analysis may be extended to decadal time scales and climatric records of pollution potential may be produced to give an insight into air pollution history in New Zealand. Additionally, an improved understanding will aid in the development of automated air quality forecasts which are not yet implemented for any of the major urban agglomerations throughout the country.Classification tree analysis as used in this study is an appealing method for exploring complex multivariate relationships for a wide range of applications. Graphical representation of the complex analysis in a decission tree provides easy interpretations of the results. Additionally, the classification is absolute in the sense that each observation gets assigned to exactly one terminal node and no overlaps occur. However, classification trees are very sensitive to data input, especially outliers, which adds to the need of further analysis on a range of temporal and spatial scales to gain further confidence of the results presented here.

5. CONCLUSION

In this study strong evidence was found that efforts to improve air quality in Christchurch, New Zealand have produced observable emission reduction in recent years. Classification tree analysis allowed creation of a record of pollution probability classes that provided a means to investigate PM10 concentrations independent of meteorological variation. Comparison of two specific years showed that although atmospheric conditions in these years was very similar, exceedences were reduced by 50%, thus providing evidence for emission reduction. Results indicate that classification tree analysis was able to sperate out distinct local atmospheric conditions as diffrerences between identified classes are also observable on a synoptic scale and are in line with expected forcings. Finally, pollution probability distribution from year to year enabled general assumptions to be made on winter time air quality in Christchurch.

References

Arya S.P. 1999, 'Air pollution meteorology and dispersion', Oxford University Press.Bluett J., Appelhans T., Dey K., Fisher G., Sturman A. and Wilton E. 2007, 'Using air quality data to track progress

toward PM10 standards: Case study - Christchurch 1999 – 2006', NIWA Client Report: CHC2007-135, National Institue for Water and Atmospheric Research.

Breiman L., Friedman J.H., Ohlsen R. and Stone C.J. 1984, 'Classification and regression trees', Wadsworth, Belmont, CA.

Cheng C., Campbell M., Li Q., Li G., Auld H., Day N., Pengelly D., Gingrich S. and Yap D. 2007, 'A Synoptic Climatological Approach to Assess Climatic Impact on Air Quality in South-central Canada. Part I: Historical Analysis', Water, Air & Soil Pollution, 182, 131-148.

Corsmeier U., Kossmann M., Kalthoff N. and Sturman A. 2006, 'Temporal evolution of winter smog within a nocturnal boundary layer at Christchurch, New Zealand', Meteorology and Atmospheric Physics, 91, 129-148.

Figure 4: Frequency of exceedence probability classes between 1993 - 2008.

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Elminir H.K. 2005, 'Dependence of urban air pollutants on meteorology', Science of The Total Environment, 350, 225 – 237.

Heidorn K.C. and Yap D. 1986, 'A synoptic climatology for surface ozone concentrations in southern Ontario, 1976–1981', Atmospheric Environment, 20, 695–703.

Hendrikx J., Owens I., Carran W. and Carran A. 2005, 'Avalanche activity in an extreme maritime climate: The application of classification trees for forecasting', Cold Regions Science and Technology, 43, 104 – 116.

Hothorn T., Hornik K. and Zeileis A. 2006, 'Unbiased Recursive Partitioning', Journal of Computational and Graphical Statistics, 15, 651-674.

Kalkstein L.S. and Corrigan P. 1986, 'A synoptic climatological approach for geographical analysis: Assessment of sulfur dioxide concentrations', Annals of the Association of American Geographers, 76, 381–395.

Khan B.A., de Freitas C.R. and Shooter D. 2007, 'Application of synoptic weather typing to an investigation of nocturnal ozone concentration at a maritime location, New Zealand', Atmospheric Environment, 41, 5636 – 5646.

Kidson J.W. 2000, 'An analysis of New Zealand synoptic types and their use in defining weather regimes', International Journal of Climatology, 20, 299-316.

Kossmann M. and Sturman A. 2004, 'The surface wind field during winter smog nights in Christchurch and coastal Canterbury, New Zealand', International Journal of Climatology, 24(1), 93-108.

Kuo C.Y., Chen P.T., Lin Y.C., Lin C.Y., Chen H.H. and Shih J.F. 2008, 'Factors affecting the concentrations of PM10 in central Taiwan', Chemosphere, 70, 1273 – 1279.

Makra L., Mika J. Bartzokas A., Béczi R. and Sümeghy Z. 2009, ‘Comparison of objective air-mass types and the Péczely weather types and their ability to classify levels of air pollutants in Szeged, Hungary’, Int. J. Environment and Pollution, Vol. 36, Nos. 1/2/3, pp.81–98.

McGregor G.R. and Bamzelis D. 1995, 'Synoptic typing and its application to the investigation of weather air pollution relationships, Birmingham, United Kingdom', Theoretical and Applied Climatology, 51, 223–236.

Meyer D., Zeileis A. and Hornik K. 2006, 'The Strucplot Framework: Visualizing Multi-Way Contingency Tables with vcd', Journal of Statistical Software, 17(3), 1-48.

MfE 2002, 'Ambient Air Quality Guidelines', Air Quality Report, Ministry for the Environment.Oke T.R. 1987, 'Boundary layer climates', 2nd ed., Routledge.Owens I.F. And Tapper N.J. 1977, 'The Influence of Meteorological Factors on Air Pollution Occurrence in

Christchurch', In Proceedings of the air and envrionment conference, Rotorua, New Zealand, 403 – 420.Rost J., Holst T., Sähn E., Klingner M., Anke K., Ahrens D. and Mayer H. 2009, 'Variability of PM10 concentrations

dependent on meteorological conditions’, Int. J. Environment and Pollution, Vol. 36, Nos. 1/2/3, pp.3–18.Sánchez M.L., Pascual D., Ramos C. and Pérez I. 1990, 'Forecasting particulate pollutant concentrations in a city

from meteorological variables and regional weather patterns', Atmospheric Environment, 24, 1509 – 1519.Slini T., Kaprara A., Karatzas K. and Moussiopoulos N. 2006, 'PM10 forecasting for Thessaloniki, Greece',

Environmental Modelling & Software, 21, 559 – 565.Wise E.K. And Comrie A.C. 2005, 'Meteorologically adjusted urban air quality trends in the Southwestern United

States', Atmospheric Environment, 39, m2969 – 2980.Yap D. and Chung Y. S. 1977, 'Relationship of ozone to meteorological conditions in southern Ontario', In

Preprint, 70th annual meeting of the air pollution control association. Toronto no. 77–20.4.Zawar-Reza P. and Spronken-Smith R.A. 2005, 'Air pollution climatology', In Oliver J.E. (Ed) Encyclopaedia of

World Climatology, p21-32, Cornwall: Springer.Zawar-Reza P., Appelhans T., Gharaylou M. and Shamsipour A. 2009, 'Mesoscale controls on particulate matter

pollution for a mega-city in a semi-arid mountainous environment: Tehran, Iran', International Journal of Environment and Pollution, In Press.

Zeileis A., Meyer D. and Hornik K. 2007, 'Residual-based Shadings for Visualizing (Conditional) Independence', Journal of Computational and Graphical Statistics, 16(3), 507-525.

Zheng H., Chen L., Han X., Zhao X. and Ma Y. 2009, 'Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions', Agriculture, Ecosystems & Environment, 132, 98 – 105.

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