Quantifying the contributions of road and air traffic to ambient ultrafine particles in two urban communities

Traffic-related activities are widely acknowledged as a primary source of urban ambient ultrafine particles (UFPs). However, a notable gap exists in quantifying the contributions of road and air traffic to size-resolved and total UFPs in urban areas. This study aims to delineate and quantify the traffic's contributions to size-resolved and total UFPs in two urban communities. To achieve this, stationary sampling was conducted at near-road and near-airport communities in Seattle, Washington State, to monitor UFP number concentrations during 2018-2020. Comprehensive correlation analyses among all variables were performed. Furthermore, a fully adjusted generalized additive model, incorporating meteorological factors, was developed to quantify the contributions of road and air traffic to size-resolved and total UFPs. The study found that vehicle emissions accounted for 29% of total UFPs at the near-road site and 13% at the near-airport site. Aircraft emissions contributed 14% of total UFPs at the near-airport site. Notably, aircraft predominantly emitted UFP sizes below 20 nm, while vehicles mainly emitted UFP sizes below 50 nm. These findings reveal the variability in road and air traffic contributions to UFPs in distinct areas. Our study emphasizes the pivotal role of traffic layout in shaping urban UFP 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 andR 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.

Cohort profile: The Assessing Imperial Valley Respiratory Health and the Environment (AIRE) study

BACKGROUND

The Children's Assessing Imperial Valley Respiratory Health and the Environment (AIRE) study is a prospective cohort study of environmental influences on respiratory health in a rural, southeastern region of California (CA), which aims to longitudinally examine the contribution of a drying saline lake to adverse health impacts in children.

OBJECTIVES

This cohort was established through a community-academic partnership with the goal of assessing the health effects of childhood exposures to wind-blown particulate matter (PM) and inform public health action. We hypothesize that local PM sources are related to poorer children's respiratory health.

POPULATION

Elementary school children in Imperial Valley, CA.

DESIGN

Prospective cohort study.

METHODS

Between 2017 and 2019, we collected baseline information on 731 children, then follow-up assessments yearly or twice-yearly since 2019. Data have been collected on children's respiratory health, demographics, household characteristics, physical activity and lifestyle, via questionnaires completed by parents or primary caregivers. In-person measurements, conducted since 2019, repeatedly assessed lung function, height, weight and blood pressure. Exposure to air pollutants has been assessed by multiple methods and individually assigned to participants using residential and school addresses. Health data will be linked to ambient and local sources of PM, during and preceding the study period to understand how spatiotemporal trends in these environmental exposures may relate to respiratory health.

PRELIMINARY RESULTS

Analyses of respiratory symptoms indicate a high prevalence of allergies, bronchitic symptoms and wheezing. Asthma diagnosis was reported in 24% of children at enrolment, which exceeds both CA state and US national prevalence estimates for children.

CONCLUSIONS

The Children's AIRE cohort, while focused on the health impacts of the drying Salton Sea and air quality in Imperial Valley, is poised to elucidate the growing threat of drying saline lakes and wind-blown dust sources to respiratory health worldwide, as sources of wind-blown dust emerge in our changing climate.

Estimates of Population Highly Annoyed from Transportation Noise in the United States: An Unfair Share of the Burden by Race and Ethnicity

Transportation is one of the most pervasive sources of community noise. In this study, we used a spatially-resolved model of transportation-related noise with established transportation noise exposure-response functions to estimate the population highly annoyed (HA) due to aviation, road, and railway traffic sources in the United States. Additionally, we employed the use of the Fair Share Ratio to assess race/ethnicity disparities in traffic noise exposures. Our results estimate that in 2020, 7.8 million (2.4%) individuals were highly annoyed by aviation noise, while 5.2 million (1.6%) and 7.9 million (2.4%) people were highly annoyed by rail and roadway noise, respectively, across the US. The Fair Share Ratio revealed that Non-Hispanic Asian, Black, NHPI, and Other, and Hispanic populations were disproportionally highly annoyed by transportation noise nationwide. Notably, Hispanic populations experienced the greatest share of high annoyance from aviation noise (1.69 times their population share). Non-Hispanic Black populations experienced the greatest share of high annoyance from railway noise (1.48 times their population share). Non-Hispanic Asian populations experienced the greatest share of high annoyance from roadway noise (1.51 times their population share). Analyses at the state and Urban Area levels further highlighted varying disparities in transportation noise exposure and annoyance across different race ethnicity groups, but still suggested that Non-Hispanic White populations were less annoyed by all sources of transportation noise compared to non-White populations. Our findings indicate widespread presence of transportation noise annoyance across the US and emphasize the need for targeted source-specific noise mitigation strategies and policies to minimize the disproportionate impact of transportation noise in the US.

School and childcare facility air quality decision-makers' perspectives on using low-cost sensors for wildfire smoke response

BACKGROUND

During wildfire smoke episodes, school and childcare facility staff and those who support them rely upon air quality data to inform activity decisions. Where ambient regulatory monitor data is sparse, low-cost sensors can help inform local outdoor activity decisions, and provide indoor air quality data. However, there is no established protocol for air quality decision-makers to use sensor data for schools and childcare facilities. To develop practical, effective toolkits to guide the use of sensors in school and childcare settings, it is essential to understand the perspectives of the potential end-users of such toolkit materials.

METHODS

We conducted 15 semi-structured interviews with school, childcare, local health jurisdiction, air quality, and school district personnel regarding sensor use for wildfire smoke response. Interviews included sharing PM2.5 data collected at schools during wildfire smoke. Interviews were transcribed and transcripts were coded using a codebook developed both a priori and amended as additional themes emerged.

RESULTS

Three major themes were identified by organizing complementary codes together: (1) Low-cost sensors are useful despite data quality limitations, (2) Low-cost sensor data can inform decision-making to protect children in school and childcare settings, and (3) There are feasibility and public perception-related barriers to using low-cost sensors.

CONCLUSIONS

Interview responses provided practical implications for toolkit development, including demonstrating a need for toolkits that allow a variety of sensor preferences. In addition, participants expected to have a wide range of available time for monitoring, budget for sensors, and decision-making types. Finally, interview responses revealed a need for toolkits to address sensor uses outside of activity decisions, especially assessment of ventilation and filtration.

Conversions between noise exposure metrics 24-hour Leq, Ldn, and Lden: the impact of diurnal local bus traffic patterns on population annoyance in the United States

Noise during evening and nighttime hours tends to be associated with high annoyance, which is reflected in the use of community noise exposure metrics, such as the L d n and L d e n , that include penalties during these hours. Transportation noise sources may exhibit distinct diurnal patterns, but the impact of these patterns on different noise metrics has not been thoroughly evaluated, especially within the United States. In this study, we utilized General Transit Feed Specification (GTFS) data from 24 major cities in the U.S. to quantify diurnal traffic patterns for local buses, and the impact of these patterns on differences in noise metrics, such as L Day , L Evening , L Night , L d n , and L d e n , compared to the 24-hour L Aeq24 , Using mathematical conversions between the noise metrics, we found on average across the cities that the L d n was between 2.8 to 3.6 dB higher than the L Aeq24 , and the L d e n was also 3.6 to 3.8 dB higher than the L Aeq24 for noise from local buses. This increase was mainly due to noise during daytime (L Day ) that was higher than the 24-hour average noise, and dB penalties added to the L d n and L d e n metrics, which compensate for less bus traffic during evening and nighttime hours. We discuss the relevance of these conversions and the observed differences between the 24-hour L Aeq24 and the L d n and L d e n , which are used for health impact assessments of high annoyance, on public transportation planning.

Wildfire Smoke Monitoring for Agricultural Safety and Health in Rural Washington

OBJECTIVES

This study aimed to evaluate the performance of a low-cost smoke sampling platform relative to environmental and occupational exposure monitoring methods in a rural agricultural region in central Washington state.

METHODS

We co-located the Thingy AQ sampling platform alongside cyclone-based gravimetric samplers, a nephelometer, and an environmental beta attenuation mass (E-BAM) monitor during August and September of 2020. Ambient particulate matter concentrations were collected during a smoke and non-smoke period and measurements were compared across sampling methods.

RESULTS

We found reasonable agreement between observations from two particle sensors within the Thingy AQ platform and the nephelometer and E-BAM measurements throughout the study period, though the measurement range of the sensors was greater during the smoke period compared to the non-smoke period. Occupational gravimetric sampling methods did not correlate with PM2.5 data collected during smoke periods, likely due to their capture of larger particle sizes than those typically measured by PM2.5 ambient air quality instruments during wildfire events.

CONCLUSION

Data collected before and during an intense wildfire smoke episode in September 2020 indicated that the low-cost smoke sampling platform provides a strategy to increase access to real-time air quality information in rural areas where regulatory monitoring networks are sparse if sensor performance characteristics under wildfire smoke conditions are understood. Improving access to spatially resolved air quality information could help agricultural employers protect both worker and crop health as wildfire smoke exposure increases due to the impacts of climate change. Such information can also assist employers with meeting new workplace wildfire smoke health and safety rules.

Assessing the effectiveness of portable HEPA air cleaners for reducing particulate matter exposure in King County, Washington homeless shelters: Implications for community congregate settings

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/O_OPNC). Based on the LMER models, a 10 % increase in the hourly, daily, and total time PACs were used significantly reduced I/O_OPNC by 0.034 [95 % CI: 0.028, 0.040; p < 0.001], 0.051 [95 % CI: 0.020, 0.078; p < 0.001], and 0.252 [95 % CI: 0.150, 0.328; p < 0.001], respectively, indicating that keeping PACs on resulted in significantly lower I/O_OPNC. 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.

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.

An applied environmental justice framework for exposure science

On the 30th anniversary of the Principles of Environmental Justice established at the First National People of Color Environmental Leadership Summit in 1991 (Principles of Environmental Justice), we continue to call for these principles to be more widely adopted. We propose an environmental justice framework for exposure science to be implemented by all researchers. This framework should be the standard and not an afterthought or trend dismissed by those who believe that science should not be politicized. Most notably, this framework should be centered on the community it seeks to serve. Researchers should meet with community members and stakeholders to learn more about the community, involve them in the research process, collectively determine the environmental exposure issues of highest concern for the community, and develop sustainable interventions and implementation strategies to address them. Incorporating community "funds of knowledge" will also inform the study design by incorporating the knowledge about the issue that community members have based on their lived experiences. Institutional and funding agency funds should also be directed to supporting community needs both during the "active" research phase and at the conclusion of the research, such as mechanisms for dissemination, capacity building, and engagement with policymakers. This multidirectional framework for exposure science will increase the sustainability of the research and its impact for long-term success.

Use of a remote telemonitoring platform significantly improves medication optimisation in heart failure patients

Background

Guideline directed medical therapy (GDMT) has been shown to reduce morbidity and mortality in patients with heart failure with reduced ejection fraction (HFrEF). Despite this, a large number of eligible patients do not receive these treatments or have prolonged delays in achieving optimal doses.

Purpose

To determine whether a telemonitoring-supported, remote medication optimisation programme could increase the proportion of HFrEF patients reaching maximum tolerated GDMT, in a shorter period of time compared to usual care.

Methods

A prospective, randomised controlled trial recruited 108 patients with a diagnosis of HFrEF from the ambulatory heart function clinic of a North American cardiac centre. All patients were enrolled onto a non-invasive remote monitoring platform which allowed daily nurse coordinator-led assessment of patient-reported symptoms and trends in heart rate, blood pressure and weight. In the remote titration intervention group, telemonitoring data were used by treating physicians to make decisions on optimisation of GDMT every two weeks, which was enacted by the patient's nurse coordinator, with no physician visit required. Patients in the control group were reviewed in clinic by their treating physician, where medication doses were optimised as per standard of care. The proportion of patients achieving maximum tolerated GDMT, and the time taken for this were compared between groups. Continuous data are presented as mean±standard deviation and compared with Student's t-test, while categorical data are shown as number (%) and compared using the Chi-squared test.

Results

108 patients (69.4% male, mean age 54.1±15.4 years) were recruited with a median follow-up of 740 days. Baseline characteristics and medication prescription were similar between groups (56 randomised to remote titration, RT, 52 to usual care, UC, see Table). There were three withdrawals from the RT group and two from the UC group. Significantly more patients in the RT group 52/53 (98.1%) achieved the primary outcome, reaching maximum tolerated GDMT, compared with 42/50 (84.0%) in the UC group (p=0.01). The RT group achieved GDMT earlier (123±70 vs. 183±136 days, p=0.01) with a 40% reduction in clinic visits (p&lt;0.01). In a time-to-event analysis, time to optimisation was significantly shorter in the intervention group (median 105 vs. 165 days, p[log rank] &lt;0.01, see Figure). There was a similar increase in prescription of GDMT in both groups and no differences in hospitalisation or urgent clinic review suggesting that there was no excess hazard of remote titration.

Conclusion

Remote titration of GDMT in HFrEF patients resulted in more patients achieving maximum tolerated doses, on average two months earlier, with a reduction in clinic visits and no excess adverse outcomes. Telemonitoring-supported remote GDMT titration is effective, safe and could reduce healthcare costs associated with the management of HFrEF.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): DHB is supported by a post-doctoral fellowship award from TRANSFORM-HF (Ontario, Canada).

Partnership to Develop and Deliver Curriculum Supporting Student-led Air Quality Research in Rural Washington State

BACKGROUND

This article describes the process and educational materials developed and implemented for high school students through a partnership between an urban public university and a rural, non-profit university. This specific partnership was novel but originated within a long-standing community-academic partnership. This project took place in a rural community impacted by air pollution and a higher asthma hospitalization rate compared with the rest of the state.

OBJECTIVES

The objectives of this article are to describe the development and implementation of a high school program where students conducted their own research on local air quality using low-cost monitors with the guidance of undergraduate student mentors.

METHODS

University faculty, researchers, and students collaborated to develop an air quality curriculum relevant to local issues. This curriculum was delivered to high school students through an existing after school program, and guided students in conducting their own research on community air pollution. The students used university-provided low-cost monitors for their research, and presented their research to community members. Student learning was supported through hands-on activities and conducting research projects. Student projects examined air quality variation indoors within their school, outdoors in their community, and at home.

CONCLUSIONS

This curriculum can be adapted for use with students in many different communities. It will likely be most successful and engaging if adapted to local air pollution sources and issues, and implemented through an existing programmatic structure with a high mentor to student ratio.

Potential Impacts of Different Occupational Outdoor Heat Exposure Thresholds among Washington State Crop and Construction Workers and Implications for Other Jurisdictions

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.

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.

Potential impacts of Washington State's wildfire worker protection rule on construction workers

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. Several data sources were linked in this study-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 9.4-42.7% larger across Washington counties (one county was 75.8%). Lastly, the 'encouraged' threshold of the Washington emergency rule (20.5 μg m-3) 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 m-3), 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.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection

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 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.

Characterization of cooking-related ultrafine particles in a US residence and impacts of various intervention strategies

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 150,000-500,000 particles/cm3. 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) × 1012 and 1.1 (1.2) × 1012 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.

Changes in physical activity levels and mental health during COVID-19: Prospective findings among adult twin pairs

BACKGROUND

Physical distancing and other COVID-19 pandemic mitigation strategies have negatively impacted physical activity (PA) levels and mental health in cross-sectional studies. The purpose of this study was to investigate associations between changes in PA and mental health outcomes during the COVID-19 pandemic, following implementation of mitigation strategies, in a sample of adult twins.

METHODS

This was a prospective study of 3,057 adult twins from the Washington State Twin Registry. Study participants completed online surveys in 2020, at baseline (March 26 -April 5), and three follow-up waves (W1: April 20 -May 3; W2: Jul 16 -Aug 2; W3: Sept 16 -Oct 1). Physical activity was operationalized as self-reported moderate-to-vigorous PA (MVPA) and neighborhood walking (minutes/week), and mental health outcomes, operationalized as self-reported anxiety and perceived stress were assessed in the three waves of follow-up. Latent growth curve models (LGCMs) were used to assess changes in PA and mental health outcomes over time. Parallel LGCMs were used to estimate the cross-sectional, parallel, and prospective associations between PA and mental health over time. All models took into within-pair correlations and adjusted for age, sex, and race.

RESULTS

Individuals' amount of MVPA and walking decreased over time, whereas levels of anxiety remained stable, and stress increased slightly. Cross-sectional associations observed between both PA predictors and mental health outcomes were weak. After taking into account cross-sectional associations between PA and mental health outcomes, changes in PA over time were not associated with changes in mental health outcomes over time.

CONCLUSIONS

Over a time period aligned with COVID-19 mitigation strategies and social restrictions, changes in physical activity was not associated with changes in anxiety or stress levels in the current sample. Nonetheless, the average decline in PA over time is worrisome. Public health resources should continue to promote PA as a means to improve physical health during the pandemic.

Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements

Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles' properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors' performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.

Residential cooking-related PM2.5: Spatial-temporal variations under various intervention scenarios

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.

Source Apportionment of Environmental Combustion Sources using Excitation Emission Matrix Fluorescence Spectroscopy and Machine Learning

The link between particulate matter (PM) air pollution and negative health effects is well-established. Air pollution was estimated to cause 4.9 million deaths in 2017 and PM was responsible for 94% of these deaths. In order to inform effective mitigation strategies in the future, further study of PM and its health effects is important. Here, we present a method for identifying sources of combustion generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy and machine learning (ML) algorithms. PM samples were collected during a health effects exposure assessment panel study in Seattle. We use archived field samples from the exposure study and the associated positive matrix factorization (PMF) source apportionment based on X-ray fluorescence and light absorbing carbon measurements to train convolutional neural network and principal component regression algorithms. We show EEM spectra from cyclohexane extracts of the archived filter samples can be used to accurately apportion mobile and vegetative burning sources but were unable to detect crustal dust, Cl-rich, secondary sulfate and fuel oil sources. The use of this EEM-ML approach may be used to conduct PM exposure studies that include source apportionment of combustion sources.

Smoking is Associated With Increased Risk of Not Achieving Glycemic Target, Increased Glycemic Variability, and Increased Risk of Hypoglycemia for People With Type 1 Diabetes

BACKGROUND

The prevalence of smoking and diabetes is increasing in many developing countries. The aim of this study was to investigate the association of smoking with inadequate glycemic control and glycemic variability with continuous glucose monitoring (CGM) data in people with type 1 diabetes.

METHODS

Forty-nine smokers and 320 nonsmokers were obtained from the Novo Nordisk Onset 5 trial. After 16 weeks of treatment with continuous subcutaneous insulin infusion, risk of not achieving glycemic target and glycemic variability from six CGM measures was investigated. Analyzes were carried out with logistic regression models (glycemic target) and general linear models (glycemic variability). Finally, CGM median profiles were examined for the identification of daily glucose excursions.

RESULTS

A 4.7-fold (95% confidence interval: 1.5-15.4) increased risk of not achieving glycemic target was observed for smokers compared with nonsmokers. Increased time in hyperglycemia, decreased time in range, increased time in hypoglycemia (very low interstitial glucose), and increased fluctuation were observed for smokers compared with nonsmokers from CGM measures. CGM measures of coefficient of variation and time in hypoglycemia were not statistically significantly different. Examination of CGM median profiles revealed that risk of morning hypoglycemia is increased for smokers.

CONCLUSIONS

In conclusion, smoking is associated with inadequate glycemic control and increased glycemic variability for people with type 1 diabetes with especially risk of morning hypoglycemia. It is important for clinicians to know that if the patient has type 1 diabetes and is smoking, a preemptive action to treat high glycated hemoglobin levels should not necessarily be treatment intensification due to the risk of hypoglycemia.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

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.

Field measurements of PM2.5 infiltration factor and portable air cleaner effectiveness during wildfire episodes in US residences

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.

Cumulative Impacts and COVID-19: Implications for Low-Income, Minoritized, and Health-Compromised Communities in King County, WA

Few studies have assessed how the intersection of social determinants of health and environmental hazards contributes to racial disparities in COVID-19. The aim of our study was to compare COVID-19 disparities in testing and positivity to cumulative environmental health impacts, and to assess how unique social and environmental determinants of health relate to COVID-19 positivity in Seattle, King County, WA, at the census tract level. Publicly available data (n = 397 census tracts) were obtained from Public Health–Seattle & King County, 2018 ACS 5-year estimates, and the Washington Tracking Network. COVID-19 testing and positive case rates as of July 12, 2020, were mapped and compared to Washington State Environmental Health Disparities (EHD) Map cumulative impact rankings. We calculated odds ratios from a series of univariable and multivariable logistic regression analyses using cumulative impact rankings, and community-level socioeconomic, health, and environmental factors as predictors and having ≥ 10% or < 10% census tract positivity as the binary outcome variable. We found a remarkable overlap between Washington EHD cumulative impact rankings and COVID-19 positivity in King County. Census tracts with ≥ 10 % COVID-19 positivity had significantly lower COVID-19 testing rates and higher proportions of people of color and faced a combination of low socioeconomic status–related outcomes, poor community health outcomes, and significantly higher concentrations of fine particulate matter (PM_2.5). King County communities experiencing high rates of COVID-19 face a disproportionate cumulative burden of environmental and social inequities. Cumulative environmental health impacts should therefore systematically be considered when assessing for risk of exposure to and health complications resulting from COVID-19.

Solid-phase excitation-emission matrix spectroscopy for chemical analysis of combustion aerosols

Exposure to ultrafine combustion aerosols such as particulate matter (PM) from residential woodburning, forest fires, cigarette smoke, and traffic emission have been linked to adverse health outcomes. Excitation-emission matrix (EEM) spectroscopy presents a sensitive and cost-effective alternative for analysis of PM organic fraction. However, as with other analytical chemistry methods, the miniaturization is hindered by a solvent extraction step and a need for benchtop instrumentation. We present a methodology for collecting and in-situ analysis of airborne nanoparticles that eliminates labor-intensive sample preparation and miniaturizes the detection platform. Nanoparticles are electrostatically collected onto a transparent substrate coated with solid-phase (SP) solvent-polydimethylsiloxane (PDMS). The PM organic fraction is extracted into PDMS and analyzed in-situ, thus avoiding liquid-phase extraction. In the SP-EEM analysis, we evaluated external and internal excitation schemes. Internal excitation shows the lowest scattering interference but leads to signal masking from PDMS fluorescence for λ<250nm. The external excitation EEM spectra are dependent on the excitation light incident angle; ranges of 30-40° and 55-65° show the best results. SP-EEM spectra of woodsmoke and cigarette smoke samples are in good agreement with the EEM spectra of liquid-phase extracts. The SP-EEM technique can be used to develop wearable sensors for exposure assessments and environmental monitoring.

Health Impact Assessment of the 2020 Washington State Wildfire Smoke Episode: Excess Health Burden Attributable to Increased PM2.5 Exposures and Potential Exposure Reductions

Major wildfires starting in the summer of 2020 along the west coast of the United States made PM2.5 concentrations in this region rank among the highest in the world. Washington was impacted both by active wildfires in the state and aged wood smoke transported from fires in Oregon and California. This study aims to estimate the magnitude and disproportionate spatial impacts of increased PM2.5 concentrations attributable to these wildfires on population health. Daily PM2.5 concentrations for each county before and during the 2020 Washington wildfire episode (September 7-19) were obtained from regulatory air monitors. Utilizing previously established concentration-response function (CRF) of PM2.5 (CRF of total PM2.5) and odds ratio (OR) of wildfire smoke days (OR of wildfire smoke days) for mortality, we estimated excess mortality attributable to the increased PM2.5 concentrations in Washington. On average, daily PM2.5 concentrations increased 97.1 μg/m3 during the wildfire smoke episode. With CRF of total PM2.5, the 13-day exposure to wildfire smoke was estimated to lead to 92.2 (95% CI: 0.0, 178.7) more all-cause mortality cases; with OR of wildfire smoke days, 38.4 (95% CI: 0.0, 93.3) increased all-cause mortality cases and 15.1 (95% CI: 0.0, 27.9) increased respiratory mortality cases were attributable to the wildfire smoke episode. The potential impact of avoiding elevated PM2.5 exposures during wildfire events significantly reduced the mortality burden. Because wildfire smoke episodes are likely to impact the Pacific Northwest in future years, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid excess health burden.

Energy consumption of using HEPA-based portable air cleaner in residences: A monitoring study in Seattle, US

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 PM2.5 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 PM2.5, 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 PM2.5, 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 PM2.5, temperature, and RH levels. The implications for PAC operation mode selection in residential environments were discussed.

Distinct Ultrafine Particle Profiles Associated with Aircraft and Roadway Traffic

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.

Impacts of implementing Healthy Building guidelines for daily PM2.5 limit on premature deaths and economic losses in urban China: A population-based modeling study

Given a large fraction of people's exposure to urban PM_2.5 occur indoors, reducing indoor PM_2.5 levels may offer a more feasible and immediate way to save substantial lives and economic losses attributable to PM_2.5 exposure. We aimed to estimate the premature mortality and economic loss reductions associated with achieving the newly established Chinese indoor air guideline and a few hypothetical indoor PM_2.5 guideline values. We used outdoor PM_2.5 concentrations from 1497 monitoring sites in 339 Chinese cities in 2015, coupled with a steady-state mass balance model, to estimate indoor concentrations of outdoor-infiltrated PM_2.5. Using province-specific time-activity patterns for urban residents, we estimated outdoor and indoor exposures to PM_2.5 of outdoor origin. We then proceeded to use localized census-based concentration-response models and the value of statistical life estimates to calculate premature deaths and economic losses attributable to PM_2.5 exposure across urban China. Finally, we estimated potentially avoidable mortality and corresponding economic losses by meeting the current 24-hour based guideline and various hypothetical indoor limits for PM_2.5. In 2015 in urban areas of mainland China, the city-specific annual mean outdoor and indoor PM_2.5 concentrations ranged 9-108 μg/m³ and 5-56 μg/m³, respectively. Indoor exposures contributed 62%-91% daily and 68%-83% annually to the total time-weighted exposures. The potential reductions in total deaths and economic losses for the scenario in which daily indoor concentrations met the current guideline of 75 μg/m³, 37.5 μg/m³, and 25 μg/m³ were 16.9 (95% CI: 0.7-62.1) thousand, 87.7 (95% CI: 9.7-197.7) thousand, and 165.5 (95% CI: 30.8-304.0) thousand, respectively. The corresponding reductions in economic losses were 5.7 (95% CI: 0.2-34.8) billion, 29.4 (95% CI: 2.4-109.6) billion, and 55.2 (95% CI: 7.7-168.0) billion US Dollars, respectively. Deaths and economic losses would be reduced exponentially within the range of 0-75 μg/m³ for hypothetical indoor PM_2.5 limits. The findings demonstrate the effectiveness of reducing indoor concentrations of outdoor-originated PM_2.5 in saving substantial lives and economic losses in China. The analysis provides quantitative evidence to support the implementation of an indoor air quality guideline or standard for PM_2.5.

Community-Level Factors Associated with COVID-19 Cases and Testing Equity in King County, Washington

Individual-level Coronavirus Disease 2019 (COVID-19) case data suggest that certain populations may be more impacted by the pandemic. However, few studies have considered the communities from which positive cases are prevalent, and the variations in testing rates between communities. In this study, we assessed community factors that were associated with COVID-19 testing and test positivity at the census tract level for the Seattle, King County, Washington region at the summer peak of infection in July 2020. Multivariate Poisson regression was used to estimate confirmed case counts, adjusted for testing numbers, which were associated with socioeconomic status (SES) indicators such as poverty, educational attainment, transportation cost, as well as with communities with high proportions of people of color. Multivariate models were also used to examine factors associated with testing rates, and found disparities in testing for communities of color and communities with transportation cost barriers. These results demonstrate the ability to identify tract-level indicators of COVID-19 risk and specific communities that are most vulnerable to COVID-19 infection, as well as highlight the ongoing need to ensure access to disease control resources, including information and education, testing, and future vaccination programs in low-SES and highly diverse communities.

Impacts of the COVID-19 responses on traffic-related air pollution in a Northwestern US city

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.

Health Impact Assessment of PM2.5 attributable mortality from the September 2020 Washington State Wildfire Smoke Episode.. [PMID: 32995819 PMCID: PMC7523160 DOI: 10.1101/2020.09.19.20197921]

Major wildfires that started in the summer of 2020 along the west coast of the U.S. have made PM_2.5 concentrations in cities in this region rank among the highest in the world. Regions of Washington were impacted by active wildfires in the state, and by aged wood smoke transported from fires in Oregon and California. This study aims to assess the population health impact of increased PM_2.5 concentrations attributable to the wildfire. Average daily PM_2.5 concentrations for each county before and during the 2020 Washington wildfire episode were obtained from the Washington Department of Ecology. Utilizing previously established associations of short-term mortality for PM_2.5, we estimated excess mortality for Washington attributable to the increased PM_2.5 levels. On average, PM_2.5 concentrations increased 91.7 μg/m³ during the wildfire episode. Each week of wildfire smoke exposures was estimated to result in 87.6 (95% CI: 70.9, 103.1) cases of increased all-cause mortality, 19.1 (95% CI: 10.0, 28.2) increased cardiovascular disease deaths, and 9.4 (95% CI: 5.1, 13.5) increased respiratory disease deaths. Because wildfire smoke episodes are likely to continue impacting the Pacific Northwest in future years, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid this excess health burden.

Occupational Exposure to Particulate Matter and Volatile Organic Compounds in Two Indoor Cannabis Production Facilities

Legal commercial cultivation and processing of cannabis is a rapidly growing industry in multiple countries. However, to date little effort has been made to characterize and identify the various occupational hazards that workers may be facing in the cannabis production industry, including airborne contaminants that may affect the human respiratory system. In the current study, we quantified occupational exposures to particulate matter (PM) and volatile organic compounds (VOCs) in various task zones of two indoor cannabis facilities in Washington State. Full-shift (8-h) area measurements of PM and VOCs were collected in each task zone. Measurement devices were placed near the employee's work area in order to attempt to estimate the personal exposure to the contaminants. In each task zone we measured particle number concentration, particle mass concentration (PMC), cumulative size distribution of the particles, and total terpene mass concentrations. The mean PMCs were greater in task zones that required the employees to manipulate the cannabis plants and materials. The arithmetic mean PMC for the trim task was 60 µg m-3, preroll task was 45 µg m-3, grow task was 42 µg m-3, and the referent office area was 27 µg m-3. When comparing each task zone PMC to the office referent PMC, the trim task, and the preroll task were significantly higher than the referent group (P-values both <0.05). The arithmetic mean terpene mass concentration for the trim task was 36 mg m-3, preroll task was 9.9 mg m-3, grow task was 15 mg m-3, and for the office referent space was 4.9 mg m-3. Compared with the office space, only the trim task area had significantly elevated terpene mass concentrations (P-value <0.01). We observed a weak but statistically significant correlation between PMC and total terpene mass concentrations (rho = 0.42, P < 0.02). Overall, we observed that exposures to respiratory hazards were highest in task zones where cannabis plants and material were manipulated by workers, including the trim, preroll, and the grow task areas. These observations can help inform the employer of the task zones where exposure to respiratory hazards are the highest, and where it may be beneficial to deploy control measures to reduce worker exposures.

Combined Burden of Heat and Particulate Matter Air Quality in WA Agriculture

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.

Excitation-Emission Matrix Spectroscopy for Analysis of Chemical Composition of Combustion Generated Particulate Matter

Analysis of particulate matter (PM) is important for the assessment of human exposures to potentially harmful agents, notably combustion-generated PM. Specifically, polycyclic aromatic hydrocarbons (PAHs) found in ultrafine PM have been linked to cardiovascular diseases and carcinogenic and mutagenic effects. In this study, we quantify the presence and concentrations of PAHs with lower molecular weight (LMW, 126 < MW < 202) and higher molecular weight (HMW, 226 < MW < 302), i.e., smaller and larger than Pyrene, in combustion-generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy. Laboratory combustion PM samples were generated in a laminar diffusion inverted gravity flame reactor (IGFR) operated on ethylene and ethane. Fuel dilution by Ar in 0% to 90% range controlled the flame temperature. The colder flames result in lower PM yields however, the PM PAH content increases significantly. Temperature thresholds for PM transition from low to high organic carbon content were characterized based on the maximum flame temperature (T_max,c ∼ 1791 to 1857 K) and the highest soot luminosity region temperature (T*_c ∼ 1600 to 1650K). Principal component regression (PCR) analysis of the EEM spectra of IGFR samples correlates to GCMS data with R² = 0.988 for LMW and 0.998 for HMW PAHs. PCR-EEM analysis trained on the IGFR samples was applied to PM samples from woodsmoke and diesel exhaust, the model accurately predicts HMW PAH concentrations with R² = 0.976 and overestimates LMW PAHs.

Performance of a Low-Cost Sensor Community Air Monitoring Network in Imperial County, CA

Air monitoring networks developed by communities have potential to reduce exposures and affect environmental health policy, yet there have been few performance evaluations of networks of these sensors in the field. We developed a network of over 40 air sensors in Imperial County, CA, which is delivering real-time data to local communities on levels of particulate matter. We report here on the performance of the Network to date by comparing the low-cost sensor readings to regulatory monitors for 4 years of operation (2015-2018) on a network-wide basis. Annual mean levels of PM10 did not differ statistically from regulatory annual means, but did for PM2.5 for two out of the 4 years. R2s from ordinary least square regression results ranged from 0.16 to 0.67 for PM10, and increased each year of operation. Sensor variability was higher among the Network monitors than the regulatory monitors. The Network identified a larger number of pollution episodes and identified under-reporting by the regulatory monitors. The participatory approach of the project resulted in increased engagement from local and state agencies and increased local knowledge about air quality, data interpretation, and health impacts. Community air monitoring networks have the potential to provide real-time reliable data to local populations.

Using Vehicles' Rendezvous for In Situ Calibration of Instruments in Fleet Vehicle-Based Air Pollution Mobile Monitoring

This study examines the feasibility of the in situ calibration of instruments for fleet vehicle-based mobile monitoring of ultrafine particles (UFPs) and black carbon (BC) by comparing rendezvous vehicle measurements. Two vehicles with identical makes and models of UFP and BC monitors as well as GPS receivers were sampled within 140 m of each other for 2 h in total during winter in Seattle, Washington. To identify an optimal intervehicle distance for rendezvous calibration, 6 different buffers within 0-140 m for UFP monitors and 5 different buffers within 0-90 m for BC monitors were chosen, and the results of calibration were compared against a reference scenario, which consisted of mobile colocation measurements with both sets of the UFP and BC monitors deployed in one of the vehicles. Results indicate that the optimal distances for rendezvous calibration are 10-80 m for UFP monitors and 0-30 m for BC monitors. In comparison with the mobile colocation calibration, the rendezvous calibration shows a normalized root mean squared deviation of 6-14% and a normalized mean absolute deviation of 4-8% for these monitors. Criteria for applying a rendezvous calibration approach are presented, and an extension of this approach to an instrumented fleet of mobile monitoring vehicles is discussed.

Use of low-cost PM monitors and a multi-wavelength aethalometer to characterize PM2.5 in the Yakama Nation Reservation

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/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 375-880nm, 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.

Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study

Low-cost air monitoring sensors are an appealing tool for assessing pollutants in environmental studies. Portable low-cost sensors hold promise to expand temporal and spatial coverage of air quality information. However, researchers have reported challenges in these sensors' operational quality. We evaluated the performance characteristics of two widely used sensors, the Plantower PMS A003 and Shinyei PPD42NS, for measuring fine particulate matter compared to reference methods, and developed regional calibration models for the Los Angeles, Chicago, New York, Baltimore, Minneapolis-St. Paul, Winston-Salem and Seattle metropolitan areas. Duplicate Plantower PMS A003 sensors demonstrated a high level of precision (averaged Pearson's r = 0.99), and compared with regulatory instruments, showed good accuracy (cross-validated R² = 0.96, RMSE = 1.15 µg/m³ for daily averaged PM_2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson's r = 0.84) and accuracy (cross-validated R² = 0.40, RMSE = 4.49 µg/m³). Region-specific Plantower PMS A003 models, calibrated with regulatory instruments and adjusted for temperature and relative humidity, demonstrated acceptable performance metrics for daily average measurements in the other six regions (R² = 0.74-0.95, RMSE = 2.46-0.84 µg/m³). Applying the Seattle model to the other regions resulted in decreased performance (R² = 0.67-0.84, RMSE = 3.41-1.67 µg/m³), likely due to differences in meteorological conditions and particle sources. We describean approach to metropolitan region-specific calibration models for low-cost sensors that can be used with cautionfor exposure measurement in epidemiological studies.

Excitation Emission Matrix Fluorescence Spectroscopy for Combustion Generated Particulate Matter Source Identification

The inhalation of particulate matter (PM) is a significant health risk associated with reduced life expectancy due to increased cardio-pulmonary disease and exacerbation of respiratory diseases such as asthma and pneumonia. PM originates from natural and anthropogenic sources including combustion engines, cigarettes, agricultural burning, and forest fires. Identifying the source of PM can inform effective mitigation strategies and policies, but this is difficult to do using current techniques. Here we present a method for identifying PM source using excitation emission matrix (EEM) fluorescence spectroscopy and a machine learning algorithm. We collected combustion generated PM2.5 from wood burning, diesel exhaust, and cigarettes using filters. Filters were weighted to determine mass concentration followed by extraction into cyclohexane and analysis by EEM fluorescence spectroscopy. Spectra obtained from each source served as training data for a convolutional neural network (CNN) used for source identification in mixed samples. This method can predict the presence or absence of the three laboratory sources with an overall accuracy of 89% when the threshold for classifying a source as present is 1.1 μg/m3 in air over a 24-hour sampling time. The limit of detection for cigarette, diesel and wood are 0.7, 2.6, 0.9 μg/m3, respectively, in air assuming a 24-hour sampling time at an air sampling rate of 1.8 liters per minute. We applied the CNN algorithm developed using the laboratory training data to a small set of field samples and found the algorithm was effective in some cases but would require a training data set containing more samples to be more broadly applicable.

Momentary mood response to natural outdoor environments in four European cities

Exposure to natural outdoor environments (NOE) has been shown in population-level studies to reduce anxiety and psychological distress. This study investigated how exposure to one's everyday natural outdoor environments over one week influenced mood among residents of four European cities including Barcelona (Spain), Stoke-on-Trent (United Kingdom), Doetinchem (The Netherlands) and Kaunas (Lithuania). Participants (n = 368) wore a smartphone equipped with software applications to track location and mood (using mobile ecological momentary assessment (EMA) software), for seven consecutive days. We estimated random-effects ordered logistic regression models to examine the association between mood (positive and negative affect), and exposure to green space, represented by two binary variables indicating exposure versus no exposure to NOE using GPS tracking and satellite and aerial imagery, 10 and 30 min prior to participants' completing the EMA. Models were adjusted for home city, day of the week, hour of the day, EMA survey type, residential NOE exposure, and sex, age, education level, mental health status and neighbourhood socioeconomic status. In addition, we tested for heterogeneity of effect by city, sex, age, residential NOE exposure and mental health status. Within 10 min of NOE exposure, compared to non-exposure, we found that overall there was a positive relationship with positive affect (OR: 1.39, 95% CI: 1.06, 1.81) of EMA surveys, and non-significant negative association with negative affect (OR: 0.80, 95% CI: 0.58, 1.10). When stratifying, associations were consistently found for Stoke-on-Trent inhabitants and men, while findings by age group were inconsistent. Weaker and less consistent associations were found for exposure 30 min prior to EMA. Our findings support increasing evidence of psychological and mental health benefits of exposure to natural outdoor environments, especially among urban populations such as those included in our study.

Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report

Air quality data from satellites and low-cost sensor systems, together with output from air quality models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much-needed air quality information in countries without them. Each of these technologies has strengths and limitations that need to be considered when integrating them to develop a robust and diverse global air quality monitoring network. To address these issues, the American Thoracic Society, the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, and the National Institute of Environmental Health Sciences convened a workshop in May 2017 to bring together global experts from across multiple disciplines and agencies to discuss current and near-term capabilities to monitor global air pollution. The participants focused on four topics: 1) current and near-term capabilities in air pollution monitoring, 2) data assimilation from multiple technology platforms, 3) critical issues for air pollution monitoring in regions without a regulatory-quality stationary monitoring network, and 4) risk communication and health messaging. Recommendations for research and improved use were identified during the workshop, including a recognition that the integration of data across monitoring technology groups is critical to maximizing the effectiveness (e.g., data accuracy, as well as spatial and temporal coverage) of these monitoring technologies. Taken together, these recommendations will advance the development of a global air quality monitoring network that takes advantage of emerging technologies to ensure the availability of free, accessible, and reliable air pollution data and forecasts to health professionals, as well as to all global citizens.

Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes

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 μm in diameter) hourly average concentration is equal to or greater than 35 μg m-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.

Exposures to Air Pollution and Noise from Multi-Modal Commuting in a Chinese City

Background: Modern urban travel includes mixtures of transit options, which potentially impact individual pollution exposures and health. This study aims to investigate variations in traffic-related air pollution and noise levels experienced in traffic in Chengdu, China. Methods: Real-time PM_2.5, black carbon (BC), and noise levels were measured for four transportation modes (car, bus, subway, and shared bike) on scripted routes in three types of neighborhoods (urban core, developing neighborhood, and suburb). Each mode of transportation in each neighborhood was sampled five times in summer and winter, respectively. After quality control, mixed effect models were built for the three pollutants separately. Results: Air pollutants had much higher concentrations in winter. Urban Core had the highest PM_2.5 and BC concentrations across seasons compared to the other neighborhoods. The mixed effect model indicated that car commutes were associated with lower PM_2.5 (−34.4 μg/m³; 95% CI: −47.5, −21.3), BC (−2016.4 ng/m³; 95% CI: −3383.8, −648.6), and noise (−9.3 dBA; 95% CI: −10.5, −8.0) levels compared with other modes; subway commutes had lower PM_2.5 (−11.9 μg/m³; 95% CI: 47.5, −21.3), but higher BC (2349.6 ng/m³; 95% CI: 978.1, 3722.1) and noise (3.0 dBA; 95% CI: 1.7, 4.3) levels than the other three modes of transportation. Conclusion: Personal exposure to air pollution and noise vary by season, neighborhood, and transportation modes. Exposure models accounting for environmental, meteorological, and behavioral factors, and duration of mixed mode commuting may be useful for health studies of urban traffic microenvironments.

A Focused Review of Smartphone Diet-Tracking Apps: Usability, Functionality, Coherence With Behavior Change Theory, and Comparative Validity of Nutrient Intake and Energy Estimates

BACKGROUND

Smartphone diet-tracking apps may help individuals lose weight, manage chronic conditions, and understand dietary patterns; however, the usabilities and functionalities of these apps have not been well studied.

OBJECTIVE

The aim of this study was to review the usability of current iPhone operating system (iOS) and Android diet-tracking apps, the degree to which app features align with behavior change constructs, and to assess variations between apps in nutrient coding.

METHODS

The top 7 diet-tracking apps were identified from the iOS iTunes and Android Play online stores, downloaded and used over a 2-week period. Each app was independently scored by researchers using the System Usability Scale (SUS), and features were compared with the domains in an integrated behavior change theory framework: the Theoretical Domains Framework. An estimated 3-day food diary was completed using each app, and food items were entered into the United States Department of Agriculture (USDA) Food Composition Databases to evaluate their differences in nutrient data against the USDA reference.

RESULTS

Of the apps that were reviewed, LifeSum had the highest average SUS score of 89.2, whereas MyDietCoach had the lowest SUS score of 46.7. Some variations in features were noted between Android and iOS versions of the same apps, mainly for MyDietCoach, which affected the SUS score. App features varied considerably, yet all of the apps had features consistent with Beliefs about Capabilities and thus have the potential to promote self-efficacy by helping individuals track their diet and progress toward goals. None of the apps allowed for tracking of emotional factors that may be associated with diet patterns. The presence of behavior change domain features tended to be weakly correlated with greater usability, with R² ranging from 0 to .396. The exception to this was features related to the Reinforcement domain, which were correlated with less usability. Comparing the apps with the USDA reference for a 3-day diet, the average differences were 1.4% for calories, 1.0% for carbohydrates, 10.4% for protein, and -6.5% for fat.

CONCLUSIONS

Almost all reviewed diet-tracking apps scored well with respect to usability, used a variety of behavior change constructs, and accurately coded calories and carbohydrates, allowing them to play a potential role in dietary intervention studies.

Real-Time Monitoring of Spray Drift from Three Different Orchard Sprayers

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.

Deliberating performance targets workshop: Potential paths for emerging PM2.5 and O3 air sensor progress

The United States Environmental Protection Agency held an international two-day workshop in June 2018 to deliberate possible performance targets for non-regulatory fine particulate matter (PM_2.5) and ozone (O₃) air sensors. The need for a workshop arose from the lack of any market-wide manufacturer requirement for Ozone documented sensor performance evaluations, the lack of any independent third party or government-based sensor performance certification program, and uncertainty among all users as to the general usability of air sensor data. A multi-sector subject matter expert panel was assembled to facilitate an open discussion on these issues with multiple stakeholders. This summary provides an overview of the workshop purpose, key findings from the deliberations, and considerations for future actions specific to sensors. Important findings concerning PM_2.5 and O₃ sensors included the lack of consistent performance indicators and statistical metrics as well as highly variable data quality requirements depending on the intended use. While the workshop did not attempt to yield consensus on any topic, a key message was that a number of possible future actions would be beneficial to all stakeholders regarding sensor technologies. These included documentation of best practices, sharing quality assurance results along with sensor data, and the development of a common performance target lexicon, performance targets, and test protocols.

Real-time particle monitoring of pesticide drift from an axial fan airblast orchard sprayer

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.

The association between park facilities and the occurrence of physical activity during park visits

Prior research has found a positive relationship between the variety of park facilities and park-based physical activity (PA), but has not provided an estimate of the effect that additional different PA facilities have on whether an individual is active during a park visit. Using objective measures of park visits and PA from an urban sample of 225 adults in King County, Washington, we compared the variety of PA facilities in parks visited where an individual was active to PA facilities in parks where the same individual was sedentary. Each additional different PA facility at a park was associated with a 6% increased probability of being active during a visit. Adding additional different PA facilities to a park appears to have a moderate effect on whether an individual is active during a park visit, which could translate into large community health impacts when scaled up to multiple park visitors.