Patterns of distributive environmental inequity under different PM2.5 air pollution scenarios for Salt Lake County public schools

Joint polygenic and environmental risks for childhood attention-deficit/hyperactivity disorder (ADHD) and ADHD symptom dimensions

Background

attention-deficit/hyperactivity disorder (ADHD) is associated with both polygenic liability and environmental exposures, both intrinsic to the family, such as family conflict, and extrinsic, such as air pollution. However, much less is known about the interplay between environmental and genetic risks relevant to ADHD-a better understanding of which could inform both mechanistic models and clinical prediction algorithms.

Methods

Two independent data sets, the population-based Adolescent Brain Cognitive Development Study (ABCD) (N = 11,876) and the case-control Oregon-ADHD-1000 (N = 1449), were used to examine additive (G + E) and interactive (GxE) effects of selected polygenic risk scores (PRS) and environmental factors in a cross-sectional design. Genetic risk was measured using PRS for nine mental health disorders/traits. Exposures included family income, family conflict/negative sentiment, and geocoded measures of area deprivation, lead exposure risk, and air pollution exposure (nitrogen dioxide and fine particulate matter).

Results

ADHD PRS and family conflict jointly predicted concurrent ADHD symptoms in both cohorts. Additive-effects models, including both genetic and environmental factors, explained significantly more variation in symptoms than any individual factor alone (joint R 2 = .091 for total symptoms in ABCD; joint R 2 = .173 in Oregon-ADHD-1000; all delta-R 2 p-values <2e-7). Significant effect size heterogeneity across ancestry groups was observed for genetic and environmental factors (e.g., Q = 9.01, p = .011 for major depressive disorder PRS; Q = 13.34, p = .001 for area deprivation). GxE interactions observed in the full ABCD cohort suggested stronger environmental effects when genetic risk is low, though they did not replicate.

Conclusions

Reproducible additive effects of PRS and family environment on ADHD symptoms were found, but GxE interaction effects were not replicated and appeared confounded by ancestry. Results highlight the potential value of combining exposures and PRS in clinical prediction algorithms. The observed differences in risks across ancestry groups warrant further study to avoid health care disparities.

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM_2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM_2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM_2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM_2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Neighborhood Composition and Air Pollution in Chicago: Monitoring Inequities With a Dense, Low-Cost Sensing Network, 2021

Objectives. To evaluate the efficacy of a novel, real-time sensor network for routine monitoring of racial and economic disparities in fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in diameter) exposures at the neighborhood level. Methods. We deployed a dense network of low-cost PM2.5 sensors in Chicago, Illinois, to evaluate associations between neighborhood-level composition variables (percentage of Black residents, percentage of Hispanic/Latinx residents, and percentage of households below poverty) and interpolated PM2.5. Relationships were assessed in spatial lag models after adjustment for all composition variables. Models were fit with data both from the overall period and during high-pollution episodes associated with social events (July 4, 2021) and wildfires (July 23, 2021). Results. The spatial lag models showed that racial/ethnic composition variables were associated with higher PM2.5 levels. Levels were notably higher in neighborhoods with larger compositions of Hispanic/Latinx residents across the entire study period and notably higher in neighborhoods with larger Black populations during the July 4 episode. Conclusions. As a complement to sparse regulatory networks, dense, low-cost sensor networks can capture spatial variations during short-term air pollution episodes and enable monitoring of neighborhood-level inequities in air pollution exposures in real time. (Am J Public Health. 2022;112(12):1765-1773. https://doi.org/10.2105/AJPH.2022.307068).

Disparities in Air Pollutants Across Racial, Ethnic, and Poverty Groups at US Public Schools

We investigate socioeconomic disparities in air quality at public schools in the contiguous US using high resolution estimates of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations. We find that schools with higher proportions of people of color (POC) and students eligible for the federal free or reduced lunch program, a proxy for poverty level, are associated with higher pollutant concentrations. For example, we find that the median annual NO2 concentration for White students, nationally, was 7.7 ppbv, compared to 9.2 ppbv for Black and African American students. Statewide and regional disparities in pollutant concentrations across racial, ethnic, and poverty groups are consistent with nationwide results, where elevated NO2 concentrations were associated with schools with higher proportions of POC and higher levels of poverty. Similar, though smaller, differences were found in PM2.5 across racial and ethnic groups in most states. Racial, ethnic, and economic segregation across the rural-urban divide is likely an important factor in pollution disparities at US public schools. We identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we highlight that disparities exist not only across urban and non-urban lines but also within urban environments.

Industrial Particulate Pollution and Historical Land Use Contribute Metals of Concern to Dust Deposited in Neighborhoods Along the Wasatch Front, UT, USA

The Salt Lake Valley, UT, USA, is proximal to the desiccating Great Salt Lake (GSL). Prior work has found that this lakebed/playa contributes metals-laden dust to snow in the Wasatch and Uinta Mountains. Dust and industrial particulate pollution are also delivered to communities along the Wasatch Front, but their sources, compositions, and fluxes are poorly characterized. In this study, we analyzed the dust deposited in 18 passive samplers positioned near the GSL, in cities in and near the Salt Lake Valley for total dust flux, the <63 µm dust fraction, 87Sr/86Sr, and trace element geochemistry. We compared spatial patterns in metal flux and abundance with community-level socioeconomic metrics. We observed the highest dust fluxes at sites near the GSL playa. Within the urban corridor, 87Sr/86Sr and trace element relative abundances suggest that most of the dust to which people are regularly exposed may be fugitive dust from local soil materials. The trace metal content of dust deposited along the Wasatch Front exceeded Environmental Protection Agency screening levels and exhibited enrichment relative to both the upper continental crust and the dust collected adjacent to GSL. Sources of metals to dust deposited along the Wasatch Front may include industrial activities like mining, oil refining, as well as past historical pesticide and herbicide applications. Arsenic and vanadium indicated a statistically significant positive correlation with income, whereas lead, thallium, and nickel exhibited higher concentrations in the least wealthy and least white neighborhoods.

Location-specific strategies for eliminating US national racial-ethnic [Formula: see text] exposure inequality

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ([Formula: see text]), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.

Communities of color are disproportionately exposed to long-term and short-term PM2.5 in metropolitan America

We conducted a novel investigation of neighborhood-level racial/ethnic exposure disparities employing measures aligned with long-term and short-term PM_2.5 air pollution benchmarks across metropolitan contexts of the contiguous United States, 2012-2016. We used multivariable generalized estimating equations (GEE) to quantify PM_2.5 exposure disparities based on the census tract composition of people of color (POC) and POC groups (Hispanic/Latina/x/o, Black, Asian). We examined eight census tract-level measures of longer-to-shorter term exposures derived from data on modeled daily ambient PM_2.5 concentrations. We found associations between increased POC composition and greater exposure to all PM_2.5 measures, with associations strengthening across measures of longer-to-shorter term exposures. In a GEE with a negative binomial distribution, a standard deviation increase in POC composition predicted a 0.6% increase (incidence rate ratio (IRR): 1.006, 95% confidence interval (CI): 1.005-1.008) in the number of days PM_2.5 concentrations were ≥5 μg/m³ (longest-term benchmark). In a GEE with an inverse Gaussian distribution, a standard deviation increase in POC composition predicted a 0.110 μg/m³ (1.0%) increase (B: 0.110, 95% CI: 0.076-0.143) in mean PM_2.5 concentration. In GEEs with a negative binomial distribution, the effect of a standard deviation increase in POC composition on exposure strengthened to 2.6% (IRR:1.026, 95% CI:1.017-1.035), 3.4% (IRR:1.034, 95% CI:1.022-1.047), 4.2% (IRR:1.042, 95% CI:1.025-1.058), 16.2% (IRR:1.162, 95% CI:1.117-1.210), 22.7% (IRR:1.227, 95% CI:1.137-1.325) and 28.3% (IRR:1.283, 95% CI:1.144-1.439) with respect to the number of days PM_2.5 concentrations were ≥10, 12, 15, 25, 35 and 55.5 μg/m³. POC group models indicated exposure disparities based on greater Hispanic/Latina/x/o, Asian, and Black composition. Evidence for stronger POC associations with shorter-term (higher concentration) PM_2.5 exceedances suggests that reducing PM_2.5 would attenuate racial/ethnic exposure disparities.

Redlining, racism and food access in US urban cores

In the 1930s, the Home Owners' Loan Corporation (HOLC) graded the mortgage security of urban US neighborhoods. In doing so, the HOLC engaged in the practice, imbued with racism and xenophobia, of "redlining" neighborhoods deemed "hazardous" for lenders. Redlining has caused persistent social, political and economic problems for communities of color. Linkages between redlining and contemporary food access remain unexamined, even though food access is essential to well-being. To investigate this, we used a census tract-level measure of low-income and low grocery store food access from the US Department of Agriculture Food Access Research Atlas, redlining data from Mapping Inequality Project, and demographic data from the American Community Survey. We employed generalized estimating equations with robust covariance estimates to analyze data pertaining to 10,459 census tracts in 202 US cities. Tracts that the HOLC graded as "C" ("decline in desirability") and "D" ("hazardous") had reduced contemporary food access compared to those graded "A" ("best"). Increases in contemporary census tract proportions of Black, Hispanic, or other racial/ethnic minority residents, as well as disabled residents, were associated with reduced food access. Increases in contemporary proportions of residents age 75 years and older or those without a car were associated with better food access. Tracts that underwent housing redevelopment since being graded had better food access, while those undergoing gentrification had reduced food access. Results suggest that issues of redlining, housing discrimination, racism, ableism, displacement, and food inaccessibility are deeply intertwined.

Exploring the distributional environmental justice implications of an air quality monitoring network in Los Angeles County

Non-governmental air quality monitoring networks include low-cost, networked air pollution sensors hosted at homes and schools that display real-time pollutant concentration estimates on publicly accessible websites. Such networks can empower people to take health-protective actions, but their unplanned organization may produce an uneven spatial distribution of sensors. Barriers to acquiring sensors may disenfranchise particular social groups. To test this directly, we quantitatively examine if there are social inequalities in the distribution of sensors in a non-governmental air quality monitoring network (PurpleAir) in Los Angeles County, California. We paired sociodemographic data from the American Community Survey and estimates of PM_2.5 concentrations from the USEPA's Downscaler model at the census tract level (n = 2203) with a sensors per capita (SPC) variable, which is based on population proximity to PurpleAir sensors (n = 696) in Los Angeles County. Findings from multivariable generalized estimating equations (GEEs) controlling for clustering by housing age and value reveal patterns of environmental injustice in the distribution of PurpleAir sensors across Los Angeles County census tracts. Tracts with higher percentages of Hispanic/Latino/a and Black residents and lower median household income had decreased SPC. There was a curvilinear (concave) relationship between the percentage of renter-occupants and SPC. Sensors were concentrated in tracts with greater percentages of adults and seniors (vs. children), higher occupied housing density, and higher PM_2.5 pollution. Results reveal social inequalities in the self-organizing PurpleAir network, suggesting another layer of environmental injustice such that residents of low-income and minority neighborhoods have reduced access to information about local air pollution.

Children's exposure to vehicular pollution: Environmental injustice in Texas, USA

Distributive environmental justice research on children's exposure to vehicular pollution is underdeveloped and few empirical studies have been conducted in the US. This study seeks to address this gap by examining if socially disadvantaged children are disproportionately located in public school districts burdened by higher vehicular pollution in Texas-the second largest US state based on population size. Vehicular pollution exposure is measured using two variables: (1) an index developed by the US Environmental Protection Agency that combines traffic proximity and volume; and (2) outdoor concentrations of nitrogen dioxide (NO₂), a widely used proxy for traffic-related air pollution. These variables are linked to school district level data on socio-demographic characteristics of children obtained from the latest American Community Survey. Statistical analysis is based on multivariable generalized estimating equations that account for spatial clustering of school districts. Results reveal significantly greater traffic proximity and NO₂ exposure in Texas school districts with higher percentages of children, after controlling for clustering, population density, and other socio-demographic factors. Districts exposed to higher levels of traffic proximity and NO₂ exposure also contain significantly greater proportions of racial/ethnic minority, foreign-born, disabled, and socioeconomically vulnerable children. These findings highlight the urgent need to develop mitigation strategies for reducing vehicular pollution exposure, especially in districts with higher proportions of socially disadvantaged students that could be additionally burdened with limited resources. School districts represent a policy relevant analytic unit since school district boards can act as advocates for the environmental health of children and implement mitigation strategies for reducing pollution exposure.

Environmental inequity in hilly neighborhood using multi-source data from a health promotion view

The study focused on the justice of residents' opportunity to engage in healthy behavior under different environments is not vast, especially in a hilly dwelling environment. Therefore, this paper investigates environmental inequalities in a hilly urban environment in the context of the booming real estate market in China, comprised of health promotion-related elements, namely, built environment, physical activity facilities, street infrastructure, green spaces, and environmental perceptions. The multi-source data are used to calculate environmental attributes and the socioeconomic status of communities. We take the central districts of Dalian city as the research area and measure environmental equity across different socioeconomic residential areas using the Kruskal-Wallis one-way analysis of variance. The results reveal the spatial disparities in physical activity facilities, street greening, and positive perceptions between different communities. However, green injustice is mitigated in the hilly neighborhoods when we consider only ground-level greenness. This paper studies environmental justice by taking a health-enhancing view, and the results of this study can provide guidance on hilly urban development for government leaders and planners.

Technical note: Understanding the effect of COVID-19 on particle pollution using a low-cost sensor network

The 2020 coronavirus pandemic and the following quarantine measures have led to significant changes in daily life worldwide. Preliminary research indicates that air quality has improved in many urban areas as a result of these measures. This study takes a neighborhood-scale approach to quantifying this change in pollution. Using data from a network of citizen-hosted, low-cost particulate matter (PM) sensors, called Air Quality & yoU (AQ&U), we obtained high-spatial resolution measurements compared to the relatively sparse state monitoring stations. We compared monthly average estimated PM_2.5 concentrations from February 11 to May 11, 2019 at 71 unique locations in Salt Lake County, UT, USA with the same (71) sensors' measurements during the same timeframe in 2020. A paired t-test showed significant reductions (71.1% and 21.3%) in estimated monthly PM_2.5 concentrations from 2019 to 2020 for the periods from March 11-April 10 and April 11-May 10, respectively. The March time period corresponded to the most stringent COVID-19 related restrictions in this region. Significant decreases in PM_2.5 were also reported by state monitoring sites during March (p < 0.001 compared to the previous 5-year average). While we observed decreases in PM_2.5 concentrations across the valley in 2020, it is important to note that the PM_2.5 concentrations did not improve equally in all locations. We observed the greatest reductions at lower elevation, more urbanized areas, likely because of the already low levels of PM_2.5 at the higher elevation, more residential areas, which were generally below 2 μg/m³ in both 2019 and 2020. Although many of measurements during March and April were near or below the estimated detection limit of the low-cost PM sensors and the federal equivalent measurements, every low-cost sensor (51) showed a reduction in PM_2.5 concentration in March of 2020 compared to 2019. These results suggest that the air quality improvement seen after March 11, 2020 is due to quarantine measures reducing traffic and decreasing pollutant emissions in the region.

Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes

Short-term exposure to fine particulate matter (PM_2.5) pollution is linked to numerous adverse health effects. Pollution episodes, such as wildfires, can lead to substantial increases in PM_2.5 levels. However, sparse regulatory measurements provide an incomplete understanding of pollution gradients. Here, we demonstrate an infrastructure that integrates community-based measurements from a network of low-cost PM_2.5 sensors with rigorous calibration and a Gaussian process model to understand neighborhood-scale PM_2.5 concentrations during three pollution episodes (July 4, 2018, fireworks; July 5 and 6, 2018, wildfire; Jan 3-7, 2019, persistent cold air pool, PCAP). The firework/wildfire events included 118 sensors in 84 locations, while the PCAP event included 218 sensors in 138 locations. The model results accurately predict reference measurements during the fireworks (n: 16, hourly root-mean-square error, RMSE, 12.3-21.5 μg/m³, n(normalized)RMSE: 14.9-24%), the wildfire (n: 46, RMSE: 2.6-4.0 μg/m³; nRMSE: 13.1-22.9%), and the PCAP (n: 96, RMSE: 4.9-5.7 μg/m³; nRMSE: 20.2-21.3%). They also revealed dramatic geospatial differences in PM_2.5 concentrations that are not apparent when only considering government measurements or viewing the US Environmental Protection Agency's AirNow visualizations. Complementing the PM_2.5 estimates and visualizations are highly resolved uncertainty maps. Together, these results illustrate the potential for low-cost sensor networks that combined with a data-fusion algorithm and appropriate calibration and training can dynamically and with improved accuracy estimate PM_2.5 concentrations during pollution episodes. These highly resolved uncertainty estimates can provide a much-needed strategy to communicate uncertainty to end users.

Human Health and Economic Costs of Air Pollution in Utah: An Expert Assessment

Air pollution causes more damage to health and economy than previously understood, contributing to approximately one in six deaths globally. However, pollution reduction policies remain controversial even when proven effective and cost negative, partially because of misunderstanding and growing mistrust in science. We used an expert assessment to bridge these research–policy divides in the State of Utah, USA, combining quantitative estimates from 23 local researchers and specialists on the human health and economic costs of air pollution. Experts estimated that air pollution in Utah causes 2480 to 8000 premature deaths annually (90% confidence interval) and decreases the median life expectancy by 1.1 to 3.6 years. Economic costs of air pollution in Utah totaled $0.75 to $3.3 billion annually, up to 1.7% of the state’s gross domestic product. Though these results were generally in line with available estimates from downscaled national studies, they were met with surprise in the state legislature, where there had been an almost complete absence of quantitative health and economic cost estimates. We discuss the legislative and personal responses of Utah policy makers to these results and present a framework for increasing the assimilation of data into decision making via regional expert assessment. In conclusion, combining quantitative assessments from local experts is a responsive and cost-effective tool to increase trust and information uptake during time-sensitive policy windows.

Effects of PM2.5 on Third Grade Students' Proficiency in Math and English Language Arts

Fine particulate air pollution is harmful to children in myriad ways. While evidence is mounting that chronic exposures are associated with reduced academic proficiency, no research has examined the frequency of peak exposures. It is also unknown if pollution exposures influence academic proficiency to the same degree in all schools or if the level of children's social disadvantage in schools modifies the effects, such that some schools' academic proficiency levels are more sensitive to exposures. We address these gaps by examining the percentage of third grade students who tested below the grade level in math and English language arts (ELA) in Salt Lake County, Utah primary schools (n = 156), where fine particulate pollution is a serious health threat. More frequent peak exposures were associated with reduced math and ELA proficiency, as was greater school disadvantage. High frequency peak exposures were more strongly linked to lower math proficiency in more advantaged schools. Findings highlight the need for policies to reduce the number of days with peak air pollution.

Evergreen Needle Magnetization as a Proxy for Particulate Matter Pollution in Urban Environments

We test the use of magnetic measurements of evergreen needles as a proxy for particulate matter pollution in Salt Lake City, Utah. Measurements of saturation isothermal remanent magnetization indicate needle magnetization increases with increased air pollution. Needle magnetization shows a high degree of spatial variability with the largest increases in magnetization near roadways. Results from our magnetic measurements are corroborated by scanning electron microscopy of needle surfaces and by inductively coupled plasma mass spectrometry of metal concentrations in residues collected from sampled needles. Low-temperature magnetic analysis suggests the presence of small (<20 nm) partially oxidized magnetite particles on needles collected adjacent to a major roadway. Magnetization may be a low-cost proxy for certain metal concentrations (including lead) during periods of increased particulate pollution. The spatial resolution of our method appears capable of resolving changes in ambient particulate matter pollution on the scale of tens to hundreds of meters. Questions remain regarding the timescales over which evergreen needles retain particulate matter accumulated during atmospheric inversion events in Salt Lake City. Results presented here corroborate previous studies that found needle magnetization is a fast, cost-effective measure of particulate matter pollution. This method has the potential to provide high spatial resolution maps of biomagnetically monitored particulate matter in polluted urban environments year-round.