Stove stacking (concurrent use of multiple stoves and/or fuels) is a poorly quantified practice in regions where efforts to transition household energy to cleaner stoves/or fuels are on-going. Using biomass-burning stoves alongside clean stoves undermines health and environmental goals. This review synthesizes stove stacking data gathered from eleven case studies of clean cooking programs in low/middle-income country settings. Analyzed data are from ministry and program records, research studies, and informant interviews. Thematic analysis identify key drivers of stove stacking behavior in each setting. Significant (28%-100%) stacking with traditional cooking methods was observed in all cases. Reason for traditional fuel use includes: costs of clean fuel; mismatches between cooking technologies and household needs; and unreliable fuel supply. National household surveys often focus on ‘primary’ cookstoves and miss stove stacking data. Thus more attention should be paid to discontinuation of traditional stove use, not solely adoption of cleaner stoves/fuels. Future energy policies and programs should acknowledge the realities of stacking and incorporate strategies at the design stage to transition away from polluting stoves/fuels. Seven principles for clean cooking system program design and policy are presented, focused on a shift toward “cleaner stacking” that could yield household air pollution reductions approaching WHO targets.

Smoke from biomass burning has been linked to reduced birth weight; association with other birth outcomes is poorly understood. Our objective was to evaluate effects of exposure to biomass smoke on birth weight, preterm birth and stillbirth.

Rationale: Approximately 2.8 billion people are exposed daily to household air pollution from polluting cookstoves. The effects of prenatal household air pollution on lung development are unknown. Objectives: To prospectively examine associations between prenatal household air pollution and infant lung function and pneumonia in rural Ghana. Methods: Prenatal household air pollution exposure was indexed by serial maternal carbon monoxide personal exposure measurements. Using linear regression, we examined associations between average prenatal carbon monoxide and infant lung function at age 30 days, first in the entire cohort (n = 384) and then stratified by sex. Quasi-Poisson generalized additive models explored associations between infant lung function and pneumonia. Measurements and Main Results: Multivariable linear regression models showed that average prenatal carbon monoxide exposure was associated with reduced time to peak tidal expiratory flow to expiratory time (β = −0.004; P = 0.01), increased respiratory rate (β = 0.28; P = 0.01), and increased minute ventilation (β = 7.21; P = 0.05), considered separately, per 1 ppm increase in average prenatal carbon monoxide. Sex-stratified analyses suggested that girls were particularly vulnerable (time to peak tidal expiratory flow to expiratory time: β = −0.003, P = 0.05; respiratory rate: β = 0.36, P = 0.01; minute ventilation: β = 11.25, P = 0.01; passive respiratory compliance normalized for body weight: β = 0.005, P = 0.01). Increased respiratory rate at age 30 days was associated with increased risk for physician-assessed pneumonia (relative risk, 1.02; 95% confidence interval, 1.00–1.04) and severe pneumonia (relative risk, 1.04; 95% confidence interval, 1.00–1.08) in the first year of life. Conclusions: Increased prenatal household air pollution exposure is associated with impaired infant lung function. Altered infant lung function may increase risk for pneumonia in the first year of life. These findings have implications for future respiratory health. Clinical trial registered with www.clinicaltrials.gov (NCT 01335490).

After more than three decades of access to low-cost liquefied petroleum gas (LPG) financed by large direct government subsidies, more than 90% of Ecuadorian households cook primarily with LPG. Due to the large fiscal burden of the LPG subsidy, increases in electricity from hydropower, and other sociopolitical factors, the Government of Ecuador launched a major induction stove program (PEC) to reduce the demand for LPG. We assess the effects of the LPG subsidies and PEC using government records, interviews, academic literature, newspaper reports, household surveys, and focus groups. Household surveys, conducted in rural, northern Ecuadorian households (n=383), characterized cooking patterns and fuel access. Focus groups (n=6) were carried out with a subset of surveyed households to better characterize survey findings. The LPG subsidy was developed as part of broad social support reforms in the early 1970s, without specific aims to reduce the health impacts of household air pollution from woodfuel or provide economic benefits as part of the transition to a clean cooking fuel. Nonetheless, the subsidy has resulted in nearly all Ecuadorian households cooking primarily with LPG. PEC has generated the sale of 740,000 induction stoves since its inception in 2014, short of the goal of 3.5 million. Among the rural households surveyed, LPG use, acceptance, and satisfaction was high, however, more than three-quarters of those surveyed reported weekly woodfuel use. Induction stove ownership (17%) and use as a primary cooking fuel (1%) was low among the rural households surveyed; furthermore, households owning induction stoves reported very low satisfaction with the stoves. Here we show that nationally-representative surveys reporting only “primary cooking fuef” use may underestimate solid fuel use as a supplemental household cooking energy, particularly in rural areas where fuel availability issues play a stronger role in decisions about what fuels to use.

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM_2.5 is strongly correlated with reference PM_2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m³, followed by PurpleAir PA-II (4.54 μg/m³) and Clarity Node-S (13.68 μg/m³). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R²: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m³ for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM_2.5 concentration in Accra is 23.4 μg/m³, which is 1.6 times the World Health Organization Daily PM_2.5 guideline of 15 μg/m³. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.