Search NASA⌕ Search

SEARCH · Search NASA

Results for “census”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The role of building characteristics, demographics, and urban heat islands in shaping residential energy use

This study assesses the effects of residential building structure, socio-demographics, and ambient temperature conditions of a neighborhood to overall energy expenditures of a household. Using annual utility billing data, existing tax-lot records, temperature data, and U.S. Census demographic data, we ask two research questions: (1) What role do differences in neighborhood ambient heat, structural conditions and behavior play in helping to explain residential energy use? And (2) In what ways do energy expenditures spatially cluster in relation to urban heat islands? Using linear regression, spatial regression, and spatial clustering techniques, we evaluated the role of physical location of these households in relation to ambient temperatures. Significant positive relationships among energy expenditures and building size, average household size, White population, and population over 65 were observed; Significant negative relationships among energy expenditures and building density, renter population and population with a bachelor’s degree or higher were also observed. Negative relationships among morning and afternoon measures of heat, suggest that some areas spend less on energy use as a result of the neighborhood scale thermal environment. Spatial analysis of ambient temperature and energy expenditures show that energy use throughout the city is spatially clustered, with more affluent areas using more energy, regardless of whether heat islands are present. The results help to frame the role that increasing ambient temperatures in cities play in the spatial disparity in energy demand.

Residential Buildings, Energy Expenditure, Socio-d↗
DOE OSTI2024

Estimating geographic origins of corn and soybean biomass for biofuel production: A detailed dataset

Sustainable fuel initiatives in the United States such as the Environmental Protection Agency’s Renewable Fuel Stan- dard and the Department of Energy’s Sustainable Aviation Fuel Grand Challenge have increased the production of corn ethanol and soybean biodiesel. However, the lack of precise information regarding biomass sourcing at a localized level has hindered accurate understanding of both biofuel costs and environmental impact of these production pathways. By harnessing the power of geospatial analysis and leveraging United States Department of Agriculture (USDA) crop cen- sus data, this dataset fills this critical knowledge gap. This dataset offers a novel estimation of geospatial biomass sourc- ing for biofuel production in the United States by synthe- sizing 2017 USDA crop census data, biorefinery data from the United States Energy Information Administration, and publicly available information about biomass sourcing for biofuel production. This dataset provides a detailed under- standing of biomass use for first generation biofuel pro- duction, enabling stakeholders to make informed decisions about resource allocation, investment strategies, and infras- tructure development. Furthermore, the county-level gran- ularity of the dataset allows for increased fidelity in the techno-economic assessments and life-cycle analyses of first- generation biofuels in the United States.

09 BIOMASS FUELS↗
DOE OSTI2021

Decarbonizing via disparities: Problematizing the relationship between social identity and solar energy transitions in the United States

As solar adoption across the United States continues to grow, so do the gaps between rural and urban communities in how they choose to embrace these technologies, leading to serious questions of social justice and equity by researchers and policymakers alike. While recent studies have examined the racial and social justice elements of solar adoption alongside institutions' role in shaping pro-solar policies, codes, and code enforcement, an opportunity exists to discuss how the place and composition of the body politic in terms of race/ethnicity and rurality exists. This paper establishes a methodology for examining location and body politic composition concerning adopting all types of solar (residential, non-residential, utility-scale), utilizing the State of Georgia as a case study. Results indicate that the approach yields useful and informative findings; namely, there is a significant difference in adopting non-residential and utility-scale solar between rural and urban counties. We conclude by discussing further opportunities to expand on this analysis and the impact of assessing solar adoption in terms of value alignment between a body politic and the policies that shape the adoption of sustainable energy technologies. Finally, combining solar adoption information for the State of Georgia with Census data, this study compares solar adoption trends across counties--grouped by urban/rural classification and racial and ethnic majority.

14 SOLAR ENERGY↗
DOE OSTI2024

Identifying Disadvantaged Communities in the United States: An Energy-Oriented Mapping Tool that Aggregates Environmental and Socioeconomic Burdens

This paper defines a policy-relevant nationwide composite index to identify communities disproportionately impacted by environmental, energy, and climate injustices in the United States. We review existing vulnerability indicators and indices to assess the tradeoffs of different design parameters, including variable selection, geographic unit, dimensionality reduction, weighting, and aggregation methods. From this methodological review, we create the first nationwide, census tract-level index of cumulative burden that includes energy-relevant indicators alongside climate, social, environmental, and economic indicators, and is flexible to the inclusion of additional data sources. We provide a summary of the sources of inputs used to develop a definition for "disadvantaged communities" that can be used to prioritize energy investments. We discuss use-cases for this index including the implementation of the Justice40 Initiative, which calls for 40% of certain federal clean energy benefits to flow to disadvantaged communities in the United States. We use our results to examine historic allocations of federal energy investments and show that communities that we identify as disadvantaged received about 37% fewer funds per capita than non-disadvantaged communities.

cumulative burden↗
DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗
DOE OSTI2026

Scalable Generation of High-fidelity Synthetic Population Ensembles

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the U.S. via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. The study involves two scenarios: creating ensembles for (1) 17 U.S. metropolitan areas in 2019 and (2) full U.S. Census Divisions in 2023, with each scenario consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system within a research cloud, comprised of virtual containerizations, GPU-enhanced functionality, and orchestrated deployments of UrbanPop’s maturing Likeness Python ecosystem. Results demonstrate we maintained high-fidelity approximations of residential totals by areas of interest and the demographic characteristics of neighborhoods while reducing manual workflow burdens. Finally, we discuss plans to fine-tune and further develop our automated workflows for truly distributed job orchestration to increase computational efficiency, as well as provide an outlook for broadening applications of the ensembles.

Cluster computing↗
DOE OSTI2026

Investigating the Determinants of Household Capabilities Burden During Power Outages: The Case of Winter Storm Uri

Existing research primarily uses census data to identify the vulnerability of communities to hazards. These vulnerability indices provide aggregated data and are not hazard-specific nor well-validated with post-event data. In contrast, our study uses household survey data (n=1065) to understand which Texan households suffered the greatest loss of their capabilities due to power outages and other utility service disruptions during Winter Storm Uri. Inspired by the Capabilities Approach, our measures of burden include the number of household capability types disrupted during the outages (e.g., cooking, heating, refrigeration), the severity of impact for each disrupted capability, and the additional time and financial costs of coping with these disruptions. We perform a clustering analysis, and find two distinct groups in our data, consisting of ‘lesser burden' and ‘heavier burden' households. Results indicate that the households experiencing the heaviest capabilities burden were most likely to experience longer power outages and the loss of water services. They were also more likely to have a Hispanic-Latino household member, lack access to a generator, live in a rented home, have larger households with more young children, fewer adults over 65, lower household incomes, been impacted by the COVID-19 pandemic, and more family characteristics that made life harder. We also fit a logistic regression model to assess the role of outage, household, and community characteristics in predicting differences in capabilities burden. Our results offer insights into enumerating the consequences of utility service disruptions on households, which can inform more targeted and equitable resilience strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Patterns of indoor radon concentrations, radon-hazard potential, and radon testing on a small geographic scale in Utah

Currently, there are no publicly-available estimates of indoor radon concentration at scales smaller than the county. Radon-hazard potential soil maps that reflect underlying geologic factors can be created at small geographic scale and linked to residential and census data. We determined the association between residential radon tests and high radon-hazard potential soil at the residential and block group levels using a large Utah-based dataset. We also identified characteristics of block groups with limited tests in the dataset.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Geospatial analysis of freight accessibility and job attraction: The role of interstate ramps, airports, ports, and rail

The number of jobs within an industry is significantly influenced by geographical location, with transportation infrastructure playing a key role. While previous research has largely focused on how access to jobs affects employment, less attention has been given to how transportation infrastructure impacts business operations and job attraction. Here, this study addresses this gap by examining how the ease of transporting products to key transportation facilities affects job numbers in freight-intensive industries. Using job data from the Longitudinal Employment Household Dynamics dataset at the Census Tract level, we applied a non-parametric model to assess the impact of proximity to interstate ramps, rail intermodals, ports, and airports. Our analysis revealed that closer transportation infrastructure generally has a greater impact on employment. Specifically, interstate ramps are crucial for attracting jobs, particularly in rural areas, while airport proximity is essential for industries dealing with high-value, time-sensitive goods, as seen notably in Massachusetts. The importance of transportation facilities varies considerably across states and industries. The findings and method in this study can be used by transportation agencies for freight planning.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

HIV drug resistance during antiretroviral therapy scale-up in Uganda, 2012–19: a population-based, longitudinal study

Background With scale-up of antiretroviral therapy (ART) in sub-Saharan Africa, increasing pretreatment HIV drug resistance has been reported; however, the broader effect of ART expansion on population-level resistance patterns remains insufficiently quantified. We aimed to estimate the longitudinal prevalence of drug resistance and resistance-conferring mutations. Methods This study used data collected as part of the Rakai Community Cohort Study (RCCS), an open population-based census and cohort study conducted in southern Uganda. At each survey round, residents aged 15–49 years are invited to participate and receive a structured questionnaire that obtains sociodemographic, behavioural, and health information, including self-reported past and current ART use. Voluntary HIV testing is conducted using a rapid test algorithm and a venous blood sample. People with HIV provide samples for viral load quantification and deep sequencing. We analysed RCCS survey, HIV viral load, and deep sequencing (which was used to predict resistance) data from five survey rounds. The key outcomes were the population prevalence of viraemic people with HIV with non-nucleoside reverse transcriptase inhibitor (NNRTI), nucleoside reverse transcriptase inhibitor (NRTI), protease inhibitor, or multiclass resistance among all participants (regardless of HIV serostatus) in the 2015 and 2017 surveys. Prevalence of class-specific resistance and resistance-conferring substitutions were estimated using robust log-Poisson regression. Findings Between Aug 10, 2011, and Nov 4, 2020, there were 43 361 participants in the RCCS and 7923 (18·27%) people with HIV. Over five survey rounds, 93 622 participant visits occurred, among which 17 460 (18·65%) were from people with HIV. Over the analysis period, the median age of study participants remained similar (28 years [22–35] in 2012 and 29 years [21–38] in 2019). Sufficient data were available to reliably genotype 4072 (90·03%) of 4523 participant visits from 3407 people with HIV for at least one drug. Overall population prevalence of resistance contributed by viraemic pretreatment people with HIV decreased between 2012 and 2017 from 0·56% (95% CI 0·42–0·75) to 0·25% (0·18–0·33) for NNRTI and from 0·24% (0·15–0·37) to 0·05% (0·02–0·10) for NRTI (prevalence ratio 0·44 [0·29–0·68] for NNRTI and 0·21 [0·09–0·47] for NRTI). Between 2012 and 2017, NNRTI resistance among viraemic pretreatment people with HIV increased from 4·86% (3·69–6·42) to 9·61% (7·27–12·7; prevalence ratio 1·98 [1·34–2·91]). The prevalence of NNRTI and NRTI resistance was substantially higher among viraemic treatment-experienced people with HIV (51·49% [46·24–57·34] for NNRTI and 36·46% [30·06–44·22] for NRTI in 2017) than among pretreatment people with HIV. NNRTI and NRTI resistance was predominantly attributable to rtK103N and rtM184V. inT97A was observed at a similar prevalence among viraemic treatment-experienced (9·96% [6·41–15·48]) and viraemic pretreatment (10·56% [8·01–13·93]) people with HIV; no major dolutegravir resistance mutations were observed. Interpretation Despite rising NNRTI resistance among pretreatment people with HIV, overall population prevalence of pretreatment HIV drug-resistant viraemia decreased due to increasing ART uptake and viral suppression. This finding underscores the crucial role of achieving and maintaining high ART coverage in reducing transmission of drug-resistant HIV. The high prevalence of mutations conferring resistance to components of first-line ART regimens among viraemic people with HIV is potentially concerning. Funding National Institutes of Health, Johns Hopkins University Center for AIDS Research, Bill & Melinda Gates Foundation, and the US Centers for Disease Control and Prevention.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Residential segregation and outdoor urban moist heat stress disparities in the United States

The combined impact of urbanization-induced warming and drying on large-scale heat stress disparities remains unknown, with multicity studies using satellite-derived land surface temperature as a proxy for these disparities. Here, using high-resolution urban-resolving numerical model simulations for 2014–2018, we find pervasive disparities in all-sky average maximum summertime air temperature and moist heat stress metrics across US cities, with higher outdoor heat stress exposure in poorer and primarily non-white census tracts. Ninety-four percent of the US urban population (228 million) live in cities where heat stress burdens the poor, with heat stress inequities between white and non-white populations strongly associated with residential segregation. Similarly, historically redlined neighborhoods show higher heat stress than their non-redlined counterparts, demonstrating how historical segregation relates to present-day environmental inequalities. Our results provide quantitative estimates of physiologically relevant heat stress disparities at the US national scale and highlight potential biases when using satellites as a proxy for these.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hazard-Based Model of Activity Generation Using Vehicle Trajectory Data

Travel behavior modeling suffers from lack of longitudinal data due to the high cost of procurement and difficulty with ensuring data quality. In the recent years ubiquitous data intercepted from social media, cell detail record, GPS traces are increasingly being explored to analyze human mobility. However, these data lack key demographic information which limits their use for developing causal models of traveler behavior. The primary contribution of the paper lies in using vehicle trajectory data from Ann Arbor along with multiple other data sources for developing a hazard-based activity generation model. The different category of data used in the analysis include (i) vehicle trajectory data (VTD), (ii) American Census Survey (ACS), (iii) land use data, (iv) household travel survey (HTS) data, and (v) smart location data from Environmental Protection Agency (EPA). Home and work are identified as the two most frequently visited locations from the VTD and validated against land use categories of the region. Next a random utility-based model is developed using HTS data to identify other activity purposes as a function of the polynomials of distance from home, activity duration and departure time. The developed model is then applied to identify the non-home activity purposes in the VTD. Demographic and accessibility information are appended to the VTD from ACS and smart location databases respectively. The fmal database is then used to develop a hazard-based activity generation model. (C) 2020 The Authors. Published by Elsevier B.V.

activity generation↗
DOE OSTI2021

Is a Prescribed Fire Sufficient to Slow the Spread of Woody Plants in an Infrequently Burned Grassland? A Case Study in Tallgrass Prairie

In many mesic grasslands, such as the central Great Plains in North America, frequent fire is a key regulator of ecological processes. Long periods of infrequent fire facilitate the conversion of herbaceous-dominated grassland to woody-dominated shrubland or woodland. At the Konza Prairie Biological Station in northeast Kansas, one infrequently burned portion of the landscape has undergone transformation from grassland to woodland after nearly 30 yr without fire. In Spring 2017, a prescribed burn was implemented to assess fire effectiveness on woody plant mortality. A postfire census of 3 000+ individual woody plants identified the distribution of species by size (height), topographic position, and slope on the landscape. Mortality and canopy fire damage were calculated for each individual. In lowland locations with near-continuous shrub cover (30.7% of the landscape), woody plants were unaffected by fire. However, in upland and slope locations, where shrubs and trees were sparser, survival probability varied by topographic position and species. In these locations 68% of all woody individuals experienced 90% or greater fire damage to the canopy, with 56% of these individuals exhibiting new canopy regrowth within 2 mo after the fire. Here, the two most abundant woody shrubs, Cornus drummondii and Rhus aromatica, showed high survival at all height classes and landscape positions. The two abundant tree species, Gleditsia triacanthos and Juniperus virginiana, showed increased survival probability with tree height that varied by landscape position. Survival of J. virginiana also varied according to proximity and size of neighboring clonal shrubs, providing a mechanism for persistence of this fire-sensitive tree species even at small height classes. The probability survival curves developed here are useful for managers assessing when to prescribe fire to maximize mortality for J. virginiana and provide insight relevant for broader ecological understanding of woody encroachment within grasslands throughout the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Identifying schools at high-risk for elevated lead in drinking water using only publicly available data

Estimating the risk of lead contamination of schools' drinking water at the State level is a complex, important, and unexplored challenge. Variable water quality among water systems and changes in water chemistry during distribution affect lead dissolution rates from pipes and fittings. In addition, the locations of lead-bearing plumbing materials are uncertain. We tested the capability of six machine learning models to predict the likelihood of lead contamination of drinking water at the schools' taps using only publicly available datasets. The predictive features used in the models correspond to those with a proven correlation to the dominant, but commonly unavailable, factors that govern lead leaching: the presence of lead-bearing plumbing materials and water quality conducive to lead corrosion. By combining water chemistry data from public reports, socioeconomic information from the US census, and spatial features using Geographic Information Systems, we trained and tested models to estimate the likelihood of lead contaminated tap water in over 8,000 schools across California and Massachusetts. Our best-performing model was a Random Forest, with a 10-fold cross validation score of 0.88 for Massachusetts and 0.78 for California using the average Area Under the Receiver Operating Characteristic Curve (ROC AUC) metric. The model was then used to assign a lead leaching risk category to half of the schools across California (the other half was used for training). There was good agreement between the modeled risk categories and the actual lead leaching outcomes for every school; however, the model overestimated the lead leaching risk in up to 17% of the schools. This model is the first of its kind to offer a tool to predict the risk of lead leaching in schools at the State level. Further use of this model can help deploy limited resources more effectively to prevent childhood lead exposure from school drinking water.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Prevalence of Americans reporting a family history of cancer indicative of increased cancer risk: Estimates from the 2015 National Health Interview Survey

The collection and evaluation of family health history in a clinical setting presents an opportunity to discuss cancer risk, tailor cancer screening recommendations, and identify people with an increased risk of carrying a pathogenic variant who may benefit from referral to genetic counseling and testing. National recommendations for breast and colorectal cancer screening indicate that men and women who have a first-degree relative affected with these types of cancers may benefit from talking to a healthcare provider about starting screening at an earlier age and other options for cancer prevention. The prevalence of reporting a first-degree relative who had cancer was assessed among adult respondents of the 2015 National Health Interview Survey who had never had cancer themselves (n = 27,999). We found 35.6% of adults reported having at least one first-degree relative with cancer at any site. Significant differences in reporting a family history of cancer were observed by sex, age, race/ethnicity, educational attainment, and census region. Nearly 5% of women under age 50 and 2.5% of adults under age 50 had at least one first-degree relative with breast cancer or colorectal cancer, respectively. We estimated that 5.8% of women had a family history of breast or ovarian cancer that may indicate increased genetic risk. A third of U.S. adults who have never had cancer report a family history of cancer in a first-degree relative. Furthermore, this finding underscores the importance of using family history to inform discussions about cancer risk and screening options between healthcare providers and their patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Estimating the Benefits of Sustainable Aviation Fuel Usage at Chicago O’Hare International Airport on Ultrafine Particle Exposure and Mortalities Reductions

The expanding commercial aviation sector necessitates diverse energy sources, and sustainable aviation fuels (SAFs) have emerged as a promising option. Widespread SAF adoption can help meet transportation fuel demand and offer health benefits for people residing near airports or along airport landing and takeoff (LTO) pathways, where elevated levels of aircraft-derived air pollution often exist. Blending SAF with traditional jet fuels can reduce ultrafine particle (UFP) emissions, which may improve health of near airport population. We analyzed a population of about 8 million people in 1925 census tracts around the Chicago O’Hare International Airport (ORD). We conducted a risk assessment to estimate anticipated UFP reductions for three adoption scenarios using blends of traditional jet fuels with 5, 25, and 50% SAF across all flights landing and taking off from ORD. We calculated baseline estimates of UFP emissions using ORD flight data, a dispersion model, and a calibration function derived from mobile monitoring data. We used this baseline UFP emission profile across the study area to estimate population-weighted UFP, as well as the attributable case reductions (ACRs) and attributable mortality rate reductions (AMRRs) across the demographic distribution around the airport, based on the SAF blending scenarios. We found a positive association of SAF blending with UFP reductions, particularly near the airport and along LTO flight pathways. Our study showed that the population-weighted UFP across different demographics was similar. ACRs were largely dependent on individual demographic populations, while AMRRs for all populations were relatively similar, with an estimated 0.3 (95% range: 0.2−0.3), 1.1 (0.9−1.4), and 1.8 (1.5−2.2) fewer mortalities per 100,000 people per year expected with the adoption of 5, 25, and 50% SAF blends, respectively. This study indicates that communities near ORD, across a range of demographics, may benefit similarly from SAF adoption, thus highlighting how SAF adoption may offer an opportunity to improve health outcomes like aviation UFP-related mortalities around airports.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Temporal Trends of Racial and Socioeconomic Disparities in Population Exposures to Upstream Oil and Gas Development in California

People living near oil and gas development are exposed to multiple environmental stressors that pose health risks. Some studies suggest these risks are higher for racially and socioeconomically marginalized people, which may be partly attributable to disparities in exposures. We examined whether racially and socioeconomically marginalized people in California are disproportionately exposed to oil and gas wells and associated hazards. We longitudinally assessed exposure to wells during three time periods (2005–2009, 2010–2014, and 2015–2019) using sociodemographic data at the census block group‐level. For each block group and time period, we assessed exposure to new, active, retired, and plugged wells, and cumulative production volume. We calculated risk ratios to determine whether marginalized people disproportionately resided near wells (within 1 km). Averaged across the three time periods, we estimated that 1.1 million Californians (3.0%) lived within 1 km of active wells. Nearly 9 million Californians (22.9%) lived within 1 km of plugged wells. The proportion of Black residents near active wells was 42%–49% higher than the proportion of Black residents across California, and the proportion of Hispanic residents near active wells was 4%–13% higher than their statewide proportion. Disparities were greatest in areas with the highest oil and gas production, where the proportion of Black residents was 105%–139% higher than statewide. Socioeconomically marginalized residents also had disproportionately high exposure to wells. Though oil and gas production has declined in California, marginalized communities persistently had disproportionately high exposure to wells, potentially contributing to health disparities.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗