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  1. The impacts of wildfires along the wildland urban interface (WUI) on atmospheric particulate concentrations and composition are an understudied source of air pollution exposure. To assess the residual impacts of the 2021 Marshall Fire (Colorado), a wildfire that predominantly burned homes and other human-made materials, on homes within the fire perimeter that escaped the fire, we performed a combination of fine particulate matter (PM2.5) filter sampling and chemical analysis, indoor dust collection and chemical analysis, community scale PurpleAir PM2.5 analysis, and indoor particle number concentration measurements. Following the fire, the chemical speciation of dust collected in smoke-affected homes in the burned zone showed elevated concentrations of the biomass burning marker levoglucosan (medianlevo = 4147 ng g−1), EPA priority toxic polycyclic aromatic hydrocarbons (median Σ16PAH = 1859.3 ng g−1), and metals (median Σ20Metals = 34.6 mg g−1) when compared to samples collected in homes outside of the burn zone 6 months after the fire. As indoor dust particles are often resuspended and can become airborne, the enhanced concentration of hazardous metals and organics within dust samples may pose a threat to human health. Indoor airborne particulate organic carbon (median = 1.91 μg m−3), particulate elemental carbon (median = .02 μg m−3), and quantified semi-volatile organic species in PM2.5 were found in concentrations comparable to ambient air in urban areas across the USA. Particle number and size distribution analysis at a heavily instrumented supersite home located immediately next to the burned area showed indoor particulates in low concentrations (below 10 μg m−3) across various sizes of PM (12 nm–20 μm), but were elevated by resuspension from human activity, including cleaning. 
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    Free, publicly-accessible full text available January 1, 2024
  2. Abstract Background

    Household air pollution (HAP) from cooking with solid fuels has adverse health effects. REACCTING (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana) was a randomized cookstove intervention study that aimed to determine the effects of two types of “improved” biomass cookstoves on health using self-reported health symptoms and biomarkers of systemic inflammation from dried blood spots for female adult cooks and children, and anthropometric growth measures for children only.


    Two hundred rural households were randomized into four different cookstove groups. Surveys and health measurements were conducted at four time points over a two-year period. Chi-square tests were conducted to determine differences in self-reported health outcomes. Linear mixed models were used to assess the effect of the stoves on inflammation biomarkers in adults and children, and to assess the z-score deviance for the anthropometric data for children.


    We find some evidence that two biomarkers of oxidative stress and inflammation, serum amyloid A and C-reactive protein, decreased among adult primary cooks in the intervention groups relative to the control group. We do not find detectable impacts for any of the anthropometry variables or self-reported health.


    Overall, we conclude that the REACCTING intervention did not substantially improve the health outcomes examined here, likely due to continued use of traditional stoves, lack of evidence of particulate matter emissions reductions from “improved” stoves, and mixed results for HAP exposure reductions.

    Clinical trial registry Institutes of Health); Trial Registration Number:NCT04633135; Date of Registration: 11 November 2020 – Retrospectively registered.


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  3. Abstract. Advances in ambient environmental monitoring technologies are enabling concerned communities and citizens to collect data to better understand their local environment and potential exposures. These mobile, low-cost tools make it possible to collect data with increased temporal and spatial resolution, providing data on a large scale with unprecedented levels of detail. This type of data has the potential to empower people to make personal decisions about their exposure and support the development of local strategies for reducing pollution and improving health outcomes. However, calibration of these low-cost instruments has been a challenge. Often, a sensor package is calibrated via field calibration. This involves colocating the sensor package with a high-quality reference instrument for an extended period and then applying machine learning or other model fitting technique such as multiple linear regression to develop a calibration model for converting raw sensor signals to pollutant concentrations. Although this method helps to correct for the effects of ambient conditions (e.g., temperature) and cross sensitivities with nontarget pollutants, there is a growing body of evidence that calibration models can overfit to a given location or set of environmental conditions on account of the incidental correlation between pollutant levels and environmental conditions, including diurnal cycles. As a result, a sensor package trained at a field site may provide less reliable data when moved, or transferred, to a different location. This is a potential concern for applications seeking to perform monitoring away from regulatory monitoring sites, such as personal mobile monitoring or high-resolution monitoring of a neighborhood. We performed experiments confirming that transferability is indeed a problem and show that it can be improved by collecting data from multiple regulatory sites and building a calibration model that leverages data from a more diverse data set. We deployed three sensor packages to each of three sites with reference monitors (nine packages total) and then rotated the sensor packages through the sites over time. Two sites were in San Diego, CA, with a third outside of Bakersfield, CA, offering varying environmental conditions, general air quality composition, and pollutant concentrations. When compared to prior single-site calibration, the multisite approach exhibits better model transferability for a range of modeling approaches. Our experiments also reveal that random forest is especially prone to overfitting and confirm prior results that transfer is a significant source of both bias and standard error. Linear regression, on the other hand, although it exhibits relatively high error, does not degrade much in transfer. Bias dominated in our experiments, suggesting that transferability might be easily increased by detecting and correcting for bias. Also, given that many monitoring applications involve the deployment of many sensor packages based on the same sensing technology, there is an opportunity to leverage the availability of multiple sensors at multiple sites during calibration to lower the cost of training and better tolerate transfer. We contribute a new neural network architecture model termed split-NN that splits the model into two stages, in which the first stage corrects for sensor-to-sensor variation and the second stage uses the combined data of all the sensors to build a model for a single sensor package. The split-NN modeling approach outperforms multiple linear regression, traditional two- and four-layer neural networks, and random forest models. Depending on the training configuration, compared to random forest the split-NN method reduced error 0 %–11 % for NO2 and 6 %–13 % for O3. 
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  4. Abstract

    Atmospheric iron solubility varies depending on whether the particles are collected in rural or urban areas, with urban areas showing increased iron solubility. In this study, we investigate if the iron species present in different environments affects its ultimate solubility. Field data are presented from the Platte River Air Pollution and Photochemistry Experiment (PRAPPE), aimed at understanding the interactions between organic carbon and trace elements in atmospheric particulate matter (PM). 24‐hr PM2.5samples were collected during the summer and winter (2016–2017), at three different sites on the Eastern Colorado plains: an urban, agricultural, and a mixed site. Downtown Denver had an average total and water‐soluble iron air concentration of 181.2 and 7.7 ng m−3, respectively. Platteville, the mixed site, had an average of total iron of 76.1 ng m−3, with average water‐soluble iron concentration of 9.1 ng m−3. Jackson State Park (rural/agricultural) had the lowest total iron average of 31.5 ng m−3and the lowest water‐soluble iron average, 1.3 ng m−3. The iron oxidation state and chemical speciation of 97 samples across all sites and seasons was probed by X‐ray absorption near edge structure (XANES) spectroscopy. The most common iron phases observed were almandine (Fe₃Al₂Si₃O₁₂) (Denver 21%, Platteville 16%, Jackson 24%), magnetite (Fe3O4) (Denver 9%, Platteville 4%, Jackson 5%) and Fe (III)dextran (Denver 5%, Platteville 13%, Jackson 5%), a surrogate for Fe‐organic complexes. Additionally, native iron [Fe(0)] was found in significant amounts at all sites. No correlation was observed between iron solubility and iron oxidation state or chemical speciation.

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