Improving the CalEnviroScreen Score at the CA-Baja CA borderSan Diego-Tijuana Air Quality Task Force meetingTijuana, Baja California, September 26, 2018Penelope (Jenny) JE Quintana, PhD, MPH, San Diego State University

Javier Emmanuel Castillo Quiñones, PhD, Universidad Autónoma de Baja California, Tijuana

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▪ Science-based tool

▪ Uses 19 indicators of environmental, health and socioeconomic conditions

▪ Evaluates multiple pollution sources

▪ While accounting for a community’s vulnerability to pollution

▪ http://oehha.ca.gov/calenviroscreen/maps-data

▪ Border communities felt as if they had only 180 degrees of environmental exposures used for their scores, not 360 degrees.

▪ Purpose of this CARB (CA Air Resources Board) contract is to map emissions sources near border, model potential effects, and calculate new scores

2California Communities Environmental Health Screening Tool

Investigators:• San Diego State University: Graduate School of Public Health, Penelope Quintana (PI of Project) and

Zohir Chowdhury, have measured emissions from border crossings, currently also OEHHA-funded project to deploy community air sensing in the border community of San Ysidro, CA to input values to CalEnviroScreen.

• San Diego State University: Dept. of Geography, Atsushi Nara, Trent Biggs and Fernando De Salas, who provide vital mapping, GIS, and modeling expertise

• Molina Center for Energy and the Environment, Luisa Molina, Miguel Zavala and Victor Almanza of the Molina Center for Strategic Studies in Energy and the Environment, who are experts in measuring and modeling air pollution in Mexico, and who led the CAL-MEX 2010 air pollution campaign in Tijuana, Baja California

• UABC, Campus Tijuana: Dept of Chemistry and Chemical Engineering, Emmanuel Castillo Quiñones,, who has overseen student collection of air pollution data in Tijuana in past studies, and participated in Cal-Mex 2010

• UABC, Campus Mexicali: Dept of Geography, Judith Ley Garcia,, who has worked in social geography and environmental disparities

• James Sadd, Occidental College, Manuel Pastor, University of Southern California who developed the EJSM and who have a deep understanding of data needs and gaps in relation to utility for the EJSM and CES

• Lynn Russell, expert on regional atmospheric aerosols

• With advisory role: Dr. Margarito Quintero Núñez, Director de Planeación y Política Ambiental Secretaríade Protección al Ambiente

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Technical plan: task overview • Task 1. Locate, aggregate and characterize existing data on emission sources on

the Mexican side of the California-Baja California border, focusing on those sources that impact communities in California

• Task 1.1 Conduct planning meetings.

• Task 1.2 Compile existing data.

• Task 1.3. Create process to evaluate and edit data (online map validation and groundtruthing)

• EXPLORATORY AIM - Detection of burning activities in the border region such as agricultural burns in the Mexico area through automated detection using satellite images (MODIS)

• Task 2. Model-based identification of the influence of major sources

• Task 3. Evaluate impact on CalEnviroScreen (CES) and Environmental Justice Screening Method (EJSM) scores, and identify data gaps

• Task 4. Create and provide formatted data and detailed methodology in a format that can be incorporated into CalEnviroScreen and EJSM, provide final report

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MNEI (Mexico National Emissions Inventory) for Baja California 2013 -mappedMNEI 2014 Received from SEMARNAT in Jun 5th, 2018 • Extracted 78 relevant emission facilities by Molina Center (Dr. Miguel

Zavala)• 59 facilities match with MNEI 2013 records• 12 new emission facilities• Locations of 7 facilities cannot be validated

RETC (Registro de Emisiones y Transferencia de Contaminantes) for Baja California ( 2013, 2016 mapped, 2014, 2015 in progress)

Also 472 gas stations• 27 Landfills• 59 Brick Kilns• 46 Dry Cleaning• 15 Feedlots/Ranches• Major Railroad lines through Mexicali

Task 1.2 - Compile Existing Data8

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Task 1.3 Evaluate and edit dataData Validation Procedures

• Step-By-Step Instructions on Validation Procedures

SDSU Method - 8 Methods of Validation Based on:

• Lat/Long initial result

• Google Maps

• Google Earth

• Geocoding

• Google Search

• Business Name Search

• Call Business

• Call Current Business or Tenant

• Field Investigation (Ground Truthing)

Task 1.3 - Evaluate and Edit Data9

Groundtruthing (Direct observations in the field)• Validate both site location and emission characteristics by business

types• Emission facilities within a 1,500 m buffer from the border• Use mobile device field data collection tool, Arc Collector

Arc Collector

Reference: http://www.esri.com/software/arcgis/collector-for-arcgis

Web-GIS map is available at:• https://arcg.is/1yeSie

Key GIS layers: • Facility of Interest (FOI) Layer

All facility of interest collected in this projectAttribute fields for emission data include both MNEI 2013 and 2014

• FOI 1500m LayerFacility of interest within the 1.5 km buffer from the U.S.-Mexico borderAttribute fields for emission data include both MNEI 2013 and 2014

• FOI 1500m field validationA facility of interest layer used for groundtruthingContaining field notes and images taken in the field

10Task 1.2 - Compile Existing DataTask 1.3 - Evaluate and Edit Data

Task 1.2 - Compile Existing DataTask 1.3 - Evaluate and Edit Data

11• https://arcg.is/1yeSie

1/1/2017

1/1/2017

1/9/2017

1/25/2017

2/2/2017

3/6/2017

3/14/2017

3/22/2017

• An exploratory activity will be to have the SDSU Geography researchers detect burning activities such as agricultural burns in the Mexicali area.

• MODIS - Thermal Anomalies & Fire• Cell size: ~1,000m x ~1,000m

• Cycle: 1 day x 2 (Terra & Aqua)

MODIS – Fire Mask – 8 day composite From 1/1/2017 To 3/30/2017 (11 images)

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Task 1 - Exploratory Aim

11Find burning activities (e.g., agricultural burns) in the US-Mexico border area

Data Source: MODIS Fire Product• 1,545 HDF files from 2000 – 2018Time Series Analysis • Find relationships between fire activities and agricultural land-uses

# o

f fire

pix

els (

1km

x

1km

)

Monthly fire activities from March 2000 to February 2018

# o

f fire

eve

nts

Monthly fire activities from February 2000 to February 2018

Fire events

Task 1 - Exploratory Aim

12Find burning activities (e.g., agricultural burns) in the US-Mexico border area

Data Source: MODIS Fire Product• 1,545 HDF files from 2000 – 2018Time Series Analysis • Find relationships between fire activities and agricultural land-uses

2016 2017

Task 1 - Exploratory Aim

Fire Events

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Task 1 - Exploratory Aim

Mexicali

Tijuana

Find burning activities (e.g., agricultural burns) in the US-Mexico border area

Identify crop fields: Land Cover – 2010 (Landsat, 30m)

Not urban or Crop FieldCrop FieldUrban

http://www.cec.org/tools-and-resources/map-files/land-cover-2010-landsat-30m

Task 2: Drs. Luisa Molina, Miguel Zavala, Victor Almanza, Molina Center

• The database obtained from task 1 was used to identify the emission sources with the largest potential for impacting areas in San Diego, Calexico and adjacent US-Mexico border communities using a combination of short and long-term meteorological and air quality modeling simulations.

• A prerequisite to identify the influence of major emission sources impacting sensitive areas in the California-Mexico border is to understand the meteorological patterns that drive the dispersion, formation and fate of air pollutants in the region.

• Long-term time-series analysis of meteorological and air quality observations

• Simulation of the local (~ 1 km grids) and regional (~ 3 km grid) meteorology in the California-Mexico border using the WRF model.

• Application of backward trajectories techniques to identify potential emission locations influencing sensitive areas in the California-Mexico border region.

• Application of forward trajectories with FLEXPART-WRF aimed to prioritize the influence of specific potential emission sources.

Selection of events with southern wind component

San Diego – Tijuana: 21 episodes (28 days)Calexico – Mexicali: 35 episodes (77 days)

San Diego – Tijuana

Calexico – Mexicali

Forward dispersion, release points: Tijuana, Mexicali February 28 to March 1, 2014, 12 hrs

Backward trajectories

US

Mexico

San Ysidro

Task 3 –ongoing . Impact on CES and EJSM scores (James Sadd/Manuel Pastor –:• EJ Screening Method (EJSM): The EJSM includes hazard proximity in its exposure scoring. The measured distance between hazardous

facilities/uses and locations to be scored are used in a calculation of the hazard proximity metric. Currently, this metric does not reflect hazards proximate to border areas, but these new data will allow these to be included in calculating the hazard proximity metric, which will result in higher values for border census tracts. Because the EJSM Hazard Proximity Score only considers hazards with a 3000 ft distance, only those facilities and uses located within that distance of the international border will affect any change in the Score. These include:

• MNEI and RETC facilities

• Gas stations• Railroad land uses

• CalEnviroScreen (CES): CalEnviroScreen is Periodic updates of CalEnviroScreen include consideration and adoption of new data sources or improved metrics. Shortly after this project was approved, CES released a new version (CES3) in April of this year, and a newer version is being developed. This has required that we be flexible and forward looking in producing data useful to CES.

• · MNEI and RETC facilities: can be used to improve the data quality of the Toxic Releases Indicator by combining these facilities with TRI facilities in preparation of this Indicator (by outside vendor)

• · Landfills/Brick Kilns/Manufacturing facilities: can be used to improve the Environmental Effects component by incorporating them in a manner consistent with the way in which similar facilities within California are used. This depends on the ancillary information available for each facility, such as volume produced, number of employees, and facility size; the information available would determine exactly how each facility or class of facilities would be included.

• · Railroad land uses: there is potential for this data to inform the CES Diesel PM Indicator, but details have not been worked out until this data is complete.

• · MODIS fire detection information: there is potential for its use, but too little is known at this time to speculate how it might inform CES metrics or scoring.

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Thanks for your attention!

Contact:Penelope (Jenny) Quintana jquintan@sdsu.eduJavier Emmanuel Castillo Quiñones castillo@uabc.edu.mx

Mapping activities: Atsushi Nara anara@sdsu.eduJudith Ley Garcia jley@uabc.edu.mx

Trajectory analysis:Luisa Molina ltmolina@mit.edu

Effect on environmental justice mapping scores:James Sadd jsadd@oxy.edu

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(EXTRA SLIDES)20

Task 4.

• Create and provide formatted data and detailed methodology in a format that can be incorporated into CalEnviroScreen and EJSM, and prepare and submit final report:

• Create a final geodatabase or shapefiles formatted for direct input to CES and EJSM programming, as well as full documentation of methods and procedures for data collection, validation and modeling.

• We will utilize both open-source and proprietary software resources to produce the final geodatabase including ArcGIS, PostgreSQL, PostGIS, and Python. Our data management framework is capable of flexibly converting one format to another in order to meet CES and EJSM data format requirements. The type of metadata that will be included is ISO19115-1, which defines the schema required for describing geographic information and services.

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Spatial Database

Transportation Network

Remote Sensing Imagery

Topographic

Data Management

Modules

Emissions Sources

Census Demographics

(Mexico)

Administrative Boundary

PostgreSQL&

PostGISPython

(& ArcGIS)

Input

…

KML/KMZ Metadata

CES Format

Geodatabase/Shapefile

Output

EJSMFormat GeoJSON Raster

Image

Data Management Framework

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Task 1.2 - Compile Existing DataTask 1.3 - Evaluate and Edit Data

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Facility of InterestWithin a 50km buffer from U.S.-Mexico border 1098

MNEI 558business/location validated 502business/location not valid or cannot be validated 56

Feedlot 16business/location validated 16business/location not valid or cannot be validated 0

Landfill 15business/location validated 14business/location not valid or cannot be validated 1

Gas Station 400business/location validated 396business/location not valid or cannot be validated 4

Brick Kilns 57business/location validated 55business/location not valid or cannot be validated 2

Dry Cleaning 52business/location validated 48business/location not valid or cannot be validated 4

Outside 50km buffer 125Total 1223

Facility of InterestWithin a 1.5km buffer from U.S.-Mexico border 209

MNEI 100business/location validated 74business/location not valid or cannot be validated 26

Feedlot 2business/location validated 0business/location not valid or cannot be validated 2

Landfill 2business/location validated 1business/location not valid or cannot be validated 1

Gas Station 73business/location validated 69business/location not valid or cannot be validated 4

Brick Kilns 5business/location validated 3business/location not valid or cannot be validated 2

Dry Cleaning 27business/location validated 23business/location not valid or cannot be validated 4

Total 209

All FOI within a 50 km buffer from the U.S.-Mexico Border(FOIs outside of the 1.5km buffer were validated only using remove validation methods)

All FOI within a 1.5 km buffer from the U.S.-Mexico Border(FOIs were validated using both remote validation and groudtruthing)

Task 2. DatasetsDatabase Data type Sampling

periodSampling frequency Spatial Extent

Air Quality System (AQS-EPA)

Air Quality and Meteorological Information System (AQMIS-CARB)

Criteria Pollutants: O3, SO2, CO, PM10, PM2.5, NOx 1980-2017 1 hour Southern California

Toxic Pollutants: HAPS, VOCs 1980-2017 1 hour Southern California

Meteorological parameters: P, T, RH, U, V 1980-2017 1 hour Southern California

National Data Bouy Center (NDBC) Meteorological parameters: P, T, RH, U, V 1980-2017 1 hour Southern California

NOAA's National Estuarine Research Reserve System's (NERRS)

Meteorological parameters: P, T, RH, U, V 1980-2017 1 hour Southern California

Servicio Meteorológico Nacional (SMN)

EMAs meteorological parameters: T, wind, RH, P 2000-2016 10 minutes Baja California

National Center for Atmospheric Research (NCAR)

Reanalysis 2014 6 hours Global

Monitoring stations

Potential Area of Influence (PAI) vs Potential Source Regions (PSR)

Trajectory Analysis

Identification of southern wind events

Potential Area of Influence

Potential Source Regions

- Forward trajectories- Event specific

(few episodes)

- Backward trajectories- Frequency analysis

(all episodes)

Task 2. Framework

California-Mexico Border Emission Sources Spatial

Database

Trajectory Analysis

Output Task 1

PAI and PSR

Long-term Air Quality Database

WRF modeling

Transport patterns

Output Task 2

Task 2 28WRF Modeling

• 1-way nesting

D1: 45 x 45 km, 116 x 85

D2: 15 x 15 km, 151 x 127

D3: 3 x 3 km, 161 x 136

D4: 1 x 1 km, 127 x 127 SD/ TIJUANAD5: 1 x 1 km, 103 x 103 Calexico / Mexicali

• 35 vertical layers

• 2014 NCEP FNL reanalysis nudging every 6 hrs

• YSU PBL scheme

• RRTMG radiation scheme

• Grell convective parametrization

• 36 hrs run/12 hrs spin up

• 15 min outputsD1

D2

D3

D4 D5

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ExposuresEnvironmental

EffectsSensitive

PopulationsSocioeconomic

Factors

Ozone PM 2.5

Diesel PM Pesticides

Toxic releases

from facilities

Traffic density

Drinking water quality

Cleanup sites Groundwater threats

Hazardous waste

Impaired water bodies

Solid Waste

Children and Elderly

Asthma ER visits

Low Birth Weight Infants

Educational attainment

Linguistic isolation

Poverty

Unemployment

Pollution Burden Population Characteristics