Oral histories document community mobilisation to participate in decision-making regarding a hazardous waste thermal treatment facility.

Colfax, Louisiana hosts a commercial hazardous waste thermal treatment (TT) facility, which treats fireworks, explosives, and military ordnances by open-burn/open-detonation one mile from the edge of the nearest community. Seventy-one percent of Colfax's residents are Black, and forty-six percent live below poverty, indicating the community's structural vulnerability. This community-based study originated at the behest of Colfax community members. We hypothesized that the close relationships among members of this enclave may have enhanced the community's ability to mobilize in opposition to the TT facility. We conducted semi-structured oral history interviews with nineteen community members and examined the social and interorganizational networks used by the Colfax community to claim its role in decision-making regarding the TT facility after years of exclusion from this process. Interview transcripts were analyzed through the lens of community capacity theory to gain insight into how interactions among community members about the environmental hazards led to social mobilization and improved participation in the decision-making process using codes for communication, organization, and outcome. Additionally, we reviewed Louisiana Department of Environmental Quality records for complaints about the facility to gauge public participation. One notable theme across several interviews was exclusion from the initial decision-making process related to the facility. However, interviewees noted a sustained effort was made among community members to educate themselves about the facility, organize a response through neighbor-to-neighbor contact, and take action by submitting formal complaints and participating in public hearings. Through the lens of environmental justice, this study illustrates an evolving condition of procedural justice.

Associations of COVID-19 Hospitalizations, ICU Admissions, and Mortality with Black and White Race and Their Mediation by Air Pollution and Other Risk Factors in the Louisiana Industrial Corridor, March 2020-August 2021.

Louisiana ranks among the bottom five states for air pollution and mortality. Our objective was to investigate associations between race and Coronavirus Disease 2019 (COVID-19) hospitalizations, intensive care unit (ICU) admissions, and mortality over time and determine which air pollutants and other characteristics may mediate COVID-19-associated outcomes. In our cross-sectional study, we analyzed hospitalizations, ICU admissions, and mortality among positive SARS-CoV-2 cases within a healthcare system around the Louisiana Industrial Corridor over four waves of the pandemic from 1 March 2020 to 31 August 2021. Associations between race and each outcome were tested, and multiple mediation analysis was performed to test if other demographic, socioeconomic, or air pollution variables mediate the race-outcome relationships after adjusting for all available confounders. Race was associated with each outcome over the study duration and during most waves. Early in the pandemic, hospitalization, ICU admission, and mortality rates were greater among Black patients, but as the pandemic progressed, these rates became greater in White patients. However, Black patients were disproportionately represented in these measures. Our findings imply that air pollution might contribute to the disproportionate share of COVID-19 hospitalizations and mortality among Black residents in Louisiana.

Spatial associations of long-term exposure to diesel particulate matter with seasonal and annual mortality due to COVID-19 in the contiguous United States.

BACKGROUND: People with certain underlying respiratory and cardiovascular conditions might be at an increased risk for severe illness from COVID-19. Diesel Particulate Matter (DPM) exposure may affect the pulmonary and cardiovascular systems. The study aims to assess if DPM was spatially associated with COVID-19 mortality rates across three waves of the disease and throughout 2020. METHODS: We tested an ordinary least squares (OLS) model, then two global models, a spatial lag model (SLM) and a spatial error model (SEM) designed to explore spatial dependence, and a geographically weighted regression (GWR) model designed to explore local associations between COVID-19 mortality rates and DPM exposure, using data from the 2018 AirToxScreen database. RESULTS: The GWR model found that associations between COVID-19 mortality rate and DPM concentrations may increase up to 77 deaths per 100,000 people in some US counties for every interquartile range (0.21 µg/m3) increase in DPM concentration. Significant positive associations between mortality rate and DPM were observed in New York, New Jersey, eastern Pennsylvania, and western Connecticut for the wave from January to May, and in southern Florida and southern Texas for June to September. The period from October to December exhibited a negative association in most parts of the US, which seems to have influenced the year-long relationship due to the large number of deaths during that wave of the disease. CONCLUSIONS: Our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease. That influence appears to have waned over time as transmission patterns evolved.

Risk factors among Black and White COVID-19 patients from a Louisiana Hospital System, March, 2020 - August, 2021.

Objectives: To investigate relationships between race and COVID-19 hospitalizations, intensive care unit (ICU) admissions, and mortality over time and which characteristics, may mediate COVID-19 associations. Methods: We analyzed hospital admissions, ICU admissions, and mortality among positive COVID-19 cases within the ten-hospital Franciscan Ministries of Our Lady Health System around the Mississippi River Industrial Corridor in Louisiana over four waves of the pandemic from March 1, 2020 - August 31, 2021. Associations between race and each outcome were tested, and multiple mediation analysis was performed to test if other demographic, socioeconomic, or air pollution variables mediate the race-outcome relationships. Results: Race was associated with each outcome over the study duration and during most waves. Early in the pandemic, hospitalization, ICU admission, and mortality rates were greater among Black patients, but as the pandemic progressed these rates became greater in White patients. However, Black patients were still disproportionately represented in these measures. Age was a significant mediator for all outcomes across waves, while comorbidity and emissions of naphthalene and chloroprene acted as mediators for the full study period. Conclusions: The role of race evolved throughout the pandemic in Louisiana, but Black patients bore a disproportionate impact. Naphthalene and chloroprene air pollution partially explained the long-term associations. Our findings imply that air pollution might contribute to the increased COVID-19 hospitalizations and mortality among Black residents in Louisiana but likely do not explain most of the effect of race.

Spatial Associations of Long-term Exposure to Diesel Particulate Matter with Seasonal and Annual Mortality Due to COVID-19 in the Contiguous United States.

Background People with certain underlying respiratory and cardiovascular conditions might be at an increased risk for severe illness from COVID-19. Diesel Particulate Matter (DPM) exposure may affect the pulmonary and cardiovascular systems. The study aims to assess if DPM was spatially associated with COVID-19 mortality across three waves of the disease and throughout 2020. Methods We tested an ordinary least square (OLS) model, then two global models, spatial lag model (SLM) and spatial error model (SEM), designed to explore spatial dependence, and a geographically weighted regression (GWR) model designed to explore local associations. Results The GWR model found that associations between COVID-19 deaths and DPM concentrations may increase up to 57, 36, 43, and 58 deaths per 100,000 people in some US counties for every 1 µg/m 3 increase in DPM concentration. Relative significant positive association are observed in New York, New Jersey, eastern Pennsylvania, and western Connecticut for the wave from January to May, and in southern Florida and southern Texas for June to September. The period from October to December exhibit a negative association in most parts of the US, which seems to have influenced the year-long relationship due to the large number of deaths during that wave of the disease. Conclusions Our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease, but that influence appears to have waned over time as transmission patterns evolved.

A Community-Integrated Geographic Information System Study of Air Pollution Exposure Impacts in Colfax, LA.

A community-integrated geographic information systems (CIGIS) study assimilating qualitative and quantitative information about human exposures and health was conducted in Colfax, Louisiana, which hosts a commercial open burn/open detonation thermal treatment (TT) facility that destroys waste from Superfund sites, explosives, military ordnances, and propellants. Fifty-eight percent of residents identified as Black, and median annual income was $16,318, with 90% of the population living below the poverty line. We conducted oral history interviews of twenty-nine residents and mined public records to document the community's experiences. Interviews focused on themes of Colfax's history, changing community fabric, resident health, and air pollution. The oral histories and public comments by community members provided information about lived experiences, including several health conditions, toleration of noise and vibration, property damage, and resulting changes to activity levels. These statements provided insight into the extent of suffering experienced by the local community. We also ran dispersion models for dates in 2020 when the waste stream composition, mass, and burn/smoldering times were provided in the facility's public records. The dispersion models placed the air pollution at the homes of residents during some of the time, and waste stream records from the TT facility agree with community testimony about health effects based on the known health effects of those compounds. CIGIS integration of our community-based qualitative data and maps with quantitative air pollution dispersion model output illustrated alignment between community complaints of impacts to health and property, known toxicological information about waste stream compounds, and dispersion model output.

A critical review of environmentally persistent free radical (EPFR) solvent extraction methodology and retrieval efficiency.

Long-lived environmentally persistent free radical (EPFR) exposures have been shown in toxicology studies to lead to respiratory and cardiovascular effects, which were thought to be due to the persistence of EPFR and their ability to produce reactive oxygen species. To characterize EPFR exposure and resulting health impacts, it is necessary to identify and systematize analysis protocols. Both direct measurement and solvent extraction methods have been applied to analyze environmental samples containing EPFR. The use of different protocols and solvents in EPFR analyses makes it difficult to compare results among studies. In this work, we reviewed EPFR studies that involved solvent extraction and carefully reported the details of the extraction methodology and retrieval recovery. EPFR recovery depends on the structure of the radical species and the solvent. For the limited number of studies available for review, the polar solvents had superior recovery in more studies. Radicals appeared to be more oxygen-centered following extraction for fly ash and particulate matter (PM) samples. Different solvent extraction methods to retrieve EPFR may produce molecular products during the extraction, thus potentially changing the sample toxicity. The number of studies reporting detailed methodologies is limited, and data in these studies were not consistently reported. Thus, inference about the solvent and protocol that leads to the highest EPFR extraction efficiency for certain types of radicals is not currently possible. Based on our review, we proposed reporting criteria to be included for future EPFR studies.

Disparities in Distribution of Particulate Matter Emissions from US Coal-Fired Power Plants by Race and Poverty Status After Accounting for Reductions in Operations Between 2015 and 2017.

Objectives. To investigate potential changes in burdens from coal-fired electricity-generating units (EGUcfs) that emit fine particulate matter (PM2.5, defined as matter with a nominal mean aerodynamic diameter of ≤ 2.5 µm) among racial/ethnic and economic groups after reduction of operations in 92 US EGUcfs.Methods. PM2.5 burdens calculated for EGUs listed in the 2008, 2011, and 2014 National Emissions Inventory were recalculated for 2017 after omitting emissions from 92 EGUcfs. The combined influence of race/ethnicity and poverty on burden estimates was characterized.Results. Omission of 92 EGUcfs decreased PM2.5 burdens attributable to EGUs by 8.6% for the entire population and to varying degrees for every population subgroup. Although the burden decreased across all subgroups, the decline was not equitable. After omission of the 92 EGUcfs, burdens were highest for the below-poverty and non-White subgroups. Proportional disparities between White and non-White subgroups increased. In our combined analysis, the burden was highest for the non-White-high-poverty subgroup.Conclusions. Our results indicate that subgroups living in poverty experience the greatest absolute burdens from EGUcfs. Changes as a result of EGUcf closures suggest a shift in burden from White to non-White subgroups. Policymakers could use burden analyses to jointly promote equity and reduce emissions.

Influence of exposure measurement errors on results from epidemiologic studies of different designs.

In epidemiologic studies of health effects of air pollution, measurements or models are used to estimate exposure. Exposure estimates have errors that propagate to effect estimates in exposure-response models. We critically evaluate how types of exposure measurement error influenced bias and precision of effect estimates to understand conditions affecting interpretation of exposure-response models for epidemiologic studies of exposure to PM2.5, NO2, and SO2. We reviewed available literature on exposure measurement error for time-series and long-term exposure epidemiology studies. For time-series studies, time-activity error (daily exposure concentration did not account for variation in exposure due to time-activity during a day) and nonambient (indoor) sources negatively biased the effect estimates and increased standard error, so uncertainty grew with increasing bias while underestimating the true health effect in these studies. Spatial error (deviation between true exposure concentration at an individual's location and concentration at a receptor) was ascribed to negatively biased effect estimates in most cases. Positive bias occurred for spatially variable pollutants when the variance of error correlated with the exposure estimate. For long-term exposure studies, most spatial errors did not bias the effect estimate. For both time-series and long-term exposure studies reviewed, large uncertainties were observed when exposure concentration was modeled with low spatial and temporal resolution for a spatially variable pollutant.

Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status.

OBJECTIVES: To quantify nationwide disparities in the location of particulate matter (PM)-emitting facilities by the characteristics of the surrounding residential population and to illustrate various spatial scales at which to consider such disparities. METHODS: We assigned facilities emitting PM in the 2011 National Emissions Inventory to nearby block groups across the 2009 to 2013 American Community Survey population. We calculated the burden from these emissions for racial/ethnic groups and by poverty status. We quantified disparities nationally and for each state and county in the country. RESULTS: For PM of 2.5 micrometers in diameter or less, those in poverty had 1.35 times higher burden than did the overall population, and non-Whites had 1.28 times higher burden. Blacks, specifically, had 1.54 times higher burden than did the overall population. These patterns were relatively unaffected by sensitivity analyses, and disparities held not only nationally but within most states and counties as well. CONCLUSIONS: Disparities in burden from PM-emitting facilities exist at multiple geographic scales. Disparities for Blacks are more pronounced than are disparities on the basis of poverty status. Strictly socioeconomic considerations may be insufficient to reduce PM burdens equitably across populations.

A cross-disciplinary evaluation of evidence for multipollutant effects on cardiovascular disease.

BACKGROUND: The current single-pollutant approach to regulating ambient air pollutants is effective at protecting public health, but efficiencies may be gained by addressing issues in a multipollutant context since multiple pollutants often have common sources and individuals are exposed to more than one pollutant at a time. OBJECTIVE: We performed a cross-disciplinary review of the effects of multipollutant exposures on cardiovascular effects. METHODS: A broad literature search for references including at least two criteria air pollutants (particulate matter [PM], ozone [O3], oxides of nitrogen, sulfur oxides, carbon monoxide) was conducted. References were culled based on scientific discipline then searched for terms related to cardiovascular disease. Most multipollutant epidemiologic and experimental (i.e., controlled human exposure, animal toxicology) studies examined PM and O3 together. DISCUSSION: Epidemiologic and experimental studies provide some evidence for O3 concentration modifying the effect of PM, although PM did not modify O3 risk estimates. Experimental studies of combined exposure to PM and O3 provided evidence for additivity, synergism, and/or antagonism depending on the specific health endpoint. Evidence for other pollutant pairs was more limited. CONCLUSIONS: Overall, the evidence for multipollutant effects was often heterogeneous, and the limited number of studies inhibited making a conclusion about the nature of the relationship between pollutant combinations and cardiovascular disease.

Maternal Exposure to Nitrogen Dioxide, Intake of Methyl Nutrients, and Congenital Heart Defects in Offspring.

Nutrients that regulate methylation processes may modify susceptibility to the effects of air pollutants. Data from the National Birth Defects Prevention Study (United States, 1997-2006) were used to estimate associations between maternal exposure to nitrogen dioxide (NO2), dietary intake of methyl nutrients, and the odds of congenital heart defects in offspring. NO2 concentrations, a marker of traffic-related air pollution, averaged across postconception weeks 2-8, were assigned to 6,160 nondiabetic mothers of cases and controls using inverse distance-squared weighting of air monitors within 50 km of maternal residences. Intakes of choline, folate, methionine, and vitamins B6 and B12 were assessed using a food frequency questionnaire. Hierarchical regression models, which accounted for similarities across defects, were constructed, and relative excess risks due to interaction were calculated. Relative to women with the lowest NO2 exposure and high methionine intake, women with the highest NO2 exposure and lowest methionine intake had the greatest odds of offspring with a perimembranous ventricular septal defect (odds ratio = 3.23, 95% confidence interval: 1.74, 6.01; relative excess risk due to interaction = 2.15, 95% confidence interval: 0.39, 3.92). Considerable departure from additivity was not observed for other defects. These results provide modest evidence of interaction between nutrition and NO2 exposure during pregnancy.

NO to NO2 Conversion Rate Analysis and Implications for Dispersion Model Chemistry Methods using Las Vegas, Nevada Near-Road Field Measurements.

The nitrogen dioxide/oxides of nitrogen (NO2/NOX) ratio is an important surrogate for NO to NO2 chemistry in dispersion models when estimating NOX impacts in a near-road environment. Existing dispersion models use different techniques and assumptions to represent NO to NO2 conversion and do not fully characterize all of the important atmospheric chemical and mechanical processes. Thus, "real-world" ambient measurements must be analyzed to assess the behavior of NO2/NOX ratios near roadways. An examination of NO2/NOX ratio data from a field study conducted in Las Vegas, Nevada (NV), from mid-December, 2008 through mid-December, 2009 provides insights into the appropriateness of assumptions about the NO2/NOX ratio included in dispersion models. Data analysis indicates multiple factors affect the downwind NO2/NOX ratio. These include spatial gradient, background ozone (O3), source emissions of NO and NO2, and background NO2/NOX ratio. Analysis of the NO2/NOX ratio spatial gradient indicates that under high O3 conditions, the change in the ratio is fairly constant once a certain O3 threshold (≥ 30 ppb) is reached. However, under low O3 conditions (< 30 ppb), there are differences between weekdays and weekends, most likely due to a decline in O3 concentrations during the weekday morning hours, reducing the O3 available to titrate the emitted NO, allowing lower NO2/NOX ratios. These results suggest that under high O3 conditions, NOX chemistry is driving the NO2/NOX ratios whereas under low O3 conditions, atmospheric mixing is the driving factor.

Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway gradients from near-road monitoring data.

This paper describes a new regression modeling approach to estimate on-road nitrogen dioxide (NO2) and oxides of nitrogen (NOX) concentrations and near-road spatial gradients using data from a near-road monitoring network. Field data were collected in Las Vegas, NV at three monitors sited 20, 100, and 300 m from Interstate-15 between December, 2008 and January, 2010. Measurements of NO2 and NOX were integrated over 1-hour intervals and matched with meteorological data. Several mathematical transformations were tested for regressing pollutant concentrations against distance from the roadway. A logit-ln model was found to have the best fit (R2 = 94.7%) and also provided a physically realistic profile. The mathematical model used data from the near-road monitors to estimate on-road concentrations and the near-road gradient over which mobile source pollutants have concentrations elevated above background levels. Average and maximum on-road NO2 concentration estimates were 33 ppb and 105 ppb, respectively. Concentration gradients were steeper in the morning and late afternoon compared with overnight when stable conditions preclude mixing. Estimated on-road concentrations were also highest in the late afternoon. Median estimated on-road and gradient NO2 concentrations were lower during summer compared with winter, with a steeper gradient during the summer, when convective mixing occurs during a longer portion of the day On-road concentration estimates were higher for winds perpendicular to the road compared with parallel winds and for atmospheric stability with neutral-to-unstable atmospheric conditions. The concentration gradient with increasing distance from the road was estimated to be sharper for neutral-to-unstable conditions when compared with stable conditions and for parallel wind conditions compared with perpendicular winds. A regression of the NO2/NOX ratios yielded on-road ratios ranging from 0.25 to 0.35, substantially higher than the anticipated tail-pipe emissions ratios. The results from the ratios also showed that the diurnal cycle of the background NO2/NOX ratios were a driving factor in the on-road and downwind NO2/NOX ratios.

Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina.

Air pollution health studies of fine particulate matter (diameter ≤2.5 µm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 µg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 µg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.

Multiple biomarker models for improved risk estimation of specific cardiovascular diseases related to metabolic syndrome: a cross-sectional study.

BACKGROUND: Metabolic syndrome (MetS) is the co-occurrence of several conditions that increase risk of chronic disease and mortality. Multivariate models for calculating risk of MetS-related diseases based on combinations of biomarkers are promising for future risk estimation if based on large population samples. Given biomarkers' nonspecificity and commonality in predicting diseases, we hypothesized that unique combinations of the same clinical diagnostic criteria can be used in different multivariate models to develop more accurate individual and cumulative risk estimates for specific MetS-related diseases. METHODS: We utilized adult biomarker and cardiovascular disease (CVD) data from the National Health and Nutrition Examination Survey as part of a cross-sectional analysis. Serum C-reactive protein (CRP), glycohemoglobin, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, fasting glucose, and apolipoprotein-B were modeled. CVDs included congestive heart failure, coronary heart disease, angina, myocardial infarction, and stroke. Decile analysis for disease prevalence in each biomarker group and multivariate logistic regression for estimation of odds ratios were employed to measure the joint association between multiple biomarkers and CVD diagnoses. RESULTS: Of the biomarkers considered, glycohemoglobin, triglycerides, and CRP were consistently associated with the CVD outcomes of interest in decile analysis and were selected for the final models. Associations were overestimated when using single-marker models in comparison with full models; individual odds ratios decreased an average of 16.4% from the single-biomarker models to the joint association models for CRP, 6.6% for triglycerides, and 1.4% for glycohemoglobin. However, joint associations were stronger than any single-marker estimate. Additionally, reduced models produced unique combinations of biomarkers for specific CVD outcomes. CONCLUSION: The reduced joint association modeling results suggest that unique combinations of biomarkers with their related measure of association can be used to produce more accurate cumulative risk estimates for each CVD. Additionally, our results indicate that the use of multiple biomarkers in a single multivariate model may provide increased accuracy of individual biomarker association estimates by controlling for statistical artifacts and spurious relationships due to co-biomarker confounding.

Cross-species coherence in effects and modes of action in support of causality determinations in the U.S. Environmental Protection Agency's Integrated Science Assessment for Lead.

The peer-reviewed literature on the health and ecological effects of lead (Pb) indicates common effects and underlying modes of action across multiple organisms for several endpoints. Based on such observations, the United States (U.S.) Environmental Protection Agency (EPA) applied a cross-species approach in the 2013 Integrated Science Assessment (ISA) for Lead for evaluating the causality of relationships between Pb exposure and specific endpoints that are shared by humans, laboratory animals, and ecological receptors (i.e., hematological effects, reproductive and developmental effects, and nervous system effects). Other effects of Pb (i.e., cardiovascular, renal, and inflammatory responses) are less commonly assessed in aquatic and terrestrial wildlife limiting the application of cross-species comparisons. Determinations of causality in ISAs are guided by a framework for classifying the weight of evidence across scientific disciplines and across related effects by considering aspects such as biological plausibility and coherence. As illustrated for effects of Pb where evidence across species exists, the integration of coherent effects and common underlying modes of action can serve as a means to substantiate conclusions regarding the causal nature of the health and ecological effects of environmental toxicants.