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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 (NO

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 (NO

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models’ accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort’s residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 “gold-standard” monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m3 to 0.92 and 1.63 μg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m3 to 0.79 and 0.88 μg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model’s prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Pesticide spray drift represents an important exposure pathway that may cause illness among orchard workers. To strike a balance between improving spray coverage and reducing drift, new sprayer technologies are being marketed for use in modern tree canopies to replace conventional axial fan airblast (AFA) sprayers that have been used widely since the 1950s. We designed a series of spray trials that used mixed-effects modeling to compare tracer-based drift volume levels for old and new sprayer technologies in an orchard work environment. Building on a smaller study of 6 trials (168 tree rows) that collected polyester line drift samples (n = 270 measurements) suspended on 15 vertical masts downwind of an AFA sprayer application, this study included 9 additional comparison trials (252 tree rows; n = 405 measurements) for 2 airblast tower sprayers: the directed air tower (DAT) and the multi-headed fan tower (MFT). Field-based measurements at mid (26 m) and far (52 m) distances showed that the DAT and MFT sprayers had 4–15 and 35–37% less drift than the AFA. After controlling for downwind distance, sampling height, and wind speed, model results indicated that the MFT [−35%; 95% confidence interval (CI): −22 and −49%; P < 0.001] significantly reduced drift levels compared to the AFA, but the DAT did not (−7%; 95% CI: −19 and 6%; P = 0.29). Tower sprayers appear to be a promising means by which to decrease drift levels through shorter nozzle-to-tree canopy distances and more horizontally directed aerosols that escape the tree canopy to a lesser extent. Substitution of these new technologies for AFA sprayers is likely to reduce the frequency and magnitude of pesticide drift exposures and associated illnesses. These findings, especially for the MFT, may fit United States Environmental Protection Agency’s Drift Reduction Technology (DRT) one-star rating of 25–50% reduction. An ‘AFA buyback’ incentive program could be developed to stimulate wider adoption of new drift-reducing spray technologies. However, improved sprayer technologies alone do not eliminate drift. Applicator training, including proper sprayer calibration and maintenance, and application exclusion zones (AEZs) can also contribute to minimizing the risks of drift exposure. With regard to testing DRTs and establishing AEZs, our study findings demonstrate the need to define the impact of airblast sprayer type, orchard architecture, sampling height, and wind speed.

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 stop sites representative of the cohort. We collected about 29 two-minute visit measures at each site during all seasons, days of the week, and most times of day during a one-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and regulatory monitoring 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 (1,177 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 0.50 (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.SynopsisWe develop well-performing, long-term average pollutant exposure prediction models for epidemiologic application from an innovative and extensive short-term mobile monitoring campaign.Abstract Figure

BACKGROUND AND AIM: A growing body of evidence has begun linking traffic-related air pollution (TRAP) to brain health, including dementia. Ultrafine particles (UFP, 100 nm diameter) and black carbo...

BACKGROUND AND AIM: Mobile monitoring has recently made it possible to measure the long-term trends of less commonly measured pollutants. While many different monitoring approaches have been taken,...

Background: Air pollution may be associated with elevated dementia risk. Prior research has limitations that may affect reliability, and no studies have evaluated this question in a population-based cohort of men and women in the United States. Objectives: We evaluated the association between time-varying, 10-y average fine particulate matter (PM2.5) exposure and hazard of all-cause dementia. An additional goal was to understand how to adequately control for age and calendar-time-related confounding through choice of the time axis and covariate adjustment. Methods: Using the Adult Changes in Thought (ACT) population-based prospective cohort study in Seattle, we linked spatiotemporal model-based PM2.5 exposures to participant addresses from 1978 to 2018. Dementia diagnoses were made using high-quality, standardized, consensus-based protocols at biennial follow-ups. We conducted multivariable Cox proportional hazards regression to evaluate the association between time-varying, 10-y average PM2.5 exposure and time to event in a model with age as the time axis, stratified by apolipoprotein E (APOE) genotype, and adjusted for sex, education, race, neighborhood median household income, and calendar time. Alternative models used calendar time as the time axis. Results: We report 1,136 cases of incident dementia among 4,166 individuals with nonmissing APOE status. Mean [mean ± standard deviation (SD)] 10-y average PM2.5 was 10.1 (±2.9) μg/m3. Each 1-μg/m3 increase in the moving average of 10-y PM2.5 was associated with a 16% greater hazard of all-cause dementia [1.16 (95% confidence interval: 1.03, 1.31)]. Results using calendar time as the time axis were similar. Discussion: In this prospective cohort study with extensive exposure data and consensus-based outcome ascertainment, elevated long-term exposure to PM2.5 was associated with increased hazard of all-cause dementia. We found that optimal control of age and time confounding could be achieved through use of either age or calendar time as the time axis in our study. Our results strengthen evidence on the neurodegenerative effects of PM2.5. https://doi.org/10.1289/EHP9018

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM(2.5) in epidemiological studies. Using sparse regulatory PM(2.5) measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models’ accuracy in predicting PM(2.5) spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort’s residential locations). In this study, we used the PM(2.5) measurements from a publicly available commercial low-cost PM(2.5) network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 “gold-standard” monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R(2) and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m(3) to 0.92 and 1.63 μg/m(3), respectively. The external spatial validation R(2) and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m(3) to 0.79 and 0.88 μg/m(3), respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model’s prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Mobile monitoring makes it possible to estimate the long-term trends of less commonly measured pollutants through the collection of repeated short-term samples. While many different mobile monitoring approaches have been taken, few studies have looked at the importance of study design when the goal is application to epidemiologic cohort studies. Air pollution concentrations include random variability and systematic variability, and we hypothesize that mobile campaigns benefit from temporally balanced designs that randomly sample from all seasons of the year, days of the week, and hours of the day. We carried out a simulation study of fixed-site monitors to better understand the role of short-term mobile monitoring design on the prediction of long-term air pollution exposure surfaces. Specifically, we simulated three archetypal sampling designs using oxides of nitrogen (NOx) monitoring data from 69 California air quality system (AQS) sites: (1) a year-around, Balanced Design, (2) a Rush Hours Design, and (3) a Business Hours Design. We used Monte Carlo resampling to investigate the range of possible outcomes (i.e., the resulting annual average concentration prediction) from each design against the 9truth9, the actual monitoring data. We found that the Balanced Design consistently yielded the most accurate annual averages; Rush Hours and Business Hours Designs generally resulted in comparatively more biased estimates and model predictions. Importantly, the superior performance of the Balanced Design was evident when predictions were evaluated against true concentrations but less detectable when predictions were evaluated against the measurements from the same sampling campaign since these were themselves biased. This result is important since mobile monitoring campaigns that use their own measurements to test the robustness of the results may underestimate the level of bias in their results. Appropriate study design is crucial for mobile monitoring campaigns aiming to assess accurate long-term exposure in epidemiologic cohorts. Campaigns should aim to implement balanced designs that sample during all seasons of the year, days of the week, and all or most hours of the day to produce generally unbiased, long-term averages. Furthermore, differential exposure misclassification could result from unbalanced designs, which may result in misleading health effect estimates in epidemiologic investigations.

Background: Air pollution may be associated with elevated dementia risk, but prior research has limitations that may affect reliability, and no prior studies have evaluated this question in a community-based cohort of men and women in the United States. Methods: We evaluated the association of long-term fine particulate matter (PM2.5) exposure with all-cause dementia during the period 1978-2018 in the Adult Changes in Thought (ACT) study based in Seattle, Washington, USA. PM2.5 exposures linked to participant addresses were obtained from a spatiotemporal model spanning 40 years. Research quality dementia diagnoses were made using standardized diagnosis protocols at biennial follow-ups. We conducted multivariate Cox proportional hazards regression to evaluate the association between time-varying, 10-year average PM2.5 exposure and time to event in a model based on age, stratified by apolipoprotein E (APOE) genotype, and adjusted for sex, education, race, neighborhood median household income, and calendar time. Findings: Mean (standard deviation) 10-year average PM2.5 was 10.0 (2.9) µg/m3, which is below the current national air quality standard. Each 1 µg/m3 increase in the moving average of 10-year PM2.5 was associated with a 16% greater hazard of all-cause dementia (1.16 (1.03, 1.31). Interpretation: In this community-based prospective cohort study with uniquely extensive exposure data and research quality outcome ascertainment, elevated long-term exposure to PM2.5 was associated with an increased hazard of all-cause dementia. These results add to growing evidence on the neurodegenerative effects of air pollution and suggest that reducing exposures across the population could contribute to reducing the burden of dementia. Funding: NIEHS F31ES030972-02, NIEHS T32ES015459, NIA T32AG052354, NIEHS and NIA R01ES026187, U01 AG006781, NIEHS ES026187, NIA AG05136, R01 AG056711 and U01 NS091272, the University of Washington Retirement Association Aging Fellowship, and the Seattle Chapter of the Achievement Rewards for College Scientists (ARCS) Foundation. Declaration of Interests: The authors have no conflicts of interest to disclose. Ethics Approval Statement: ACT participants signed forms indicating their informed consent to enroll in the study. Study procedures were approved by the University of Washington and Kaiser Permanente Institutional Review Boards.

In Washington State, a majority of reported pesticide-related illnesses and application-related complaints involve drift. We employed real-time particle monitors (Dylos) during a series of experimental spray events investigating drift. Sections of an orchard block were randomly sprayed by an axial fan airblast sprayer, while monitors sampled particulate matter above and below the canopy at various downwind locations. We found elevated particle mass concentrations (PMC) at all distances (16–74 m). The 75th percentile PMC while spraying was significantly greater than the control periods by 107 (95% CI 94–121) μg/m3, after adjusting for sampler height and wind speed. The 75th percentile PMC below the canopy was significantly greater than above the canopy by 9.4 (95% CI 5.2–12) μg/m3, after adjusting for spraying and wind speed. In a restricted analysis of the spray events, the 75th percentile PMC significantly decreased by 2.6 (95% CI −3.2 to −1.7) μg/m3 for every additional meter away from the edge of the spray quadrant, after adjusting for canopy height and wind speed. Our results were consistent with a larger study that performed passive sampling during the same spray events, suggesting that real-time monitoring can be used as a screening tool for pesticide drift. Compared with traditional methods of drift sampling, real-time monitoring is overall an easily employed, affordable sampling technique, and it can provide minute-by-minute measurements that can be coupled with meteorological measurements to better understand how changes in wind speed and direction affect drift.

In Washington State, half of all pesticide-related illnesses in agriculture result from drift, the off-target movement of pesticides. Of these, a significant proportion involve workers on another farm and orchard airblast applications. We compared the spray drift exposure reduction potential of two modern tower sprayers – directed air tower (DAT) and multi-headed fan tower (MFT), in relation to a traditional axial fan airblast (AFA) sprayer. We employed real-time particle monitors (Dylos DC1100) during a randomized control trial of orchard spray applications. Sections of a field were randomly sprayed by three alternating spray technologies – AFA, DAT and MFT – while monitors sampled particulate matter above and below the canopy at various downwind locations in a neighboring field. Geometric mean particle mass concentrations (PMC) outside the intended spray area were elevated during all applications at all of our sampling distances (16–74 m, 51–244 ft). After adjusting for wind speed and sampling height, the 75th percentile (95% confidence interval) PMC level was significantly greater during spray events than background levels by 105 (93, 120) μg/m3, 49 (45, 54) μg/m3 and 26 (22, 31) μg/m3 during AFA, DAT and MFT applications, respectively. Adjusted PMC levels were significantly different between all three sprayers. In this study, tower sprayers significantly reduced spray drift exposures in a neighboring orchard field when compared to the AFA sprayer, with the MFT sprayer producing the least drift; however these tower sprayers did do not fully eliminate drift.

Pesticide spray drift represents an important cause of crop damage and farmworker illness, especially among orchard workers. We drew upon exposure characteristics from known human illness cases to design a series of six spray trials that measured drift from a conventional axial fan airblast sprayer operating in a modern orchard work environment. Polyester line drift samples (n = 270; 45 per trial) were suspended on 15 vertical masts downwind of foliar applications of zinc, molybdenum, and copper micronutrient tracers. Samples were analyzed using inductively coupled plasma mass spectrometry and resulting masses were normalized by sprayer tank mix concentration to create tracer-based drift volume levels. Mixed-effects modeling described these levels in the context of spatial variability and buffers designed to protect workers from drift exposure. Field-based measurements showed evidence of drift up to 52 m downwind, which is approximately 1.7 times greater than the 30 m (100 ft) ‘Application Exclusion Zone’ defined for airblast sprayers by the United States Environmental Protection Agency Worker Protection Standard. When stratified by near (5 m), mid (26 m), and far (52 m) distances, geometric means and standard deviations for drift levels were 257 (1.8), 52 (2.0), and 20 (2.3) µl, respectively. Fixed effect model coefficients showed that higher wind speed [0.53; 95% confidence interval (CI): 0.35, 0.70] and sampling height (0.16; 95% CI: 0.11, 0.20) were positively associated with drift; increasing downwind distance (−0.05; 95% CI: −0.06, −0.04) was negatively associated with drift. Random effects showed large within-location variability, but relatively few systematic changes for individual locations across spray trials after accounting for wind speed, height, and distance. Our study findings demonstrate that buffers may offer drift exposure protection to orchard workers from airblast spraying. Variables such as orchard architecture, sampling height, and wind speed should be included in the evaluation and mitigation of risks from drift exposure. Data from our study may prove useful for estimating potential exposure and validating orchard-based bystander exposure models.