School Bus Rebate Program and Student Educational Performance Test Scores.

Importance: Students who ride older school buses are often exposed to high levels of exhaust during their commutes, which may adversely affect health and school attendance. As a result, the US Environmental Protection Agency (EPA) has awarded millions of dollars to school districts to replace older, highly polluting school buses with newer, cleaner buses. Objective: To leverage the EPA's randomized allocation of funding under the 2012-2016 School Bus Rebate Programs to estimate the association between replacing old, highly polluting buses and changes in district-average standardized test scores. Design, Setting, and Participants: This study examined changes in reading and language arts (RLA) and math test scores among US school district applicants to the EPA's 2012-2016 national School Bus Rebate Programs 1 year before and after each lottery by selection status. Data analysis was conducted from January 15 to July 30, 2023. Exposure: Selection to receive EPA funding to replace older school buses with newer, cleaner alternatives. Main Outcomes and Measures: School district changes in RLA and math test scores among students in grades 3 through 8 before and after the EPA funding lotteries by selection status were measured using an intention-to-treat approach. Results: This study included 1941 school district applicants to the 2012-2106 EPA School Bus Rebate Programs. These districts had a mean (SD) of 14.6 (33.7) schools per district, 8755 (23 776) students per district, and 41.3% (20.2%) of students with free lunch eligibility. Among the applicants, 209 districts (11%) were selected for the clean bus funding. District-average student test scores did not improve among selected districts overall. In secondary analyses, however, districts replacing the oldest, highest polluting buses (ie, pre-1990) experienced significantly greater improvements in district-average test scores in the year after the lottery for RLA and math (SD improvement in test scores, 0.062 [95% CI, 0.050-0.074] and 0.025 [95% CI, 0.011-0.039], respectively) compared with districts without replacements. Conclusions and Relevance: In this study, the EPA funding was not associated with student test scores overall, but in secondary analyses, the replacement of the oldest school buses was associated with improved educational performance. These findings support prioritizing clean bus replacement of the oldest buses as an actionable way for improving students' educational performance.

Coarse Particulate Matter and Markers of Inflammation and Coagulation in the Multi-Ethnic Study of Atherosclerosis (MESA) Population: A Repeat Measures Analysis.

BACKGROUND: In contrast to fine particles, less is known of the inflammatory and coagulation impacts of coarse particulate matter (PM10-2.5, particulate matter with aerodynamic diameter ≤10µm and>2.5µm). Toxicological research suggests that these pathways might be important processes by which PM10-2.5 impacts health, but there are relatively few epidemiological studies due to a lack of a national PM10-2.5 monitoring network. OBJECTIVES: We used new spatiotemporal exposure models to examine associations of both 1-y and 1-month average PM10-2.5 concentrations with markers of inflammation and coagulation. METHODS: We leveraged data from 7,071 Multi-Ethnic Study of Atherosclerosis and ancillary study participants 45-84 y of age who had repeated plasma measures of inflammatory and coagulation biomarkers. We estimated PM10-2.5 at participant addresses 1 y and 1 month before each of up to four exams (2000-2012) using spatiotemporal models that incorporated satellite, regulatory monitoring, and local geographic data and accounted for spatial correlation. We used random effects models to estimate associations with interleukin-6 (IL-6), C-reactive protein (CRP), fibrinogen, and D-dimer, controlling for potential confounders. RESULTS: Increases in PM10-2.5 were not associated with greater levels of inflammation or coagulation. A 10-µg/m3 increase in annual average PM10-2.5 was associated with a 2.5% decrease in CRP [95% confidence interval (CI): -5.5, 0.6]. We saw no association between annual average PM10-2.5 and the other markers (IL-6: -0.7%, 95% CI: -2.6, 1.2; fibrinogen: -0.3%, 95% CI: -0.9, 0.3; D-dimer: -0.2%, 95% CI: -2.6, 2.4). Associations consistently showed that a 10-µg/m3 increase in 1-month average PM10-2.5 was associated with reduced inflammation and coagulation, though none were distinguishable from no association (IL-6: -1.2%, 95% CI: -3.0 , 0.5; CRP: -2.5%, 95% CI: -5.3, 0.4; fibrinogen: -0.4%, 95% CI: -1.0, 0.1; D-dimer: -2.0%, 95% CI: -4.3, 0.3). DISCUSSION: We found no evidence that PM10-2.5 is associated with higher inflammation or coagulation levels. More research is needed to determine whether the inflammation and coagulation pathways are as important in explaining observed PM10-2.5 health impacts in humans as they have been shown to be in toxicology studies or whether PM10-2.5 might impact human health through alternative biological mechanisms. https://doi.org/10.1289/EHP12972.

Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling.

Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM2.5 and PM10 regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.

Exposure to Primary Air Pollutants Generated by Highway Traffic and Daily Mortality Risk in Near-Road Communities: A Case-Crossover Study.

Most epidemiologic studies fail to capture the impact of spatiotemporal fluctuations in traffic on exposure to traffic-related air pollutants in the near-road population. Using a case-crossover design and the Research LINE source (R-LINE) dispersion model with spatiotemporally resolved highway traffic data, we quantified associations between primary pollutants generated by highway traffic-particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5), oxides of nitrogen (NOx), and black carbon (BC)-and daily nonaccidental, respiratory, cardiovascular, and cerebrovascular mortality among persons who had resided within 1 km (0.6 mile) of major highways in the Puget Sound area of Washington State between 2009 and 2013. We estimated these associations using conditional logistic regression, adjusting for time-varying covariates. Although highly resolved modeled concentrations of PM2.5, NOx, and BC from highway traffic in the hours before death were used, we found no evidence of an association between mortality and the preceding 24-hour average PM2.5 exposure (odds ratio = 0.99, 95% confidence interval: 0.96, 1.02) or exposure during shorter averaging periods. This work did not support the hypothesis that mortality risk was meaningfully higher with greater exposures to PM2.5, NOx, and BC from highways in near-road populations, though we did incorporate a novel approach to estimate exposure to traffic-generated air pollution based on detailed traffic congestion data.

Exposure to Air Pollution in Relation to Risk of Dementia and Related Outcomes: An Updated Systematic Review of the Epidemiological Literature.

BACKGROUND: Dementia is a devastating neurologic condition that is common in older adults. We previously reviewed the epidemiological evidence examining the hypothesis that long-term exposure to air pollution affects dementia risk. Since then, the evidence base has expanded rapidly. OBJECTIVES: With this update, we collectively review new and previously identified epidemiological studies on air pollution and late-life cognitive health, highlighting new developments and critically discussing the merits of the evidence. METHODS: Using a registered protocol (PROSPERO 2020 CRD42020152943), we updated our literature review to capture studies published through 31 December 2020, extracted data, and conducted a bias assessment. RESULTS: We identified 66 papers (49 new) for inclusion in this review. Cognitive level remained the most commonly considered outcome, and particulate matter (PM) remained the most commonly considered air pollutant. Since our prior review, exposure estimation methods in this research have improved, and more papers have looked at cognitive change, neuroimaging, and incident cognitive impairment/dementia, though methodological concerns remain common. Many studies continue to rely on administrative records to ascertain dementia, have high potential for selection bias, and adjust for putative mediating factors in primary models. A subset of 35 studies met strict quality criteria. Although high-quality studies of fine particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) and cognitive decline generally supported an adverse association, other findings related to PM2.5 and findings related to particulate matter with aerodynamic diameter ≤10µm (PM10, NO2, and NOx) were inconclusive, and too few papers reported findings with ozone to comment on the likely direction of association. Notably, only a few findings on dementia were included for consideration on the basis of quality criteria. DISCUSSION: Strong conclusions remain elusive, although the weight of the evidence suggests an adverse association between PM2.5 and cognitive decline. However, we note a continued need to confront methodological challenges in this line of research. https://doi.org/10.1289/EHP8716.

Traffic Congestion as a Risk Factor for Mortality in Near-Road Communities: A Case-Crossover Study.

Existing epidemiologic research on traffic largely neglects localized fluctuations. We leveraged finely resolved congestion data to investigate short-term associations with mortality in communities near roadways. We identified all nonaccidental, cardiovascular, cerebrovascular, and respiratory deaths (2009-2013) within 1 km of a highway in the Puget Sound region of Washington State. Using a case-crossover design, we examined the association of congestion 0-150 m, 151-300 m, and 301-1,000 m upwind of a decedent's home with mortality, adjusting for meteorology, holidays, and influenza activity. Among 9,449 deaths, we observed higher odds of cerebrovascular and respiratory mortality with greater upwind congestion, especially congestion near the decedent's home. For each 10-minute-km increase in upwind congestion within 150 m, the odds of cerebrovascular mortality were 1.08 (95% confidence interval (CI): 0.88, 1.33); within 151-300 m, the odds of cerebrovascular mortality were 1.05 (95% CI: 0.98, 1.12) times higher. We observed similar patterns for respiratory mortality, with 1.06 (95% CI: 0.76, 1.50) times higher odds of death with greater upwind congestion within 150 m and 1.02 (95% CI: 0.95, 1.10) times higher odds within 151-300 m. No increased odds of mortality were observed at greater distances, for overall mortality, or with downwind congestion. Unexpectedly, lower odds of cardiovascular mortality were suggested with greater congestion. This work demonstrates the use of nontraditional data to characterize the impacts of near-road exposures.