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The pervasiveness of "stacking" between traditional and clean fuels/stoves is moving the household clean cooking research and policy agenda from programs that are centered on one-fuel or stove, to multi-clean fuel/device interventions that could result in a more effective displacement of traditional biomass stoves. However, there is little recognition and knowledge of the benefits realistic clean-stacking cooking solutions can have on indoor air quality and health. In this paper, particulate matter (PM2.5) and carbon monoxide (CO) concentration levels that are associated with stove-stacking options (Patsari-U-shaped open fire (U-type), Patsari-liquefied petroleum gas (LPG) stove, U-type-LPG, and Patsari-U-type-LPG) common within rural Mexico were evaluated while using a controlled cooking cycle (CCC) from the Purepecha Highlands, which mimics the most common dishes prepared daily within the region as well as their sequential arrangement. The results confirms that the WHO Interim Target-1 (IT1) of 35 μg/m3 for PM2.5 is exceeded whenever woodburning open fires are part of the stacking combination (Patsari-U-type 107 ± 9 μg/m3, U-type-LPG 131 ± 55 μg/m3 and Patsari-U-type-LPG 107 ± 22 μg/m3). However, well-designed and properly operated woodburning chimney stoves, such as the Patsari can meet the IT1, either used exclusively (21 ± 8 μg/m3) or as a "clean stacking" option with LPG (24 ± 5 μg/m3). Given stove stacking patterns, evaluating the health and environmental consequences of stove transitions while assuming the total replacement of traditional fires by clean cooking options will lead to misplaced expectations, and programs should evaluate more realistic "clean-stacking" options. [ABSTRACT FROM AUTHOR]

This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM2.5 and PMcoarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM2.5 and PMcoarse were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) R2 and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM2.5 (CV R2 = 0.47, RMSE = 1.5 µg/m3) and PMcoarse (CV R2 = 0.65, RMSE = 8.07 µg/m3). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m3 for PM2.5 and 17.7 µg/m3 for PMcoarse. Variable importance measures pointed to seasonality and length of roads as drivers of PM2.5, and seasonality, type of farmland, and length of roads as drivers of PMcoarse. Predicted PM2.5 was elevated near the US/Mexico border and predicted PMcoarse was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities. [ABSTRACT FROM AUTHOR]