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Toxic Tides and Environmental Injustice: Social Vulnerability to Sea Level Rise
and Flooding of Hazardous Sites in Coastal California
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Download Hi-Res ImageDownload to MS-PowerPointCite This:Environ. Sci. Technol.
2023, XXXX, XXX, XXX-XXX
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TOXIC TIDES AND ENVIRONMENTAL INJUSTICE: SOCIAL VULNERABILITY TO SEA LEVEL RISE
AND FLOODING OF HAZARDOUS SITES IN COASTAL CALIFORNIA
* Lara J. Cushing*
Lara J. Cushing
Department of Environmental Health Sciences, University of California Los
Angeles, Los Angeles, California 90095, United States
*Email: lcushing@ucla.edu
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https://orcid.org/0000-0003-0640-6450
,
* Yang Ju
Yang Ju
School of Architecture and Urban Planning, Nanjing University, Nanjing, China
210093
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* Scott Kulp
Scott Kulp
Climate Central, Princeton, New Jersey 08542, United States
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* Nicholas Depsky
Nicholas Depsky
Energy and Resources Group, University of California, Berkeley, Berkeley,
California 94720, United States
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* Seigi Karasaki
Seigi Karasaki
Energy and Resources Group, University of California, Berkeley, Berkeley,
California 94720, United States
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* Jessie Jaeger
Jessie Jaeger
PSE Healthy Energy, Oakland, California 94612, United States
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* Amee Raval
Amee Raval
Asian Pacific Environmental Network, Oakland, California 94612, United States
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* Benjamin Strauss
Benjamin Strauss
Climate Central, Princeton, New Jersey 08542, United States
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, and
* Rachel Morello-Frosch*
Rachel Morello-Frosch
Department of Environmental Science, Policy and Management & School of Public
Health, University of California, Berkeley, Berkeley, California 94720,
United States
*Email: rmf@berkeley.edu
More by Rachel Morello-Frosch
Cite this: Environ. Sci. Technol. 2023, XXXX, XXX, XXX-XXX
Publication Date (Web):May 2, 2023
PUBLICATION HISTORY
* Received11 October 2022
* Accepted20 March 2023
* Revised17 March 2023
* Published online2 May 2023
https://doi.org/10.1021/acs.est.2c07481
© 2023 The Authors. Published by American Chemical Society
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Supporting Info (1)»Supporting Information Supporting Information
SUBJECTS:
* Color,
* Fossil fuels,
* Groundwaters,
* Lipids,
* Mathematical methods
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ABSTRACT
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Sea level rise (SLR) and heavy precipitation events are increasing the frequency
and extent of coastal flooding, which can trigger releases of toxic chemicals
from hazardous sites, many of which are in low-income communities of color. We
used regression models to estimate the association between facility flood risk
and social vulnerability indicators in low-lying block groups in California. We
applied dasymetric mapping techniques to refine facility boundaries and
population estimates and probabilistic SLR projections to estimate facilities’
future flood risk. We estimate that 423 facilities are at risk of flooding in
2100 under a high emissions scenario (RCP 8.5). One unit standard deviation
increases in nonvoters, poverty rate, renters, residents of color, and
linguistically isolated households were associated with a 1.5–2.2 times higher
odds of the presence of an at-risk site within 1 km (ORs [95% CIs]: 2.2 [1.8,
2.8], 1.9 [1.5, 2.3], 1.7 [1.4, 1.9], 1.5 [1.2, 1.9], and 1.5 [1.2, 1.9],
respectively). Among block groups near at least one at-risk site, the number of
sites increased with poverty, proportion of renters and residents of color, and
lower voter turnout. These results underscore the need for further research and
disaster planning that addresses the differential hazards and health risks of
SLR.
KEYWORDS:
* GIS
* climate change
* environmental equity
* exposure analysis
* participatory research
* climate resilience
*
*
*
SYNOPSIS
Little research exists on the environmental justice implications of sea level
rise-driven flooding of coastal hazardous facilities. This study estimates
future flood risks to potentially hazardous sites and associated threats to
socially disadvantaged populations.
INTRODUCTION
ARTICLE SECTIONS
Jump To
* Abstract
* Introduction
* Materials and Methods
* Results
* Discussion
* Supporting Information
* Author Information
* Acknowledgments
* References
--------------------------------------------------------------------------------
The frequency of extreme coastal flooding across much of the world is projected
to more than double by 2050 due to sea level rise (SLR). (1) In California, SLR
has closely mirrored global average rates of about 0.3 cm per year over the past
several decades. (2) Assuming greenhouse gas emissions continue to rise, SLR of
0.2 to 0.5 m is expected between 2000 and 2050 and 0.5 to 1.4 m by the end of
the century. (3) These projections pose significant implications for coastal
communities in California where more than 68,000 people live within 0.3 m
elevation of the local mean high tide line, and more than 145,000 live within
0.9 m. (4) In the coming decades, even larger areas will experience coastal
flooding during high tides, storm surges, high precipitation, and El Niño events
due to higher average sea levels. (5)
Past flood and storm surge events have led to releases of toxic substances into
the environment from industrial, hazardous waste, and legacy contamination
sites. (6−8) For example, flooding caused by Hurricanes Katrina and Rita
resulted in an estimated 166 releases of hazardous substances, largely due to
emergency shut down and start-up operations at industrial facilities. (9−11)
SLR-amplified flood heights of future storm surges and tidal events will
increase flood risks at hazardous sites in coastal areas and the possibility of
similar natural hazard-triggered technological (“natech”) disasters. (12,13)
Less severe flooding can also contribute to contaminant releases via damage from
debris flow, corrosion of pipelines and other infrastructure, power failures,
and impediments to operator access. (14)
Flood-induced contaminant releases are more likely to impact low-income
households and people of color because they are more likely to live near
industrial and hazardous waste facilities. (15−18) Socially disadvantaged
communities also have fewer resources to anticipate, mitigate, cope with, or
recover from the effects of flooding. Prior research shows people of color and
the poor are less likely than others to own a car enabling evacuation, more
likely to suffer injury or die during the aftermath of an extreme flood event,
and less likely to return and rebuild afterward. (19−21) In the case of
Hurricane Harvey, pollutant releases from petrochemical facilities associated
with flooding disproportionately impacted neighborhoods with higher proportions
of low income and Hispanic residents. (8)
In this analysis, we present the first assessment, of which we are aware, of the
number and location of hazardous facilities at risk of future flooding due to
SLR in California and assess the environmental justice implications. We consider
a wide variety of sites that have hazardous substances on site and have
documented excess contaminant releases to air, land, and floodwaters during
previous flood events, including refineries, industrial facilities, and sewage
treatment plants, as well as cleanup sites where SLR may cause changes in
groundwater movement that leads to the spread of below-ground hazardous
substances. We combine information on the location of hazardous sites and
present-day population demographics with SLR projections to assess inequalities
in future flood risk projections under two greenhouse gas emissions scenarios.
We consider proximity to sites at-risk of flooding in 2050 and 2100 with respect
to current community demographics and indicators of social vulnerability to
characterize inequities in potential exposure and test the hypotheses that (1)
SLR will increase the number of sites at risk of a 1-in-100 year flood in 2050
and 2100 and (2) vulnerable and socially marginalized populations are more
likely to live near at-risk sites.
We collaborated with an advisory committee comprised of environmental justice
advocates working on community-based climate resilience strategies in the San
Francisco Bay Area, Central Coast, and Southern California who provided guidance
on the study design, methods, interpretation, and dissemination of results. This
community-engaged strategy sought to facilitate translation of our analytical
results to inform policy and resilience planning in vulnerable regions
throughout California. Such integration of data- and community-driven methods
also allows for ground-truthing of analytical results, thus enhancing
methodological rigor, public relevance, and policy reach of the research more
broadly. (22−24)
MATERIALS AND METHODS
ARTICLE SECTIONS
Jump To
* Abstract
* Introduction
* Materials and Methods
* Results
* Discussion
* Supporting Information
* Author Information
* Acknowledgments
* References
--------------------------------------------------------------------------------
Project methods were codeveloped in partnership with a five-member advisory
committee, starting with the development of a funding proposal to support the
work and continuing in an iterative process through the study design,
implementation, and development of online data visualization tools for results
dissemination. Committee members were staff of organizations focused on
environmental justice, public health, and climate change and working on climate
resilience policy. Following an initial in-person meeting, we interacted with
the committee virtually, due to the COVID-19 pandemic, through a series of
regular and ad-hoc meetings to gain feedback on our study design and research
methods including data cleaning, metrics development, statistical analysis, and
interpretation of results. We collectively chose the greenhouse gas emissions
scenarios, timeframes (2050 and 2100), and range of SLR estimates we would
investigate, as well as the flood risk metrics, categorization of sites
considering both their potential hazards and ease of interpretation, and the
demographic and social vulnerability metrics to include. One advisory board
member (A. Raval) contributed to the writing of this manuscript. The committee
also coordinated and hosted a series of four public webinars (one state-wide,
one each in the San Francisco Bay Area, Central Coast, and Southern California
regions) to share preliminary findings and gather feedback from a broader range
of roughly 350 community and agency stakeholders. Feedback received via these
webinars resulted in the addition of more extreme SLR scenarios in our study to
align with statewide guidance and considerations of groundwater movement.
HAZARDOUS SITES
We compiled data on the location of active industrial facilities and other
potentially hazardous sites from four sources: the U.S. Environmental Protection
Agency’s (EPA) Facility Registry Service (FRS), (25) the U.S. Energy Information
Administration’s (EIA) Energy Atlas, (26) the U.S. Army Corp of Engineers’
(USACE) Waterborne Commerce Statistics Center, (27) and the Enverus database
(28) of active oil and gas well permits (Table 1). To ensure we captured all
sites from these sources with potential SLR-related flood risk, we included
sites located in any of the 29 California counties containing land area within a
3 km Euclidean distance of the 10 m elevation above the current high tide line
(see Supporting Information, Figure S1 study area map). The FRS compiles
information from more than 30 national and 45 state data systems on facilities
subject to federal environmental regulation in the United States. We excluded
FRS records with poor locational information. This included records with
horizontal accuracy values greater than 50 m or imprecise location descriptions
(e.g., latitude and longitude coordinates derived from zip codes). To focus the
analysis on sites most likely to pose risks of SLR-related contaminant releases,
we also excluded records with a “site type name” indicating that contamination
had been remediated (“contamination addressed”) or where the “active status” was
classified as “No”, “Closed”, “Permanently Closed”, “Retired”, or “Permanently
shut down, Cancelled, Postponed, or No Longer Planned”, or sites with
“environmental interest” end dates before 2020, indicating the site was no
longer regulated under a given environmental interest type. We chose to retain
inactive facilities and facilities with expired permits (“active status” is
“inactive” or “expired”) because residual hazardous materials may remain at
these sites.
Table 1. Sites and Data Sources Included in the Analysis (N = 10,390)a
categorysourcecountdescriptionpower plants (nuclear and fossil fuel)EPA Facility
Registry Service (FRS)79electricity generating facilities that provide power to
the electric grid using primarily nuclear, coal, oil, gas, or other fossil
fuelsanimal operationsFRS42facilities primarily engaged in feeding cattle for
fattening, milking dairy cattle, or concentrated animal feeding operations
(CAFOs)sewage treatment facilitiesFRS341operating sewer systems or sewage
treatment facilities that collect, treat, and dispose of wastehazardous waste
treatment and disposalFRS107facilities designated for the treatment and/or
disposal of hazardous waste or establishments primarily involved in the combined
activity of collecting and/or hauling of hazardous wastes within a local area
and operating treatment or disposal facilities for hazardous wasteToxic Release
Inventory facilitiesFRS3595facilities required to report to the Toxic Release
Inventory (TRI) that handle, manufacture, use, or store certain flammable or
toxic substancessolid waste landfills and incineratorsFRS271operating landfills,
combustors, and/or incinerators designated for the disposal of nonhazardous
solid wastecleanup sites and sites with radioactive materialFRS68contaminated
sites including military sites (BRAC), Superfund sites on the National
Priorities List (NPL), and sites with radioactive contamination, radionuclide
emissions, or involved in radioactive waste disposal are also
included.refineriesEIA U.S. Energy Atlas13facilities involved in the manufacture
of fossil fuelsfossil fuel ports and terminalsEIA U.S. Energy Atlas & USACE
Waterborne Commerce Statistics Center66facilities involved in the transport of
fossil fuelsactive oil and gas wellsEnverus5808active oil and gas wells used for
production or enhanced oil recovery
a
Categories are mutually exclusive, and sites that could fall into more than one
category were assigned using the hierarchy of categories as listed in descending
order.
We then classified FRS sites into one of 7 mutually exclusive categories using
(1) the environmental permits or regulatory programs (“environmental interest”)
given in the FRS; (2) the North American Industry Classification System (NAICS)
code; and/or (3) keyword filters (see Supporting Information Table S1 for
details). We applied a hierarchy to ensure that each category was mutually
exclusive and to eliminate duplicate entries, because many FRS sites have
multiple environmental permits and/or NAICS codes and thus multiple records in
the FRS.
FRS data were supplemented with information on petroleum refineries from the EIA
Energy Atlas. To ensure petroleum refineries were not duplicated between the FRS
and EIA data sets, we manually identified and removed all refineries contained
in the EIA data set from the FRS data set using facility names and coordinates.
Additional fossil fuel infrastructure was included from the EIA Energy Atlas
(petroleum product terminals and crude oil rail terminals) and USACE data set
(petroleum ports). Finally, we obtained active oil and gas well locations from
Enverus and filtered based on production type to limit to production or
stimulation wells (see Supporting Information Table S2 for details).
Active oil and gas well locations were represented as points based on the
longitude and latitude coordinates provided by Enverus. All other sites were
regeocoded using the Google API and joined to tax parcel data from DMP LightBox
to better approximate the geographic extent of each site from its geographic
coordinates. (29) Roughly 80% of these newly geocoded site locations fell within
tax parcel boundaries; we assumed the intersecting parcel geometries
approximated the extents of these sites and used the resulting polygons in our
subsequent flood risk projections. The remaining 20% of sites did not intersect
tax parcels, usually because their point locations were along roadways adjacent
to the facility property. For these sites, we calculated the median parcel area
by site category based on those sites that intersected parcels. We then created
a circular buffer equal to the corresponding median areas by category to
estimate site extents at these locations (see schematic Supporting Information
Figure S2). Parcels and circular buffer areas that extended beyond the mean high
tide line were clipped at the coast.
In a final round of data cleaning, we removed 139 duplicate sites that 1) were
part of the same category and 2) had identical coordinates after geocoding and
3) the same or a similar address (based on a fuzzy text match). We retained
those facilities with identical coordinates and similar addresses if they were
assigned to different categories (n = 15). We dropped facilities with identical
coordinates in the same category if they had different addresses (n = 14)
because we determined geocodes were inaccurate upon visual inspection for these
sites.
The final facilities data set consisted of 10,390 sites in 29 California
counties, classified into the following categories: nuclear and fossil fuel
power plants (n = 79), animal operations (n = 42), sewage treatment facilities
(n = 341), hazardous waste treatment and disposal (n = 107), Toxic Release
Inventory (TRI) facilities (n = 3,595), landfills and incinerators (n = 271),
cleanup sites (including National Priority List Superfund sites and sites with
radioactive material; n = 68), refineries (n = 13), fossil fuel ports and
terminals (n = 66), and oil and gas wells (n = 5808) (Table 1).
SEA LEVEL RISE AND FLOOD RISK PROJECTIONS
To assess site vulnerability, we followed the method described by Kulp and
Strauss (30) and Buchanan et al. (31) In brief, our analyses used probabilistic
sea level rise projections (32) assuming low (Reference Concentration Pathway
[RCP] 4.5) and high (RCP 8.5) greenhouse gas emission scenarios for the years
2050 and 2100. These projections incorporated local vertical land movement, such
as subsidence caused by tectonic activity, large-scale underground fluid
extraction, and glacial isostatic adjustment. Coastal flood height return level
curves from Tebaldi et al. (33) are defined at each of seven U.S. tide stations
in California with more than 30 years of hourly water level records.
In these analyses, we estimate Pannual(H ≥ Elevi | Y = y), the total annual
probability P of at least one coastal flood exceeding the land elevation Elevi
of each site € in year y, integrated across the full distribution of SLR
projections for each emissions scenario. In this context, we defined Elevi as
the 25th percentile of land elevation within the parcel/circular buffer of site
i. We derived land elevations from NOAA’s Coastal Topographic Lidar digital
elevation model (34) and refined the Buchanan et al. approach (31) to better
incorporate levees and other flood control structures. Hydrological connectivity
to the ocean was enforced.
We applied eq (1) from Buchanan et al., (30,31) which integrates the localized
SLR projections and flood risk statistics, to estimate these annual
probabilities. We considered sites to be at-risk if their projected annual
probabilities exceeded 0.01 (i.e., threatened by a 1-in-100 year flood event).
Furthermore, by summing these probabilities across administrative areas (e.g.,
across block groups), we derived that area’s total expected annual exposure
(EAE) or the expected number of hazardous sites exposed in a given year.
EXTREME SLR SCENARIOS AND GROUNDWATER ENCROACHMENT
Our main analysis using RCP 8.5 corresponds to a central projection of about 0.9
m of SLR by 2100. To facilitate integration of our findings into state-level
climate resilience planning, we additionally estimated how many facilities would
be at risk of inundation under more extreme levels of SLR in accordance with
California agency guidance documents recommending consideration of 6.9 feet
(∼2.1 m) of SLR (“Medium Risk-Aversion” scenario for residential and commercial
development) and 10.1 feet (∼3.1 m) of SLR (“High-Risk Aversion” scenario for
critical infrastructure). (35,36) For our analysis of “extreme” scenarios, site
risk was classified on a yes/no basis depending on each site’s elevation and
connectivity to the sea.
Rising groundwater due to SLR may lead to groundwater emergence and the movement
of contaminated groundwater inland. (37) This movement can affect the release
and spread of surface and subsurface toxic substances at contaminated sites.
(38) We integrated data on projected groundwater depths available in half meter
increments of SLR from the U.S. Geological Survey. (39) We used groundwater rise
estimates corresponding most closely to the degree of SLR in the two extreme
scenarios above: 2 m (∼6.6 feet) and 3 m (∼9.8 feet). We followed published
guidance to select the parameters of the groundwater modeling data (i.e., the
Mean Higher-High Water marine boundary condition and a horizontal hydraulic
conductivity of 1.0 m/day). (40) We considered a site to be at-risk of
groundwater encroachment if it spatially overlapped with groundwater tables 1 m
or less below the surface.
COMMUNITY DEMOGRAPHICS AND SOCIAL VULNERABILITY MEASURES
We estimated the following census block-group level measures using the U.S.
Census American Community Survey’s (ACS) 2013–2017 five-year estimates: (41) age
(% under the age of 18 and % 65 and older), race/ethnicity (% people of color,
defined as the inverse of % non-Hispanic White), poverty (% below twice the
federal poverty line), housing tenure (% renters), vehicle ownership (% of
households without a vehicle), family structure (% single parent-headed
households), linguistic isolation (% of households where no one 14 years or
older speaks English “very well”). We derived a presence/absence measure of
affordable housing (market-based or government subsidized) using data from
CoStar and Urban Land Institute’s 2017 Naturally Occurring Affordable Housing
Analysis and the National Housing Trust’s 2017 Affordable Housing Programs.
(31,42) We used voter turnout data from California’s Statewide Database (43) on
redistricting to derive the percent of registered voters who voted during the
2016 general elections as an indicator of civic engagement, following Maizlish’s
(2016) methodology. (44) Finally, we used CalEnviroScreen 4.0 to identify
disadvantaged communities. (45) CalEnviroScreen is a relative ranking of
California census tracts that combines indicators of pollution burden from
multiple environmental hazards and population vulnerability to pollutant
exposures. We defined disadvantaged communities as census tracts with the
highest quartile of cumulative environmental burdens and social vulnerability
(in keeping with an earlier version of CalEnviroScreen) but with updated
CalEnviroScreen 4.0 cumulative impact percentiles. (46)
We then took this population data and geographically refined it to a higher
spatial resolution using dasymetric mapping methods to define exposed blocks
groups and characterize populations near at-risk sites (see Supporting
Information Figure S2, schematic illustration of definition of exposed block
groups). Dasymetric mapping refers to the disaggregation of spatial data–in this
case census block boundaries–to finer spatial units of analysis using ancillary
data. It has been used in prior environmental justice and sea level rise
vulnerability analyses (47,48) and helps to accurately distinguish between
residences and unpopulated space, especially in sparsely populated settings
where census blocks (the smallest census geographic unit) can still be
relatively large (>50 km2). This approach helps to ensure our analysis focuses
on the places where people live and reduces measurement error in the estimation
of distance between residences and hazardous sites at risk. Our method was
utilized and is detailed further elsewhere. (17,49,50) Briefly, we created a
high-resolution (i.e., sub-block) map of populations residing near potentially
hazardous facilities of resolution that was comparable to the facility
boundaries and digital elevation data we used; this entailed developing a
statewide, 100 m-resolution “target” population grid within census blocks for
which population counts were observed in the 2010 census, using two ancillary
data sources: (1) a statewide database of more than 12 million individual tax
parcel boundaries from DMP LightBox (29) and (2) spatial building footprint data
for nearly 11 million buildings in California. The latter is part of a
nationwide layer developed by Microsoft using satellite imagery and machine
learning classification techniques. (51)
Within each census block, population was apportioned to small residential
parcels and/or building footprints following a tiered approach. First, we
identified all residential parcels within each census block based on land use
descriptions provided in the statewide parcel data set. If residential parcels
were identified in a given block and relatively small (<1 acre or roughly 4047
m2), we assumed its population to be located within these residential areas
alone. This parcel-based apportionment accounted for 91.8% of California’s
population. Second, for blocks containing no small residential parcels but with
a nonzero population count according to the 2010 Census, we allocated population
evenly across all building footprint areas identified within them. This was
common for blocks in wilderness areas or zones of low-density agriculture, with
parcels classified as “open space” or “agricultural” in the statewide parcel
database, but which still contain residences. This building footprint-based
apportionment accounted for 7.9% of California’s population.
Finally, for the remaining census blocks that contained neither residential
parcels nor building footprints but had a nonzero population count, we assumed
that population counts were evenly distributed across the entire block area.
This “default” method of population apportionment was applied to 0.3% of the
state’s population. Population values apportioned to these target zones within
each block were based on 2010 decennial census values at the block-level but
scaled to match the 2013–2017 ACS vintage based on population growth rates
observed in parent block-groups between the 2010 census and the 2013–2017 ACS.
ANALYTIC APPROACH
We first examined the distribution of at-risk sites by county, year, and
emissions scenario for the 10,390 facilities in 29 counties. Further analysis
focused on the 18 counties from our initial universe with at least one site at
risk under RCP 8.5 by 2100. We conducted a block-group-level analysis that
included all block groups within a 3-km Euclidean distance of the 10-m elevation
line in these 18 counties (hereafter “low-lying” block groups). For the main
analysis, exposed block groups were defined as those low-lying block groups with
dasymetrically mapped populated areas within one kilometer of at least one
at-risk site (see Supporting Information Figure S2 schematic). In sensitivity
analyses, we used a 3-km buffer to define exposed block groups. We examined two
additional outcome variables for each exposed block group: the total number of
at-risk sites and sum of annual flood event probabilities (the area’s expected
annual exposure, EAE) from all at-risk sites nearby. Following a similar logic
to our definition of exposed block groups, we only kept sites (n = 5,914 and
5,921 respectively) that were within 1 or 3 km from populated areas when
calculating outcome variables for each block group.
We derived descriptive statistics and correlation coefficients between each
social vulnerability indicator and our outcomes. We then conducted a series of
multivariate regression models that included one vulnerability indicator,
block-group population density (people per square kilometer), and county fixed
effects as independent variables. We included population density as a potential
confounder given prior research demonstrating an association between population
density and the likelihood of a proximate industrial facility, and since
disadvantaged populations tend to be more densely populated. (52,53) We included
county fixed effects to control for regional demographic differences and to
compare block groups with and without at-risk sites within the same coastal
county. We scaled vulnerability indicators by unit standard deviation (SD) using
the mean and SD from block groups in the 18 counties to facilitate comparisons
across indicators. We did not include multiple vulnerability indicators in the
same model due to multicollinearity (see Supporting Information Figure S3
correlation coefficients). We used a logistic model to estimate the odds of an
at-risk site nearby (yes/no variable), a negative binomial model to estimate the
number of sites nearby (count variable), and a linear model to estimate EAE
(continuous variable). Models of the number of at-risk sites and EAE only
included the subset of block groups that had at least one at-risk site within
one kilometer (exposed block groups). We estimated county clustered robust
standard errors to control for the spatial autocorrelation.
Finally, we used generalized additive models to examine nonlinear associations
between the continuous vulnerability indicators and our outcomes. For the
logistic model estimating the odds of an at-risk site nearby and the binomial
model estimating the number of at-risk sites nearby, we assessed whether the
associations were nonloglinear. For the linear model estimating EAE across
at-risk sites, we assessed whether the association was nonlinear. In the
generalized additive models, we applied a penalized splines function to create
smooth terms for the vulnerability indicators, through which nonlinearity was
tested. Similar to earlier models, we also included county fixed effects and
population density. We used effective degrees of freedom (edf) and its
significance for the smooth terms to assess the significance of nonlinearity. An
edf value closer to one indicates linearity, whereas higher values indicate
nonlinearity.
RESULTS
ARTICLE SECTIONS
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* Abstract
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Of the 29 counties considered in our analysis, 14 and 18 contained at-risk sites
in 2050 and 2100, respectively, under the high emissions (RCP 8.5) scenario,
with the San Francisco Bay Area and Los Angeles/Orange County regions having the
highest total number of at-risk sites (Figure 1). Under the high emissions
scenario, 129 (1.2%) sites were estimated to be at risk in 2050, and 423 (4.1%)
sites were estimated to be at risk in 2100 (Table 2). The largest number of
at-risk sites were TRI facilities and oil and gas wells (Table 2). By 2100,
roughly a fifth of coastal California sewage treatment facilities, refineries,
and fossil fuel ports and terminals were estimated to be at-risk under the
scenario of continued high greenhouse gas emissions. Under the low emissions
scenario (RCP 4.5), 51 fewer sites were found to be at risk in 2100 (a 12%
reduction; Table 2).
FIGURE 1
Figure 1. Number of sites at risk of flooding due to SLR in (a) 2050 and (b)
2100 under a high emissions scenario (RCP 8.5) by county and type.
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Table 2. Number and Type of Sites at Risk of SLR-Related Flooding by Scenario
and Year Across 29 California Counties
no. (%) at risk, RCP 4.5no. (%) at risk, RCP 8.5categoryno. of facilities in
analysis2050210020502100power plants (nuclear and fossil
fuel)793 (3.8)9 (11.4)4 (5.1)9 (11.4)animal
operations421 (2.4)1 (2.4)1 (2.4)1 (2.4)sewage treatment
facilities34133 (9.7)57 (16.7)34 (10.0)62 (18.2)hazardous waste treatment and
disposal1076 (5.6)14 (13.1)6 (5.6)16 (15.0)Toxic Release Inventory
facilities359559 (1.6)145 (4.0)61 (1.7)181 (5.0)solid waste landfills and
incinerators27110 (3.7)15 (5.5)10 (3.7)16 (5.9)cleanup sites and sites with
radioactive
material683 (4.4)6 (8.8)3 (4.4)7 (10.3)refineries131 (7.7)2 (15.4)1 (7.7)3 (23.1)fossil
fuel ports and terminals665 (7.6)10 (15.2)5 (7.6)13 (19.7)active oil and gas
wells58084 (0.1)113 (1.9)4 (0.1)115 (2.0)total10390125 (1.2)372 (3.6)129 (1.2)423 (4.1)
Under more extreme levels of sea level rise in accordance with California
guidance for a medium- risk aversion, 603 (5.8%) facilities were projected to be
at risk of coastal flooding, and we estimated that groundwater would encroach to
<1 m below the surface of an additional 199 sites (Supporting Information Table
S3). Under California’s high-risk aversion scenario, we identified 736 (7.1%)
facilities at risk of coastal flooding and an additional 173 with projected
groundwater encroachment.
On average, populations living near (<1 km) at-risk sites had higher proportions
of residents living in poverty, residents of color, renters, linguistically
isolated households, elderly populations (defined as age 65 and older), children
(defined as < 18 years old), single parent households, lower voter turnout, and
lower car ownership (Table 3). In multivariate models considering the high
emissions scenario (RCP 8.5), all vulnerability factors except % under age 18
and the presence of affordable housing were associated with an increased odds of
an at-risk site within 1 km in both 2050 and 2100 (Figure 2). Disadvantaged
community status as defined by CalEnviroScreen 4.0 was the most strongly
associated with the presence of an at-risk site, with disadvantaged status being
associated with a nearly 7-fold increase in the odds of an at-risk site within 1
km in 2100. This was followed by low voter turnout, poverty, housing tenure,
race/ethnicity, linguistic isolation, vehicle ownership, single parent
households, and elderly (see Supporting Information Table S4 for full model
results).
Table 3. Distribution of Community Characteristics within Low-Lying Block Groups
with and without at-Risk Sites within 1 km in 2100 under RCP 8.5 across 18
California Counties (n = 7,211)
no. of at-risk sites (n = 6,381)a total population: 10,154,202
median [25th, 75th percentile]≥1 at-risk site (n = 831)a
total population: 1,388,531 median [25th, 75th percentile]P-valueb% voters not
voting23.9 [17.6, 30.9]27.0 [18.9, 35.8]<0.01% poverty22.1 [12.0, 38.7]29.4
[15.5, 51.2]<0.01% of renter-occupied units43.5 [22.9, 68.7]52.9 [30.6,
75.6]<0.01% people of color57.1 [33.2, 81.0]68.9 [42.4, 86.7]<0.01% linguistic
isolation5.2 [1.0, 12.2]7.3 [2.7, 15.3]<0.01% without a car4.1 [1.2, 10.1]6.3
[1.9, 13.9]<0.01% single parent household15.3 [8.8, 24.7]17.2 [9.5, 28.7]<0.01%
elderly21.2 [11.4, 35.5]23.6 [13.0, 38.8]<0.01% under 1820.0 [14.6, 25.5]21.1
[14.0, 27.4]0.01
a
N is slightly lower for some individual vulnerability indicators due to missing
data.
b
The P-value is from the Mann–Whitney U-test.
FIGURE 2
Figure 2. Association between individual block group vulnerability factors and
the presence-absence of an at-risk site within 1 km among all low-lying block
groups. Models considered one vulnerability factor at a time. All models
controlled for population density and county fixed effects. Disadvantaged status
(as defined by CalEnviroscreen) and presence of affordable housing are binary
predictors; all other variables are continuous and were scaled by unit standard
deviation to facilitate comparisons. Confidence intervals were calculated using
robust standard errors. The dashed line indicates no association.
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When limiting our analysis to the universe of exposed block groups (with at
least one at-risk site), the number of at-risk sites within 1 km was also
unequally distributed with respect to all vulnerability factors except
linguistic isolation and % under 18 (Figure 3a). Lower voter turnout and
disadvantaged community status were the most strongly associated with the number
of at-risk sites within 1 km (incidence rate ratio (IRR) and 95% CI: 1.3 [1.2,
1.4] in 2050 and 1.2 [1.1, 1.3] in 2100 per unit SD increase in % of voters not
voting and 1.2 [1.1, 1.3] in 2050 and 1.4 [1.2, 1.8] in 2100 for disadvantaged
vs not disadvantaged communities), followed by poverty, residents of color, and
housing tenure (see Supporting Information Table S5 for full model results).
FIGURE 3
Figure 3. Association between individual block group vulnerability factors and
(a) the total number of at-risk sites within 1 km and (b) the sum of EAE across
sites within 1 km, among exposed block groups. Models considered one
vulnerability factor at a time. All models controlled for population density and
county fixed effects. Disadvantaged status (as defined by CalEnviroscreen 4.0)
and presence of affordable housing are binary predictors; all other variables
are continuous and were scaled by unit standard deviation to facilitate
comparisons. Confidence intervals were calculated using robust standard errors.
The dashed line indicates no association.
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Flood risk severity as measured by EAE across all at-risk sites within 1 km was
not as strongly associated with most of the vulnerability factors we considered
when comparing among exposed block groups (Figure 3b). Lower voter turnout,
poverty, and housing tenure were however all associated with increased mean EAE
in 2100. A one unit SD increase in the % of residents living in poverty was
associated with a 0.19 higher mean EAE (95% CI [0.05, 0.33]). An SD unit
increase in the % of voters not voting and % of renters was associated with a
0.20 (95% CI [0.03, 0.37]) and 0.15 (95% CI [0.01, 0.28]) higher mean EAE,
respectively (see Supporting Information Table S6 for full model results).
We found little evidence of non(log)linear associations (edf close to 1) between
our vulnerability indicators and the outcomes with a few exceptions (Supporting
Information, Figure S4). For example, we found that the association between %
residents of color and odds of an at-risk site within 1 km was nonmonotonic in
both 2050 (Supporting Information, Figure S4(a)) and 2100 (Supporting
Information, Figure S4(b)), with the relationship being generally negative in
block groups with less than 20% residents of color, but positive for the rest of
the block groups. We found similar patterns between % population under age 18
and the odds of an at-risk site within 1 km. When looking at the sum of EAE
across all at-risk sites within 1 km, we saw nonmonotonic associations between %
voters not voting in 2050 (Supporting Information, Figure S4(e)) and between %
elderly and sum of EAE across all at-risk sites in 2100 (Supporting Information,
Figure S4(f)).
Findings from the sensitivity analysis considering a 3 km buffer distance to
define exposed block groups were largely consistent in terms of the direction
and statistical significance of associations with our vulnerability metrics
(Supporting Information Tables S4–S6). Effect estimates were in general slightly
attenuated in both 2050 and 2100 in the comparison of the odds of a nearby
at-risk site across all low-lying block groups. Among exposed block groups,
associations were in general slightly stronger at the 3 km distance between our
vulnerability measures and the number of at-risk sites and EAE in 2100.
DISCUSSION
ARTICLE SECTIONS
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* Abstract
* Introduction
* Materials and Methods
* Results
* Discussion
* Supporting Information
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* References
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By the end of the century, our analysis projects that 423 potentially hazardous
sites in California will be threatened by a 1-in-100 year flood event due to sea
level rise if greenhouse gas emissions continue unabated. The majority (88%) of
these sites will remain under threat even if greenhouse gas emissions are
stabilized and reduced. By 2050, we estimate 129 total sites will be at risk
statewide, all but four of which will be at risk under both the low and high
emission scenarios because most SLR by midcentury is driven by past rather than
future emissions. With their highly industrialized coastlines, the San Francisco
Bay Area and Los Angeles/Long Beach regions have the greatest number of at-risk
sites.
Oil infrastructure–including actively producing oil and gas wells, refineries,
and fossil fuel ports and terminals–makes up a large fraction of the at-risk
sites we identified. While flooding after Hurricanes Katrina, Rita, and Harvey
was more severe than what might be expected under incremental SLR, these extreme
weather events nevertheless provide an indication of the types of contaminant
releases that can be expected from flood-damaged oil infrastructure. Flooding
following these hurricanes resulted in numerous documented oil spills, pipeline
ruptures, and corrosion, as well as excess air pollutant releases during
intentional shutdowns, flaring, and start-up operations at petrochemical
facilities. (8−11,54,55) Because we did not include pipelines in our analysis,
we underestimated the extent of oil and gas infrastructure that may threaten
communities with contaminant releases due to SLR. Prior analyses suggest about
90 to 290 km of natural gas pipelines are projected to be flooded by century’s
end due to SLR in the San Francisco Bay Area alone. (56)
We estimated that nearly a fifth of California’s sewage treatment facilities are
at-risk by 2100, due to their frequent close proximity to low-lying coastal
areas to reduce the cost of discharging treated effluent. A prior study
estimated that a 0.9 m (3 feet) SLR flooding scenario in California could affect
sewage treatment service to approximately 2.6 million residents. (57) Four
counties in the San Francisco Bay Area (San Francisco, Alameda, Contra Costa,
San Mateo, Santa Clara) accounted for the largest proportion of at-risk TRI
facilities, in large measure due to the history of industrial development in
this region along coastal areas, that includes clusters of diversified economies
based on metalworking, oil refining, chemical and pharmaceutical manufacturing,
food products, and the semiconductor industry. (58)
In low-lying counties statewide, socially marginalized populations including
those with lower levels of voter turnout and higher proportions of residents of
color, poor, linguistically isolated households, and households without a
vehicle had a higher likelihood of living near an at-risk hazardous facility.
These findings are broadly consistent with scholarship on environmental
injustice and a report that looked at SLR and contaminated sites listed or being
considered for inclusion under the Superfund program along the East and Gulf
Coasts. Findings from that analysis concluded that people of color and
low-income communities were disproportionately represented among the populations
living within 1.6, 4.8, and 8.0 km (1, 3, and 5 miles) of clean up sites at risk
of coastal flooding under low, medium, and high SLR scenarios. (59) Another
analysis of former hazardous manufacturing facilities in 6 U.S. cities
identified more than 6000 “relic” industrial sites with elevated flood risk over
the next 30 years (2050), with socially vulnerable groups, including people of
color and low income, disproportionately likely to live in these areas. (60) A
2011 study of SLR threats to California infrastructure also found that
communities of color were more likely to be affected in areas experiencing
potential SLR-related flooding threats, (61) although this analysis examined
fewer site and facility categories and assessed fewer scenarios.
Strengths of our study include the use of tax parcel data to better approximate
the extent of facility boundaries, a probabilistic approach to estimating
SLR-related flood risk, dasymetric mapping to more precisely estimate
populations and community demographics near at-risk sites, consideration of
SLR-related groundwater rise, and splines to assess non(log)linear associations.
Prior studies have shown that utilizing dasymetric mapping methods rather than
census-block boundaries results in more accurate estimates of populations at
risk of flooding. (62) Prior environmental justice studies have also shown the
importance of assessing nonlinear associations in the relationship between
demographics and environmental hazards, because they are not always monotonic
and assuming a linear relationship may underestimate disparities. (17,63) The
involvement of environmental justice collaborators also strengthened the rigor
and policy relevance of our analysis. For example, they informed the inclusion
criteria for FRS facilities by identifying when important local industrial sites
in their community were omitted because of overly strict criteria. The addition
of the extreme SLR scenarios and groundwater projections was the result of
dialogue with residents in impacted communities and agency officials through a
webinar series facilitated by our environmental justice partners. These webinars
also served to spark discussion of research and policy priorities to enhance the
climate resilience of marginalized populations in California and resulted in the
use of our flood risk projections by regulatory agency staff to inform
SLR-related planning (personal communication).
Several factors may cause average annual exposure of hazardous sites to diverge
from our projections. Our flood models assume that the frequency and magnitude
of flood events will remain static over the coming century. However, recent
studies suggest that tropical cyclone activity will change, and intensity could
increase due to the warming climate, (64−66) leading to even more damaging
impacts annually to coastal populations. (67) Additionally, our approach to
estimating annual probabilities of flood level exceedance does not consider
nonlinear interactions between extreme flood events and local topography (i.e.,
a “bathtub” approach). In some situations, these dynamics may cause increased
flood levels at inland locations, especially where marshlands shrink and land
use becomes more developed. (68) Conversely, this approach also does not account
for floodwater level attenuation, which may cause us to overestimate exposure
during extreme storm events where land is particularly wide and flat. (69,70)
Errors in the secondary data on the location of hazardous sites and industrial
facilities may have also caused us to over- or underestimate the number of
at-risk sites. We did not consider all types of potentially hazardous sites,
omitting for example underground storage tanks, brownfields, and non-National
Priority List Superfund sites that may result in contamination releases if
flooded. We additionally do not attempt to project future changes in flood risk
mitigation or population and demographic shifts, given the uncertainty in trying
to predict this information. It is therefore possible that actions to mitigate
flood risk near hazardous sites, gentrification, and other factors could change
the associations we observed between social vulnerability and proximity to
at-risk sites.
Our findings indicate that environmental justice should be prioritized in policy
and community-resilience planning related to sea level rise and climate
adaptation. Future in-depth site-specific work is needed to more fully
characterize the threats posed by flooding at individual locations identified as
at-risk in our statewide analysis, including the impact of factors beyond the
overland flooding by seawater that was the focus of our analysis. This could
include the ways in which increased precipitation due to climate change and
groundwater movement due to SLR may contribute to the spread of contaminants and
potential exposure threats to nearby communities. Unlike other parts of the
country, California’s coastline has relatively high elevation, and the state
does not typically experience extreme tropical storms or hurricane events. It is
therefore likely that SLR-related flooding threats at industrial and hazardous
sites are even greater in other regions such as the Gulf and East Coasts and
Puerto Rico. Additional research is needed to more systematically identify
at-risk sites and nearby vulnerable communities in these regions in order to
proactively undertake mitigation measures that prevent contaminant releases due
to flooding.
SUPPORTING INFORMATION
ARTICLE SECTIONS
Jump To
* Abstract
* Introduction
* Materials and Methods
* Results
* Discussion
* Supporting Information
* Author Information
* Acknowledgments
* References
--------------------------------------------------------------------------------
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acs.est.2c07481.
* Facility inclusion criteria and categorization methods, an illustration of
the dasymetric mapping method and study area, facility flood risk and
groundwater projections using medium- and high-risk aversion scenarios,
correlation coefficients between vulnerability metrics, and full model
results for effect estimates shown in Figures 2 and 3 and generalized
additive models (PDF)
* es2c07481_si_001.pdf (2.63 MB)
Toxic Tides and Environmental Injustice: Social Vulnerability to Sea Level Rise
and Flooding of Hazardous Sites in Coastal California
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S1
Supplemental Material
Toxic tides and environmental in
justice: Social vulnerability to sea level rise and flooding of
hazardous sites in coastal California
Lara
J. Cushing, Yang Ju, Scott Kulp
, Nicholas Depsky,
Seigi Karasaki, Jessie Jaeger,
Amee Raval,
Ben
jamin
Strauss, Rachel Morello
-Frosch
Table of Contents
Table S1. Detailed inclusion criteria and counts for sites
derived from the FRS
........................................
S2
Table S2. Det
ailed inclusion criteria and facility counts for sites
derived from other data sources
..........
S4
Table S
3. Additional facilities at risk of SLR flooding or groundwater encroachment in
medium
- and high
-
risk aversion scenarios
................................................................................................................................
S5
Table S4. Association between block group social
vulnerability and
the
presence of at
-risk sites among
low
-lying block groups in 18 California counties
........................................................................................
S6
Table S5. Association between block group social
vulnerability and
the
number of at
-risk sites among
exposed block groups
.................................................................................................................................
S7
Table S6. Association between block group social
vulnerability and
the
sum of expected annual exp
osure
(EAE) across at
-risk sites among exposed block groups
.............................................................................
S8
Figure S1. Study area of low
-lying California counties and number of hazardous sites considered in the
analysis
........................................................................................................................................................
S9
Figure S2. Schematic illustration of determination of at
-risk block groups
.............................................
S10
Figure S3. Spearman’s correlation coefficients between social vulnerability
metrics
.............................
S11
Figure S4. Non-
(log)linear associations between continuous vulnerability factors and
flood risk
measures.
................................................................................................................................................
S14
Figure S5. Associations between block group social
vulnerability and (a) change in EAE between 2050
-
2100 (mean difference and 95% CI) and (b) change in number of facilities exposed
between 2050
-2100
(IRR and 95% CI) under RCP 8.5 (n=830).
..................................................................................................
S15
S2
Table S1
.
Detailed inclusion criteria and counts for sites
derived from the Facility Registry Service (
FRS)
Category Name
Interest Type or Primary Name
Keywords
a
NAICS Code(s)
Condition
# of CA Facilities
Final # of CA Facilities
within Study Area
b
Power
Plants
(Nuclear & Fossil
Fuel)
ELECTRIC POWER GENERATOR
(NUCLEAR BASED)
NA
Interest type
2
99
79
ELECTRIC POWER GENERATOR
(COAL BASED)
221112
Fossil Fuel Electric
Power Generation
Interest type AND
NAICS
0
97
ELECTRIC POWER GENERATOR
(GAS BASED)
96
ELECTRIC POWER GENERATOR
(OIL BASED)
1
ELECTRIC POWER GENERATOR
(OTHER FOSSIL FUEL BASED)
0
Animal Operations
CONCENTRATED ANIMAL FEEDING
OPERATION
NA
Interest type
100
118
42
STATE MASTER | ICIS
-NPDES
UNPERMITTED
112210
Hog and Pig
Farming
Interest type AND
NAICS
0
18
112112
Cattle Feedlots
3
1121120
0
11212
Dairy Cattle and Milk
Production
1
112120
14
Sewage Treatment
Facilities
Not exclusively 'AIR EMISSIONS
CLASSIFICATION UNKNOWN'
22132
Sewage
Treatment
Facilities
Interest type AND
NAICS
263
561
341
221320
298
Hazardous Waste
Treatment &
Disposal
Not exclusively 'AIR EMISSIONS
CLASSIFICATION UNKNOWN'
562211
Hazardous Waste
Treatment and Disposal
Interest type AND
NAICS
126
126
107
Toxic Release
Inventory Facilities
TRI REPORTER
Exclude 32411/324110 (Petroleum
Refineries) & 424710 (Bulk
Terminals)
Interest type AND
NAICS
4,358
4,358
3,595
Solid Waste
Landfills (Including
Incinerators)
Contains: 'LANDFILL', 'DUMP',
'DISPOSAL', 'WASTE', 'SANITARY',
'SANITATION', 'ILLEGAL',
'STATION', 'TRANSFER', 'SOLID',
'PIT', 'RECOVERY', 'BURNSITE',
'LNDFILL', 'LNFLL', 'LANDFLL',
'LNDFLL', 'RUBBISH', 'SWDS',
'SALVAGE', 'RECYCLING'
562212
Solid Waste Landfill
Primary name AND
NAICS
1,234
1,263
271
562213
Solid Waste
Combustors and
Incinerators
29
Cleanup Sites &
Other Sites with
BRAC
NA
Interest type
14
93
68
RAD NESHAPS
0
RAD NPL
0
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AUTHOR INFORMATION
ARTICLE SECTIONS
Jump To
* Abstract
* Introduction
* Materials and Methods
* Results
* Discussion
* Supporting Information
* Author Information
* Acknowledgments
* References
--------------------------------------------------------------------------------
* Corresponding Authors
* Lara J. Cushing - Department of Environmental Health Sciences, University
of California Los Angeles, Los Angeles, California 90095, United States;
https://orcid.org/0000-0003-0640-6450; Email: lcushing@ucla.edu
* Rachel Morello-Frosch - Department of Environmental Science, Policy and
Management & School of Public Health, University of California, Berkeley,
Berkeley, California 94720, United States; Email: rmf@berkeley.edu
* Authors
* Yang Ju - School of Architecture and Urban Planning, Nanjing University,
Nanjing, China 210093
* Scott Kulp - Climate Central, Princeton, New Jersey 08542, United States
* Nicholas Depsky - Energy and Resources Group, University of California,
Berkeley, Berkeley, California 94720, United States
* Seigi Karasaki - Energy and Resources Group, University of California,
Berkeley, Berkeley, California 94720, United States
* Jessie Jaeger - PSE Healthy Energy, Oakland, California 94612, United
States
* Amee Raval - Asian Pacific Environmental Network, Oakland, California
94612, United States
* Benjamin Strauss - Climate Central, Princeton, New Jersey 08542, United
States
* Author Contributions
L.J.C. – Conceptualization, methodology, project administration, funding
acquisition, writing–original draft. Y.J. – Data curation, formal analysis,
writing–original draft. S.Kulp – Data curation, software, formal analysis,
writing–review and editing. N.D. – Data curation, validation,
writing–original draft. S.Karasaki – Validation, investigation, data
curation, visualization, writing–original draft. J.J. – Data curation,
validation. A.R. – Conceptualization, methodology, funding acquisition,
writing–review and editing. B.S. – Conceptualization, methodology, funding
acquisition, writing–review and editing. R.M.-F. – Conceptualization,
methodology, project administration, funding acquisition, writing original
draft– review and editing.
*
* Notes
This document has not been formally reviewed by the EPA. The views expressed
in this document are solely those of the authors and do not necessarily
reflect those of the EPA. The EPA does not endorse any products or commercial
services mentioned in this publication.
The authors declare no competing financial interest.
ACKNOWLEDGMENTS
ARTICLE SECTIONS
Jump To
* Abstract
* Introduction
* Materials and Methods
* Results
* Discussion
* Supporting Information
* Author Information
* Acknowledgments
* References
--------------------------------------------------------------------------------
We thank the Toxic Tides Advisory Council–comprised of community leaders from
the Asian Pacific Environmental Network, Central Coast Alliance for a
Sustainable Economy, Physicians for Social Responsibility Los Angeles, Public
Health Institute, and WE ACT for Environmental Justice–for their expertise,
guidance, and support throughout this research project. This work was funded by
the California Strategic Growth Council (#CCRP0022) awarded to the University of
California, Berkeley, the U.S. Environmental Protection Agency (Assistance
Agreement No. 84003901 awarded to the University of California Los Angeles), and
the JPB and Kresge Foundations. Y.J. is also supported by the “Yuxiu Young
Scholars Program” and the Fundamental Research Funds for the Central
Universities (#2022300171) at Nanjing University.
REFERENCES
ARTICLE SECTIONS
Jump To
* Abstract
* Introduction
* Materials and Methods
* Results
* Discussion
* Supporting Information
* Author Information
* Acknowledgments
* References
--------------------------------------------------------------------------------
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action.
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Casey, J. A.; Cushing, L.; Depsky, N.; Morello-Frosch, R. Climate Justice
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ESTHAG; ISSN:1520-5851. (American Chemical Society)
Methane superemitters emit non-methane copollutants that are harmful to
human health. Yet, no prior studies have assessed disparities in exposure
to methane superemitters with respect to race/ethnicity, socioeconomic
status, and civic engagement. To do so, we obtained the location, category
(e.g., landfill, refinery), and emission rate of California methane
superemitters from Next Generation Airborne Visible/IR Imaging Spectrometer
(AVIRIS-NG) flights conducted between 2016 and 2018. We identified block
groups within 2 km of superemitters (exposed) and 5-10 km away (unexposed)
using dasymetric mapping and assigned level of exposure among block groups
within 2 km (measured via no. of superemitter categories and total methane
emissions). Analyses included 483 superemitters. The majority were
dairy/manure (n = 213) and oil/gas prodn. sites (n = 127). Results from
fully adjusted logistic mixed models indicate environmental injustice in
methane superemitter locations. For example, for every 10% increase in
non-Hispanic Black residents, the odds of exposure increased by 10% (95%
confidence interval (CI): 1.04, 1.17). We obsd. similar disparities for
Hispanics and Native Americans but not with indicators of socioeconomic
status. Among block groups located within 2 km, increasing proportions of
non-White populations and lower voter turnout were assocd. with higher
superemitter emission intensity. Previously unrecognized racial/ethnic
disparities in exposure to California methane superemitters should be
considered in policies to tackle methane emissions.
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18. 18
Morello-Frosch, R.; Lopez, R. The Riskscape and the Color Line: Examining
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The riskscape and the color line: Examining the role of segregation in
environmental health disparities
Morello-Frosch, Rachel; Lopez, Russ
Environmental Research (2006), 102 (2), 181-196CODEN: ENVRAL;
ISSN:0013-9351. (Elsevier)
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inequality with discrimination, environmental degrdn., and health. More
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segregation and environmental health disparities. We begin with an overview
of race-based segregation in the United States and propose a framework for
understanding its implications for environmental health disparities. We
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in ambient air pollution burdens across racial groups and go on to discuss
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health outcomes. We conclude with a discussion of the research and policy
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environmental health disparities.
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presents an alternative to traditional population-based biomedical research
practices by encouraging active and equal partnerships between community
members and academic investigators. The National Institute of Environmental
Health Sciences (NIEHS), the premier biomedical research facility for
environmental health, is a leader in promoting the use of CBPR in instances
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tool to enhance our knowledge of the causes and mechanisms of disorders
having an environmental etiology, reduce adverse health outcomes through
innovative intervention strategies and policy change, and address the
environmental health concerns of community residents.
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(Springer)
Rising sea levels are increasing the exposure of populations and
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of future exposure or project rates of sea level rise, here, we est. growth
rates of exposure, likely to be a key factor in how effectively coastal
communities can adapt. These rates may not correlate well with sea level
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distributions and sea level rise projections to compute the expected annual
exposure of population, housing, roads, and property value in 327
medium-to-large coastal municipalities circumscribing the contiguous USA,
and identify those localities that could experience rapid exposure growth
sometime this century. We define a rate threshold of 0.25% additive
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present-day exceedance. With unchecked carbon emissions under
Representative Concn. Pathway (RCP) 8.5, the no. of cities exceeding the
threshold reaches 33 (18-59, 90% CI) by 2050 and 90 (22-196) by 2100,
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the end-of-century total to 28 (12-105), vs. a baseline of 7 cities in
2000. The methods and results presented here offer a new way to illustrate
the consequences of different emission scenarios or mitigation efforts, and
locally assess the urgency of coastal adaptation measures.
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Environmental Research Letters (2020), 15 (12), 124020CODEN: ERLNAL;
ISSN:1748-9326. (IOP Publishing Ltd.)
The frequency of coastal floods around the United States has risen sharply
over the last few decades, and rising seas point to further future
acceleration. Residents of low-lying affordable housing, who tend to be
low-income persons living in old and poor quality structures, are esp.
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we provide the first nationwide assessment of recent and future risks to
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housing units exposed to extreme coastal water levels and of how often
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New York, and Massachusetts have the largest no. of units exposed to
extreme water levels both in abs. terms and as a share of their affordable
housing stock. Some top-ranked cities could experience numerous coastal
floods reaching higher than affordable housing sites each year. As the top
20 cities account for 75% of overall exposure, limited, strategic and
city-level efforts may be able to address most of the challenge of
preserving coastal-area affordable housing stock.
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Sea level rise (SLR) will cause shallow unconfined coastal aquifers to
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these impacts. Pumping may accelerate land subsidence rates, thereby
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jurisdiction levels will need information regarding where groundwater
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Library of Science)
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eight different sociodemog. variables across the state of California using
data from the 2020 census. These layers constitute the 'CA-POP' dataset,
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block populations using fine-scale residential tax parcel boundaries and
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In comparison to a no. of existing gridded population products, CA-POP
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data vintage, higher resoln., more accurate building footprint data, and in
some cases more sophisticated but parsimonious and transparent dasymetric
mapping methodologies. A general accuracy assessment of the CA-POP
dasymetric mapping methodol. was conducted by producing a population grid
that was constrained by population observations within block groups instead
of blocks, enabling a comparison of this grid's population apportionment to
block-level census values, yielding a median abs. relative error of approx.
30% for block group-to-block apportionment. However, the final CA-POP grids
are constrained by higher-resoln. census block-level observations, likely
making them even more accurate than these block group-constrained grids
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disaggregation is not possible due to the absence of observational data at
the sub-block scale. The CA-POP grids are freely available as GeoTIFF
rasters online at github.com/njdepsky/CA-POP, for total population,
Hispanic/Latinx population of any race, and non-Hispanic populations for
the following groups: American Indian/Alaska Native, Asian,
Black/African-American, Native Hawaiian and other Pacific Islander, White,
other race or multiracial (two or more races) and residents under 18 years
old (i.e. minors).
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Environmental Science & Technology (2011), 45 (18), 7754-7760CODEN: ESTHAG;
ISSN:0013-936X. (American Chemical Society)
Recently, concerns have centered on how to expand knowledge on the limited
science related to the cumulative impact of multiple air pollution
exposures and the potential vulnerability of poor communities to their
toxic effects. The highly intercorrelated nature of exposures makes
application of std. regression-based methods to these questions problematic
due to well-known issues related to multicollinearity. Our paper addresses
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consisting of a pattern of exposure values. These profiles are grouped into
clusters and assocd. with a deprivation outcome. Specifically, we examine
how profiles of NO2-, PM2.5-, and diesel- (road and off-road) based
exposures are assocd. with the no. of individuals living under poverty in
census tracts (CT's) in Los Angeles County. Results indicate that higher
levels of pollutants are generally assocd. with higher poverty counts,
though the assocn. is complex and nonlinear. Our approach is set in the
Bayesian framework, and as such the entire model can be fit as a unit using
modern Bayesian multilevel modeling techniques via the freely available
WinBUGS software package, though we have used custom-written C++ code
(validated with WinBUGS) to improve computational speed. The modeling
approach proposed thus goes beyond single-pollutant models in that it
allows us to det. the assocn. between entire multipollutant profiles of
exposures with poverty levels in small geog. areas in Los Angeles County.
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53. 53
Sadd, J. L.; Pastor, M.; Morello-Frosch, R.; Scoggins, J.; Jesdale, B.
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Playing it safe: assessing cumulative impact and social vulnerability
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International journal of environmental research and public health (2011), 8
(5), 1441-59 ISSN:.
Regulatory agencies, including the U.S. Environmental Protection Agency (US
EPA) and state authorities like the California Air Resources Board (CARB),
have sought to address the concerns of environmental justice (EJ) advocates
who argue that chemical-by-chemical and source-specific assessments of
potential health risks of environmental hazards do not reflect the multiple
environmental and social stressors faced by vulnerable communities. We
propose an Environmental Justice Screening Method (EJSM) as a relatively
simple, flexible and transparent way to examine the relative rank of
cumulative impacts and social vulnerability within metropolitan regions and
determine environmental justice areas based on more than simply the
demographics of income and race. We specifically organize 23 indicator
metrics into three categories: (1) hazard proximity and land use; (2) air
pollution exposure and estimated health risk; and (3) social and health
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base map by intersecting land use data with census block polygons, and
calculates hazard proximity measures based on locations within various
buffer distances. These proximity metrics are then summarized to the census
tract level where they are combined with tract centroid-based estimates of
pollution exposure and health risk and socio-economic status (SES)
measures. The result is a cumulative impacts (CI) score for ranking
neighborhoods within regions that can inform diverse stakeholders seeking
to identify local areas that might need targeted regulatory strategies to
address environmental justice concerns.
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Hurricane Katrina struck an area dense with industry, causing numerous
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effects of these releases in order to inform analysis of risk from future
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and communication systems, and difficulty acquiring supplies and
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* Figures
* References
* Support Info
* ABSTRACT
High Resolution Image
Download MS PowerPoint Slide
FIGURE 1
Figure 1. Number of sites at risk of flooding due to SLR in (a) 2050 and (b)
2100 under a high emissions scenario (RCP 8.5) by county and type.
High Resolution Image
Download MS PowerPoint Slide
FIGURE 2
Figure 2. Association between individual block group vulnerability factors
and the presence-absence of an at-risk site within 1 km among all low-lying
block groups. Models considered one vulnerability factor at a time. All
models controlled for population density and county fixed effects.
Disadvantaged status (as defined by CalEnviroscreen) and presence of
affordable housing are binary predictors; all other variables are continuous
and were scaled by unit standard deviation to facilitate comparisons.
Confidence intervals were calculated using robust standard errors. The dashed
line indicates no association.
High Resolution Image
Download MS PowerPoint Slide
FIGURE 3
Figure 3. Association between individual block group vulnerability factors
and (a) the total number of at-risk sites within 1 km and (b) the sum of EAE
across sites within 1 km, among exposed block groups. Models considered one
vulnerability factor at a time. All models controlled for population density
and county fixed effects. Disadvantaged status (as defined by CalEnviroscreen
4.0) and presence of affordable housing are binary predictors; all other
variables are continuous and were scaled by unit standard deviation to
facilitate comparisons. Confidence intervals were calculated using robust
standard errors. The dashed line indicates no association.
High Resolution Image
Download MS PowerPoint Slide
* REFERENCES
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Sheffield Perry; Schwartz Rebecca
Journal of exposure science & environmental epidemiology (2021), 31 (3),
454-460 ISSN:.
BACKGROUND: Hurricane Harvey facilitated exposure to various toxic
substances and floodwater throughout the greater Houston metropolitan
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integrated disparities in exposure to toxic incidents following Hurricane
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relationship between flooding, socioeconomic status (SES), and toxic site
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flood water depths during Hurricane Harvey in the greater Houston area
were compiled from multiple sources. A multivariable logistic regression
was performed to predict the odds of a toxic site release by flooding at
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of 1403 toxic sites (5.9%) had reported releases during Hurricane Harvey.
The proportion of toxic sites with reported incidents across increasing
SES index quintiles were 8.35, 7.67, 5.14, 4.55, and 0.51, respectively.
The odds of an incident were lower in the highest SES quintile areas
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Flooding was similar at toxic sites with and without incidents, and was
distributed similarly and highest at toxic sites located in lower SES
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during Hurricane Harvey, areas with lower SES were more likely to have a
toxic release during the storm, after accounting for number of toxic
sites. Improving quality of maintenance, safety protocols, number of
storm-resilient facilities may minimize this disproportionate exposure
and its subsequent adverse outcomes among socioeconomically vulnerable
populations.
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Walker; Alexeeff George
American journal of public health (2015), 105 (11), 2341-8 ISSN:.
OBJECTIVES: We used an environmental justice screening tool
(CalEnviroScreen 1.1) to compare the distribution of environmental
hazards and vulnerable populations across California communities.
METHODS: CalEnviroScreen 1.1 combines 17 indicators created from 2004 to
2013 publicly available data into a relative cumulative impact score. We
compared cumulative impact scores across California zip codes on the
basis of their location, urban or rural character, and racial/ethnic
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zip codes were 6.2, 5.8, 1.9, 1.8, and 1.6 times greater for Hispanics,
African Americans, Native Americans, Asian/Pacific Islanders, and other
or multiracial individuals, respectively, than for non-Hispanic Whites.
Environmental hazards were more regressively distributed with respect to
race/ethnicity than poverty, with pesticide use and toxic chemical
releases being the most unequal. CONCLUSIONS: Environmental health
hazards disproportionately burden communities of color in California.
Efforts to reduce disparities in pollution burden can use simple
screening tools to prioritize areas for action.
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status, and civic engagement. To do so, we obtained the location,
category (e.g., landfill, refinery), and emission rate of California
methane superemitters from Next Generation Airborne Visible/IR Imaging
Spectrometer (AVIRIS-NG) flights conducted between 2016 and 2018. We
identified block groups within 2 km of superemitters (exposed) and 5-10
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categories and total methane emissions). Analyses included 483
superemitters. The majority were dairy/manure (n = 213) and oil/gas
prodn. sites (n = 127). Results from fully adjusted logistic mixed models
indicate environmental injustice in methane superemitter locations. For
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of exposure increased by 10% (95% confidence interval (CI): 1.04, 1.17).
We obsd. similar disparities for Hispanics and Native Americans but not
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voter turnout were assocd. with higher superemitter emission intensity.
Previously unrecognized racial/ethnic disparities in exposure to
California methane superemitters should be considered in policies to
tackle methane emissions.
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ISSN:0013-9351. (Elsevier)
Environmental health researchers, sociologists, policy-makers, and
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these issues through the lens of racial residential segregation can offer
new insights into the junctures of the political economy of social
inequality with discrimination, environmental degrdn., and health. More
importantly, this line of inquiry may highlight whether obsd.
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paper examines theor. and methodol. questions related to racial
residential segregation and environmental health disparities. We begin
with an overview of race-based segregation in the United States and
propose a framework for understanding its implications for environmental
health disparities. We then discuss applications of segregation measures
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groups and go on to discuss the applicability of these methods for other
environmental exposures and health outcomes. We conclude with a
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community-based participatory research (CBPR) projects. CBPR methodology
presents an alternative to traditional population-based biomedical
research practices by encouraging active and equal partnerships between
community members and academic investigators. The National Institute of
Environmental Health Sciences (NIEHS), the premier biomedical research
facility for environmental health, is a leader in promoting the use of
CBPR in instances where community-university partnerships serve to
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of the causes and mechanisms of disorders having an environmental
etiology, reduce adverse health outcomes through innovative intervention
strategies and policy change, and address the environmental health
concerns of community residents.
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(Springer)
Rising sea levels are increasing the exposure of populations and
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of future exposure or project rates of sea level rise, here, we est.
growth rates of exposure, likely to be a key factor in how effectively
coastal communities can adapt. These rates may not correlate well with
sea level rise rates due to varying patterns of topog. and development.
We integrate exposure assessments based on LiDAR elevation data with
extreme flood event distributions and sea level rise projections to
compute the expected annual exposure of population, housing, roads, and
property value in 327 medium-to-large coastal municipalities
circumscribing the contiguous USA, and identify those localities that
could experience rapid exposure growth sometime this century. We define a
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yr, based on its rarity of present-day exceedance. With unchecked carbon
emissions under Representative Concn. Pathway (RCP) 8.5, the no. of
cities exceeding the threshold reaches 33 (18-59, 90% CI) by 2050 and 90
(22-196) by 2100, including the cities of Boston and Miami. Sharp cuts
under RCP 2.6 limit the end-of-century total to 28 (12-105), vs. a
baseline of 7 cities in 2000. The methods and results presented here
offer a new way to illustrate the consequences of different emission
scenarios or mitigation efforts, and locally assess the urgency of
coastal adaptation measures.
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Environmental Research Letters (2020), 15 (12), 124020CODEN: ERLNAL;
ISSN:1748-9326. (IOP Publishing Ltd.)
The frequency of coastal floods around the United States has risen
sharply over the last few decades, and rising seas point to further
future acceleration. Residents of low-lying affordable housing, who tend
to be low-income persons living in old and poor quality structures, are
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(SLR), we provide the first nationwide assessment of recent and future
risks to affordable housing from SLR and coastal flooding in the United
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largest no. of units exposed to extreme water levels both in abs. terms
and as a share of their affordable housing stock. Some top-ranked cities
could experience numerous coastal floods reaching higher than affordable
housing sites each year. As the top 20 cities account for 75% of overall
exposure, limited, strategic and city-level efforts may be able to
address most of the challenge of preserving coastal-area affordable
housing stock.
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ISSN:2073-4441. (MDPI AG)
Sea level rise (SLR) will cause shallow unconfined coastal aquifers to
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infrastructure, soil behavior, human health, and nearshore ecosystems.
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adding to surface flooding problems. Levees and seawalls may not prevent
these impacts. Pumping may accelerate land subsidence rates, thereby
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jurisdiction levels will need information regarding where groundwater
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Library of Science)
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for eight different sociodemog. variables across the state of California
using data from the 2020 census. These layers constitute the 'CA-POP'
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boundaries and Microsoft's remotely-sensed building footprint layer as
ancillary datasets. In comparison to a no. of existing gridded population
products, CA-POP shows good concordance and offers a no. of benefits,
including more recent data vintage, higher resoln., more accurate
building footprint data, and in some cases more sophisticated but
parsimonious and transparent dasymetric mapping methodologies. A general
accuracy assessment of the CA-POP dasymetric mapping methodol. was
conducted by producing a population grid that was constrained by
population observations within block groups instead of blocks, enabling a
comparison of this grid's population apportionment to block-level census
values, yielding a median abs. relative error of approx. 30% for block
group-to-block apportionment. However, the final CA-POP grids are
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over a given region, but for which error assessments of population
disaggregation is not possible due to the absence of observational data
at the sub-block scale. The CA-POP grids are freely available as GeoTIFF
rasters online at github.com/njdepsky/CA-POP, for total population,
Hispanic/Latinx population of any race, and non-Hispanic populations for
the following groups: American Indian/Alaska Native, Asian,
Black/African-American, Native Hawaiian and other Pacific Islander,
White, other race or multiracial (two or more races) and residents under
18 years old (i.e. minors).
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Michael
Environmental Science & Technology (2011), 45 (18), 7754-7760CODEN:
ESTHAG; ISSN:0013-936X. (American Chemical Society)
Recently, concerns have centered on how to expand knowledge on the
limited science related to the cumulative impact of multiple air
pollution exposures and the potential vulnerability of poor communities
to their toxic effects. The highly intercorrelated nature of exposures
makes application of std. regression-based methods to these questions
problematic due to well-known issues related to multicollinearity. Our
paper addresses these problems by using, as its basic unit of inference,
a profile consisting of a pattern of exposure values. These profiles are
grouped into clusters and assocd. with a deprivation outcome.
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and off-road) based exposures are assocd. with the no. of individuals
living under poverty in census tracts (CT's) in Los Angeles County.
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with higher poverty counts, though the assocn. is complex and nonlinear.
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model can be fit as a unit using modern Bayesian multilevel modeling
techniques via the freely available WinBUGS software package, though we
have used custom-written C++ code (validated with WinBUGS) to improve
computational speed. The modeling approach proposed thus goes beyond
single-pollutant models in that it allows us to det. the assocn. between
entire multipollutant profiles of exposures with poverty levels in small
geog. areas in Los Angeles County.
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53. 53
Sadd, J. L.; Pastor, M.; Morello-Frosch, R.; Scoggins, J.; Jesdale, B.
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Jesdale Bill
International journal of environmental research and public health (2011),
8 (5), 1441-59 ISSN:.
Regulatory agencies, including the U.S. Environmental Protection Agency
(US EPA) and state authorities like the California Air Resources Board
(CARB), have sought to address the concerns of environmental justice (EJ)
advocates who argue that chemical-by-chemical and source-specific
assessments of potential health risks of environmental hazards do not
reflect the multiple environmental and social stressors faced by
vulnerable communities. We propose an Environmental Justice Screening
Method (EJSM) as a relatively simple, flexible and transparent way to
examine the relative rank of cumulative impacts and social vulnerability
within metropolitan regions and determine environmental justice areas
based on more than simply the demographics of income and race. We
specifically organize 23 indicator metrics into three categories: (1)
hazard proximity and land use; (2) air pollution exposure and estimated
health risk; and (3) social and health vulnerability. For hazard
proximity, the EJSM uses GIS analysis to create a base map by
intersecting land use data with census block polygons, and calculates
hazard proximity measures based on locations within various buffer
distances. These proximity metrics are then summarized to the census
tract level where they are combined with tract centroid-based estimates
of pollution exposure and health risk and socio-economic status (SES)
measures. The result is a cumulative impacts (CI) score for ranking
neighborhoods within regions that can inform diverse stakeholders seeking
to identify local areas that might need targeted regulatory strategies to
address environmental justice concerns.
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Hurricane Katrina struck an area dense with industry, causing numerous
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and effects of these releases in order to inform analysis of risk from
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or reconstruction (55%), than experienced releases. To reduce the risk of
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* SUPPORTING INFORMATION
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The Supporting Information is available free of charge at
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* Facility inclusion criteria and categorization methods, an illustration of
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groundwater projections using medium- and high-risk aversion scenarios,
correlation coefficients between vulnerability metrics, and full model
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additive models (PDF)
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