The increasing frequency and severity of wildfires due to climate change pose health risks to migrant farm workers laboring in wildfire‐prone regions. This study focuses on Sonoma County, California, investigating the effectiveness of air monitoring and safety protections for farmworkers. The analysis employs AirNow and PurpleAir PM2.5data acquired during the 2020 wildfire season, comparing spatial variability in air pollution. Results show significant differences between the single Sonoma County AirNow station data and the PurpleAir data in the regions directly impacted by wildfire smoke. Three distinct wildfire pollution episodes with elevated PM2.5levels are identified to examine the regional variations. This study also examines the system used to exempt farmworkers from wildfire mandatory evacuation orders, finding incomplete information, ad hoc decision‐making, and scant enforcement. In response, we make policy recommendations that include stricter requirements for employers, real‐time air quality monitoring, post‐exposure health screenings, and hazard pay. Our findings underscore the need for significant consideration of localized air quality readings and the importance of equitable disaster policies for protecting the health of farmworkers (particularly those who are undocumented migrants) in the face of escalating wildfire risks.
In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.
more » « less- NSF-PAR ID:
- 10525635
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Climate and Atmospheric Science
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2397-3722
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire‐related smoke PM2.5fields and variability in concentration‐response parameters from epidemiologic studies of ambient and smoke PM2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire‐attributable PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration‐response parameters derived from studies of ambient and wildfire‐specific PM2.5are used to assess health‐related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health‐related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire‐related decision‐making.
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Abstract One of the primary challenges associated with evaluating smoke models is the availability of observations. The limited density of traditional air quality monitoring networks makes evaluating wildfire smoke transport challenging, particularly over regions where smoke plumes exhibit significant spatiotemporal variability. In this study, we analyzed smoke dispersion for the 2018 Pole Creek and Bald Mountain Fires, which were located in central Utah. Smoke simulations were generated using a coupled fire‐atmosphere model, which simultaneously renders fire growth, fire emissions, plume rise, smoke dispersion, and fire‐atmosphere interactions. Smoke simulations were evaluated using PM2.5observations from publicly accessible fixed sites and a semicontinuously running mobile platform. Calibrated measurements of PM2.5made by low‐cost sensors from the Air Quality and yoU (AQ&U) network were within 10% of values reported at nearby air quality sites that used Federal Equivalent Methods. Furthermore, results from this study show that low‐cost sensor networks and mobile measurements are useful for characterizing smoke plumes while also serving as an invaluable data set for evaluating smoke transport models. Finally, coupled fire‐atmosphere model simulations were able to capture the spatiotemporal variability of wildfire smoke in complex terrain for an isolated smoke event caused by local fires. Results here suggest that resolving local drainage flow could be critical for simulating smoke transport in regions of significant topographic relief.
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Abstract Previous research on the health and air quality impacts of wildfire smoke has largely focused on the impact of smoke on outdoor air quality; however, many people spend a majority of their time indoors. The quality of indoor air on smoke-impacted days is largely unknown. In this analysis, we use publicly available data from an existing large network of low-cost indoor and outdoor fine particulate matter (PM2.5) monitors to quantify the relationship between indoor and outdoor particulate air quality on smoke-impacted days in 2020 across the western United States (US). We also investigate possible regional and socioeconomic trends in this relationship for regions surrounding six major cities in the western US. We find indoor PM2.5concentrations are 82% or 4.2
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