Goals of the Meeting. Presentation - November 1, 2006 by Manuel Pastor, James Sadd, Rachel Morello-Frosch

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1 Air Pollution and Environmental Justice: Integrating Indicators of

Decision-making Source: CBE Source: David Woo Prepared by: Manuel Pastor,

Goals of the Meeting Introduce project coordinated by CJTC and supported by

feedback on project plan/framework, including local community-based study

1 1 Air Pollution and Environmental Justice: Integrating Indicators of Cumulative Impact and Socio-economic Vulnerability into Regulatory Decision-making Source: CBE Source: David Woo Prepared by: Manuel Pastor, UCSC Jim Sadd, Occidental College Rachel Morello-Frosch, Brown University 2 Goals of the Meeting Introduce project coordinated by CJTC and supported by California Air Resources Board and California Energy Commission Solicit feedback on project plan/framework, including local community-based study Solicit input on initial thoughts about environment justice (EJ) screening indicators methdology 1

2 3 Project Summary: Integrating Indicators of Cumulative Impact and Community Vulnerability into Regulatory Decision-making Address data and analytical needs for implementation of 2004 EJ Working Group Recommendations Analyze air pollution data for disparities statewide and regionally (facility location, exposures, estimated health risks) Examine air pollution data in relation to health (birth outcomes) Conduct local-scale study utilizing community-based participatory research (CBPR) methods to: ground-truth information from emissions inventory data Conduct PM sampling using low cost monitors Develop indicators of cumulative impact and community vulnerability/resilience using existing data sources Relevance for research, policy, and regulation Develop screening methods with indicators to flag locations and populations that may be of regulatory concern for disparate impact Consider alternative siting scenarios for CEC 4 Data Base & Analytical Work Integrate data on air pollution and environmental justice Compare NATA, CHAPIS, TRI and other data, using newest iterations of each Work with air districts to consider use of regional data surfaces (e.g. BAAQMD CARE data) Advise CARB on spatial techniques to create more reliable interpolated grid for air pollution exposure concentrations Conduct multivariate modeling to examine disparate impact of estimated risks associated with pollution exposures, taking into account contextual variables such as: land use, SES, civic engagement, segregation, and other measures Apply controls for spatial autocorrelation in the models 2

3 Presentation - November 1, 2006 by Manuel 5 Assembling the Data Toxic Release Inventory annual self-reports from industrial facilities, with analysis attempting to weight emissions by toxicity geo-coded as of US EPA s National Air Toxics Assessment 1999 models concentration estimates of air toxics and diesel PM to census tracts. CARB criteria air pollutant monitoring data PM 10, PM 2.5, NO 2, SO 2, CO, ozone CHAPIS emissions database made available by CARB with geo-coded locations of emitters. ASPEN Grid: ambient air toxics and diesel PM exposure data modeled by CARB utilizing CEIDARS inventory. 6 TRI Air Release Facilities 2003 TRI Air Releases (2003) Percent People of Color (2000) Lowest Third (<26%) Middle Third (>26% <77%) Highest Third (>77%) 3

4 County Bay Area: 2000 Income by Proximity to an Active Toxic Release (2003) Median household income (relative to state) 164 Income Relative to State (State = within 0.5 miles within 1 mile within 2.5 mile more than 2.5 miles away Proximity to an active TRI County Los Angeles Area: 2000 Income by Proximity to an Active Toxic Release (2003) Median household income (relative to state) Income Relative to State (State = within 0.5 miles within 1 mile within 2.5 mile more than 2.5 miles away Proximity to an active TRI 4

5 9 30% 25% 9-County Bay Area: 2000 Immigration Status by Proximity to an Active Toxic Release (2003) Percent '80s and '90s Immigrants Percent Population 20% 15% 10% 27% 25% 22% 5% 13% 0% within 0.5 miles within 1 mile within 2.5 mile more than 2.5 miles away Proximity to an active TRI 10 30% 5-County Los Angeles Area: 2000 Immigration Status by Proximity to an Active Toxic Release (2003) 25% Percent Population 20% 15% 10% 29% 27% 24% Percent '80s and '90s Immigrants 5% 13% 0% within 0.5 miles within 1 mile within 2.5 mile more than 2.5 miles away Proximity to an active TRI 5

6 11 100% 9-County Bay Area: Children in Schools (2005) by Race/Ethnicity and Proximity to an Active Toxic Release (2003) 80% 20% 20% 24% 24% Other Asian/Pacific Islander Percent Children 60% 40% 20% 45% 15% 18% 37% 31% 15% 14% 26% 29% 24% 6% 44% Latino African American Anglo 0% within 0.5 miles 0.5 to 1 mile 1 to 2.5 miles more than 2.5 miles away Proximity to an active TRI % 5-County Los Angeles Area: Children in Schools (2005) by Race/Ethnicity and Proximity to an Active Toxic Release (2003) 11% 8% 12% 9% Other 80% 44% Asian/Pacific Islander Percent Children 60% 40% 63% 69% 52% 8% Latino 8% African American 20% 0% 11% 7% 14% 15% 27% 38% within 0.5 miles 0.5 to 1 mile 1 to 2.5 miles more than 2.5 miles away Proximity to an active TRI Anglo 6

7 13 50% 9-County Bay Area: Percent Households Within One Mile of an Active TRI by Income and Race/Ethnicity Asian/Pacific Islander 40% Percent Households 30% Latino African American 20% Non-Hispanic White 10% <$10K $10K- $15K $15K- $25K $25K- $35K $35K- $50K Household Income $50K- $75K $75K- $100K >$100K 14 50% 5-County Los Angeles Area: Percent Households Within One Mile of an Active TRI by Income and Race/Ethnicity Asian/Pacific Islander 40% Percent Households 30% Latino African American 20% Non-Hispanic White 10% <$10K $10K- $15K $15K- $25K $25K- $35K $35K- $50K Household Income $50K- $75K $75K- $100K >$100K 7

8 15 Logistic Regression Results: Probability of a Tract Being Located Within 1 Mile of an Active TRI 9-County San Francisco Bay Area 5-County Los Angeles Area Model Variables Sign Sig. Sign Sig. Sign Sig. Sign Sig. % owner occupied housing units * - ** ln(median household income) *** + ** ln(median household income)² *** - ** ln(population density) - ** - ** + *** + *** % industrial land use or % employed in manufacturing + *** + *** + *** + *** % african american households + *** + *** + ** + ** % latino households + *** excluded + *** excluded % asian/pacific islander households + excluded - excluded % latino households, not linguistically isolated excluded + excluded + *** % asian/pacific islander households, not linguistically isolated excluded + excluded + *** % latino households, linguistically isolated excluded + *** excluded + *** % asian/pacific islander households, linguistically isolated excluded - excluded - *** Constant *** - ** -2 Log-Likelihood Model Chi-Square % Predicted Correctly Sample Size U.S. EPA s National Air Toxics Assessment (NATA) Gaussian dispersion model estimates longterm annual average outdoor concentrations by census tract for base year Concentration estimates include: 177 air toxics (of 187 listed under the 1990 Clean Air Act) Diesel particulates The model includes ambient concentration estimates from mobile and stationary emissions sources: Manufacturing (point and area) e.g., refineries, chrome plating Non-Manufacturing (point and area) e.g., utilities, hospitals, dry cleaners Mobile (on road and off road) e.g., cars, trucks, air craft, agricultural equipment Modeled air pollutant concentration estimates allocated to tract centroids. 8

9 17 Estimating Cancer and Respiratory Risks Associated with Ambient Air Toxics Exposures Risk estimates are derived based on U.S. EPA and California Risk Guidelines for risk assessment Assumes exposures are chronic over a lifetime Risks are additive across pollutants Ecological study combines Tract-level cumulative cancer and non-cancer risk estimates with tract-level SES measures 18 Respiratory Hazard Associated with Ambient Air Toxics - NATA 1999 San Francisco Bay Area, 1999 NATA Concentration-Based Respiratory Hazard Ratio by 2000 Census Tracts NATA 1999 Concentration-Based Respiratory Hazard Ratio Miles d:\data\gisca\ca_ej_nata96_and_nata99.apr - fields: Census Tracts view: Bay Area - Teale Albers - NATA99 layout: BayArea_NATA99_CRsptotsum - CRsptotsum - Nata 1999 Concentration-Based Respiratory Hazard Ratio (All Sources) Scale equals: 1 to 550,000 9

10 19 Respiratory Hazard Associated with Ambient Air Toxics (NATA 1999) Respiratory Hazard Ratio (concentration based) NATA Demographics of Respiratory Hazard, California Total, SCAQMD and BAAQMD NATA 1999 data, respiratory hazard estimated using additional RfC's and exposure estimates 100% 75% 50% 25% nh other nh API nh native nh black nh white hispanic 0% lowest respiratory hazard Q2 Q3 highest respiratory hazard lowest respiratory hazard Q2 Q3 highest respiratory hazard lowest respiratory hazard Q2 Q3 highest respiratory hazard California total 5 Southern California counties 9 Bay Area counties population in tracts ranked by respiratory hazard ratios 10

11 21 Estimated Individual Lifetime Cancer Risk by Race/Ethnicity & Tract Median Household Income (NATA 1999/2000 Census) - Southern CA cancer risk, per million total population hispanics nh whites nh blacks nh native nh asian less than $30,000 $30,000-$44,999 $45,000-$59,999 $60,000 and higher 22 Estimated Individual Lifetime Cancer Risk by Race/Ethnicity & Tract Median Household Income (NATA 1999/2000 Census) - Bay Area cancer risk, per million total population hispanics nh whites nh blacks nh native nh asian less than $30,000 $30,000-$44,999 $45,000-$59,999 $60,000 and higher 11

12 23 Estimated Respiratory Hazard by Race/Ethnicity & Tract Median Household Income - Southern California (NATA 1999/2000 Census) respiratory hazard total population hispanics nh whites nh blacks nh native nh asian 5 0 less than $30,000 $30,000-$44,999 $45,000-$59,999 $60,000 and higher 24 Estimated Respiratory Hazard by Race/Ethnicity & Tract Median Household Income - Bay Area (NATA 1999/2000 Census) respiratory hazard total population hispanics nh whites nh blacks nh native nh asian 5 0 less than $30,000 $30,000-$44,999 $45,000-$59,999 $60,000 and higher 12

13 25 Bay Area AQMD Respiratory Hazard model variables: R 2 adj Relationship parameter t p-value 52.45% % owner occupied housing units *** ln(median household income) *** ln(population density) *** % industrial/commercial/transportation land use *** % african american *** % latino % asian/pacific islander *** % linguistically isolated Cancer Risk model variables: R 2 adj Relationship parameter t p-value 55.33% % owner occupied housing units *** ln(median household income) *** ln(population density) *** % industrial/commercial/transportation land use *** % african american *** % latino *** % asian/pacific islander % linguistically isolated *** *** p<0.05 **p< Southern California AQMD Respiratory Hazard model variables: R 2 adj Relationship parameter t p-value 61.62% % owner occupied housing units *** ln(median household income) *** ln(population density) *** % industrial/commercial/transportation land use *** % african american *** % latino *** % asian/pacific islander *** % linguistically isolated *** Cancer Risk model variables: R 2 adj Relationship parameter t p-value 64.28% % owner occupied housing units *** ln(median household income) *** ln(population density) *** % industrial/commercial/transportation land use *** % african american *** % latino *** % asian/pacific islander *** % linguistically isolated *** *** p<

analysis -- cohort study) Preterm birth Low birth weight Examine whether SES measures affect any observed

14 27 What about health outcomes? Exploring pollution links to: Asthma hospitalization rates (ecological study) Poor birth outcomes (secondary data analysis -- cohort study) Preterm birth Low birth weight Examine whether SES measures affect any observed pollution/health outcome associations 28 Asthma Hospitalization Rate Source: Community Action to Fight Asthma 200 Asthma Hospitalization Rate per zip code polygon

29 Connecting to Health Outcomes Multivariate Modeling Asthma Hospitalization Rate (age adjusted per 10,000 people) Counties included: Alameda Mariposa Contra Costa Merced San Francisco Tulare San

ln(concentration-based respiratory hazard ratio) + * ln(population density) + * % population below 150 percent povery + * % households built in 1949 or earlier + * % population latino + * % african

15 29 Connecting to Health Outcomes Multivariate Modeling Asthma Hospitalization Rate (age adjusted per 10,000 people) Counties included: Alameda Mariposa Contra Costa Merced San Francisco Tulare San Mateo Kern Santa Clara Los Angeles Sacramento San Diego Solano Imperial Sonoma model variables Log Likelihood Parameter Stat. Sig. ln(concentration-based respiratory hazard ratio) + * ln(population density) + * % population below 150 percent povery + * % households built in 1949 or earlier + * % population latino + * % african american + * % asian/pacific islander + * % with a B.A. or higher education level - * % linguistically isolated - * * indicates significance at the.10 level 30 Relationship to Poor Birth Outcomes Monitored criteria pollutant data and risk estimates from modeled air toxics data integrated with tract level SES measures Pollution data and SES measures modeled in relation to birth outcomes geocoded by tract (LBW, preterm birth) Relationship between birth outcomes and ambient monitoring data (ozone, NO 2, CO, PM 10, PM 2.5, SO 2 ) Association between birth outcomes and risk estimates from air toxics data Examine whether pollution-poor birth outcome relationship is possibly stronger in poorer neighborhoods. High Poverty Neighborhoods Air Quality Poor Birth Outcomes Low Poverty Neighborhoods Air Quality Poor Birth Outcomes 15

31 Community-based Local Scale Study Overview: Community-based Done in conjunction with a community-based organization Partners in design, data collection, interpretation Identify/locate sources of

Community-based Local Scale Study Specific area: Oakland s Hegenberger Corridor neighborhood Specific goals: locate emissions that may not be systematically incorporated into ARB inventories identify

16 31 Community-based Local Scale Study Overview: Community-based Done in conjunction with a community-based organization Partners in design, data collection, interpretation Identify/locate sources of community concern Also work with UC Berkeley professors on project, including potential deployment of cheap, portable and accurate monitors for pilot study for community-based PM air monitoring 32 Community-based Local Scale Study Specific area: Oakland s Hegenberger Corridor neighborhood Specific goals: locate emissions that may not be systematically incorporated into ARB inventories identify estimates of community-level pollution burdens, incorporate community as partners in study design, data collection and interpretation; capture community knowledge in a form useful to our analysis check results with official data sources 16

17 S a n Le a nd r o 14t h 9 8 t h Presentation - November 1, 2006 by Manuel 33 Hegenberger Corridor NATA 1999 Excess Cancer Risk (all sources- concentration-based estimate) H e ge n b e rg er 34 Hegenberger Corridor study site Hegenberger 14th San Leandro 0.5 miles.-,880 98th 17

18 35 Hegenberger Corridor Schools Hegenberger ÊÚ ACORN Woodland Elementary EnCompass Elementary Highland Elementary ÊÚ ÊÚ Rise Community 14th ÊÚ ÊÚ ÊÚ LPS - Oakland Webster Academy San Leandro Elmhurst Middle ÊÚ.-,880 98th Monarch Academy ÊÚ 36 Hegenberger Corridor Cumulative Impacts TRI Air Release (2003) DTSC Hazardous Waste Sites Intermodal Facilities CHAPIS Air Toxics Releases Railroads Hegenberger San Leandro 14th.-,880 98th 18

19 37 Community-based Local Scale Study Community members will map three types of observations: Location and descriptive information on small emissions sources Verify locational accuracy of emissions sources recorded in regulatory data Assess air quality with spot sampling that corresponds to areas of concern: odor complaints direct observations of problematic operations 38 Community-based Local Scale Study How Data will be collected Community members will systematically cover entire study area Use handheld GPS receivers to record location of observations Use air photography where appropriate Capacity building for community Oakland office CBE Support community members who will actually collect the data. Regularly compile and maintain the completed field data sets organize meetings for community members to view, discuss and error-check the data 19

39 Community PM Monitoring: The Challenge There are thousands of compounds, for assessing health effects from combustion pollution, but small particles are the

No true passive monitors basic physics Electronic monitors using light-scattering or other techniques are expensive Gold standard technology, pumps and filters,

20 39 Community PM Monitoring: The Challenge There are thousands of compounds, for assessing health effects from combustion pollution, but small particles are the best single measure. However, small particles are difficult and expensive to measure. No true passive monitors basic physics Electronic monitors using light-scattering or other techniques are expensive Gold standard technology, pumps and filters, is feasible, but bulky. Slide: Courtesy of Kirk Smith, UCB 40 Realization in 1993 There is actually a cheap reliable particle detector in use in hundreds of millions of locations throughout the world. Slide: Courtesy of Kirk Smith, UCB 20

41 Temperature and Humidity Sensor Data Logger Slide: Courtesy of Kirk Smith, UCB 42 Pilot Testing New

retrofitted to meet sampling needs and conditions in California where PM levels tend to be lower Easy use,

21 41 Temperature and Humidity Sensor Data Logger Slide: Courtesy of Kirk Smith, UCB 42 Pilot Testing New Monitors Current monitors used in developing countries where PM levels are very high Monitor will be retrofitted to meet sampling needs and conditions in California where PM levels tend to be lower Easy use, cheap and flexible deployment by community groups in diverse settings (indoor/outdoor, schools, workplaces, homes, buses, industrial zones, etc). Yield sampling results that can be compared directly to national standards Deploy 10 final prototype devices, software and necessary training for use in community-selected sites in Oakland. 21

43 Community-based Local Study Phases Begin summer 2007 To incorporate air quality monitoring, we plan to deploy ten new UCB-L particle air quality monitors; mid-2008 Pending successful development

Kirk Smith of University California at Berkeley) on the local-scale study in mid-2008 one of Smith s doctoral students will handle logistics and raw data analysis will work with team to train and

22 43 Community-based Local Study Phases Begin summer 2007 To incorporate air quality monitoring, we plan to deploy ten new UCB-L particle air quality monitors; mid-2008 Pending successful development Collaboration with Dr. Kirk Smith of University California at Berkeley) on the local-scale study in mid-2008 one of Smith s doctoral students will handle logistics and raw data analysis will work with team to train and supervise community members in monitor use. 44 Addressing Cumulative Impact and Community Vulnerability Emerging question in EJ research and policy is: Cumulative impact from multiple pollution sources/exposures The independent variables associated with siting patterns, metrics of group exposure, and estimated health risks Vulnerability to siting or disamenities Measures of civic engagement or proxy indicators of potential community voice (e.g. ethnic churning, segregation) Vulnerability factors that may modify or confound pollution outcome relationships SES (individual/contextual) Segregation (contextual) Developing vulnerability measures to apply in multivariate modeling 22

23 Presentation - November 1, 2006 by Manuel 45 Cumulative Impact: View of Multiple Hazards Percent People of Color (2000) Lowest Third (<41%) Middle Third (>41% <80%) Highest Third (>80%) Percent People of Color by Tract 46 Cumulative Impact: View of Multiple Hazards Toxic Release Inventory Air Releases 23

24 Presentation - November 1, 2006 by Manuel 47 Cumulative Impact: View of Multiple Hazards Chrome Plating Facilities 48 Cumulative Impact: View of Multiple Hazards ARB CHAPIS Stationary Facilities 24

25 Presentation - November 1, 2006 by Manuel 49 Cumulative Impact: View of Multiple Hazards Intermodal Transportation Facilities 50 Cumulative Impact: View of Multiple Hazards Railroads 25

26 Cumulative Impact & Vulnerability Framework Regional Vulnerability Vulnerability Indicators Community Vulnerability Built Environment Land Use/Zoning Traffic Density Housing Quality Social Environment Political Empowerment Poverty Rate Access to Health Care Food Security Regulatory Enforcement Racial Residential Segregation Income Inequality Individual Vulnerability Social support Income Health Insurance Status Diet/Nutritional Status Health Behaviors Exposure to Discrimination CI Indicators Pollutant Source Industrial Facility/ Transportation Corridor Emissions Chemicals Emitted Community Impact Exposure Outdoor Pollution Levels Internal Dose Chemical Body Burden Response & Resilience Detoxification Capacity Individual Impact Health Effect Asthma/ Birth Outcomes Ability to Recover Co-Morbidity/ Mortality Develop Screening Methods Use analytical results to develop indicators of cumulative impact and community vulnerability that would be: Applicable to various geographic levels Transparent, quantifiable, understandable and relevant to policy-makers and communities Can be derived from existing data sources Indicators will be reviewed by community EJ groups (along with CARB staff) Integrate into an environmental justice screening methodology which could be used for: regulatory decision-making enforcement activities community outreach Identify areas for special regulatory attention Greenlining assessment 26

27 53 Dimensions of Cumulative Impact & Community Vulnerability Indicators of Cumulative Impact Land Use Point Sources: Toxic Release Inventory Facilities, TSDFs Measures Distance from and density of active facilities Toxicity-weighted emissions burdens Area Sources Chrome Plating Facilities Dry Cleaners CHAPIS Facilities Measures Distance from and density of active facilities Mobile Sources Rail yards Airports Ports Traffic Density Measures Distance from and density of active facilities Distance -weighted traffic density Cumulative Measure across all source types Cumulative scoring based on quartile distribution of facility density and traffic density in a neighborhood. Add facilities (Score 1-4) and traffic density scores together (Score 1-4) 54 Dimensions of Cumulative Impact & Community Vulnerability Indicators of Cumulative Impact Air Quality Air Toxics National Air Toxics Assessment (NATA) ARB Modeled Air Toxics Estimates Measures Estimates of cumulative cancer risk and respiratory hazard (total and source-specific) (Morello-Frosch 2002 and Reynolds 2003) Source-specific estimates of cumulative cancer and respiratory hazard Scoring based on quartile distribution of air toxics by relevant health endpoints and structural properties (i.e. metals, chlorinated solvents, aromatic solvents that are mutually exclusive) (Wilhelm et al 2006) Criteria Pollutants California air quality monitoring data (PM10, PM2.5, ozone, NO2, SO2) Measures Annual average air quality measures for 5 critieria pollutants Scoring based on quartile distribution of each pollutant Aggregate Index of long-term exposure to 5 pollutants by weighting long-term regional exposure estimates by each pollutant NAAQS standard (Kyle et al 2002) 27

28 55 Dimensions of Cumulative Impact & Community Vulnerability Indicators of Vulnerability/Resilience SES Poverty Educational Attainment Level Wealth (Home ownership) Income Occupational Status Measures Scoring based on quartile distribution of each SES measure. Each SES score is then added together Civic Engagement/Political Empowerment Lingusitic Isolation Immigration Status Voter Turnout Measures Scoring based on quartile distribution of each empowerment/civic engagement measure. Each score is then added together. Health Status/Health Care Access Asthma Hospitalization Rates (children & adults) Rates of poor birth outcomes (low birth weight, preterm birth) Measures Scoring based on quartile distributions of each health outcome measure 56 Dimensions of Cumulative Impact & Community Vulnerability Sensitive Land Uses Schools Day Care Centers Measures Cumulative scoring based on quartile distribution of facility density Regional Inequality Racial Residential Segregation Income Inequality Indicators of Vulnerability/Resilience Measures Cumulative scoring based on quartile distribution of two inequality measures 28

29 57 Alternative Siting Scenarios for CEC Special Analysis for California Energy Commission (CEC) Includes site assessment for a potential power plant to examine environmental justice impacts Evaluate different assessment methods and approaches, incorporating new cumulative impact and community vulnerability indicators to explore these tools Suggest ways to implement these into screening methodology for future site assessment 58 Study Outputs Assessment of how new and existing data sets can be leveraged to answer EJ policy & regulatory questions Statistical analyses of demographics, community vulnerability, and disparities in air pollution burdens Application of vulnerability measures to health outcome analysis. Explanations of how strength of relationships is affected by spatial scale, autocorrelation, inclusion of land use, and construction of vulnerability measures Analysis of local study and relationship to state emissions inventory databases Explanation of screening methodology and options for its application and further development/dissemination 29

30 59 Questions, Comments? 30

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