Post on 19-Jan-2016
description
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
###
#####
#######
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