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Background: An estimated 3 billion people, largely in low- and middle-income countries, rely on unclean fuels for cooking, heating, and lighting to meet household energy needs. The resulting exposure to household air pollution (HAP) is a leading cause of pneumonia, chronic lung disease, and other adverse health effects. In the last decade, randomized controlled trials of clean cooking interventions to reduce HAP have been conducted. We aim to provide guidance on how to interpret the findings of these trials and how they should inform policy makers and practitioners.Methods: We assembled a multidisciplinary working group of international researchers, public health practitioners, and policymakers with expertise in household air pollution from within academia, the American Thoracic Society, funders, nongovernmental organizations, and global organizations, including the World Bank and the World Health Organization. We performed a literature search, convened four sessions via web conference, and developed consensus conclusions and recommendations via the Delphi method.Results: The committee reached consensus on 14 conclusions and recommendations. Although some trials using cleaner-burning biomass stoves or cleaner-cooking fuels have reduced HAP exposure, the committee was divided (with 55% saying no and 45% saying yes) on whether the studied interventions improved measured health outcomes.Conclusions: HAP is associated with adverse health effects in observational studies. However, it remains unclear which household energy interventions reduce exposure, improve health, can be scaled, and are sustainable. Researchers should engage with policy makers and practitioners working to scale cleaner energy solutions to understand and address their information needs.

The primary source of measurement error from widely used particulate matter (PM) PurpleAir sensors is ambient relative humidity (RH). Recently, the US EPA developed a national correction model for PM2.5 concentrations measured by PurpleAir sensors (Barkjohn model). However, their study included few sites in the southeastern US, the most humid region of the country. To provide high-quality spatial and temporal data and inform community exposure risks in this area, our study developed and evaluated PurpleAir correction models for use in the warm–humid climate zones of the US. We used hourly PurpleAir data and hourly reference-grade PM2.5 data from the EPA Air Quality System database from January 2021 to August 2023. Compared with the Barkjohn model, we found improved performance metrics, with error metrics decreasing by 16 %–23 % when applying a multilinear regression model with RH and temperature as predictive variables. We also tested a novel semi-supervised clustering method and found that a nonlinear effect between PM2.5 and RH emerges around RH of 50 %, with slightly greater accuracy. Therefore, our results suggested that a clustering approach might be more accurate in high humidity conditions to capture the nonlinearity associated with PM particle hygroscopic growth. [ABSTRACT FROM AUTHOR]

We use air pollution exposure measurements and household survey data from four studies conducted across three countries in sub‐Saharan Africa (SSA) to analyze the association between carbon monoxide (CO) exposure from cooking with biomass and indicators of cognitive impairment. While there is strong evidence on the relationship between ambient air pollution exposure and cognitive impairment from studies in high‐income countries, relatively little research has focused on household air pollution (HAP) in low‐income country settings where risks of HAP exposure are high. This study is the first to our knowledge to focus on the association between HAP exposure (specifically CO exposure) and cognitive impairment across diverse settings in SSA. We use 24‐hour measurements of primary cooks' exposure to CO across four study sites: urban Zambia (n = 493); urban Malawi (n = 130); rural Malawi (n = 102); and urban Rwanda (n = 2,576). We model the estimated percent carboxyhemoglobin (%COHb) of cooks and map values to a toxicological profile for risk of cognitive impairment. We find that across all study settings, cooks' average %COHb levels are below levels of daily concern, but that cooks who use charcoal for preparing greater than 40% of meals are more likely to spend additional time at higher levels of risk. For the urban Zambia sample, we compare %COHb and frequency of charcoal use to a series of cognitive test scores and find no consistent relationships between %COHb and cognitive test scores. High levels of daily CO exposure from cooks across SSA highlight the potential for longer‐term negative cognitive (and other) health outcomes motivating additional research and efforts to characterize and mitigate risk. [ABSTRACT FROM AUTHOR]

Low-cost gas and particulate matter sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference-grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using colocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modeling approaches to calibrate the electrochemical gas sensors: k -nearest neighbors (k NN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, O x , NO, and NO 2 sensors, we found that k NN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models were also the most transferable approach when applied to deployment data collected in Malawi. We compared k NN hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, partly due to proximity to residential biomass combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using colocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH >70 %) conditions and influence from emissions from nearby residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi. Data recovery (30 %–80 %) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural, low-income settings would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory-grade monitoring infrastructure. [ABSTRACT FROM AUTHOR]

ABSTRACT Atmospheric transformations determine the contribution of emissions from combustion systems to fine particulate matter (PM) mass. For example, combustion systems emit vapors that condense onto existing particles or form [...]

Traditional solid fuel cookstoves emit gas- and particle-phase pollutants that contribute to household air pollution, human disease, and climate impacts. Forced-draft semi-gasifier stoves are an attractive intermediate step to zero-emitting stoves due to their reported lower emissions in laboratory and field studies, and potential for increased availability in more rural locales. However, emissions from these stoves have been shown to be highly variable and sensitive to stove design, fuel type, secondary air velocity, and operation mode. We measured carbon monoxide (CO), particulate matter (PM 2.5), organic and elemental carbon, and particle number (15–685 nm) emissions of the widely adopted Mimi Moto pellet-fed, gasifier stove for different operating conditions under two modified protocols, the Water Boiling Test (WBT) and an updated laboratory testing protocol ISO 19867-1 (ISO). We categorized operating conditions into three approaches: Startup (varying ignition material), Shutdown (varying fan speed during a 45-min burnout period), and Refuel (varying the height of charred pellets added for re-ignition). Refueling led to the largest and most variable emissions, but lab emissions were all lower than high field emissions (e.g., similar to those of traditional solid fuels) and remained primarily in ISO Tiers 5 and 4 for CO and PM 2.5 , aspirational and second-best, respectively. We find large relative differences in emissions when comparing our results to similar studies conducted with the Mimi Moto and ISO protocol, suggesting small operational differences can have large emissions implications. To minimize emissions, we recommend using kerosene for ignition, turning the fan off when pellets are done burning and flame has extinguished, and reigniting with fresh pellets instead of pellet char. Improved training and maintenance are needed in real-world applications to decrease the frequency of high-emission events. Tightly constrained testing and detection limits remain challenges to fully understanding factors contributing to these events. • In modified testing, no lab tests were as high emitting as the 'worst' field tests. • Varying refueling practices led to the largest and most variable emissions. • Fairly large differences with ISO protocol tests elsewhere, showing sensitivity to small changes. • Kerosene ignition (vs. kindling or char) and 'fan-off' burnout minimize emissions. • Particle emissions are in the ultrafine range and relatively consistent across conditions tested. [ABSTRACT FROM AUTHOR]

The volatility of organic aerosols (OA) has emerged as a property of primary importance in understanding their atmospheric lifecycle, and thus abundance and transport. However, quantitative estimates of the thermodynamic (volatility) and kinetic parameters dictating ambient OA gas-particle partitioning, such as saturation concentrations (C*), enthalpy of evaporation (ΔHvap) and evaporation coefficient (γe), are highly uncertain. Here, we present measurements of ambient OA volatility at two sites in the southeastern U.S., one at biogenic-volatile-organic-compound (BVOC)-dominated rural setting in Alabama as part of the Southern Oxidant and Aerosol Study (SOAS) in June-July, 2013, and another at a more anthropogenically-influenced urban location in North Carolina during October-November, 2013. These measurements applied a dual-thermodenuder (TD) system, in which temperature and residence times are varied in parallel, to constrain equilibrium and kinetic aerosol volatility properties. Gas-particle partitioning parameters were determined via evaporation kinetic model fits to the dual-TD observations. OA volatility parameters values derived from both datasets were similar despite the fact that measurements were collected in distinct settings and seasons. The OA volatility distributions also did not vary dramatically over the campaign period nor strongly correlate with OA components identified via positive matrix factorization of aerosol mass spectrometer data. A large portion (40-70%) of measured ambient OA at both sites was composed of very low volatility organics (C*≤ 0.1 μg m-3). An effective ΔHvap of bulk OA of ~ 80-100 kJ mol-1 and a γe value of ~ 0.5 best describe the evaporation observed in the TDs. This range of ΔHvap values is substantially higher than that typically assumed for simulating OA in atmospheric models (30-40 kJ mol-1). TD data indicate that γe is on the order of 0.1 to 0.5, indicating that repartitioning timescales for atmospheric OA are on the order of several minutes to an hour under atmospheric conditions. The OA volatility distributions resulting from fits were compared to those simulated in the Weather, Research and Forecasting model with Chemistry (WRF/Chem) with a current treatment of SOA formation. The substantial fraction of low-volatility material observed in our measurements is largely missing from simulations, and OA mass concentrations are underestimated. The large discrepancies between simulations and observations indicate a need to treat low volatility OA in atmospheric models. Volatility parameters extracted from ambient measurements enable evaluation of emerging treatments for OA (e.g., secondary OA using the volatility basis set or formed via aqueous chemistry) in atmospheric models. [ABSTRACT FROM AUTHOR]