Explore the vast range of titles available.

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.

An integrated network of environmental monitors was developed to continuously measure several airborne hazards in a manufacturing facility. The monitors integrated low-cost sensors to measure particulate matter, carbon monoxide, ozone and nitrogen dioxide, noise, temperature and humidity. The monitors were developed and tested in situ for three months in several overlapping deployments, before a full cohort of 40 was deployed in a heavy vehicle manufacturing facility for a year of data collection. The monitors collect data from each sensor and report them to a central database every 5 min. The work includes an experimental validation of the particle, gas and noise monitors. The R2 for the particle sensor ranges between 0.98 and 0.99 for particle mass densities up to 300 μg/m3. The R2 for the carbon monoxide sensor is 0.99 for concentrations up to 15 ppm. The R2 for the oxidizing gas sensor is 0.98 over the sensitive range from 20 to 180 ppb. The noise monitor is precise within 1% between 65 and 95 dBA. This work demonstrates the capability of distributed monitoring as a means to examine exposure variability in both space and time, building an important preliminary step towards a new approach for workplace hazard monitoring.

We propose the core collaborative community science framework, an original conceptual framework that integrates and modifies best practices from community science and collective impact groups to support investigations of environmental health and justice. The core collaborative community science framework differs from more typical frameworks for community science, which often frame projects as static and either scientist or community led; these framings can limit the potential for co-production and action-oriented models of science. Frameworks are lacking to help community science collaborators determine the contributions and leadership needed to initiate, sustain, and link together multiple projects that jointly support local learning and action, as well as contribute to broader scientific knowledge of complex social-ecological systems. The core collaborative community science framework offers three main innovations and contributions: (1) It invests in a core collaborative group structure, designed to increase community capacity and resilience through an expanded network of partners dedicated to the reduction of systematic inequities and injustices; (2) It seeds and supports multiple, diverse research projects implemented across complex social-ecological systems, focusing first on community-identified needs, and then on the questions community science can help answer; and (3) It facilitates dynamic shared responsibilities and leadership for partners from community, research, and government institutions, recognizing the need for shared contributions at all project phases. We offer examples from the Green Duwamish Learning Landscape in Washington, USA to show how project partners have coordinated their work focused on social, ecological, and human health and navigated challenges related to funding, staffing, and governance. We share insights on how to help integrate community science within the social fabric of communities, especially those faced with environmental health and justice challenges.

Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.

Heavy metal concentrations often vary at small spatial scales not captured by air monitoring networks, with implications for environmental justice in industrial‐adjacent communities. Pollutants measured in moss tissues are commonly used as a screening tool to guide use of more expensive resources, like air monitors. Such studies, however, rarely address environmental justice issues or involve the residents and other decision makers expected to utilize results. Here, we piloted a community science approach, engaging over 55 people from nine institutions, to map heavy metals using moss in two industrial‐adjacent neighborhoods. This area, long known for disproportionately poor air quality, health outcomes, and racial inequities, has only one monitor for heavy metals. Thus, an initial understanding of spatial patterns is critical for gauging whether, where, and how to invest further resources toward investigating heavy metals. Local youth‐led sampling of the moss Orthotrichum lyellii from trees across a 250 × 250 m sampling grid (n = 79) and generated data comparable to expert‐collected samples (n = 19). We mapped 21 chemical elements measured in moss, including 6 toxic “priority” metals: arsenic, cadmium, chromium, cobalt, lead, and nickel. Compared to other urban O. lyellii studies, local moss had substantially higher priority metals, especially arsenic and chromium, encouraging community members to investigate further. Potential hotspots of priority metals varied somewhat but tended to peak near the central industrial core where many possible emission sources, including legacy contamination and converge. Informed by these findings, community members successfully advocated regulators for a second study phase—a community‐directed air monitoring campaign to evaluate residents' exposure to heavy metals—as is needed to connect moss results back to the partnership's core goal of understanding drivers of health disparities. This follow‐up campaign will measure metals in the PM10 fraction owing to clues in the current study that airborne soil and dust may be locally important carriers of priority metals. Future work will address how our approach combining bioindicators and community science ultimately affects success addressing longstanding environmental justice concerns. For now, we illustrate the potential to co‐create new knowledge, to help catalyze and strategize next steps, in a complex air quality investigation.

Policy action in the coming decade will be crucial to achieving globally agreed upon goals to decarbonize the economy and build resilience to a warmer, more extreme climate. Public health has an essential role in climate planning and action: “Co-benefits” to health help underpin greenhouse gas reduction strategies, while safeguarding health—particularly of the most vulnerable—is a frontline local adaptation goal. Using the structure of the core functions and essential services (CFES), we reviewed the literature documenting the evolution of public health’s role in climate change action since the 2009 launch of the US CDC Climate and Health Program. We found that the public health response to climate change has been promising in the area of assessment (monitoring climate hazards, diagnosing health status, assessing vulnerability); mixed in the area of policy development (mobilizing partnerships, mitigation and adaptation activities); and relatively weak in assurance (communication, workforce development and evaluation). We suggest that the CFES model remains important, but is not aligned with three concepts—governance, implementation and adjustment—that have taken on increasing importance. Adding these concepts to the model can help ensure that public health fulfills its potential as a proactive partner fully integrated into climate policy planning and action in the coming decade.

ObjectiveTransit operators have reported physical and mental health impacts related to drug use on transit. We sought to characterize air and surface concentrations of methamphetamine and fentanyl on transit in Washington and Oregon states to characterize transit operator exposure to these substances and help transit agencies prioritize interventions for operator well-being.Material and MethodsWe collected 78 total dust air samples (near the transit operator and elsewhere) and 102 surface samples via methanol-wetted swab from 11 buses and 19 trains. Background environmental samples were collected in nearby urban areas. Filters and swabs were analyzed for fentanyl and methamphetamine using LC/MS/MS.ResultsOn transit samples, fentanyl was detected in 25% of air samples and 46% of surface samples; methamphetamine was detected in all air samples and 98% of surface samples. No fentanyl was found in environmental air or surface samples; methamphetamine was found in 3 (of 15) environmental air samples and 4 (of 14) environmental surface samples. The highest fentanyl air sample (0.14 µg/m3) was collected in the passenger area of a train, exceeding the ACGIH 8-hr TWA TLV of 0.1 µg/m3. No surface samples exceed the ACGIH fentanyl surface level TLV (10 ng/cm2). Other occupational standards or guidelines do not exist.ConclusionsFentanyl and methamphetamine were frequently found in the air and on surfaces of the vehicles in our study, at levels exceeding the environmental samples. Protecting transit operators from second-hand exposures, and from the stressors of witnessing and responding to smoking events, represents appropriate occupational health action consistent with the public health goal of smoke free workplaces. Where elimination is not possible, engineering and administrative controls (ventilation, cleaning) should be evaluated along with training and workplace supports for after operators observe or respond to drug events.

Background More frequent and intense wildfires will increase concentrations of smoke in schools and childcare settings. Low-cost sensors can assess fine particulate matter (PM 2.5 ) concentrations with high spatial and temporal resolution. Objective We sought to optimize the use of sensors for decision-making in schools and childcare settings during wildfire smoke to reduce children’s exposure to PM 2.5 . Methods We measured PM 2.5 concentrations indoors and outdoors at four schools in Washington State during wildfire smoke in 2020–2021 using low-cost sensors and gravimetric samplers. We randomly sampled 5-min segments of low-cost sensor data to create simulations of brief portable handheld measurements. Results During wildfire smoke episodes (lasting 4–19 days), median hourly PM 2.5 concentrations at different locations inside a single facility varied by up to 49.6 µg/m 3 (maximum difference) during school hours. Median hourly indoor/outdoor ratios across schools ranged from 0.22 to 0.91. Within-school differences in concentrations indicated that it is important to collect measurements throughout a facility. Simulation results suggested that making handheld measurements more often and over multiple days better approximates indoor/outdoor ratios for wildfire smoke. During a period of unstable air quality, PM 2.5 over the next hour indoors was more highly correlated with the last 10-min of data (mean R 2  = 0.94) compared with the last 3-h (mean R 2  = 0.60), indicating that higher temporal resolution data is most informative for decisions about near-term activities indoors. Impact statement As wildfires continue to increase in frequency and severity, staff at schools and childcare facilities are increasingly faced with decisions around youth activities, building use, and air filtration needs during wildfire smoke episodes. Staff are increasingly using low-cost sensors for localized outdoor and indoor PM 2.5 measurements, but guidance in using and interpreting low-cost sensor data is lacking. This paper provides relevant information applicable for guidance in using low-cost sensors for wildfire smoke response.

Occupational exposure assessment is almost exclusively accomplished with personal sampling. However, personal sampling can be burdensome and suffers from low sample sizes, resulting in inadequately characterized workplace exposures. Sensor networks offer the opportunity to measure occupational hazards with a high degree of spatiotemporal resolution. Here, we demonstrate an approach to estimate personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O3), and noise using hazard data from a sensor network. We simulated stationary and mobile employees that work at the study site, a heavy-vehicle manufacturing facility. Network-derived exposure estimates compared favorably to measurements taken with a suite of personal direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The root mean square error (RMSE) between network-derived exposure estimates and personal DRI measurements for mobile employees was 0.15 mg/m3, 1 ppm, 82 ppb, and 3 dBA for PM, CO, O3, and noise, respectively. Pearson correlation between network-derived exposure estimates and DRI measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed estimating personal exposure to occupational hazards it holds promise as an additional tool to be used with traditional personal sampling due to the ability to frequently and easily collect exposure information on many employees.

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 (NO 2 ) 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 NO 2 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 NO 2 ; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO 2 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 NO 2 ; CV- R 2  = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO 2 and R 2  = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO 2 and CV- R 2  = 0.51 (with LCS). Impact We developed a spatiotemporal model for nitrogen dioxide (NO 2 ) 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 NO 2 model and found the additional spatial information the sensors provided predicted NO 2 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.