Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning.

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).

Quantifying Prescribed-Fire Smoke Exposure Using Low-Cost Sensors and Satellites: Springtime Burning in Eastern Kansas.

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March-May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low-cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hr median PM2.5 to increase by 3.0-5.3 µg m-3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite-derived product aligned with our ground-based measurements. Satellite-derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States.

BACKGROUND: Ambient nitrogen dioxide (NO2) and fine particulate matter with aerodynamic diameter ≤2.5µm (PM2.5) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants. OBJECTIVES: Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO2- and PM2.5-attributable health burdens, considering NO2-attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants. METHODS: Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO2 and PM2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study. RESULTS: Despite overall decreases in the public health damages associated with NO2 and PM2.5, racial and ethnic relative disparities in NO2-attributable pediatric asthma and PM2.5-attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM2.5-attributable premature mortality and NO2-attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM2.5-attributable premature mortality have increased by 40% and NO2-attributable pediatric asthma by 10%. DISCUSSION: Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

Inequality in the Distribution of Air Pollution Attributable Mortality Within Canadian Cities.

Recent studies have identified inequality in the distribution of air pollution attributable health impacts, but to our knowledge this has not been examined in Canadian cities. We evaluated the extent and sources of inequality in air pollution attributable mortality at the census tract (CT) level in seven of Canada's largest cities. We first regressed fine particulate matter (PM2.5) and nitrogen dioxide (NO2) attributable mortality against the neighborhood (CT) level prevalence of age 65 and older, low income, low educational attainment, and identification as an Indigenous (First Nations, Métis, Inuit) or Black person, accounting for spatial autocorrelation. We next examined the distribution of baseline mortality rates, PM2.5 and NO2 concentrations, and attributable mortality by neighborhood (CT) level prevalence of these characteristics, calculating the concentration index, Atkinson index, and Gini coefficient. Finally, we conducted a counterfactual analysis of the impact of reducing baseline mortality rates and air pollution concentrations on inequality in air pollution attributable mortality. Regression results indicated that CTs with a higher prevalence of low income and Indigenous identity had significantly higher air pollution attributable mortality. Concentration index, Atkinson index, and Gini coefficient values revealed different degrees of inequality among the cities. Counterfactual analysis indicated that inequality in air pollution attributable mortality tended to be driven more by baseline mortality inequalities than exposure inequalities. Reducing inequality in air pollution attributable mortality requires reducing disparities in both baseline mortality and air pollution exposure.

Reversal of trends in global fine particulate matter air pollution.

Ambient fine particulate matter (PM2.5) is the world's leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 µg/m3) to a peak in 2011 (38.9 µg/m3) and decreased steadily afterwards (34.7 µg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Fine particulate matter composition in American Indian vs. Non-American Indian communities.

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US. METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time. RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties. CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Arctic warming by abundant fine sea salt aerosols from blowing snow.

The Arctic warms nearly four times faster than the global average, and aerosols play an increasingly important role in Arctic climate change. In the Arctic, sea salt is a major aerosol component in terms of mass concentration during winter and spring. However, the mechanisms of sea salt aerosol production remain unclear. Sea salt aerosols are typically thought to be relatively large in size but low in number concentration, implying that their influence on cloud condensation nuclei population and cloud properties is generally minor. Here we present observational evidence of abundant sea salt aerosol production from blowing snow in the central Arctic. Blowing snow was observed more than 20% of the time from November to April. The sublimation of blowing snow generates high concentrations of fine-mode sea salt aerosol (diameter below 300 nm), enhancing cloud condensation nuclei concentrations up to tenfold above background levels. Using a global chemical transport model, we estimate that from November to April north of 70° N, sea salt aerosol produced from blowing snow accounts for about 27.6% of the total particle number, and the sea salt aerosol increases the longwave emissivity of clouds, leading to a calculated surface warming of +2.30 W m-2 under cloudy sky conditions.

Prenatal air pollution, maternal immune activation, and autism spectrum disorder.

BACKGROUND: Autism Spectrum Disorder (ASD) risk is highly heritable, with potential additional non-genetic factors, such as prenatal exposure to ambient particulate matter with aerodynamic diameter < 2.5 µm (PM2.5) and maternal immune activation (MIA) conditions. Because these exposures may share common biological effect pathways, we hypothesized that synergistic associations of prenatal air pollution and MIA-related conditions would increase ASD risk in children. OBJECTIVES: This study examined interactions between MIA-related conditions and prenatal PM2.5 or major PM2.5 components on ASD risk. METHODS: In a population-based pregnancy cohort of children born between 2001 and 2014 in Southern California, 318,751 mother-child pairs were followed through electronic medical records (EMR); 4,559 children were diagnosed with ASD before age 5. Four broad categories of MIA-related conditions were classified, including infection, hypertension, maternal asthma, and autoimmune conditions. Average exposures to PM2.5 and four PM2.5 components, black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-), were estimated at maternal residential addresses during pregnancy. We estimated the ASD risk associated with MIA-related conditions, air pollution, and their interactions, using Cox regression models to adjust for covariates. RESULTS: ASD risk was associated with MIA-related conditions [infection (hazard ratio 1.11; 95% confidence interval 1.05-1.18), hypertension (1.30; 1.19-1.42), maternal asthma (1.22; 1.08-1.38), autoimmune disease (1.19; 1.09-1.30)], with higher pregnancy PM2.5 [1.07; 1.03-1.12 per interquartile (3.73 µg/m3) increase] and with all four PM2.5 components. However, there were no interactions of each category of MIA-related conditions with PM2.5 or its components on either multiplicative or additive scales. CONCLUSIONS: MIA-related conditions and pregnancy PM2.5 were independently associations with ASD risk. There were no statistically significant interactions of MIA conditions and prenatal PM2.5 exposure with ASD risk.

Satellite reveals a steep decline in China's CO2 emissions in early 2022.

Response actions to the coronavirus disease 2019 perturbed economies and carbon dioxide (CO2) emissions. The Omicron variant that emerged in 2022 caused more substantial infections than in 2020 and 2021 but it has not yet been ascertained whether Omicron interrupted the temporary post-2021 rebound of CO2 emissions. Here, using satellite nitrogen dioxide observations combined with atmospheric inversion, we show a larger decline in China's CO2 emissions between January and April 2022 than in those months during the first wave of 2020. China's CO2 emissions are estimated to have decreased by 15% (equivalent to -244.3 million metric tons of CO2) during the 2022 lockdown, greater than the 9% reduction during the 2020 lockdown. Omicron affected most of the populated and industrial provinces in 2022, hindering China's CO2 emissions rebound starting from 2021. China's emission variations agreed with downstream CO2 concentration changes, indicating a potential to monitor CO2 emissions by integrating satellite and ground measurements.

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region.

Fine particulate matter (PM2.5) exposure is a leading mortality risk factor in India and the surrounding region of South Asia. This study evaluates the contribution of emission sectors and fuels to PM2.5 mass for 29 states in India and 6 surrounding countries (Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka, and Myanmar) by combining source-specific emission estimates, stretched grid simulations from a chemical transport model, high resolution hybrid PM2.5, and disease-specific mortality estimates. We find that 1.02 (95% Confidence Interval (CI): 0.78-1.26) million deaths in South Asia attributable to ambient PM2.5 in 2019 were primarily from three leading sectors: residential combustion (28%), industry (15%), and power generation (12%). Solid biofuel is the leading combustible fuel contributing to the PM2.5-attributable mortality (31%), followed by coal (17%), and oil and gas (14%). State-level analyses reveal higher residential combustion contributions (35%-39%) in states (Delhi, Uttar-Pradesh, Haryana) with high ambient PM2.5 (>95 µg/m3). The combined mortality burden associated with residential combustion (ambient) and household air pollution (HAP) in India is 0.72 million (95% CI:0.54-0.89) (68% attributable to HAP, 32% attributable to residential combustion). Our results illustrate the potential to reduce PM2.5 mass and improve population health by reducing emissions from traditional energy sources across multiple sectors in South Asia.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South.

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

A cross-sectional analysis of ambient fine particulate matter (PM2.5) exposure and haemoglobin levels in children aged under 5 years living in 36 countries.

Low haemoglobin (Hb) concentrations and anaemia in children have adverse effects on development and functioning, some of which may have consequences in later life. Exposure to ambient air pollution is reported to be associated with anaemia, but there is little evidence specific to low- and middle-income countries (LMICs), where childhood anaemia prevalence is greatest. We aimed to determine if long-term ambient fine particulate matter (≤2.5 µm in aerodynamic diameter [PM2.5]) exposure was associated with Hb levels and the prevalence of anaemia in children aged <5 years living in 36 LMICs. We used Demographic and Health Survey data, collected between 2010 and 2019, which included blood Hb measurements. Satellite-derived estimates of annual average PM2.5 was the main exposure variable, which was linked to children's area of residence. Anaemia was defined according to standard World Health Organization guidelines (Hb < 11 g/dL). The association of PM2.5 with Hb levels and anaemia prevalence was examined using multivariable linear and logistic regression models, respectively. We examined whether the effects of ambient PM2.5 were modified by a child's sex and age, household wealth index, and urban/rural place of residence. Models were adjusted for relevant covariates, including other outdoor pollutants and household cooking fuel. The study included 154,443 children, of which 89,904 (58.2%) were anaemic. The country-level prevalence of anaemia ranged from 15.8% to 87.9%. Mean PM2.5 exposure was 33.0 (±21.6) µg/m3. The adjusted model showed that a 10 µg/m3 increase in annual PM2.5 concentration was associated with greater odds of anaemia (OR = 1.098 95% CI: 1.087, 1.109). The same increase in PM2.5 was associated with a decrease in average Hb levels of 0.075 g/dL (95% CI: 0.081, 0.068). There was evidence of effect modification by household wealth index and place of residence, with greater adverse effects in children from lower wealth quintiles and children in rural areas. Exposure to annual PM2.5 was cross-sectionally associated with decreased blood Hb levels, and greater risk of anaemia, in children aged <5 years living in 36 LMICs.

Using Parametric g-Computation to Estimate the Effect of Long-Term Exposure to Air Pollution on Mortality Risk and Simulate the Benefits of Hypothetical Policies: The Canadian Community Health Survey Cohort (2005 to 2015).

BACKGROUND: Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5µm in aerodynamic diameter (PM2.5)] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges. METHOD: We applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold (8.8 µg/m3, 7.04 µg/m3, 5 µg/m3, and 4 µg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication. RESULTS: All hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8 µg/m3, and the largest reduction of 3.40 per 1,000 participants (95% CI: -0.23, 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications. DISCUSSION: We found evidence that any intervention further reducing the long-term exposure to PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.

Effects of long-term ambient air pollution exposure on township-level pulmonary tuberculosis notification rates during 2005-2017 in Ningxia, China.

Studies examining long-term effects of ambient air pollution exposure, measured as annual averages, on pulmonary tuberculosis (TB) incidence are scarce, particularly in endemic, rural settings. We performed a small-area study in Ningxia Hui Autonomous Region (NHAR), a high TB-burden area in rural China, using township-level (n = 358 non-overlapping townships) annual TB notification data (2005-2017). We aimed to determine if annual average concentrations of ambient air pollution (particulate matter <2·5 µm [PM2·5], nitrogen dioxide [NO2] ozone [O3]) were associated with TB notification rates (as a proxy for incidence). Air pollution effects on TB notification rates at township-level were estimated as incidence rate ratios (IRR), fitted using a generalised estimating equation (GEE) adjusted for covariates (age, sex, occupation, education, ethnicity, remoteness [urban or rural], household crowding and solid fuel use). A total of 38,942 TB notifications were reported in NHAR between 2005 and 2017. The mean annual TB notification rate was 67 (standard deviation [SD]; 7) per 100,000 people. Median concentrations of PM2·5, NO2, and O3 were 42 µg/m3 (interquartile range [IQR]; 38-48 µg/m3), 15 ppb (IQR; 12-16 ppb), and 56 ppb (IQR; 56-57 ppb), respectively. In single pollutant models, adjusted for covariates, an interquartile range (IQR) increase (10 µg/m3) in PM2·5 was significantly associated with higher TB notification rates (IRR: 1∙35; 95% CI: 1·25-1·48). Comparable effects on notifications of TB were observed for increases in NO2 exposure (IRR: 1·20 per IQR (4 ppb) increase; 95% CI: 1·08-1·31). Ground-level ozone was not associated with TB notification rate in any models. The observed effects were consistent over time, in multi-pollutant models, and appeared robust to additional adjustment for indicators of household crowding, solid fuel use and remoteness. More rigorous study designs are needed to understand if improving air quality has population-level benefits on TB disease incidence in endemic settings.

Associations of Autism Spectrum Disorder with PM2.5 Components: A Comparative Study Using Two Different Exposure Models.

This retrospective cohort study examined associations of autism spectrum disorder (ASD) with prenatal exposure to major fine particulate matter (PM2.5) components estimated using two independent exposure models. The cohort included 318 750 mother-child pairs with singleton deliveries in Kaiser Permanente Southern California hospitals from 2001 to 2014 and followed until age five. ASD cases during follow-up (N = 4559) were identified by ICD codes. Prenatal exposures to PM2.5, elemental (EC) and black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-) were constructed using (i) a source-oriented chemical transport model and (ii) a hybrid model. Exposures were assigned to each maternal address during the entire pregnancy, first, second, and third trimester. In single-pollutant models, ASD was associated with pregnancy-average PM2.5, EC/BC, OM, and SO42- exposures from both exposure models, after adjustment for covariates. The direction of effect estimates was consistent for EC/BC and OM and least consistent for NO3-. EC/BC, OM, and SO42- were generally robust to adjustment for other components and for PM2.5. EC/BC and OM effect estimates were generally larger and more consistent in the first and second trimester and SO42- in the third trimester. Future PM2.5 composition health effect studies might consider using multiple exposure models and a weight of evidence approach when interpreting effect estimates.

Incident dementia and long-term exposure to constituents of fine particle air pollution: A national cohort study in the United States.

Growing evidence suggests that fine particulate matter (PM2.5) likely increases the risks of dementia, yet little is known about the relative contributions of different constituents. Here, we conducted a nationwide population-based cohort study (2000 to 2017) by integrating the Medicare Chronic Conditions Warehouse database and two independently sourced datasets of high-resolution PM2.5 major chemical composition, including black carbon (BC), organic matter (OM), nitrate (NO3-), sulfate (SO42-), ammonium (NH4+), and soil dust (DUST). To investigate the impact of long-term exposure to PM2.5 constituents on incident all-cause dementia and Alzheimer's disease (AD), hazard ratios for dementia and AD were estimated using Cox proportional hazards models, and penalized splines were used to evaluate potential nonlinear concentration-response (C-R) relationships. Results using two exposure datasets consistently indicated higher rates of incident dementia and AD for an increased exposure to PM2.5 and its major constituents. An interquartile range increase in PM2.5 mass was associated with a 6 to 7% increase in dementia incidence and a 9% increase in AD incidence. For different PM2.5 constituents, associations remained significant for BC, OM, SO42-, and NH4+ for both end points (even after adjustments of other constituents), among which BC and SO42- showed the strongest associations. All constituents had largely linear C-R relationships in the low exposure range, but most tailed off at higher exposure concentrations. Our findings suggest that long-term exposure to PM2.5 is significantly associated with higher rates of incident dementia and AD and that SO42-, BC, and OM related to traffic and fossil fuel combustion might drive the observed associations.

Vapors Are Lost to Walls, Not to Particles on the Wall: Artifact-Corrected Parameters from Chamber Experiments and Implications for Global Secondary Organic Aerosol.

Atmospheric models of secondary organic aerosol (OA) (SOA) typically rely on parameters derived from environmental chambers. Chambers are subject to experimental artifacts, including losses of (1) particles to the walls (PWL), (2) vapors to the particles on the wall (V2PWL), and (3) vapors to the wall directly (VWL). We present a method for deriving artifact-corrected SOA parameters and translating these to volatility basis set (VBS) parameters for use in chemical transport models (CTMs). Our process involves combining a box model that accounts for chamber artifacts (Statistical Oxidation Model with a TwO-Moment Aerosol Sectional model (SOM-TOMAS)) with a pseudo-atmospheric simulation to develop VBS parameters that are fit across a range of OA mass concentrations. We found that VWL led to the highest percentage change in chamber SOA mass yields (high NOx: 36-680%; low NOx: 55-250%), followed by PWL (high NOx: 8-39%; low NOx: 10-37%), while the effects of V2PWL are negligible. In contrast to earlier work that assumed that V2PWL was a meaningful loss pathway, we show that V2PWL is an unimportant SOA loss pathway and can be ignored when analyzing chamber data. Using our updated VBS parameters, we found that not accounting for VWL may lead surface-level OA to be underestimated by 24% (0.25 µg m-3) as a global average or up to 130% (9.0 µg m-3) in regions of high biogenic or anthropogenic activity. Finally, we found that accurately accounting for PWL and VWL improves model-measurement agreement for fine mode aerosol mass concentrations (PM2.5) in the GEOS-Chem model.

A cross-sectional analysis of long-term exposure to ambient air pollution and cognitive development in children aged 3-4 years living in 12 low- and middle-income countries.

Exposure to ambient air pollution may affect cognitive functioning and development in children. Unfortunately, there is little evidence available for low- and middle-income countries (LMICs), where air pollution levels are highest. We analysed the association between exposure to ambient fine particulate matter (≤2.5 µm [PM2.5]) and cognitive development indicators in a cross-sectional analysis of children (aged 3-4 years) in 12 LMICs. We linked Demographic and Health Survey data, conducted between 2011 and 2018, with global estimates of PM2.5 mass concentrations to examine annual average exposure to PM2.5 and cognitive development (literacy-numeracy and learning domains) in children. Cognitive development was assessed using the United Nations Children's Fund's early child development indicators administered to each child's mother. We used multivariable logistic regression models, adjusted for individual- and area-level covariates, and multi-pollutant models (including nitrogen dioxide and surface-level ozone). We assessed if sex and urban/rural status modified the association of PM2.5 with the outcome. We included 57,647 children, of whom, 9613 (13.3%) had indicators of cognitive delay. In the adjusted model, a 5 µg/m3 increase in annual all composition PM2.5 was associated with greater odds of cognitive delay (OR = 1.17; 95% CI: 1.13, 1.22). A 5 µg/m3 increase in anthropogenic PM2.5 was also associated with greater odds of cognitive delay (OR = 1.05; 95% CI: 1.00, 1.10). These results were robust to several sensitivity analyses, including multi-pollutant models. Interaction terms showed that urban-dwelling children had greater odds of cognitive delay than rural-dwelling children, while there was no significant difference by sex. Our findings suggest that annual average exposure to PM2.5 in young children was associated with adverse effects on cognitive development, which may have long-term consequences for educational attainment and health.