The use of low-cost sensors (LCS) for air quality monitoring has grown rapidly across a wide range of groups, including community and citizen scientists, academic researchers, environmental agencies, and the private sector. Traditional air monitoring conducted by regulatory agencies relies on expensive, regulatory-grade instruments that require frequent maintenance and rigorous quality control procedures. In contrast, the low purchase price, minimal operating costs, user-friendly design, and open data accessibility have significantly contributed to the widespread adoption of LCS. Over the past decade, hundreds of studies have proposed diverse calibration strategies to tailor LCS performance to specific project needs. This study examines the role of PM2.5 sensors in monitoring air quality across contrasting environments and highlights the importance of inter-sensor consistency. We evaluate PurpleAir (PA) PA-II sensors against regulatory-grade Federal Equivalent Method (FEM) PM2.5 instruments and develop calibration algorithms to improve data accuracy. Calibration deployments were conducted for 2–4 weeks in Raleigh, North Carolina, and Delhi, India, to assess sensor behavior under different aerosol loadings and environmental conditions. The goal of this effort is to create a robust calibration model that uses PA-measured parameters, PM2.5, temperature, and relative humidity as inputs to generate bias-corrected hourly PM2.5 values. The model relies on concurrent FEM PM2.5 measurements as the reference data during calibration development. Multiple statistical and machine-learning approaches were applied to produce a regional calibration model. Our results show that, with proper calibration, PA sensors can provide bias-corrected PM2.5 estimates within 12

While transitioning from polluting cooking fuels (e.g. wood, charcoal) to cleaner fuels, like liquefied petroleum gas (LPG), can lead to time savings, the amount of time saved is uncertain due to minimal stove use monitoring (SUM) data. Approximately three months (mean:82 days (SD:41)) of SUM data from Geocene temperature sensors was collected from 186 households in Mbalmayo, Cameroon; Obuasi, Ghana and Eldoret, Kenya. Households exclusively using LPG (mean:1 h 22 min/day) cooked for two hours/day less than those stacking LPG and polluting fuels (3 h 19 min/day), and almost three hours/day less than those exclusively using polluting fuels (4 h 10 min/day). Financially insecure households exclusively using polluting fuels cooked for ~ 45 min longer (4 h 29 min) than financially secure households (3 h 45 min). During a 24-hour household air pollution (HAP) monitoring period, average cooking time was 38 min longer (3 h 48 min vs. 3 h 10 min) and households cooked nearly once more per day (3.63 events) than during the remaining SUM period (2.72 events). Longer cooking times among financially insecure polluting fuel users suggests that LPG access may disproportionately benefit poorer households via greater time savings. Households may cook for longer-than-normal when monitored for HAP.

In sub-Saharan Africa, approximately 85% of the population uses polluting cooking fuels (e.g. wood, charcoal). Incomplete combustion of these fuels generates household air pollution (HAP), containing fine particulate matter (PM2.5 ) and carbon monoxide (CO). Due to large spatial variability, increased quantification of HAP levels is needed to improve exposure assessment in sub-Saharan Africa. The CLEAN-Air(Africa) study included 24-h monitoring of PM2.5 and CO kitchen concentrations (npm2.5 = 248/nCO = 207) and female primary cook exposures (npm2.5 = 245/nCO = 222) in peri-urban households in Obuasi (Ghana), Mbalmayo (Cameroon) and Eldoret (Kenya). HAP measurements were combined with survey data on cooking patterns, socioeconomic characteristics and ambient exposure proxies (e.g. walking time to nearest road) in separate PM2.5 and CO mixed-effect log-linear regression models. Model coefficients were applied to a larger study population (n = 937) with only survey data to quantitatively scale up PM2.5 and CO exposures. The final models moderately explained variation in mean 24-h PM2.5 (R2 = 0.40) and CO (R2 = 0.26) kitchen concentration measurements, and PM2.5 (R2 = 0.27) and CO (R2 = 0.14) female cook exposures. Primary/secondary cooking fuel type was the only significant predictor in all four models. Other significant predictors of PM2.5 and CO kitchen concentrations were cooking location and household size; household financial security and rental status were only predictive of PM2.5 concentrations. Cooking location, household financial security and proxies of ambient air pollution exposure were significant predictors of PM2.5 cook exposures. Including objective cooking time measurements (from temperature sensors) from (n = 143) households substantially improved (by 52%) the explained variability of the CO kitchen concentration model, but not the PM2.5 model. Socioeconomic characteristics and markers of ambient air pollution exposure were strongly associated with mean PM2.5 measurements, while cooking environment variables were more predictive of mean CO levels.

The immune function is suspected to play an important role in the health effects of air pollution but it remains poorly investigated in pregnant women. One-week personal measurements of exposure to nitrogen dioxide (NO 2 ), particulate matter with an aerodynamic diameter of ≤ 2.5 µm mass concentration (PM 2.5 ) and PM 2.5 oxidative potential (OP) were assessed in 270 pregnant women from the French cohort SEPAGES. PM filters were analyzed for PM 2.5 OP using the dithiothreitol (DTT) and the ascorbic acid (AA) assays. From a blood sample withdrawn at the end of the exposure measurement week, levels of 29 cytokines and chemokines were measured at baseline and after T cell and dendritic cell activation with phytohemagglutinin (PHA) and resiquimod (R848), respectively. Associations between each air pollutant and each cytokine were assessed using adjusted linear regression models. An increase in NO 2 exposure was associated with higher interleukin 10 (IL-10) and lower PHA-activated tumor necrosis factor (TNF). No association with PM 2.5 concentration was observed, but increased exposure to PM $${\text{OP}}^{{{\text{AA}}}}$$ OP AA was associated with lower baseline and R848-activated IL-8 and increased exposure to PM $${\text{OP}}^{\text{DTT}}$$ OP DTT was associated with higher PHA-activated IL-17A. Our study provides insights into the relationships between air pollution exposure and immune function among pregnant women.

Background Personal exposure to fine particulate matter (PM 2.5 ) is impacted by different sources each with different chemical composition. Determining these sources is important for reducing personal exposure and its health risks especially during pregnancy. Objective Identify main sources and their contributions to the personal PM 2.5 exposure in 213 women in the 3rd trimester of pregnancy in Los Angeles, CA. Methods We measured 48-hr integrated personal PM 2.5 exposure and analyzed filters for PM 2.5 mass, elemental composition, and optical carbon fractions. We used the EPA Positive Matrix Factorization (PMF) model to resolve and quantify the major sources of personal PM 2.5 exposure. We then investigated bivariate relationships between sources, time-activity patterns, and environmental exposures in activity spaces and residential neighborhoods to further understand sources. Results Mean personal PM 2.5 mass concentration was 22.3 (SD = 16.6) μg/m 3 . Twenty-five species and PM 2.5 mass were used in PMF with a final R 2 of 0.48. We identified six sources (with major species in profiles and % contribution to PM 2.5 mass) as follows: secondhand smoking (SHS) (brown carbon, environmental tobacco smoke; 65.3%), fuel oil (nickel, vanadium; 11.7%), crustal (aluminum, calcium, silicon; 11.5%), fresh sea salt (sodium, chlorine; 4.7%), aged sea salt (sodium, magnesium, sulfur; 4.3%), and traffic (black carbon, zinc; 2.6%). SHS was significantly greater in apartments compared to houses. Crustal source was correlated with more occupants in the household. Aged sea salt increased with temperature and outdoor ozone, while fresh sea salt was highest on days with westerly winds from the Pacific Ocean. Traffic was positively correlated with ambient NO 2 and traffic-related NO x at residence. Overall, 76.8% of personal PM 2.5 mass came from indoor or personal compared to outdoor sources. Impact We conducted source apportionment of personal PM 2.5 samples in pregnancy in Los Angeles, CA. Among identified sources, secondhand smoking contributed the most to the personal exposure. In addition, traffic, crustal, fuel oil, fresh and aged sea salt sources were also identified as main sources. Traffic sources contained markers of combustion and non-exhaust wear emissions. Crustal source was correlated with more occupants in the household. Aged sea salt source increased with temperature and outdoor ozone and fresh sea salt source was highest on days with westerly winds from the Pacific Ocean.

Prolonged exposure to fine particulate matter (PM2.5) is a known risk to respiratory health, causing chronic lung impairment. Yet, the immediate, acute effects of PM2.5 exposure on respiratory symptoms, such as cough, are less understood. This pilot study aims to investigate this relationship using objective PM2.5 and cough monitors. Fifteen participants from rural Madagascar were followed for three days, equipped with an RTI Enhanced Children’s MicroPEM PM2.5 sensor and a smartphone with the ResApp Cough Counting Software application. Univariable Generalized Estimating Equation (GEE) models were applied to measure the association between hourly PM2.5 exposure and cough counts. Peaks in both PM2.5 concentration and cough frequency were observed during the day. A 10-fold increase in hourly PM2.5 concentration corresponded to a 39% increase in same-hour cough frequency (incidence rate ratio (IRR) = 1.40; 95% CI: 1.12, 1.74). The strength of this association decreased with a one-hour lag between PM2.5 exposure and cough frequency (IRR = 1.21; 95% CI: 1.01, 1.44) and was not significant with a two-hour lag (IRR = 0.93; 95% CI: 0.71, 1.23). This study demonstrates the feasibility of objective PM2.5 and cough monitoring in remote settings. An association between hourly PM2.5 exposure and cough frequency was detected, suggesting that PM2.5 exposure may have immediate effects on respiratory health. Further investigation is necessary in larger studies to substantiate these findings and understand the broader implications.

BACKGROUND:Relatively clean cooking fuels such as liquefied petroleum gas (LPG) emit less fine particulate matter (PM2·5) and carbon monoxide (CO) than polluting fuels (eg, wood, charcoal). Yet, some clean cooking interventions have not achieved substantial exposure reductions. This study evaluates determinants of between-community variability in exposures to household air pollution (HAP) across sub-Saharan Africa. METHODS:In this measurement study, we recruited households cooking primarily with LPG or exclusively with wood or charcoal in peri-urban Cameroon, Ghana, and Kenya from previously surveyed households. In 2019-20, we conducted monitoring of 24 h PM2·5 and CO kitchen concentrations (n=256) and female cook (n=248) and child (n=124) exposures. PM2·5 measurements used gravimetric and light scattering methods. Stove use monitoring and surveys on cooking characteristics and ambient air pollution exposure (eg, walking time to main road) were also administered. FINDINGS:The mean PM2·5 kitchen concentration was five times higher among households cooking with charcoal than those using LPG in the Kenyan community (297 μg/m3, 95% CI 216-406, vs 61 μg/m3, 49-76), but only 4 μg/m3 higher in the Ghanaian community (56 μg/m3, 45-70, vs 52 μg/m3, 40-68). The mean CO kitchen concentration in charcoal-using households was double the WHO guideline (6·11 parts per million [ppm]) in the Kenyan community (15·81 ppm, 95% CI 8·71-28·72), but below the guideline in the Ghanaian setting (1·77 ppm, 1·04-2·99). In all communities, mean PM2·5 cook exposures only met the WHO interim-1 target (35 μg/m3) among LPG users staying indoors and living more than 10 min walk from a road. INTERPRETATION:Community-level variation in the relative difference in HAP exposures between LPG and polluting cooking fuel users in peri-urban sub-Saharan Africa might be attributed to differences in ambient air pollution levels. Thus, mitigation of indoor and outdoor PM2·5 sources will probably be critical for obtaining significant exposure reductions in rapidly urbanising settings of sub-Saharan Africa. FUNDING:UK National Institute for Health and Care Research.

Indoor exposure to black carbon (BC) may differ greatly from ambient concentrations measured at stationary monitoring stations. This study characterized the variation between measured indoor and outdoor levels of BC and developed models to predict BC exposures within residences. Indoor and outdoor PM2.5 filter samples were collected for five consecutive days from 66 residences in Beijing (BJ) and Nanjing (NJ), China, across two seasons. City-specific indoor BC models were developed using liner mixed effect models and assessed by R2 and root mean square error (RMSE). The mean (SD) of indoor BC concentrations in BJ and NJ were 3.0 μg/m3 (0.6 μg/m3) and 3.2 μg/m3 (0.5 μg/m3), respectively. Both the indoor and outdoor BC concentrations were significantly higher during heating season than non-heating season. In general, indoor levels of BC were strongly associated with outdoor measurements (BJ: rs = 0.74, p < 0.001; NJ: rs = 0.76, p < 0.001), but were often lower. The critical determinants influencing indoor BC exposures varied by city, including outdoor BC concentration, window opening time, presence of indoor smoking, and the volume of the room where the indoor monitor was placed. The final models accounted for 66.4%–86.5% (RMSE: 0.070–0.106) of the variance in residential-indoor BC exposures. By incorporating the key BC exposure determinants identified here, this modelling approach can improve the estimates of BC exposure and better link BC exposure to health risk assessment.

Introduction Secondhand smoke (SHS) exposure during pregnancy is linked to adverse birth outcomes, such as low birth weight and preterm birth. While questionnaires are commonly used to assess SHS exposure, their ability to capture true exposure can vary, making it difficult for researchers to harmonize SHS measures. This study aimed to compare self-reported SHS exposure with measurements of airborne SHS in personal samples of pregnant women.Methods SHS was measured on 48-hour integrated personal PM2.5 Teflon filters collected from 204 pregnant women, and self-reported SHS exposure measures were obtained via questionnaires. Descriptive statistics were calculated for airborne SHS measures, and analysis of variance tests assessed group differences in airborne SHS concentrations by self-reported SHS exposure.Results Participants were 81% Hispanic, with a mean (standard deviation [SD]) age of 28.2 (6.0) years. Geometric mean (SD) personal airborne SHS concentrations were 0.14 (9.41) mu g/m3. Participants reporting lower education have significantly higher airborne SHS exposure (p = .015). Mean airborne SHS concentrations were greater in those reporting longer duration with windows open in the home. There was no association between airborne SHS and self-reported SHS exposure; however, asking about the number of smokers nearby in the 48-hour monitoring period was most correlated with measured airborne SHS (Two + smokers: 0.30 mu g/m3 vs. One: 0.12 mu g/m3 and Zero: 0.15 mu g/m3; p = .230).Conclusions Self-reported SHS exposure was not associated with measured airborne SHS in personal PM2.5 samples. This suggests exposure misclassification using SHS questionnaires and the need for harmonized and validated questions to characterize this exposure in health studies.Implications This study adds to the growing body of evidence that measurement error is a major concern in pregnancy research, particularly in studies that rely on self-report questionnaires to measure SHS exposure. The study introduces an alternative method of SHS exposure assessment using objective optical measurements, which can help improve the accuracy of exposure assessment. The findings emphasize the importance of using harmonized and validated SHS questionnaires in pregnancy health research to avoid biased effect estimates. This study can inform future research, practice, and policy development to reduce SHS exposure and its adverse health effects.

Oxidative stress is a prominent pathway for the health effects associated with fine particulate matter (PM2.5) exposure. Oxidative potential (OP) of PM has been associated to several health endpoints, but studies on its impact on biomarkers of oxidative stress remains insufficient. 300 pregnant women from the SEPAGES cohort (France) carried personal PM2.5 samplers for a week and OP was measured using ascorbic acid (AA) and dithiothreitol (DTT) assays, and normalized by 1) PM2.5 mass (OPm) and 2) sampled air volume (OPv). A pool of three urine spots collected on the 7th day of PM sampling was analyzed for biomarkers, namely 8-hydroxy-2-deoxyguanosine (8-OHdG), malondialdehyde (MDA) and 8-isoprostaglandin-F2 alpha (8-isoPGF2 alpha). Associations were investigated using adjusted multiple linear regressions. OP effects were additionally investigated by stratifying by median PM2.5 concentration (14 mu g m(-3)). In the main models, no association was observed with 8-isoPGF2 alpha, nor MDA. An interquartile range (IQR) increase in OPmAA exposure was associated with increased 8-OHdG (percent change: 6.2 %; 95 % CI: 0.2 % to 12.6 %). In the stratified analysis, exposure to OPmAA was associated with 8-OHdG for participants exposed to low levels of PM2.5 (percent change: 11.4 %; 95 % CI: 3.3 % to 20.1 %), but not for those exposed to high levels (percent change: -1.0 %; 95 % CI: -10.6 % to 9.6 %). Associations for OPmDTT also followed a similar pattern (p-values for OPmAA-PM and OPmDTT-PM interaction terms were 0.12 and 0.11, respectively). Overall, our findings suggest that OPmAA may be associated with increased DNA oxidative damage. This association was not observed with PM2.5 mass concentration exposure. The effects of OPmAA in 8-OHdG tended to be stronger at lower (below median) vs. higher concentrations of PM2.5. Further epidemiological, toxicological and aerosol research are needed to further investigate the OPmAA effects on 8-OHdG and the potential modifying effect of PM mass concentration on this association.

The primary aim of this study is to explore the utility of machine learning algorithms for predicting personal PM2.5 exposures of elderly participants and to evaluate the effect of individual variables on model performance. Personal PM2.5 was measured on five consecutive days across seasons in 66 retired adults in Beijing (BJ) and Nanjing (NJ), China. The potential predictors were extracted from routine monitoring data (ambient PM2.5 concentrations and meteorological factors), basic questionnaires (personal and household characteristics), and time-activity diary (TAD). Prediction models were developed based on either traditional multiple linear regression (MLR) or five advanced machine learning methods. Our results revealed that personal PM2.5 exposures were well predicted by both MLR and machine learning models with predictors extracted from routine monitoring data, which was indicated by the high nested cross-validation (CV) R2 ranging from 0.76 to 0.88. The addition of predictors from either the questionnaire or TAD did not improve predictive accuracy for all algorithms. The ambient PM2.5 concentrations were the most important predictor. Overall, the random forest, support vector machine, and extreme gradient boosting algorithms outperformed the reference MLR method. Compared with the traditional MLR approach, the CV R2 of the RF model increased up to 7% (from 0.82±0.13 to 0.88±0.10), while the RMSE reduced up to 18% (from 19.8±5.4 to 16.3±4.5) in BJ.

Prior studies suggest brick workers in Nepal may be chronically exposed to hazardous levels of fine particulate matter (PM2.5) from ambient, occupational, and household sources. However, findings from these studies were based on stationary monitoring data, and thus may not reflect a worker’s individual exposures. In this study, we used RTI International’s MicroPEMs to collect 24 h PM2.5 personal breathing zone (PBZ) samples among brick workers (n = 48) to estimate daily exposures from ambient, occupational, and household air pollution sources. Participants were sampled from five job categories at one kiln. The geometric mean (GM) PM2.5 exposure across all participants was 116 µg/m3 (95% confidence interval [CI]: 94.03, 143.42). Job category was significantly (p < 0.001) associated with PBZ PM2.5 concentrations. There were significant pairwise differences in geometric mean (GM) PBZ PM2.5 concentrations among workers in administration (GM: 47.92, 95% CI: 29.81, 77.03 µg/m3) vs. firemen (GM: 163.46, 95 CI: 108.36, 246.58 µg/m3, p = 0.003), administration vs. green brick hand molder (GM: 163.35, 95% CI: 122.15, 218.46 µg/m3, p < 0.001), administration vs. top loader (GM: 158.94, 95% CI: 102.42, 246.66 µg/m3, p = 0.005), firemen vs. green brick machine molder (GM: 73.18, 95% CI: 51.54, 103.90 µg/m3, p = 0.03), and green brick hand molder vs. green brick machine molder (p = 0.008). Temporal exposure trends suggested workers had chronic exposure to hazardous levels of PM2.5 with little to no recovery period during non-working hours. Multi-faceted interventions should focus on the control of ambient and household air pollution and tailored job-specific exposure controls.

Personal exposure to PM2.5, and the elemental composition therein, may vary greatly from ambient measurements at fixed monitoring sites. Here, we characterized the differences between personal, indoor, and outdoor concentrations of PM2.5- bound elements, and predicted personal exposures to 21 PM2.5-bound elements. Personal-indoor-outdoor PM2.5 filter sam-ples were collected for five consecutive days across two seasons from 66 healthy non-smoking retired adults in Beijing (BJ) and Nanjing (NJ), China. Personal element-specific models were developed using liner mixed effects models and evaluated by R2 and root mean square error (RMSE). The mean (SD) concentrations of personal exposures varied by element and city, ranging from 2.5 (1.4) ng/m3 for Ni in BJ to 4271.2 (1614.8) ng/m3 for S in NJ. Personal exposures to PM2.5 and most elements were significantly correlated with both indoor and outdoor (except Ni in BJ) measurements, but frequently exceeded indoor levels and fell below outdoor levels. Indoor and outdoor PM2.5 elemental concentrations were the stron-gest determinants of most personal elemental exposures, with R2M ranging from 0.074 to 0.975 for indoor and from 0.078 to 0.917 for outdoor levels, respectively. Home ventilation conditions (especially window opening behavior), time-activity patterns, meteorological factors, household characteristics, and season were also key factors influencing personal exposure levels. The final models accounted for 24.2 %-94.0 % (RMSE: 0.135-0.718) of the variance in personal PM2.5 elemental exposures. By incorporating these crucial determinants, the modeling approach used here can improve PM2.5-bound elemental exposure estimates and better associate compositionally dependent PM2.5 exposures and health risks.

Air pollution is a recognized risk factor for impaired lung health and increased morbidity and mortality globally. Fine particulate matter (PM2.5) has been linked to respiratory illness, but assessing personal PM2.5 exposure remains difficult, especially in children. The MicroPEM (TM) and Enhanced Children's MicroPEM (TM) (ECM) are personal fine particulate matter exposure monitors that have not been well-evaluated for young children. We aimed to assess compliance and acceptability of these monitors and to investigate risk factors associated with fine particulate matter in mother-child pairs. Methods: Mother-child pairs, enrolled in a South African birth cohort, the Drakenstein Child Health Study, were recruited. A MicroPEM (mother) and ECM (child), issued to each participant and worn over 24 h, quantified PM2.5 exposure. Home environment, wearability, and wearing compliance were assessed. Linear regression identified associations between variables characterising the home environment and PM(2.)5 levels. Results: From August to November 2016, 86 mothers and 75 children (age 1-4 years) had results. Fossil fuels were used in 21% of homes; maternal smoking (37%) and exposure to household tobacco smoke (89%) was high. Waking wearing compliance of the devices was 59% in children and 56% in mothers. PM2.5 exposures were above the World Health Organization recommend 24-h interim target (IT-4 25 mu g/m(3)) in 38/75 (51%) children, and in 44/86 (51%) mothers. Winter, smoking and public transport were associated with higher concentrations of PM2.5 in mothers. Whereas, for the children higher concentrations of PM2.5 were associated with winter and two or more household smokers; while lower concentrations were associated with age, weight-for-age z-score and households that had fewer than two basic dimensions. Wearability for children was reported as "very easy" in the majority (n = 54, 63%) with similar acceptability in mothers (n = 55, 64%). Conclusion: The MicroPEM and ECM provide wearable low-burden personal exposure monitoring tools for women and children with reasonable rates of acceptability. High exposure to fine particulate matter in children is concerning; strategies to minimise exposure need to be strengthened.

BACKGROUND AND AIM: Cleaner cooking fuels like liquefied petroleum gas (LPG) emit less fine particulate matter (PM2.5) and carbon monoxide (CO) than polluting fuels (e.g. wood, charcoal). Yet, some clean cooking interventions have not achieved substantial exposure reductions. METHOD: The CLEAN-Air(Africa) study measured 24-hour PM2.5 and CO kitchen concentrations (n=262), female cook (n=223) and child (n=119) exposures in peri-urban Kenya, Ghana and Cameroon among households cooking primarily with LPG, wood or charcoal. Stove use monitoring was used to derive mean 'cooking' and 'non-cooking' PM2.5 and CO levels. RESULTS: The mean 24-hour PM2.5 kitchen concentration among households cooking with charcoal (317 μg/m3) was quintuple that among households using LPG (61 μg/m3) in Kenya, but only 2 μg/m3 higher in Ghana (56 versus 54 μg/m3, respectively). The mean CO kitchen concentration in households cooking with charcoal was twice the WHO guideline (7 ppm) in Kenya (15.81 ppm) but below the guideline in Ghana (1.77 ppm). The mean PM2.5 kitchen concentration among households using wood in Cameroon was four times higher while cooking (811 μg/m3) than not cooking (202 μg/m3). Among households using charcoal in Ghana, the mean PM2.5 kitchen concentration was lower when cooking (42 μg/m3) than not cooking (67 μg/m3). Mean PM2.5 cook exposures only met the WHO interim-1 target (35 μg/m3) among LPG users staying indoors and living 10 minutes from a road. CONCLUSIONS: Clean cooking interventions should be prioritized in certain sub-Saharan African communities to increase the likelihood of PM2.5 exposure reductions and associated health benefits.

BACKGROUND AND AIM: 93% of the world's children under age 15 breathe polluted air that harms their health and development. Fine-grained particulate matter (PM2.5) in household air pollution is a particular threat. A significant source of PM2.5 is use of polluting cookstoves in the home. Mothers and children suffer the highest exposures, with risks to children beginning in-utero, potentially impairing their later neurocognitive development. We examine the association between PM2.5 exposures and neurocognitive development in children at age seven. METHOD: This study follows up on a randomized controlled trial that compared effects, on Nigerian pregnant women, of cooking with polluting fuels versus ethanol. We have followed the children born to these women and measured their personal and indoor PM2.5 exposures at age seven. We assessed their neurocognitive development using the Kaufman Assessment Battery for Children, 2nd Edition (KABC-II) and the Vineland Adaptive Behavior Scales (VABS). Multiple linear regression was used to examine the association between PM2.5 exposures and neurocognitive ability, adjusting for child's age, family assets, and mother's education. RESULTS: Our sample of 198 children (mean age 7.2 years, SD 0.2) included 132 children from households using clean cookstoves (liquefied petroleum gas, ethanol) and 66 children from households using polluting cookstoves (firewood, charcoal). Indoor peak PM2.5 levels were higher in households with polluting stoves (geometric mean, 276 μg/m3) than those with clean stoves (192 μg/m3). Children's PM2.5 exposure levels were inversely associated with their KABC-II and VABS scores. A 2-fold increase in child's peak personal PM2.5 exposure was associated with a 3.9-unit reduction in KABC-II score (p0.001); a 2-fold increase in peak indoor PM2.5 exposure with a 2.0-unit reduction in VABS score (p=0.03). Clean stove use was associated with higher KABC-II scores (coef. 4.1, p=0.04). CONCLUSIONS: Household use of polluting cookstoves contributes to children's PM2.5 exposure levels, potentially impairing their neurocognitive development.