Prepared by:

Manuel Pastor, USCJim Sadd, Occidental College

Rachel Morello-Frosch, UC Berkeley

Source: CBE

Source: David Woo

Air Pollution and Environmental Justice: Integrating Indicators of Cumulative Impact and Socio-economic Vulnerability into Regulatory Decision-making Funding from the California Air Resources Board

Our Research Team

Manuel Pastor, Ph.D. in Economics, responsible for project coordination, statistical analyses, including multivariate and spatial modeling, and popularization

James Sadd, Ph.D. in Geology, responsible for developing and maintaining geographic information systems (GIS), including location of site and sophisticated geo-processing

Rachel Morello-Frosch, Ph.D. in Environmental Health Science, responsible for statistical analysis, health end-points, and estimates of risk.

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

Project Summary: Integrating Indicators of Cumulative Impact and Community Vulnerability into Regulatory Decision-making

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

Framework Study: Data Sources

Toxic Release Inventory – annual self-reports from point facilities, with analysis attempting to separate out carcinogenic releases, and facilities geo-coded as of 2003. The TRI data is standard in national studies although much analysis is flawed due to poor geographic matching.

NATA – National Air Toxics Assessment (1999). Takes into account national emissions database with modeling of stationary, mobile, and point sources. Public available NATA fails to account for cancer risk associated with diesel; we apply risk factors to modeled diesel to complete the California picture.

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San Francisco Bay Area, 2003 Toxic Release Inventory Air Release Facilitiesby 2000 Census Tract Demographics

Percent People of Color

< 34%

34 - 61%

> 61%

#SToxic Release Inventory Air Release Facilit ies (2003)

0 10 20 Miles

At First Glance . . .TRI Facilities Relative to Neighborhood Demographics

How do we determine TRI proximity?The one-mile case

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#######

Ba

yshore

Fre

eway

.-,280

.-,380

SNEW

0 0.5 1 Miles

Total Population by Census Block0 - 1010 - 100100 - 10001000 +

Census Tract Boundaries

# TRI Facility

N

1-Mile Radius

Population by Race/Ethnicity (2000) and Proximity to a TRI Facility with Air Releases (2003) in the 9-County Bay Area

33%

45%

63%

30%

21%

12%12% 8%

4%

20% 21%17%

4% 4% 4%

0%

20%

40%

60%

80%

100%

within 1 mile 1 to 2.5 miles more than 2.5 miles away

Proximity to an active TRI

Pe

rce

nta

ge

of

Po

pu

latio

n

Other

Asian/Pacific Islander

African American

Latino

Non-Hispanic White

But It Isn’t Just Income . . .Percentage Households within One Mile of an Active TRI (2003) by Income and

Race/Ethnicity in the 9-County Bay Area

10%

20%

30%

40%

50%

<$10K $10K-$15K

$15K-$25K

$25K-$35K

$35K-$50K

$50K-$75K

$75K-$100K

>$100K

Household Income

Per

cent

age

of H

ouse

hold

s

Asian/Pacific Islander

Latino

African American

Non-Hispanic White

TRI Air Releases: Race, Income, and Land Use Together

It has more African American or Latino residents

It is lower income

It has lower home ownership rates

Its land use is more industrial

It has more non-English speakers

Multivariate analysis of proximity to a TRI facility:

Considering all the factors together, a neighborhood is more likely to be near a TRI if:

Model Variables Coeff. Sign Stat. Sig. Coeff. Sign Stat. Sig.

% owner occupied housing units - ** -ln(per capita income) - *** - ***ln(population density) - ** - **% manufacturing employment + *** + ***% African American + *** + ***% Latino + *** + **% Asian/Pacific Islander - -% linguistically isolated households + ** indicates significance at the .10 level;** indicates significance at the .05 level;*** indicates significance at the .01 level N = 1403 N = 1403

San Francisco 9-County Bay Area:Probability of a Tract Being Located Within 1 Mile of an Active TRI

(Multivariate Logistical Model)

What About Ambient Air Toxics?

This category of pollutants come from a diverse array of sources

Stationary: large industrial facilities and smaller emitters, such as auto-body paint shops, chrome platers, etc.

Mobile: Cars, trucks, rail, aircraft, shipping, construction equipment

Important because largest proportion of estimated cancer risk (70% in the Bay Area) is related to mobile emissions

U.S. EPA’s National Air Toxics Assessment (NATA)

Gaussian dispersion model estimates long-term annual average outdoor concentrations by census tract for base year 1999.

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.

Lifetime Cancer Risk (per million)

Low (< -1 std. dev. below mean)

Mid-Low (-1 to 0 std. dev. below mean)

Mid-High (0 to 1 std. dev. above mean)

High (> 1 std. dev. above mean)

0 10 20 Miles

1999 NATA Estimated Cancer Risk (All Sources) by 2000 Census Tracts, 9-County Bay Area

Race, Income, and Land Use Together . . .

It is has more residents of color

It is lower income

It has lower home ownership rates

Its land use is more industrial

It is more densely populated

Considering all the factors together, the levels of estimated cancer risk and respiratory hazard from air toxics is higher if:

Model variables Coeff. Sign Stat. Sig. Coeff. Sign Stat. Sig. Coeff. Sign Stat. Sig. Coeff. Sign Stat. Sig.

% owner occupied housing units - *** - *** - *** - ***relative per capita income (tract/state) + *** + *** + *** + ***relative per capita income squared - *** - *** - *** - ***ln(population density) + *** + *** + *** + ***% industrial/commercial/transportation land use + *** + *** + *** + ***% African American + *** + *** + *** + ***% Latino + *** + ** + *** + ***% Asian/Pacific Islander + *** + *** + *** + ***% linguistically isolated households + *** -* indicates significance at the .10 level;** indicates significance at the .05 level;*** indicates significance at the .01 level N = 1402 N = 1402 N = 1402 N = 1402

Cancer Risk Respiratory Hazard

San Francisco 9-County Bay Area:Modeling Estimated Excess Cancer Risk and Respiratory Hazard

(Multivariate OLS Model)

Overview: Community-based

Done in conjunction with Communities for a Better Environment

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

Community-based Local Scale Study

14th

98th

San Leandro

Hegenb

erger

.-,880

HegenbergerCorridor

study site

0.5 miles

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16

HegenbergerCorridor

study site

0.5 miles

880

17

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

Details of Analysis

Map only residential land use and schoolsThis is where exposure takes placeSensitive land use categories

ARB land use guidelines schools, child and healthcare facilities

Census demographics at block group level These two areas intersected to create “sliver” polygons

of known demographics and land use Proximity score – assumes impact if polygon centroid

is located within one mile radius of hazard

Four levels of indicators:Proximity to HazardsLand Use categoriesHealth Risk IndicatorsSocial Vulnerability Indicators

Hazards Indicators

Toxic Release Inventory 2003

Most TRI sites replicated in other State ARB databases (CHAPIS and AB2588) Plan to replace with EPA RSEI (1987-2005 layers of toxicity- and population-weighted hazard scores

Chrome PlatersCHAPISAB2588 “hot spots”Hazardous Waste TSDs (DTSC)

Federal Superfund sitesState response sitesVoluntary cleanup sites

I-210

I-110

Pomona

Artesia

I-40

5

I-71

0

I-605

I-105

Pasad

ena

Ora

nge

Ant

elop

e V

all e

y

I-10

Cumulative Impact: Hazard Proximity Indicators

Land Use Indicators

Rail yards and RailroadsAirports PortsRefineriesDistribution centers (intermodal facilities)Parks/recreation facilities, open spaceSensitive land uses

Childcare facilities Healthcare facilitiesSchools

Traffic density– not yet implementedCalTrans AADT and truck countsMobile sources risk included in NATA health risk measures (below)

Tree canopy: not yet implemented22

22

Cumulative Impact: Land Use Indicators

I-210

I-110

Pomona

Artesia

I-40

5

I-71

0

I-605

I-105

Pasad

ena

Ora

nge

Ant

elop

e V

all e

y

I-10

Health Risk Indicators –

Polygon receives indicator if score is greater than or equal to one standard deviation above mean for LA County NATA 1999 (National Air Toxics Assessment)

Total Cancer Risk from all pollutants Respiratory Hazard from all air pollutants Logarithmic distribution of data values- scores transformed

ARB Estimated Inhalable Cancer Risk 2001Calculated from modeled air toxics concentrations using emissions from CHAPIS http://www.arb.ca.gov/toxics/cti/hlthrisk/hlthrisk.htmCorrected this data to more accurately record “hot spots”

Asthma hospitalization rate zip code level data from Ca Dept of Health Services uneven geographic coverage

Birth outcomes –not yet implemented

I-210

I-110

Pomona

Artesia

I-40

5

I-71

0

I-605

I-105

Pasad

ena

Ora

nge

Ant

elop

e V

all e

y

I-10

Cumulative Impact: Health Risk Indicators

Social Vulnerability Indicators

Polygons receive indicator if part of a block group in the disadvantaged quartile

Percent eligible to vote Percent non citizen residents Percent linguistically isolated households

(no one in household speaks English well) Percent residents of color (non-Anglo) Percent residents at below 2X nationwide

poverty level Per capita income Educational attainment – percent high

school education or less

Cumulative Impact: Social Vulnerability Indicators

0 - 23 - 56 - 89 - 1213 - 19

LA Collaborative area

City Footprints

Sum of All Indicators

I-210

I-110

Pomona

Artesia

I-40

5

I-71

0

I-605

I-105

Pasad

ena

Ora

nge

Ant

elop

e V

all e

y

I-10

I-210

I-110

Pomona

Artesia

I-40

5

I-71

0

I-605

I-105

Pasad

ena

Ora

nge

Ant

elop

e V

all e

y

I-10

Cumulative Impact: Sum of Multiple Hazards, Health Risks, and Social Vulnerability in Los Angeles

0 - 23 - 56 - 89 - 1213 - 19

LA Collaborative area

City Footprints

Sum of All Indicators

04/21/23

Continue with health impacts assessment, particularly birth outcomes

Complete environmental justice assessment of state, controlling for spatial autocorrelation and other statistical issues

Present and get feedback on screening method – taking into account tractability as well as sophistication

Complete local study to check community issues and results against secondary databases that could be used in screening approaches

Future Directions for Project