Where Is Air Quality Improving, and Who Benefits? A Study of PM2.5 and Ozone Over 15 Years

Uncertainty Reduction and Environmental Justice in Air Pollution Epidemiology: The Importance of Minority Representation

Ambient air pollution is an increasing threat to society, with rising numbers of adverse outcomes and exposure inequalities worldwide. Reducing uncertainty in health outcomes models and exposure disparity studies is therefore essential to develop policies effective in protecting the most affected places and populations. This study uses the concept of information entropy to study tradeoffs in mortality uncertainty reduction from increasing input data of air pollution versus health outcomes. We study a case scenario for short-term mortality from particulate matter (PM2.5) in North Carolina for 2001-2016, employing a case-crossover design with inputs from an individual-level mortality data set and high-resolution gridded data sets of PM2.5 and weather covariates. We find a significant association between mortality and PM2.5, and the information tradeoffs indicate that a 10% increase in mortality information reduces model uncertainty three times more than increased resolution of the air pollution model from 12 to 1 km. We also find that Non-Hispanic Black (NHB) residents tend to live in relatively more polluted census tracts, and that the mean PM2.5 for NHB cases in the mortality model is significantly higher than that of Non-Hispanic White cases. The distinct distribution of PM2.5 for NHB cases results in a relatively higher information value, and therefore faster uncertainty reduction, for new NHB cases introduced into the mortality model. This newfound influence of exposure disparities in the rate of uncertainty reduction highlights the importance of minority representation in environmental research as a quantitative advantage to produce more confident estimates of the true effects of environmental pollution.

Air quality policy should quantify effects on disparities

New tools can guide US policies to better target and reduce racial and socioeconomic disparities in air pollution exposure.

Impact of spatial structure of urban agglomerations on PM2.5 pollution：Based on resource misallocation

The spatial structure of urban agglomerations affects the economic development and environmental conditions of urban agglomerations. Severe air pollution makes green development an empty talk, and how the spatial structure of urban agglomerations affects air pollution is an urgent problem to be solved in pursuit of high-quality economic development. The panel data of 20 urban agglomerations in China from 2002 to 2017 are used to test how the spatial structure of urban agglomerations affects regional PM2.5 concentration in resource misallocation mediation effect. The empirical results show that (1) most urban agglomerations are polycentric, and only Guanzhong, Poyang Lake Ring, Wuhan and Nanqin Beifang urban agglomerations are monocentric. (2) The spatial structure and PM2.5 concentration have U-shaped characteristics, with the inflection point of centrality equal to 1. To reduce PM2.5 concentration in polycentric urban agglomerations can be achieved by increasing the centrality, while the opposite is true for monocentric urban agglomerations. (3) Improving the resource allocation of urban agglomerations can reduce PM2.5 concentration. The spatial structure of urban agglomerations with a limited degree of centrality can improve the resource allocation of urban agglomerations and further reduce PM2.5 pollution. This also indicates to a certain extent that a reasonable spatial structure of urban agglomerations is conducive to optimizing resource allocation and improving air pollution.

Using multi-sourced big data to correlate sleep deprivation and road traffic noise: A US county-level ecological study

BACKGROUND

Road traffic noise is a serious public health problem globally as it has adverse psychological and physiologic effects (i.e., sleep). Since previous studies mainly focused on individual levels, we aim to examine associations between road traffic noise and sleep deprivation on a large scale; namely, the US at county level.

METHODS

Information from a large-scale sleep survey and national traffic noise map, both obtained from government's open data, were utilized and processed with Geographic Information System (GIS) techniques. To examine the associations between traffic noise and sleep deprivation, we used a hierarchical Bayesian spatial modelling framework to simultaneously adjust for multiple socioeconomic factors while accounting for spatial correlation.

FINDINGS

With 62.90% of people not getting enough sleep, a 10 dBA increase in average sound-pressure level (SPL) or Ls10 (SPL of the relatively noisy area) in a county, was associated with a 49% (OR: 1.49; 95% CrIs:1.19-1.86) or 8% (1.08; 1.00-1.16) increase in the odds of a person in a particular county not getting enough sleep. No significant association was observed for Ls90 (SPL of the relatively quiet area). A 10% increase in noise exposure area or population ratio was associated with a 3% (1.03; 1.01-1.06) or 4% (1.04; 1.02-1.06) increase in the odds of a person within a county not getting enough sleep.

INTERPRETATION

Traffic noise can contribute to variations in sleep deprivation among counties. This study suggests that policymakers could set up different noise-management strategies for relatively quiet and noisy areas and incorporate geospatial noise indicators, such as exposure population or area ratio. Furthermore, urban planners should consider urban sprawl patterns differently in terms of noise-induced sleep problems.

Inequities in ambient fine particulate matter: A spatiotemporal analysis in Canadian communities

BACKGROUND

Exposure to fine particulate matter (PM_2.5) is associated with adverse health outcomes but communities are not randomly exposed to PM_2.5. Previous cross-sectional environmental injustice analyses in Canada found disproportionately higher exposure to PM_2.5 in low-income populations, visible minorities and immigrants. Beyond static surveillance, it is also important to evaluate how changes in PM_2.5 exposure over time may differentially impact disadvantaged communities. We examine whether communities with different sociodemographic characteristics benefited equitably from the overall decreases in ambient concentrations of PM_2.5 from 2001 to 2016 in Canada.

METHODS

We derived census tract level estimates of average annual PM_2.5 using validated satellite-based estimations of annual average PM_2.5 concentration surfaces. We investigated how the spatial distribution of PM_2.5 has evolved over 15 years (2001-2016) by comparing absolute values and rank percentiles of census tract level annual average PM_2.5 concentrations in 2001 and 2016. Using decennial census data and multivariable linear regression, we determined if sociodemographic characteristics are associated with changes in exposure to PM_2.5, accounting for geographic boundary changes between census periods.

RESULTS

Overall, ambient PM_2.5 concentrations decreased from 2001 (median of 9.1 μg/m³) to 2016 (median of 6.4 μg/m³), with varying provincial patterns. Across communities, ranked census tract specific PM_2.5 in 2001 and in 2016 are highly correlated (Spearman's rho = 0.75). We found that, on average and accounting for provincial differences and baseline PM_2.5, communities with greater density of aboriginal population, lower education, higher shelter-cost-to-income ratio, unemployment or lower income experienced smaller absolute decreases in PM_2.5 from 2001 to 2016.

CONCLUSIONS

Identifying sociodemographic groups that benefit least from decreasing exposure to PM_2.5 highlights the need to consider environmental injustice when designing or revising air pollution policies.

Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.