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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Working Safely at LANL during the COVID-19 Pandemic (Rev. 2)

The COVID-19 pandemic is an emerging, rapidly evolving public health emergency. The content of this course reflects the most up-to-date guidance and information from the Centers for Disease Control and Prevention (CDC), World Health Organization (WHO) and scientific literature available at the time the course was developed. Check the LANL COVID-19 Hub for the latest information and guidance. Updates to the course will be made as necessary to reflect evolving guidance form public health authorities and new data form the scientific literature.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Impact of Canadian Wildfires 2023 on North Atlantic's Region Air Quality: An Analysis Using ASDC Data

Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. NASA's Atmospheric Science Data Center (ASDC) is in the Science Directorate located at NASA'S Langley Research Center in Hampton, Virginia. The Science Directorate's Climate Science Branch, Atmospheric Composition Branch, and Chemistry and Dynamics Branch work with ASDC to study changes in the Earth and its atmosphere. ASDC projects focus on the Earth science disciplines: Radiation Budget, Clouds, Aerosols, and Tropospheric Composition. All the products chosen for this analysis are products hosted by ASDC and available for users to obtain via our services [1].

Hazem Mahmoud↗
NASA NTRS2009

Environmental Program

NASA's White Sands Test Facility has six core environmental compliance capabilities: remote hazardous testing of reactive, explosive and toxic materials and fluids; hypergolic fluids materials and systems testing; oxygen materials and system testing; hypervelocity impact testing; flight hardware processing; and, propulsion testing. The facility's permit status and challenges are reviewed. Historic operations and practices dating from the 1960s through the early 1980s resulted in contamination of the facility's groundwater. An environmental restoration effort has been employed to protect public health and the health of the workforce. The restoration seeks to properly handle hazardous materials and waste processes; determine the nature and extent of the contamination; stop the migration of contaminated groundwater; stabilize the plume front which has been assessed as the greatest risk to public health; and, clean-up the environment to restore it to preexisting conditions. The Plume Front Treatment System is operational and seeks to stop the westward movement of the plume to protect drinking water and irrigation well. Specifically, the treatment system will extract contaminated water from the aquifer, remove chemical using the best available technology, and return (inject) the treated water back to the aquifer. The Mid-Plume Interception Treatment System also seeks to stop the migration of containment, as well as to evaluate new technologies to accelerate cleanup, such as bioremediation.

Fischer, Holger↗
DOE OSTI2022

Foodborne illness outbreaks linked to unpasteurised milk and relationship to changes in state laws – United States, 1998–2018

Consumption of unpasteurised milk in the United States has presented a public health challenge for decades because of the increased risk of pathogen transmission causing illness outbreaks. We analysed Foodborne Disease Outbreak Surveillance System data to characterise unpasteurised milk outbreaks. Using Poisson and negative binomial regression, we compared the number of outbreaks and outbreak-associated illnesses between jurisdictions grouped by legal status of unpasteurised milk sale based on a May 2019 survey of state laws. During 2013–2018, 75 outbreaks with 675 illnesses occurred that were linked to unpasteurised milk; of these, 325 illnesses (48%) were among people aged 0–19 years. Of 74 single-state outbreaks, 58 (78%) occurred in states where the sale of unpasteurised milk was expressly allowed. Compared with jurisdictions where retail sales were prohibited (n = 24), those where sales were expressly allowed (n = 27) were estimated to have 3.2 (95% CI 1.4–7.6) times greater number of outbreaks; of these, jurisdictions where sale was allowed in retail stores (n = 14) had 3.6 (95% CI 1.3–9.6) times greater number of outbreaks compared with those where sale was allowed on-farm only (n = 13). This study supports findings of previously published reports indicating that state laws resulting in increased availability of unpasteurised milk are associated with more outbreak-associated illnesses and outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Looking Beyond Bill Savings to Equity in Renewable Energy Microgrid Deployment

Microgrids powered by renewable energy can provide backup power to critical infrastructure during grid outages. These systems can also play an important role in advancing energy justice by providing economic, environmental, health, and resilience benefits for underserved communities. The value of microgrids is often measured by the economic savings and resilience provided, but there are other energy justice factors that should be considered. This paper describes a methodology for quantifying broader costs and benefits including utility bill savings, value of resilience, social cost of carbon, public health costs, and jobs associated with the construction and operation of microgrids. We evaluate these factors at three case study sites and find that including energy justice values in the cost-benefit analysis of microgrids can change investment decisions. When climate, health, resilience, and job creation are considered, cost-optimal microgrids include more renewable generation, leading to a 52-82% reduction in emissions and diesel fuel use. The net present values of the microgrids grow from negative $626,000-843,000 in the diesel only case to $10-16 million in the hybrid microgrid case and $12-19 million in the renewable microgrid case, indicating potential for greater microgrid deployment if energy justice values are incorporated in decision making. However, we also see large increases in capital expenses, which could limit deployment unless accompanied by innovative financing measures. These findings may be useful to communities as they seek to strengthen resilience to natural disasters while also improving public health, meeting climate goals, and providing economic opportunity for residents.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Dataset of U.S. School Bus Depots

A large body of public health literature describes how undesirable or dangerous facilities, such as truck depots and industrial plants, located in or near communities can lead to health harms. Research also describes the high levels of traffic-related air and noise pollution that is linked to health harms and may be disproportionately distributed near many schools. Therefore, a primary use case for this dataset is to analyze the location of school bus depots and to create an evidence base that would better enable the work of community members, advocates, and other stakeholders toward improving air quality and public health. Other possible uses for this school bus depot dataset include electricity grid planning and reliability, given recent momentum toward school bus electrification. This dataset was created using an object-based approach with remote sensing data. The primary source of aerial imagery was the National Agriculture Imagery Program (NAIP) dataset. NAIP imagery was analyzed to locate individual school buses based on their color and size, and then classified clusters of school buses as potential depots, which were then verified visually. The resulting dataset contains 11,309 depots across the 48 contiguous U.S. states and Washington, D.C. Fifty-one percent (5,730 depots) are at schools, defined as being 350 meters or less from the nearest school. The accuracy of the dataset was assessed by comparing it with independent reference datasets containing 506 depots from the records of two school transportation companies. We found good agreement, with an omission error rate of 15.2% (77 depots). This dataset represents one of the only remote sensing projects to conduct object detection using data at the sub-meter to 1-meter resolution for a continental-scale application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Healthy Buildings Guide for Small Businesses: A Quick Reference to Improve Indoor Air Quality (Restaurants Excluded)

Indoor air pollutant concentrations can be higher than those outdoors, putting occupants at risk. These pollutants come from a variety of sources: materials used to build and furnish indoor spaces, retail products and services, and occupants themselves. Ventilation, temperature, and humidity also affect indoor air quality. It is important to consider the documented benefits of healthy indoor environments at any time – not just during a public health crisis – and it is key to consider the indoor air quality where we do business as well as at home. Small businesses vary in size, services, products, ownership style, and building mechanical systems, yet all benefit from healthy indoor spaces for employees and customers. This guide provides steps, in conjunction with public health guidance, that small businesses can take to improve and maintain indoor air quality. Small businesses often have unique ownership structures – the building owner and business owner can be different parties – so this guide addresses both building and business owner action items. There are many ways to pursue healthy indoor air, including eliminating, minimizing, and/or controlling the contaminants we bring in, and removing or diluting any existing contaminants through ventilation, filtration, air cleaning, and/or disinfection. Best results are achieved through a comprehensive approach combining various strategies, including those discussed in this guide.

air filtration↗
DOE OSTI2022

Guia de Edificios Saludables para Pequenas Empresas: Una Referencia Rapida para Mejorar la Calidad del Aire Interior (Restaurantes Excluidos)

Indoor air pollutant concentrations can be higher than those outdoors, putting occupants at risk. These pollutants come from a variety of sources: materials used to build and furnish indoor spaces, retail products and services, and occupants themselves. Ventilation, temperature, and humidity also affect indoor air quality. It is important to consider the documented benefits of healthy indoor environments at any time - not just during a public health crisis - and it is key to consider the indoor air quality where we do business as well as at home. Small businesses vary in size, services, products, ownership style, and building mechanical systems, yet all benefit from healthy indoor spaces for employees and customers. This guide provides steps, in conjunction with public health guidance, that small businesses can take to improve and maintain indoor air quality. Small businesses often have unique ownership structures - the building owner and business owner can be different parties - so this guide addresses both building and business owner action items. There are many ways to pursue healthy indoor air, including eliminating, minimizing, and/or controlling the contaminants we bring in, and removing or diluting any existing contaminants through ventilation, filtration, air cleaning, and/or disinfection. Best results are achieved through a comprehensive approach combining various strategies, including those discussed in this guide.

air filtration↗
DOE OSTI2022

Tracking ebolavirus genomic drift with a resequencing microarray

Filoviruses are emerging pathogens that cause acute fever with high fatality rate and present a global public health threat. During the 2013–2016 Ebola virus outbreak, genome sequencing allowed the study of virus evolution, mutations affecting pathogenicity and infectivity, and tracing the viral spread. In 2018, early sequence identification of the Ebolavirus as EBOV in the Democratic Republic of the Congo supported the use of an Ebola virus vaccine. However, field-deployable sequencing methods are needed to enable a rapid public health response. Resequencing microarrays (RMA) are a targeted method to obtain genomic sequence on clinical specimens rapidly, and sensitively, overcoming the need for extensive bioinformatic analysis. This study presents the design and initial evaluation of an ebolavirus resequencing microarray (Ebolavirus-RMA) system for sequencing the major genomic regions of four Ebolaviruses that cause disease in humans. The design of the Ebolavirus-RMA system is described and evaluated by sequencing repository samples of three Ebolaviruses and two EBOV variants. The ability of the system to identify genetic drift in a replicating virus was achieved by sequencing the ebolavirus glycoprotein gene in a recombinant virus cultured under pressure from a neutralizing antibody. Comparison of the Ebolavirus-RMA results to the Genbank database sequence file with the accession number given for the source RNA and Ebolavirus-RMA results compared to Next Generation Sequence results of the same RNA samples showed up to 99% agreement.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Assessing Malaria Risks in Greater Mekong Subregion based on Environmental Parameters

At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health s decision capability for malaria risk assessments and controls. The main objectives are: 1) identifying the potential breeding sites for major vector species; 2) implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) increased warning time for public health organizations to respond to malaria outbreaks; 2) optimized utilization of pesticide and chemoprophylaxis; 3) reduced likelihood of pesticide and drug resistance; and 4) reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. The NASA Earth science data sets that have been used for malaria surveillance and risk assessment include AVHRR Pathfinder, TRMM, MODIS, NSIPP, and SIESIP. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Socioeconomic factors that may influence malaria transmissions will also be incorporated into the predictive models.

Kiang, Richard↗
NASA NTRS2005

Malaria Modeling and Surveillance for the Greater Mekong Subregion

At 4,200 km, the Mekong River is the tenth longest river in the world. It directly and indirectly influences the lives of hundreds of millions of inhabitants in its basin. The riparian countries - Thailand, Myanmar, Cambodia, Laos, Vietnam, and a small part of China - form the Greater Mekong Subregion (GMS). This geographical region has the misfortune of being the world's epicenter of falciparum malaria, which is the most severe form of malaria caused by Plasmodium falciparum. Depending on the country, approximately 50 to 90% of all malaria cases are due to this species. In the Malaria Modeling and Surveillance Project, we have been developing techniques to enhance public health's decision capability for malaria risk assessments and controls. The main objectives are: 1) Identifying the potential breeding sites for major vector species; 2) Implementing a malaria transmission model to identify the key factors that sustain or intensify malaria transmission; and 3) Implementing a risk algorithm to predict the occurrence of malaria and its transmission intensity. The potential benefits are: 1) Increased warning time for public health organizations to respond to malaria outbreaks; 2) Optimized utilization of pesticide and chemoprophylaxis; 3) Reduced likelihood of pesticide and drug resistance; and 4) Reduced damage to environment. Environmental parameters important to malaria transmission include temperature, relative humidity, precipitation, and vegetation conditions. These parameters are extracted from NASA Earth science data sets. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records.

Kiang, Richard↗
DOE OSTI2021

Addressing Parental Vaccine Hesitancy and Other Barriers to Childhood/Adolescent Vaccination Uptake During the Coronavirus (COVID-19) Pandemic

Routine childhood immunizations are proven to be one of the most effective public health interventions at controlling numerous deadly diseases. Therefore, the CDC recommends routine immunizations for children and adolescent populations against vaccine-preventable diseases e.g., tetanus, pertussis, diphtheria, etc. This current review sought to examine barriers to pediatric vaccine uptake behaviors during the COVID-19 pandemic. We also explored the implications for parental vaccine hesitancy/delay during an ongoing health crisis and proposed recommendations for increasing vaccine confidence and compliance. Our review determined that the receipt for vaccinations steadily improved in the last decade for both the United States and Tennessee. However, this incremental progress has been forestalled by the COVID-19 pandemic and other barriers i.e. parental vaccine hesitancy, social determinants of health (SDoH) inequalities, etc. which further exacerbate vaccination disparities. Moreover, non-compliance to routine vaccinations could cause an outbreak of diseases, thereby, worsening the ongoing health crisis and already strained health care system. Healthcare providers are uniquely positioned to offer effective recommendations with presumptive languaging to increase vaccination rates, as well as, address parental vaccine hesitancy. Best practices that incorporate healthcare providers’ quality improvement coaching, vaccination reminder recall systems, adherence to standardized safety protocols (physical distancing, hand hygiene practices, etc.), as well as, offer telehealth and outdoor/drive-through/curbside vaccination services, etc. are warranted. Additionally, a concerted effort should be made to utilize public health surveillance systems to collect, analyze, and interpret data, thereby, ensuring the dissemination of timely, accurate health information for effective health policy decision-making e.g., vaccine distribution, etc.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

Moving NASA Remote Sensing Data to the GIS Environment for Health Studies

There has been an increasing demand by the health community for improved data on many different environmental factors relevant to the links between the environment and disease occurrence and transmission. These data are important for GIS-based monitoring, risk mapping, and surveillance of epidemiological parameters on a large number of different spatial, temporal, and spectral resolutions. Accordingly, NASA is developing new approaches to data collection and distribution in order to improve access to multiple sources of data streams to increase spatial and temporal coverage. Methods are being developed to incorporate different, scalable capabilities to handle multiple data sources by adding, deleting and replacing components as required as well as associated tools for their management. An approach has been to search for innovative solutions focused on the creation, use and manipulation of data stored in many different archives. These include data transformation and combination as well as data and information tools that can assist the public health and science community to use existing and anticipated products in new and flexible ways. This presentation will provide an inventory of geophysical parameters derived from satellite remote sensing sensors that are useful for GIS-based public health studies. The presentation will also discuss the physical and scientific limitations of access to and use of these data for health applications such as resolution and format differences, lack of software interoperability, data access problems. Finally, there will be a summary of the recent steps the NASA program has taken to bring NASA-generated satellite products to a wider range of users in the GIS community.

Vicente, Gilberto A.↗
DOE OSTI2023

Grid Interconnection and Renewables Deployment Related Air Quality and Human Health Benefits in Southeast Asia

The Association of Southeast Asian Nations (ASEAN) has increasingly focused on multilateral electricity trade, improved grid resiliency and modernization. These opportunities in the power sector have been studied through the ASEAN Interconnection Masterplan Studies, the most recent of which is the ASEAN Interconnection Masterplan Study III (AIMS III). AIMS III assessed four different scenarios of power system in the ASEAN countries that include a base scenario and three additional scenarios (called optimum RE, ASEAN RE, and High RE) considering different levels of deployment of renewable energy (RE) and cross-border generation and trade of electricity for three years (2025, 2030, 2040). In this work, we quantify the potential air quality and public health co-benefits of AIMS III scenarios. For a rapidly expanding, energy hungry region like SE Asia generation is power generation is expected to increase significantly. The AIMS III scenarios' capacity expansion modeling suggest that: 1) Generation nearly doubles from 2025 to 2040 in all four AIMS III scenarios; 2) Most of the increased generation is met by coal in all four AIMS III scenarios; 3) While renewables generation increases in all four AIMS III scenarios, the fraction of total generation (its share) generally decreases because of the much greater increase in non-renewable sources, mostly coal. Only in the High RE Target scenario do renewables represent a higher share in 2040 than in 2025, though even here it is at the expense of natural gas rather than coal; 4) As a result, emissions of gaseous and aerosol pollutants increase significantly in all scenarios. Because emissions increase in 2040 compared to 2025 for all four AIMS III scenarios, so do PM2.5 concentrations. Even for the High RE Target scenario (the scenario with highest share of renewable energy), compared to the Base scenario in 2025, in 2040 PM2.5 concentrations are higher. Compared to the Base scenario in the same years, the Optimum RE and ASEAN RE Target scenarios do not differ very much from the Base in terms of PM2.5 concentration. The High RE Target scenario, on the other hand, yields noticeably lower PM2.5 concentration. For example, in 2040 the population-weighted decrease in annual average PM2.5 concentration in the High RE Target scenario relative to the Base scenario is 0.5 ug m-3. Compared to the Base scenario in 2040, each of the alternative AIMS III scenarios is estimated to result in net reductions in power-sector air quality-related excess mortality in the ASEAN region. Yet there are a few countries for which the Optimum RE and ASEAN RE Target result in increases in PM2.5-related excess mortality (Thailand and Vietnam for the ASEAN RE Target scenario and Thailand for the Optimum RE scenario). However, for the High RE Target scenario, all countries benefit and find reductions in excess mortality resulting from the power sector scenarios modeled in AIMS III with regionwide mortality decreasing by 16,000 compared to the Base scenario in 2040. In summary, our analysis finds that changing power generation emissions is a crucial lever for improving public health in ASEAN member countries and provides a pathway for policymakers to make decision backed by a realistic power sector expansion and air quality analyses.

air quality↗
DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗
DOE OSTI2023

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2023

San Joaquin Valley Health & Air Quality: Evaluating the Overlap of Social Vulnerabilities and Air Quality in the San Joaquin Valley Air Pollution Control District

Little Manila Rising (LMR) is a nonprofit in Stockton, California that has been increasingly concerned by the air pollution in their city and the neighboring San Joaquin Valley (SJV). Geographical factors, climate conditions, and anthropogenic activities, such as agriculture burning and vehicle emissions, contribute to the high levels of air pollution in this region. To visualize the distribution of air pollution and social disparities across the SJV, LMR partnered with NASA DEVELOP. The DEVELOP team used Terra and Aqua MODIS, Sentinel-5P TROPOMI, and CALIPSO CALIOP to observe Aerosol Optical Depth (AOD), Nitrogen Dioxide (NO 2 ), and the vertical distribution of pollutants at varying pollution levels, respectively. Additionally, Suomi-NPP VIIRS provided active fire data. By leveraging NASA Earth observations along with sociodemographic and public health data, the DEVELOP team created maps identifying the areas experiencing the highest vulnerabilities and disparities in pollution exposure. The team found that AOD was slightly higher in agricultural regions, while NO 2 was consistently higher along transportation corridors and urban areas. Wildfires dominated the type of detected active fires, and there was high correlation (R 2 = 0.6924) between active fires and burn permits in agricultural tracts. Furthermore, the team identified both high AOD vulnerability and high NO 2 vulnerability in census tracts in South Stockton, an area that has been historically redlined and disinvested in, and where LMR resides. There was also a moderate correlation between air quality reported from the satellites and in-situ ground monitors. These results will support LMR’s organizing strategies for stricter enforcement of air pollution regulations and increased public health equity for community members.

Agricultural Fires↗