A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes.

BACKGROUND: Reported associations between particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) and cognitive outcomes remain mixed. Differences in exposure estimation method may contribute to this heterogeneity. OBJECTIVES: To assess agreement between PM2.5 exposure concentrations across 11 exposure estimation methods and to compare resulting associations between PM2.5 and cognitive or MRI outcomes. METHODS: We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n = 4678) and MRI outcomes (n = 1518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors. RESULTS: Exposure estimates from most methods had high agreement across sites, but low agreement within sites. Within-site exposure variation was limited for some methods. Consistently null findings for the PM2.5-cognitive outcome associations regardless of method precluded empirical conclusions about the potential impact of method on study findings in contexts where positive associations are observed. Not accounting for study site led to consistent, adverse associations, regardless of exposure estimation method, suggesting the potential for substantial bias due to residual confounding by spatially patterned factors. DISCUSSION: PM2.5 estimation methods agreed across sites but not within sites. Choice of estimation method may impact findings when participants are concentrated in small geographic areas. Understanding unmeasured confounding by factors that are spatially patterned may be particularly important in studies of air pollution and cognitive or brain health.

Olfactory tract/bulb metal concentration in Manganese-exposed mineworkers.

BACKGROUND: Manganese (Mn) is an essential micronutrient as well as a well-established neurotoxicant. Occupational and environmental exposures may bypass homeostatic regulation and lead to increased systemic Mn levels. Translocation of ultrafine ambient airborne particles via nasal neuronal pathway to olfactory bulb and tract may be an important pathway by which Mn enters the central nervous system. OBJECTIVE: To measure olfactory tract/bulb tissue metal concentrations in Mn-exposed and non-exposed mineworkers. METHODS: Using inductively coupled plasma-mass spectrometry (ICP-MS), we measured and compared tissue metal concentrations in unilateral olfactory tracts/bulbs of 24 Mn-exposed and 17 non-exposed South African mineworkers. We used linear regression to investigate the association between cumulative Mn exposures and olfactory tract/bulb Mn concentration. RESULTS: The difference in mean olfactory tract/bulb Mn concentrations between Mn-exposed and non-Mn exposed mineworkers was 0.16 µg/g (95% CI -0.11, 0.42); but decreased to 0.09 µg/g (95% CI 0.004, 0.18) after exclusion of one influential observation. Olfactory tract/bulb metal concentration and cumulative Mn exposure suggested there may be a positive association; for each mg Mn/m3-year there was a 0.05 µg/g (95% CI 0.01, 0.08) greater olfactory tract/bulb Mn concentration overall, but -0.003 (95% CI -0.02, 0.02) when excluding the three influential observations. Recency of Mn exposure was not associated with olfactory tract/bulb Mn concentration. CONCLUSIONS: Our findings suggest that Mn-exposed mineworkers might have higher olfactory tract/bulb tissue Mn concentrations than non-Mn exposed mineworkers, and that concentrations might depend more on cumulative dose than recency of exposure.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

BACKGROUND: Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. OBJECTIVE: Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. METHODS: We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. RESULTS: The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- R 2 = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and R 2 = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- R 2 = 0.51 (with LCS). IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

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.

Airway Tree Caliber and Susceptibility to Pollution-associated Emphysema: MESA Air and Lung Studies.

Rationale: Airway tree morphology varies in the general population and may modify the distribution and uptake of inhaled pollutants. Objectives: We hypothesized that smaller airway caliber would be associated with emphysema progression and would increase susceptibility to air pollutant-associated emphysema progression. Methods: MESA (Multi-Ethnic Study of Atherosclerosis) is a general population cohort of adults 45-84 years old from six U.S. communities. Airway tree caliber was quantified as the mean of airway lumen diameters measured from baseline cardiac computed tomography (CT) (2000-2002). Percentage emphysema, defined as percentage of lung pixels below -950 Hounsfield units, was assessed up to five times per participant via cardiac CT scan (2000-2007) and equivalent regions on lung CT scan (2010-2018). Long-term outdoor air pollutant concentrations (particulate matter with an aerodynamic diameter ⩽2.5 µm, oxides of nitrogen, and ozone) were estimated at the residential address with validated spatiotemporal models. Linear mixed models estimated the association between airway tree caliber and emphysema progression; modification of pollutant-associated emphysema progression was assessed using multiplicative interaction terms. Measurements and Main Results: Among 6,793 participants (mean ± SD age, 62 ± 10 yr), baseline airway tree caliber was 3.95 ± 1.1 mm and median (interquartile range) of percentage emphysema was 2.88 (1.21-5.68). In adjusted analyses, 10-year emphysema progression rate was 0.75 percentage points (95% confidence interval, 0.54-0.96%) higher in the smallest compared with largest airway tree caliber quartile. Airway tree caliber also modified air pollutant-associated emphysema progression. Conclusions: Smaller airway tree caliber was associated with accelerated emphysema progression and modified air pollutant-associated emphysema progression. A better understanding of the mechanisms of airway-alveolar homeostasis and air pollutant deposition is needed.

Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS).

BACKGROUND: Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear. OBJECTIVES: Our objective is to compare particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses. METHODS: We assigned annual PM2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region. RESULTS: With a few exceptions, relative agreement of approach-specific PM2.5 exposure estimates was high for PM2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM2.5. There was no evidence of large differences in health effects associations with PM2.5 among estimation approaches in analyses adjusted for climate region. CONCLUSIONS: Different estimation approaches produced similar spatial patterns of PM2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM2.5-health effects associations were similar among estimation approaches. PM2.5 estimates and PM2.5-health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

Traffic-related air pollution and dementia incidence in the Adult Changes in Thought Study.

BACKGROUND: While epidemiologic evidence links higher levels of exposure to fine particulate matter (PM2.5) to decreased cognitive function, fewer studies have investigated links with traffic-related air pollution (TRAP), and none have examined ultrafine particles (UFP, ≤100 nm) and late-life dementia incidence. OBJECTIVE: To evaluate associations between TRAP exposures (UFP, black carbon [BC], and nitrogen dioxide [NO2]) and late-life dementia incidence. METHODS: We ascertained dementia incidence in the Seattle-based Adult Changes in Thought (ACT) prospective cohort study (beginning in 1994) and assessed ten-year average TRAP exposures for each participant based on prediction models derived from an extensive mobile monitoring campaign. We applied Cox proportional hazards models to investigate TRAP exposure and dementia incidence using age as the time axis and further adjusting for sex, self-reported race, calendar year, education, socioeconomic status, PM2.5, and APOE genotype. We ran sensitivity analyses where we did not adjust for PM2.5 and other sensitivity and secondary analyses where we adjusted for multiple pollutants, applied alternative exposure models (including total and size-specific UFP), modified the adjustment covariates, used calendar year as the time axis, assessed different exposure periods, dementia subtypes, and others. RESULTS: We identified 1,041 incident all-cause dementia cases in 4,283 participants over 37,102 person-years of follow-up. We did not find evidence of a greater hazard of late-life dementia incidence with elevated levels of long-term TRAP exposures. The estimated hazard ratio of all-cause dementia was 0.98 (95 % CI: 0.92-1.05) for every 2000 pt/cm3 increment in UFP, 0.95 (0.89-1.01) for every 100 ng/m3 increment in BC, and 0.96 (0.91-1.02) for every 2 ppb increment in NO2. These findings were consistent across sensitivity and secondary analyses. DISCUSSION: We did not find evidence of a greater hazard of late-life dementia risk with elevated long-term TRAP exposures in this population-based prospective cohort study.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach.

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 µg/m3]) compared to the model with all LCM measurements (0.84 [0.9 µg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 µg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 µg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 µg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

The Effect of Pesticide Spray Season and Residential Proximity to Agriculture on Glyphosate Exposure among Pregnant People in Southern Idaho, 2021.

BACKGROUND: Glyphosate is one of the most heavily used pesticides in the world, but little is known about sources of glyphosate exposure in pregnant people living in agricultural regions. OBJECTIVE: Our objective was to evaluate glyphosate exposure during pregnancy in relation to residential proximity to agriculture as well as agricultural spray season. METHODS: We quantified glyphosate concentrations in 453 urine samples collected biweekly from a cohort of 40 pregnant people in southern Idaho from February through December 2021. We estimated each participant's glyphosate exposure as the geometric mean (GM) of glyphosate concentrations measured in all samples (average n=11 samples/participant), as well as the GM of samples collected during the pesticide "spray season" (defined as those collected 1 May-15 August; average n=5 samples/participant) and the "nonspray season" (defined as those collected before 1 May or after 15 August; average n=6 samples/participant). We defined participants who resided <0.5km from an actively cultivated agriculture field to live "near fields" and those residing ≥0.5km from an agricultural field to live "far from fields" (n=22 and 18, respectively). RESULTS: Among participants living near fields, urinary glyphosate was detected more frequently and at significantly increased GM concentrations during the spray season in comparison with the nonspray season (81% vs. 55%; 0.228µg/L vs. 0.150µg/L, p<0.001). In contrast, among participants who lived far from fields, neither glyphosate detection frequency nor GMs differed in the spray vs nonspray season (66% vs. 64%; 0.154µg/L vs. 0.165µg/L, p=0.45). Concentrations did not differ by residential proximity to fields during the nonspray season (0.154µg/L vs. 0.165µg/L, for near vs. far, p=0.53). DISCUSSION: Pregnant people living near agriculture fields had significantly increased urinary glyphosate concentrations during the agricultural spray season than during the nonspray season. They also had significantly higher urinary glyphosate concentrations during the spray season than those who lived far from agricultural fields at any time of year, but concentrations did not differ during the nonspray season. These findings suggest that agricultural glyphosate spray is a source of exposure for people living near fields. https://doi.org/10.1289/EHP12768.

Comparison of Particulate Air Pollution From Different Emission Sources and Incident Dementia in the US.

Importance: Emerging evidence indicates that exposure to fine particulate matter (PM2.5) air pollution may increase dementia risk in older adults. Although this evidence suggests opportunities for intervention, little is known about the relative importance of PM2.5 from different emission sources. Objective: To examine associations of long-term exposure of total and source-specific PM2.5 with incident dementia in older adults. Design, Setting, and Participants: The Environmental Predictors of Cognitive Health and Aging study used biennial survey data from January 1, 1998, to December 31, 2016, for participants in the Health and Retirement Study, which is a nationally representative, population-based cohort study in the US. The present cohort study included all participants older than 50 years who were without dementia at baseline and had available exposure, outcome, and demographic data between 1998 and 2016 (N = 27 857). Analyses were performed from January 31 to May 1, 2022. Exposures: The 10-year mean total PM2.5 and PM2.5 from 9 emission sources at participant residences for each month during follow-up using spatiotemporal and chemical transport models. Main Outcomes and Measures: The main outcome was incident dementia as classified by a validated algorithm incorporating respondent-based cognitive testing and proxy respondent reports. Adjusted hazard ratios (HRs) were estimated for incident dementia per IQR of residential PM2.5 concentrations using time-varying, weighted Cox proportional hazards regression models with adjustment for the individual- and area-level risk factors. Results: Among 27 857 participants (mean [SD] age, 61 [10] years; 15 747 [56.5%] female), 4105 (15%) developed dementia during a mean (SD) follow-up of 10.2 [5.6] years. Higher concentrations of total PM2.5 were associated with greater rates of incident dementia (HR, 1.08 per IQR; 95% CI, 1.01-1.17). In single pollutant models, PM2.5 from all sources, except dust, were associated with increased rates of dementia, with the strongest associations for agriculture, traffic, coal combustion, and wildfires. After control for PM2.5 from all other sources and copollutants, only PM2.5 from agriculture (HR, 1.13; 95% CI, 1.01-1.27) and wildfires (HR, 1.05; 95% CI, 1.02-1.08) were robustly associated with greater rates of dementia. Conclusion and Relevance: In this cohort study, higher residential PM2.5 levels, especially from agriculture and wildfires, were associated with higher rates of incident dementia, providing further evidence supporting PM2.5 reduction as a population-based approach to promote healthy cognitive aging. These findings also indicate that intervening on key emission sources might have value, although more research is needed to confirm these findings.

Exposure Disparities by Income, Race and Ethnicity, and Historic Redlining Grade in the Greater Seattle Area for Ultrafine Particles and Other Air Pollutants.

BACKGROUND: Growing evidence shows ultrafine particles (UFPs) are detrimental to cardiovascular, cerebrovascular, and respiratory health. Historically, racialized and low-income communities are exposed to higher concentrations of air pollution. OBJECTIVES: Our aim was to conduct a descriptive analysis of present-day air pollution exposure disparities in the greater Seattle, Washington, area by income, race, ethnicity, and historical redlining grade. We focused on UFPs (particle number count) and compared with black carbon, nitrogen dioxide, and fine particulate matter (PM2.5) levels. METHODS: We obtained race and ethnicity data from the 2010 U.S. Census, median household income data from the 2006-2010 American Community Survey, and Home Owners' Loan Corporation (HOLC) redlining data from the University of Richmond's Mapping Inequality. We predicted pollutant concentrations at block centroids from 2019 mobile monitoring data. The study region encompassed much of urban Seattle, with redlining analyses restricted to a smaller region. To analyze disparities, we calculated population-weighted mean exposures and regression analyses using a generalized estimating equation model to account for spatial correlation. RESULTS: Pollutant concentrations and disparities were largest for blocks with median household income of <$20,000, Black residents, HOLC Grade D, and ungraded industrial areas. UFP concentrations were 4% lower than average for non-Hispanic White residents and higher than average for racialized groups (Asian, 3%; Black, 15%; Hispanic, 6%; Native American, 8%; Pacific Islander, 11%). For blocks with median household incomes of <$20,000, UFP concentrations were 40% higher than average, whereas blocks with incomes of >$110,000 had UFP concentrations 16% lower than average. UFP concentrations were 28% higher for Grade D and 49% higher for ungraded industrial areas compared with Grade A. Disparities were highest for UFPs and lowest for PM2.5 exposure levels. DISCUSSION: Our study is one of the first to highlight large disparities with UFP exposures compared with multiple pollutants. Higher exposures to multiple air pollutants and their cumulative effects disproportionately impact historically marginalized groups. https://doi.org/10.1289/EHP11662.

Urinary Glyphosate Concentrations among Pregnant Participants in a Randomized, Crossover Trial of Organic and Conventional Diets.

BACKGROUND: Consumption of an organic diet reduces exposure to a range of agricultural pesticides. Only three studies have examined the effect of an organic diet intervention on exposure to the herbicide glyphosate, the most heavily used agricultural chemical in the world. Despite its widespread use, the primary sources of glyphosate exposure in humans are poorly understood. OBJECTIVE: Our objective was to examine the effect of an organic diet intervention on urinary glyphosate concentrations among pregnant individuals. METHODS: We conducted a 2-wk randomized crossover trial in which 39 pregnant participants living near (≤0.5km) and far (>0.5km) from agricultural fields received a 1-wk supply of conventional groceries and 1 wk of organic groceries, randomized to order. We collected daily first morning void urine samples and analyzed composite samples from each week for glyphosate. We examined differences in urinary glyphosate concentrations between the conventional week and the organic week among all participants and stratified by residential proximity to an agricultural field. RESULTS: Median specific gravity-adjusted glyphosate concentrations were 0.19µg/L and 0.16µg/L during the conventional and organic weeks, respectively. We observed modest decreases in urinary glyphosate concentrations from the conventional to organic week among far-field participants, but no difference among near-field participants. In secondary analyses excluding participants who did not meet a priori criteria of compliance with the intervention, we observed significant decreases in urinary glyphosate concentrations, particularly among far-field participants (p<0.01-0.02, depending on exclusion criteria). DISCUSSION: This trial is the first to examine the effect of an organic diet intervention on glyphosate among people living near and far from agricultural fields. Our results suggest that diet is an important contributor to glyphosate exposure in people living >0.5km from agricultural fields; for people living near crops, agriculture may be a dominant exposure source during the pesticide spray season. https://doi.org/10.1289/EHP12155.

Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology.

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Wildfire smoke exposure and emergency department visits in Washington State.

Wildfires are increasing in prevalence in western North America due to changing climate conditions. A growing number of studies examine the impact of wildfire smoke on morbidity; however, few evaluate these impacts using syndromic surveillance data that cover many emergency departments (EDs). We used syndromic surveillance data to explore the effect of wildfire smoke exposure on all-cause respiratory and cardiovascular ED visits in Washington state. Using a time-stratified case crossover design, we observed an increased odds of asthma visits immediately after and in all five days following initial exposure (lag 0 OR: 1.13; 95% CI: 1.10, 1.17; lag 1-5 ORs all 1.05 or greater with a lower CI of 1.02 or higher), and an increased odds of respiratory visits in all five days following initial exposure (lag 1 OR: 1.02; 95% CI: 1.00, 1.03; lag 2-5 ORs and lower CIs were all at least as large) comparing wildfire smoke to non-wildfire smoke days. We observed mixed results for cardiovascular visits, with evidence of increased odds emerging only several days following initial exposure. We also found increased odds across all visit categories for a 10 µg m-3 increase in smoke-impacted PM2.5. In stratified analyses, we observed elevated odds for respiratory visits among ages 19-64, for asthma visits among ages 5-64, and mixed risk estimates for cardiovascular visits by age group. This study provides evidence of an increased risk of respiratory ED visits immediately following initial wildfire smoke exposure, and increased risk of cardiovascular ED visits several days following initial exposure. These increased risks are seen particularly among children and younger to middle-aged adults.

Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs.

BACKGROUND: Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. OBJECTIVES: We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. METHODS: We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. RESULTS: Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. DISCUSSION: Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment.

BACKGROUND: Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. OBJECTIVE: We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. METHODS: We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model. RESULTS: The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. SIGNIFICANCE: A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. IMPACT STATEMENT: Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models.

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

BACKGROUND: Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations. OBJECTIVES: To develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODS: We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations. RESULTS: The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign.

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 µg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Environmental manganese exposure and cognitive control in a South African population.

OBJECTIVE: To characterize the association between environmental (residential air) manganese (Mn) exposure and cognitive performance, focusing on cognitive control, in a Black African population. METHODS: We administered the Go-No-Go, Digit Span, and Matrix Reasoning tests to population-based samples age ≥40 from a high Mn (smelter) exposed community, Meyerton (N = 629), and a demographically comparable low (background levels) non-exposed community, Ethembalethu, (N = 96) in Gauteng province, South Africa. We investigated the associations between community and performance on the cognitive tests, using linear regression. We adjusted a priori for age and sex, and examined the effect of adjustment for education, nonverbal IQ, smoking, and alcohol consumption. We measured airborne PM2.5-Mn to confirm community exposure differences. RESULTS: Compared to Ethembalethu residents, Meyerton residents' test scores were lower (poorer) for all tests: 0.55 (95 % confidence interval [CI] 0.08, 1.03) lower scores for Matrix Reasoning, 0.34 (95 % CI -0.07, 0.75) lower for Digit Span, and 0.15 (95 % CI 0.09, 0.21) lower for Go-No-Go (high frequency discriminability index [probability]). The latter represented the most marked difference in terms of z-scores (0.50, 95 % CI 0.30, 0.71 standard deviations lower). The mean of the z-score of each of the three tests was also lower (0.34, 95 % CI 0.18, 0.50 standard deviations lower). These associations were similar in men and women, but attenuated with adjustment for education. Differences for Matrix Reasoning and Digit Span between the two communities were observed only among those who had lived in Meyerton ≥10 years, whereas for Go-No-Go, differences were also apparent among those who had lived in Meyerton <10 years. Mean PM2.5-Mn at a long-term fixed site in Meyerton was 203 ng/m3 and 10 ng/m3 in Ethembalethu. CONCLUSION: Residence in a community near a high Mn emission source is associated with cognitive dysfunction, including aspects of cognitive control as assessed by the Go-No-Go test.