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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

Over four thousand portable air cleaners (PACs) with high-efficiency particulate air (HEPA) filters were distributed by Public Health - Seattle & King County to homeless shelters during the COVID-19 pandemic. This study aimed to evaluate the real-world effectiveness of these HEPA PACs in reducing indoor particles and understand the factors that affect their use in homeless shelters. Four rooms across three homeless shelters with varying geographic locations and operating conditions were enrolled in this study. At each shelter, multiple PACs were deployed based on the room volume and PAC’s clean air delivery rate rating. The energy consumption of these PACs was measured using energy data loggers at 1-min intervals to allow tracking of their use and fan speed for three two-week sampling rounds, separated by single-week gaps, between February and April 2022. Total optical particle number concentration (OPNC) was measured at 2-min intervals at multiple indoor locations and an outdoor ambient location. The empirical indoor and outdoor total OPNC were compared for each site. Additionally, linear mixed-effects regression models (LMERs) were used to assess the relationship between PAC use time and indoor/outdoor total OPNC ratios (I/OOPNC). Based on the LMER models, one percent increase in the hourly, daily and total time PACs were used significantly reduced I/OOPNCby 0.34 [95% CI: 0.28, 0.40], 0.51 [95% CI: 0.20, 0.78], 2.52 [95% CI: 1.50, 3.28], respectively, indicating that keeping PACs on resulted in significantly lower I/OOPNCor relatively lower indoor total OPNC than outdoors. The survey suggested that keeping PACs on and running was the main challenge when operating them in shelters. These findings suggested that HEPA PACs were an effective short-term strategy to reduce indoor particle levels in community congregate living settings during non-wildfire seasons and the need for formulating practical guidance for using them in such an environment.

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

The Mobile ObserVations of Ultrafine Particles study was a two-year project to analyze potential air quality impacts of ultrafine particles (UFPs) from aircraft traffic for communities near an international airport. The study assessed UFP concentrations within 10 miles of the airport in the directions of aircraft flight. Over the course of four seasons, this study conducted a mobile sampling scheme to collect time-resolved measures of UFP, CO2, and black carbon (BC) concentrations, as well as UFP size distributions. Primary findings were that UFPs were associated with both roadway traffic and aircraft sources, with the highest UFP counts found on the major roadway (I-5). Total concentrations of UFPs alone (10–1000 nm) did not distinguish roadway and aircraft features. However, key differences existed in the particle size distribution and the black carbon concentration for roadway and aircraft features. These differences can help distinguish between the spatial impact of roadway traffic and aircraft UFP emissions using a combination of mobile monitoring and standard statistical methods.

OBJECTIVES: To evaluate the combined burden of heat and air quality exposure in Washington State agriculture by: 1) characterizing the spatiotemporal pattern of heat and PM(2.5) exposures during wildfire seasons; 2) describing the potential impact of these combined exposures on agricultural worker populations; and 3) identifying data gaps for addressing this burden in rural areas. METHODS: We combined county-level data to explore data availability and estimate the burden of heat and PM(2.5) co-exposures for Washington agricultural workers from 2010 to 2018. Quarterly agricultural worker population estimates were linked with data from a weather station network and ambient air pollution monitoring sites. A geographical information system displayed counties, air monitoring sites, agricultural crops, and images from a smoke dispersion model during recent wildfire events. RESULTS: We found substantial spatial and temporal variability in high heat and PM(2.5) exposures. The largest peaks in PM(2.5) exposures tended to occur when the heat index was around 85°F and during summers when there were wildfires. Counties with the largest agricultural populations tended to have the greatest concurrent high heat and PM(2.5) exposures, and these exposures tended to be highest during the third quarter (July-September), when population counts were also highest. Additionally, we observed limited access to local air quality information in certain rural areas. CONCLUSION: Our findings inform efforts about highest risk areas, times of year, and data availability in rural areas. Understanding the spatiotemporal pattern of exposures is consistent with the precision agriculture framework and is foundational to addressing equity in rural agricultural settings.

The increased frequency of wildfires in the Western United States has raised public concerns. Exposure to wildfire smoke has been linked to an increased risk of cancer and cardiorespiratory morbidity. Evidence-driven interventions can alleviate the adverse health impact of wildfire smoke. Public health guidance during wildfires is based on regional air quality data with limited spatiotemporal resolution. We demonstrate the use of a network of low-cost particulate matter (PM) sensors to gather indoor, outdoor, and personal PM2.5 exposure data from seven locations in the urban Seattle area, along with a personal exposure monitor worn by a resident living in one of these locations during the 2020 Washington wildfire event. The data were used to determine PM concentration indoor/outdoor (I/O) ratios, PM reduction, and personal exposure levels. The result shows that locations equipped with high-efficiency particulate air (HEPA) filters and HVAC filtration systems had significantly lower I/O ratios (median I/O = 0.43) than those without air filtration (median I/O = 0.82). The median PM2.5 reduction for the locations with HEPA is 58 % compared to 20% for the locations without HEPA. The outdoor PM sensors showed a high correlation to the nearby regional air quality monitoring stations (R2 = 0.93). The personal monitor showed high variance in PM measurements as the user moved through different microenvironments and could not be fully characterized by the network of indoor or outdoor monitors. The findings imply evidence-based interventions can be developed for reducing pollution exposure based on the combination of indoor, outdoor sensors. Personal exposure monitoring in individuals' breathing zones provided the highest fidelity data capturing temporal spikes in PM exposure.

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

Driven by climate change, wildfires are increasing in frequency, duration, and intensity across the Western United States. Outdoor workers are being exposed to increasing wildfire-related particulate matter and smoke. Recognizing this emerging risk, Washington adopted an emergency rule and is presently engaged in creating a permanent rule to protect outdoor workers from wildfire smoke exposure. While there are growing bodies of literature on the exposure to and health effects of wildfire smoke in the general public and wildland firefighters, there is a gap in knowledge about wildfire smoke exposure among outdoor workers generally, and construction workers specifically, a large category of outdoor workers in Washington totaling 200,000 people. In this study, several data sources were linked including state-collected employment data and national ambient air quality data to gain insight into the risk of PM2.5 exposure among construction workers and evaluate the impacts of different air quality thresholds that would have triggered a new Washington emergency wildfire smoke rule aimed at protecting workers from high PM2.5 exposure. Results indicate the number of poor air quality days has increased in August and September in recent years. Over the last decade these months with the greatest potential for particulate matter exposure coincided with an annual peak in construction employment that was typically 9.4 to 42.7% larger across Washington counties (one county was 75.8%). Lastly, the “encouraged” threshold of the Washington emergency rule (20.5 μg/m3) would have resulted in 5.5 times more days subject to the wildfire rule on average across all Washington counties compared to its “required” threshold (55.5 μg/m3), and in 2020 the rule could have created demand for 1.35 million N-95 filtering facepiece respirators among construction workers. These results have important implications for both employers and policy makers as rules are developed. The potential policy implications of wildfire smoke exposure, exposure control strategies, and data gaps that would improve understanding of construction worker exposure to wildfire smoke are also discussed.

Occupational heat exposure is associated with substantial morbidity and mortality among outdoor workers. We sought to descriptively evaluate spatiotemporal variability in heat threshold exceedances and describe potential impacts of these exposures for crop and construction workers. We also present general considerations for approaching heat policy-relevant analyses. We analyzed county-level 2011–2020 monthly employment (Bureau of Labor Statistics Quarterly Census of Employment and Wages) and environmental exposure (Parameter-elevation Relationships on Independent Slopes Model (PRISM)) data for Washington State (WA), USA, crop (North American Industry Classification System (NAICS) 111 and 1151) and construction (NAICS 23) sectors. Days exceeding maximum daily temperature thresholds, averaged per county, were linked with employment estimates to generate employment days of exceedances. We found spatiotemporal variability in WA temperature threshold exceedances and crop and construction employment. Maximum temperature exceedances peaked in July and August and were most numerous in Central WA counties. Counties with high employment and/or high numbers of threshold exceedance days, led by Yakima and King Counties, experienced the greatest total employment days of exceedances. Crop employment contributed to the largest proportion of total state-wide employment days of exceedances with Central WA counties experiencing the greatest potential workforce burden of exposure. Considerations from this analysis can help inform decision-making regarding thresholds, timing of provisions for heat rules, and tailoring of best practices in different industries and areas.

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,...

Interventions that improve air exchange or filter the air have the potential to reduce particle exposures from residential cooking. In this study, we evaluated the effect of using a range hood, opening kitchen windows, and using portable air cleaners (PACs) in various home locations on the concentrations of ultrafine particles (UFPs) at different times and in different rooms during and after cooking. All experiments were conducted using a standardized cooking protocol in a real-world naturally-ventilated apartment located in the northwest United States. Real-time UFP measurements collected from the kitchen, living room, and bedroom locations were used to estimate parameters of a dynamic model, which included time-varying particle emission rates from cooking and particle decay. We found that 1-min mean UFP number concentrations in the kitchen and living room mostly peaked within 0–10 min after cooking ended at levels of 150000–500000 particles/cm(3). In contrast, the bedroom UFP concentrations were consistently low except for the window-open scenario. While varying considerably with time, the 1-min UFP emission rates were comparable during and within 5-min after cooking, with means (standard deviations) of 0.8 (1.1) ×10(12) and 1.1 (1.2) ×10(12) particles/min, respectively. Compared with the no-intervention scenario, keeping the kitchen windows open and using a kitchen range hood reduced the mean indoor average UFP concentrations during and 1 h after cooking by ~70% and ~35%, respectively. Along with the range hood on, utilizing a PAC in the kitchen during and after cooking further reduced the mean indoor average UFP levels during and 1 h after cooking by an additional 53%. In contrast, placing the PAC in the living room or bedroom resulted in worse efficacy, with additional 2–13% reductions. These findings provide useful information on how to reduce cooking-related UFP exposure via readily accessible intervention strategies.

Some cooking events can generate high levels of hazardous PM(2.5). This study assesses the dispersion of cooking-related PM(2.5) throughout a naturally-ventilated apartment in the US, examines the dynamic process of cooking-related emissions, and demonstrates the impact of different indoor PM(2.5) mitigating strategies. We conducted experiments with a standardized pan-frying cooking procedure under seven scenarios, involving opening kitchen windows, using a range hood, and utilizing a portable air cleaner (PAC) in various indoor locations. Real-time PM(2.5) concentrations were measured in the open kitchen, living room, bedroom (door closed), and outdoor environments. Decay-related parameters were estimated, and time-resolved PM(2.5) emission rates for each experiment were determined using a dynamic model. Results show that the 1-min mean PM(2.5) concentrations in the kitchen and living room peaked 1–7 min after cooking at levels of 200–1400 μg/m(3), which were more than 9 times higher than the peak bedroom levels. Mean (standard deviation) k(t) for the kitchen, ranging from 0.58 (0.02) to 6.62 (0.34) h(−1), was generally comparable to that of the living room (relative difference < 20%), but was 1–5 times larger than that of the bedroom. The range of PM(2.5) full-decay time was between 1–10 h for the kitchen and living room, and from 0 to > 6 h for the bedroom. The PM(2.5) emission rates during and 5 min after cooking were 2.3 (3.4) and 5.1 (3.9) mg/min, respectively. Intervention strategies, including opening kitchen windows and using PACs either in the kitchen or living room, can substantially reduce indoor PM(2.5) levels and the related full-decay time. For scenarios involving a PAC, placing it in the kitchen (closer to the source) resulted in better efficacy.

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO, CO-B4), nitric oxide (NO, NO-B4), nitrogen dioxide (NO2, NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97, NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79, O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Some cooking events can generate high levels of hazardous PM2.5. This study assesses the dispersion of cooking-related PM2.5 throughout a naturally-ventilated apartment in the US, examines the dynamic process of cooking-related emissions, and demonstrates the impact of different indoor PM2.5 mitigating strategies. We conducted experiments with a standardized pan-frying cooking procedure under seven scenarios, involving opening kitchen windows, using a range hood, and utilizing a portable air cleaner (PAC) in various indoor locations. Real-time PM2.5 concentrations were measured in the open kitchen, living room, bedroom (door closed), and outdoor environments. Decay-related parameters were estimated, and time-resolved PM2.5 emission rates for each experiment were determined using a dynamic model. Results show that the 1-min mean PM2.5 concentrations in the kitchen and living room peaked 1–7 min after cooking at levels of 200–1400 µg/m3, which were more than 9 times higher than the peak bedroom levels. Mean (standard deviation) kt for the kitchen, ranging from 0.58 (0.02) to 6.62 (0.34) h-1, was generally comparable to that of the living room (relative difference < 20%), but was 1–5 times larger than that of the bedroom. The range of PM2.5 full-decay time was between 1–10 h for the kitchen and living room, and from 0 to > 6 h for the bedroom. The PM2.5 emission rates during and 5 min after cooking were 2.3 (3.4) and 5.1 (3.9) mg/min, respectively. Intervention strategies, including opening kitchen windows and using PACs either in the kitchen or living room, can substantially reduce indoor PM2.5 levels and the related full-decay time. For scenarios involving a PAC, placing it in the kitchen (closer to the source) resulted in better efficacy.

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.

Portable air cleaners (PACs), offering both auto and manual (adjustable) operation modes, are commonly used in residences. Compared with adjustable mode, auto mode’s advantage of reducing indoor PM(2.5) has been previously demonstrated. This study examines the energy consumption of such PACs in six residences recruited in Seattle, United States, and compares the power consumption between auto and adjustable modes. Each residence went through a one-week-long PAC filtration session under auto and adjustable modes, respectively. PAC power consumption, indoor PM(2.5), temperature, and relative humidity (RH) were measured at 10-second intervals in each residence. A linear mixed-effects regression (LMER) model was used to compare the PAC power consumption between the two modes after adjusting for indoor PM(2.5), temperature, and RH. Results show that the mean (standard deviation) PAC power consumption under adjustable and auto modes were 7.0 (3.5) and 6.8 (2.6) W, respectively. The average monthly energy consumption of continuous PAC operation was estimated to be ~5 kWh for both modes. Based on the LEMR model, PAC power consumption under auto mode was approximately 3% larger than that under adjustable mode, after adjusting for living-room PM(2.5), temperature, and RH levels. The implications for PAC operation mode selection in residential environments were discussed.

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM(2.5) infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM(2.5) levels. All residences with windows closed went through an 18-to-24-hour no filtration session, with five of seven following that period with an 18-to-24-hour filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM(2.5) levels, were used for the filtration session. 10-second resolved indoor PM(2.5) levels were measured in each residence’s living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM(2.5) mass balance models were developed to estimate the PM(2.5) indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%−78% decrease of indoor PM(2.5) levels after adjusting for outdoor PM(2.5) levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h(−1) to 1.41 (3.18) h(−1) while the PM(2.5) deposition rate ranged from 0.10 (0.54) h(−1) to 0.49 (0.47) h(−1). These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people’s excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.

Rural lower Yakima Valley, Washington is home to the reservation of the Confederated Tribes and Bands of the Yakama Nation, and is a major agricultural region. Episodic poor air quality impacts this area, reflecting sources of particulate matter with a diameter of less than 2.5 micrometers (PM(2.5)) that include residential wood smoke, agricultural biomass burning and other emissions, truck traffic, backyard burning, and wildfire smoke. University of Washington partnered with the Yakama Nation Environmental Management Program to investigate characteristics of PM(2.5) using 9 months of data from a combination of low-cost optical particle counters and a 5-wavelength aethalometer (MA200 Aethlabs) over 4 seasons and an episode of summer wildfire smoke. The greatest percentage of hours sampled with PM(2.5) >12 μg/m(3) occurred during the wildfire smoke episode (59%), followed by fall (23%) and then winter (21%). Mean (SD) values of Delta-C (μg/m(3)), which has been posited as an indicator of wood smoke, and determined as the mass absorbance difference at 375-880nm, were: summer – wildfire smoke 0.34 (0.52), winter 0.27 (0.32), fall 0.10 (0.22), spring 0.05 (0.11), and summer – no wildfire smoke 0.04 (0.14). Mean (95% confidence interval) values of the absorption Ångström exponent, an indicator of the wavelength dependence of the aerosol, were: winter 1.5 (1.2-1.8), summer – wildfire smoke 1.4 (1.0-1.8), fall 1.3 (1.1-1.4), spring 1.2 (1.1-1.4), and summer – no wildfire smoke 1.2 (1.0-1.3). The trends in Delta-C and absorption Ångström exponents are consistent with expectations that a higher value reflects more biomass burning. These results suggest that biomass burning is an important contributor to PM(2.5) in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data. Further understanding of how to interpret aethalometer data in regions with complex emissions would contribute to much-needed research in communities impacted by air pollution from agricultural as well as residential sources of combustion.

The historic wildfires of August and September 2020, up and down the west coast have made Western cities in the U.S. rank among the highest in the world for dirtiest air. Washington State was impacted by active wildfire smoke in the state as well as transported aged wood smoke from Oregon and California. Particulate matter smaller than 2.5 μm in aerodynamic diameter (PM2.5) is the primary pollutant from wildfire smoke of health concern for the public. This study aims to assess the potential population health impact of increased PM2.5concentrations attributable to the wildfire smoke. Daily PM2.5concentrations for each county before and during the 2020 Washington state wildfire smoke episode was obtained from the WA Department of Ecology. Utilizing previously established associations of short-term mortality and PM2.5concentrations we estimated increases in all-cause, respiratory, and cardiovascular mortality for Washington state attributable to the increased PM2.5levels during the wildfire episode. During the episode, PM2.5concentrations increased 99.4 μg/m3on average in Washington. Each week of wildfire smoke exposures are estimated to cause 91.0 (95% CI: 73.8, 107.1) cases of increased all-cause mortality, 19.9 (95% CI: 10.4, 29.3) increased cardiovascular disease deaths, and 9.7 (95% CI: 5.4, 14.0) increased respiratory disease deaths. This is without considering the coincidence with the COVID-19 pandemic, since wildfire smoke exposure may exacerbate the effects of SARS-CoV-2 infection based on some early evidence. Because wildfire smoke episodes are likely to continue impacting the Pacific Northwest in years to come, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid this excess health burden.

This study evaluates the COVID-19 impacts on traffic-related air pollution, including ultrafine particles (UFPs), PM2.5, black carbon (BC), NO, NO2, NOx, and CO in a Northwestern US city. Hourly traffic, air pollutants, and meteorological data on/near a major freeway in the downtown of Seattle, Washington, were collected for five weeks before and ten weeks after the Washington Stay Home Order (SHO) was enacted, respectively (February 17–May 31, 2020). The pollutants between pre- and post-SHO periods were compared, and their differences were statistically tested. Besides, first-order multivariate autoregressive (MAR(1)) models were developed to reveal the impacts specific to the change of traffic due to the COVID-19 responses while controlling for meteorological conditions. Results indicate that compared with those in the post-SHO period, the median traffic volume and road occupancy decreased by 37% and 52%, respectively. As for pollutants, the median BC and PM2.5 levels significantly decreased by 25% and 33%, relatively, while NO, NO2, NOx, and CO decreased by 33%, 29%, 30%, and 17%, respectively. In contrast, neither size-resolved UFPs nor total UFPs showed significant changes between the two periods, although larger particles (≥115.5 nm) decreased by 4–29%. Additionally, significant differences were found in meteorological conditions between the two periods. Based on the MAR(1) models, controlling for meteorological conditions, the COVID-19 responses were associated with significant decreases in median levels of traffic-related pollutants including 11.5–154.0 nm particles (ranging from −3% [95% confidence interval (CI): −1%, −4%] to −12% [95% CI: −10%, −14%]), total UFPs (−7% [95% CI: −5%, −8%]), BC (−6% [95% CI: −5%, −7%]), PM2.5 (−2% [95% CI: −1%, −3%]), NO, NO2, NOx (ranging from −3% [95% CI: −2%, −4%] to −10% [95% CI: −18%, −12%]), and CO (−4% [95% CI, −3%, −5%]). These findings illustrate that the conclusion of the COVID-19 impacts on urban traffic-related air pollutant levels could be completely different in scenarios whether meteorology was adjusted for or not. Fully adjusting for meteorology, this study shows that the COVID-19 responses were associated with much more reductions in traffic-related UFPs than PM2.5 in the Seattle region, in contrast to the reverse trend from the direct empirical data comparison., Graphical abstract Unlabelled Image, Highlights • The COVID-19 impacts on traffic-related air pollution were evaluated in a US city. • This study is the first to assess the COVID-19 impacts on roadside UFPs. • Meteorology was fully adjusted by using multivariate autoregressive models. • Significant decreases showed in UFPs, black carbon, PM2.5, NO, NO2, NOx, and CO.

We have applied the absolute principal component scores (APCS) receptor model to on-road, background-adjusted measurements of NOx, CO, CO(2), black carbon (BC), and particle number (PN) obtained from a continuously moving platform deployed over nine afternoon sampling periods in Seattle, WA. Two Varimax-rotated principal component features described 75% of the overall variance of the observations. A heavy-duty vehicle feature was correlated with black carbon and particle number, whereas a light-duty feature was correlated with CO and CO(2). NO(x) had moderate correlation with both features. The bootstrapped APCS model predictions were used to estimate area-wide, average fuel-based emission factors and their respective 95% confidence limits. The average emission factors for NOx, CO, BC and PN (14.8, 18.9, 0.40 g/kg, and 4.3×10(15) particles/kg for heavy duty vehicles, and 3.2, 22.4, 0.016 g/kg, and 0.19×10(15) particles/kg for light-duty vehicles, respectively) are consistent with previous estimates based on remote sensing, vehicle chase studies, and recent dynamometer tests. Information on the spatial distribution of the concentrations contributed by these two vehicle categories relative to background during the sampling period was also obtained.

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.

Background: Nitrate contamination in groundwater disproportionately impacts agricultural Latino communities, creating a significant hazard for Latinos that rely on private wells. Private well users must conduct water testing and other well stewardship behaviors to ensure their well water is safe to drink. This study sought to identify the barriers and facilitators of private well water testing in rural, agricultural Latino communities. Methods: We conducted four focus groups with private well users, two in Spanish and two in English. We recruited 37 participants from the Lower Yakima Valley, Washington State, a rural, agricultural community with a large Latino population and elevated nitrate concentrations in groundwater. A semi-structured interview guide was developed to capture testing barriers and facilitators guided by the Risk, Attitudes, Norms, Ability, and Self-Regulation (RANAS) model. Inductive thematic analysis was conducted by two coders to identify common themes. Results: Themes emerged around barriers and facilitators to well stewardship behaviors, including well testing, treatment, and maintenance. Barriers included limited actionable information, economic limitations, lack of technical support, and challenges related to landlords and neighbors. Facilitators of well stewardship included concerns about well water contamination, knowledge of agricultural sources of contamination, do-it-yourself (DIY) home repair experience, and responsibility to protect family. Conclusions: Well stewardship programs in rural, agricultural Latino communities may want to prioritize communication that provides actionable information on well water testing, treatment, and maintenance and emphasizes the responsibility to protect family in its messaging. Additionally, programs that provide financial and technical assistance and well maintenance education that builds on DIY home repair experience may be effective in these communities.

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM2.5 (airborne particles with sizes less than 2.5 &mu, m in diameter) hourly average concentration is equal to or greater than 35 &mu, g m&minus, 3, are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region, however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM2.5 infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM2.5 levels. All residences with windows closed went through an 18-to-24-h no filtration session, with five of seven following that period with an 18-to-24-h filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM2.5 levels, were used for the filtration session. 10-s resolved indoor PM2.5 levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM2.5 mass balance models were developed to estimate the PM2.5 indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%-78% decrease of indoor PM2.5 levels after adjusting for outdoor PM2.5 levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h-1 to 1.41 (3.18) h-1 while the PM2.5 deposition rate ranged from 0.10 (0.54) h-1 to 0.49 (0.47) h-1. These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NOX) and ozone (O3) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NOX and O3, with LOOCV R2s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NOX had LOOCV R2s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O3. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NOX and O3 and are a better source of data for these models than 2-week passive badge data.

“Auto-mode” portable air cleaners (PACs) designed to turn on automatically in response to elevated indoor PM2.5 concentrations are widely used in residences. This study assesses the impacts of using such PACs equipped with high-efficiency particulate air filters on residential PM2.5 levels in six households recruited in Seattle, Washington, for a randomized crossover study, in which participants used a PAC in their homes under three intervention scenarios: (1) sham-mode filtration in which all filters in the PAC were removed, (2) auto-mode filtration in which the PAC was set to auto mode, and (3) adjustable-mode filtration in which the participants were allowed to manually adjusted the PACs. Each filtration session was one-week long. PM2.5 levels were measured at 1-min intervals in the kitchen and living room of each household. Also, an hourly time-activity log was collected from each participant. The empirical concentrations and I/O ratios of PM2.5 were compared among the three filtration modes. Additionally, linear mixed-effects regression models (LMER) were developed to reveal the impacts of different filtration modes on indoor PM2.5, while controlling for outdoor PM2.5, indoor cooking events, and indoor relative humidity, and temperature. Based on the LMER models, when indoor cooking events were present and absent, auto-mode filtration significantly reduced the indoor PM2.5 levels by 31% [95% CI: 19%, 42%] and 28% [95% CI: 22%, 34%], respectively, compared with sham-mode filtration, and reduced the indoor PM2.5 levels by 19% [95% CI: 6%, 31%] and 4% [95% CI: 4%, 11%], respectively, compared with adjustable-mode filtration. These findings suggest that using such PACs running in auto mode is a practical and effective way to reduce indoor PM2.5 levels.

Purpose: Developed in partnership with Washington State Dairy Federation, Washington State University, and University of Washington, the e-learning Dairy Safety Kit (DSK) platform provides curated ...

Rural lower Yakima Valley, Washington is home to the reservation of the Confederated Tribes and Bands of the Yakama Nation, and is a major agricultural region. Episodic poor air quality impacts this area, reflecting sources of particulate matter with a diameter of less than 2.5 μm (PM2.5) that include residential wood smoke, agricultural biomass burning and other emissions, truck traffic, backyard burning, and wildfire smoke. University of Washington partnered with the Yakama Nation Environmental Management Program to investigate characteristics of PM2.5 using 9 months of data from a combination of low-cost optical particle counters and a 5-wavelength aethalometer (MA200 Aethlabs) over 4 seasons and an episode of summer wildfire smoke. The greatest percentage of hours sampled with PM2.5 >12 μg/m3 occurred during the wildfire smoke episode (59%), followed by fall (23%) and then winter (21%). Mean (SD) values of Delta-C (μg/m3), which has been posited as an indicator of wood smoke, and determined as the mass absorbance difference at 375–880 nm, were: summer – wildfire smoke 0.34 (0.52), winter 0.27 (0.32), fall 0.10 (0.22), spring 0.05 (0.11), and summer – no wildfire smoke 0.04 (0.14). Mean (95% confidence interval) values of the absorption Angstrom exponent, an indicator of the wavelength dependence of the aerosol, were: winter 1.5 (1.2–1.8), summer – wildfire smoke 1.4 (1.0–1.8), fall 1.3 (1.1–1.4), spring 1.2 (1.1–1.4), and summer – no wildfire smoke 1.2 (1.0–1.3). The trends in Delta-C and absorption Angstrom exponents are consistent with expectations that a higher value reflects more biomass burning. These results suggest that biomass burning is an important contributor to PM2.5 in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data. Further research on the interpretation of aethalometer data in regions with complex emissions would contribute to much-needed understanding in communities impacted by air pollution from agricultural as well as residential sources of combustion.

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.

The lower Yakima Valley of Washington State is a rural area home to communities including Latinx farmworker families, the Confederated Tribes and Bands of the Yakama Nation, and Native Americans of...

The Mobile ObserVations of Ultrafine Particles (MOV-UP) Study is a two-year project intended to quantify air quality impacts of air traffic for communities located near, and below the flight paths ...

As novel environmental monitoring and exposure sensing approaches evolve, there is still the need to match the air quality concerns of communities with appropriate measurements. Designing an approp...