With the many health implications of droughts and floods known, and the many adverse secondary and tertiary effects of the Covid-19 pandemic still lingering, it is important to study the complex interactions of epidemics, droughts, and floods. To gain insights into this issue, we have constructed epidemic, drought, and flood indices for the Ming and Qing dynasties of China in a period of more than 400 years. Using adaptive fractal analysis, we find that the time series of epidemic, drought, and flood indices possess long-range correlations of different degrees in different regions of China. More importantly, the scaling behavior for the cross correlations between the epidemic and the drought indices in Northern China is characterized by a non-stationary emergent behavior rather than by a long-range correlation, while this scaling behavior is close to the boundary of stationarity and non-stationarity in the Central China. This scaling is up to about 16 years, highlighting that on average, outbreak of large-scale epidemics may occur not shorter than once every 32 years. Interestingly, the emergent behavior can be characterized as a Zipf's law for the ranked size of the epidemics, mostly in the Northern China, and sometimes also involving some regions in the Central China.

Coccidioidomycosis (Valley fever, VF) is a climate-sensitive infectious disease caused by inhaling soil-dwelling fungus , mostly reported in southwestern USA. Although soil moisture (SM) and soil temperature (ST) are known to shape the fungal lifecycle, their effects on coccidioidomycosis remain understudied. Most prior studies have relied on their proxies-precipitation and air temperature-that might not accurately capture soil hydrothermal dynamics. We conducted multivariable negative binomial regressions to estimate seasonal associations between incidence and climate drivers-including SM, ST, and wind speed from the North American Land Data Assimilation Phase 2 (NLDAS-2), and PM-based dusty-day counts-in Arizona's hyperendemic counties (Maricopa, Pima, and Pinal) from 2000 to 2022. We found higher incidence in areas with hotter, drier soils and more seasonal dusty days. Multi-year soil hydrothermal cycles-alternating wet-dry and cool-hot periods along with concurrent dry, dusty conditions-significantly influenced incidence. Notably, no antecedent dry-cool seasons were linked to increased incidence, indicating moisture and/or heat are prerequisites for fungal growth and dispersal. SM showed more consistent and widespread effects than ST across seasons and lags, with winter and spring soils most influential. Higher incidence followed wetter winters and monsoons, and dry, hot springs and falls. Our models using NLDAS-2 SM and ST data showed robust performance and generalizability across exposure seasons. Our results support adding multi-year soil indicators-with up to 3-year lead times-into early-warning systems to enhance VF forecasting and better prepare endemic regions for the challenges of a warming, drying, and increasingly variable climate.

Approximately 39% of U.S. homes are now located in the Wildland-Urban Interface (WUI) and are at elevated risk of burning during wildfires. WUI fires emit a cocktail of chemicals from the combustion of anthropogenic materials, including compounds that may differ from the burning of biogenic-only materials. There is currently limited knowledge on the mixture composition of combustible materials in WUI homes, representing a data gap and need to further characterize exposure chemistries and toxicological impacts of WUI-relevant smoke exposures. To address this issue, this study integrated combustible materials in an average American WUI home to derive what we are referring to as the "Burning ExposHome." Items such as structural materials, plumbing, furnishings, and appliances were included in the Burning ExposHome. Calculations were based on an average American household, a 2,016 sq. ft. single family home of four bedrooms, using materials typical to California due to the high incidence of WUI fires in that geographic region. All materials were sorted and summed by type of base material such as wood materials, plastics, textiles, and metals. This list is notably modular and detailed per item, allowing for the addition/subtraction of components to address future study designs. In summary, the total combustible mass of an average American home was around 46,500 kg, including 81% wood materials, 6% plastics, and 2% metals. This list of materials serves as a foundational mixture of home materials to integrate into exposure characterization, mechanistic toxicology, and ecological/human health research addressing wildfires occurring at the growing WUI.

Untreated wastewater enters the ocean at an outfall in Mexico and spreads to the San Diego-Tijuana (USA-Mexico) border region, posing significant risks to human health. Here, we developed a risk assessment tool for coastal communities, leveraging hindcast oceanographic simulations (2017-2019), to link changes in temperature and salinity at the coastline to high wastewater concentrations. We first calculated the modeled timescales (i.e., duration and return time) of wastewater exposure for popular beaches in the region. Most high wastewater exposure events occurred about once a month and lasted less than a week at the southern locations (e.g., Imperial Beach), and occurred less frequently and for shorter periods of time further north (e.g., Coronado). Using the same hindcast simulations, we then identified relationships between anomalous environmental conditions and wastewater concentration along the coastline. High wastewater concentrations were typically associated with lower salinity and temperature, reflecting the low salinity of wastewater and the colder temperatures of water originating south of the USA-Mexico border. Statistical models with only parameters of salinity and temperature anomalies captured a large proportion of the variation in wastewater-associated risk of illness (  = 0.63-0.78). We tested the risk assessment approach with several months of recent observations (January-December 2024) to show how this tool may be practically applied. This study provides an efficient method for developing risk models that utilize commonly measured environmental data, with applications to other pollution-impacted coastal locations.

The Ayeyarwady Delta in Myanmar, home to an estimated 12 million people, faces widespread arsenic contamination similar to other Asian deltas namely Bengal, Red River, and Mekong. Arsenic here primarily results from reductive dissolution of iron minerals in anoxic conditions driven by organic carbon. Here, we used digital elevation model (DEM) data to investigate how drainage density and hierarchical recharge pathways influence arsenic distribution, supported by combined data set of 136 wells (81 new, 55 from a prior study)-up to 215 m deep-along a 170 km west-to-east transect across the delta. Findings indicate arsenic hotspots in the mid-central region of the delta, where high drainage density appears to facilitate focused recharge, delivering organic carbon to underlying aquifers. Compared with other deltaic regions across Asia, the Ayeyarwady has fewer high-arsenic wells, with only 21% of our data set exceeding the local 50 μg/l limit. National screening data from 123,962 wells indicate that while only 8% exceed the regulatory limit of 50 μg/l set by Myanmar, 71% exceed the 10 μg/l guideline recommended by the World Health Organization (WHO). This highlights widespread exposure risk not addressed under the current national standard, particularly for rural communities. The observed variability in arsenic concentrations, driven by complex redox dynamics and groundwater flow patterns, indicates that contamination can occur even within short spatial intervals. A blanket-screening program focused on hotspot regions is essential to ensure that at-risk populations are not unknowingly exposed to unsafe drinking water.

Extreme weather events (EWEs) continue to threaten the health and well-being of populations across the globe. However, risk from drought and floods is not evenly distributed spatially nor are all populations equally at risk for poor health outcomes. Globally, people living with HIV/AIDS (PLHIV) face a particular set of challenges with EWE exposure including increased susceptibility to disease progression from care disruptions and medication adherence, and general population concentration in areas where rainfall is both highly variable and key to economic well-being. To mitigate the impacts of EWE exposure on PLHIV, it is necessary to understand the historical EWE exposure patterns at HIV care clinics. In this paper, we link open-source measures of drought and flood events to clinic locations from the International epidemiology Databases to Evaluate AIDS (IeDEA) network, a longitudinal study of over 2 million people living with and at risk for HIV in 44 different countries around the globe enrolling in HIV care from 2006 to present. Using generalized additive models fit to clinic-level drought and flood exposures, we show how exposures vary across and within countries, model each clinic's probability of exposure to a drought or flood to identify high-risk areas, and describe how this historical exposure record could ultimately be used to identify at-risk populations for a wide variety of study designs. While EWEs occurred at HIV care clinics around the globe, we found that clinic locations in Southern Africa are particularly vulnerable to flood and drought events as compared to other IeDEA clinic regions and locations.

Previous research has primarily focused on the impact of climatic variables and air pollution on Hand, Foot, and Mouth Disease (HFMD). However, there remains limited understanding of how air pollution levels modify these relationships across different regions and populations. This study employed a two-stage, multi-city time-series analysis using data from 13 cities in Jiangsu Province (2015-2023) to explore these effects. A multistage analytical approach, including the distributed lag non-linear model, multivariate meta-regression, and attributable risk calculation, was used to quantify the association between climatic variables, air pollutants, and HFMD. Findings indicated that HFMD incidence is closely associated with meteorological conditions, with peak risk at 24.8°C for average temperature and 89.2% for average relative humidity. Low average wind speed and short sunshine hours (SH) also contributed to increased risk. Air pollutants, such as PM, SO, and O, significantly modified these associations. For example, PM and SO increased HFMD risk at higher temperatures, while O reduced risk. Under low humidity, some pollutants exhibited protective effects, though risk increased with high humidity. NO had the strongest influence in reducing variability, while high PM and SO concentrations weakened the protective effects of SH. These findings emphasize the non-linear influence of climatic variables on HFMD risk and suggest that air pollution's modification of these relationships varies by gender, age, and location. This provides important insights for developing targeted, timely public health warnings.

We investigate the effect of extreme heat on home births in India, proposing that such extreme weather events may impede access to health facilities for childbirth. Utilizing geocoded data from the 2019-2021 Demographic and Health Survey for India, we identified the place of delivery of 208,368 births as home versus health facility. We incorporated maximum values for gridded wet-bulb globe temperatures (WBGT) and dry-bulb temperatures (DBT) corresponding to delivery dates and maternal residences. We defined context-specific extreme heat events using several percentile-based thresholds (between 80th and 95th) over varying durations (1-5 days). We used Generalized Estimating Equations (GEE) with inverse probability of treatment weighting, incorporating socioeconomic factors and state-level fixed effects, and adjusted for seasonality. We tested for effect-measure-modification by socio-economic factors (e.g., caste, wealth), healthcare access factors (e.g., rural/urban place of residence, difficultly in accessing healthcare), and contextual factors (e.g., long-term mean temperature, prevalence of institutional delivery). Both WBGT and DBT-based heatwave exposures were associated with increased likelihood of home births, with WBGT exposures demonstrating an earlier onset of significant associations at lower percentile thresholds while DBT showed stronger associations at higher thresholds and longer durations. Effect modification analyses revealed heightened impacts in warmer regions, states not designated as high-focus under the Janani Suraksha Yojana program, and non-Hindu populations. We find that extreme heat may compromise delivery at health facilities in India. Findings call for improved health system preparedness via early warning systems and advanced resource allocation to mitigate some of these effects.

California wildfires have grown increasingly frequent and intense over recent decades, raising serious public health concerns. In response, the California Air Resources Board (CARB) 2022 Scoping Plan outlines land management strategies to reduce wildfire risk and associated emissions under various climate change scenarios. This study evaluates the health benefits of CARB's official mitigation pathway, the S3 scenario, compared to a business-as-usual approach, using three global climate models (GCMs) and three future time slices. We apply the GEOS-Chem model to estimate fire-induced PM concentrations and use the U.S. EPA's BenMAP-CE tool, along with a wildfire-specific chronic mortality dose-response function, to assess associated morbidity and mortality. Results suggest that S3 can significantly reduce fire-related PM exposure, particularly in northern and central California where concentrations are typically highest-and where S3 treatments are most effective. In 2035 under the second generation Canadian Earth System Model GCM, for instance, S3 is associated with 1,927 fewer premature deaths and substantial reductions in asthma- and respiratory-related emergency room visits. However, health benefits vary by GCM and year, underscoring the influence of meteorological conditions on fire activity and health outcomes. These findings point to the importance of strategically timed and located land management actions and integrating climate variability into future mitigation planning.

Numerous studies have reported that short-term exposure to fine particulate matter (PM) is associated with mortality risk; however, results on high-risk populations and regions have been mixed. This study performed a nationwide time-stratified case-crossover study to assess the association between short-term PM and mortality in South Korea (2015-2019) by each cause of death and age group. A machine-learning ensemble PM prediction model was used to cover unmonitored districts. We estimated the excess mortality and Years of Life Lost (YLL) attributable to PM and non-compliance with the 2021 WHO guidelines (>15 μg/m). We examined the effect modifications by district-level accessibility to green spaces and medical facilities in the living sphere. In the total population, PM was positively associated with mortality for non-accidental causes (OR: 1.008 with 95% CI: 1.006-1.010), circulatory diseases (1.007, 95% CI: 1.003-1.011), and respiratory diseases (1.007, 95% CI: 1.001-1.013). Based on the point estimates, the association was generally greater in younger age groups (0-59 or 60-69 years) than in older age groups (70-80 and 80 years or older), and this pattern was pronounced in mortality for cerebrovascular diseases and pneumonia. The excess mortality fraction and YLL due to non-compliance with WHO guidelines were 0.80% and 186,808.52 years. Our findings suggest high risk populations and benefits for establishing stricter PM standards and action plans.

Climate change intensifies extreme weather, which in turn influences infectious disease transmission. As a dengue fever (DF) hotspot, Guangzhou lacks research on how extreme weather characteristics and spatial factors interact to shape DF patterns. This study analyzed DF distribution in Guangzhou from 2017 to 2019, using a zero-inflated negative binomial spatial lag (ZINB-SAR) regression model to assess the effects of daytime heatwaves (DHW), nighttime heatwaves (NHW) and extreme precipitation (EP) on DF. Results revealed that DF cases were predominantly clustered in central urban areas, with an epidemic season from May to November. The ZINB-SAR model outperformed negative binomial regression and spatial econometric models, with all spatial effect coefficients significantly positive. Analysis of lagged effects showed that each additional DHW event increased DF cases by up to 10.80% (95% CI: 6.22%-15.59%) at a 2-month lag, while NHW events increased DF by 2.73% (95% CI: -1.59%-7.23%). Threshold analysis indicated DHW intensity shifted from promoting to inhibiting DF between 0.66°C and 0.76°C, while NHW intensity transitioned between 0.95°C and 2.28°C. EP demonstrated the strongest effects at a 3-month lag, increasing DF cases by 12.05% (95% CI: 9.03%-15.17%), although its intensity was not statistically significant. Seasonal and spatial variations in DF incidence were evident. In conclusion, DHW and EP impacts were primarily driven by event frequency rather than intensity, whereas NHW effects were more dependent on intensity. These findings highlight the complex spatiotemporal interplay between extreme weather and DF in Guangzhou, providing critical evidence for developing targeted climate-adaptive disease control strategies.

Lead exposure remains a persistent environmental health threat. Soil contamination is recognized as an overlooked yet critical reservoir of childhood lead exposure due to a legacy of historical lead use in gasoline, paint, and industry. However, it is unclear whether measuring soil lead is an effective way to screen for risk at the community or neighborhood level, nor if soil lead is a significant predictor of elevated blood lead levels (EBLLs) beyond other socioeconomic and physical environment covariates. Building on prior soil sampling and conducting extensive citywide sampling and analysis, we assemble the largest data set of soil lead to date ( = 1,750) in Chicago. Combined with BLL data reported by the Chicago Department of Public Health (CDPH), municipal data, and census data, we investigated the association between soil lead concentrations, predicted BLLs from the EPA's Integrated Exposure Uptake Biokinetic (IEUBK) model, and EBLL from CDPH blood testing among children in Chicago at the community area scale. We present city-scale soil lead and IEUBK risk maps for Chicago. Furthermore, while median household income remains the strongest single predictor of EBLL prevalence in our models, we provide evidence that soil lead independently contributes significant predictive power. Our findings position systematic soil monitoring as a practical tool for primary prevention, complementing existing prevention and intervention strategies and accelerating progress toward safer cities.

Prenatal exposure to heavy metals (HMs) has been the focus of international research. However, current studies tend to examine individual metals in isolation and rely on traditional linear regression models, which may not adequately reflect the complex, non-linear and interactive effects of mixed metal exposure. The aim of this study was to investigate the relationship between maternal mixed urinary HM exposure levels during pregnancy and adverse birth outcomes such as preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA) infants using advanced machine learning methods. This study was conducted at a tertiary hospital in Guilin, from 2022 to 2023. A total of 489 pregnant women were enrolled. First-trimester urine samples were collected to quantify HM concentrations using Inductively coupled plasma mass spectrometry. Demographic and clinical data were obtained through structured questionnaires. Bayesian Kernel Machine Regression analysis revealed a significant cumulative effect of mixed metal exposure on adverse pregnancy outcomes, with distinct dose-response relationships. The risk of PTB and LBW increased monotonically with higher exposure levels; the adjusted odds ratios were elevated as metal mixture concentrations increased from the 25th to the 75th percentile. In contrast, the association with SGA exhibited a non-monotonic pattern-higher risk at lower exposure levels and a marked decline in risk at higher concentrations. Inorganic arsenic was identified as the primary toxic component in univariate models. Multivariate response modeling demonstrated the joint influence of metal mixtures on adverse outcomes (AUC = 0.697), with no significant statistical interactions between individual metals, as indicated by parallel dose-response curves ( > 0.05).

Climate change, rapid urbanization, and population growth are increasingly influencing the quality and quantity of surface water resources, especially in vulnerable reservoir systems. This study investigates the spatiotemporal changes in water features and quality of three key drinking water source lakes-Rawal, Simly, and Khanpur (RSK), located in and around Islamabad, Pakistan. Using Level 2 Landsat 5, 7 and 8 satellite data from 1991 to 2020, changes in lake surface area were assessed through the Google Earth Engine (GEE) platform. Thresholding and geospatial analysis in ArcGIS 10.8 were used to extract and visualize water bodies and surface feature changes. The study found that lake surface areas were directly linked to rainfall levels and decreased with rising temperatures especially during 1991, 2000 2010, and 2020. Water quality was assessed using standard laboratory procedures. Notably, higher bacterial counts were recorded during the wet season, indicating increased microbial contamination likely due to surface runoff. Among the heavy metals analyzed (Fe, F, As, Cu, Zn, Mn, Cr, Pb, Ni, B, Cd, P, Hg), only boron (B), nickel (Ni), and chromium (Cr) were detected above background levels, though within permissible limits. The study highlights the significant influence of climatic variables on both the physical extent and microbial quality of drinking water lakes. These findings offer critical insights for policymakers and water resource managers, providing a replicable framework for monitoring and managing similar reservoirs in other climate-sensitive regions.

Global bottom-up anthropogenic emission inventories show substantial spatial and temporal differences of short-lived pollutant emissions, which results in uncertainties in terms of air quality and human health impacts. In this study, we compare the emissions of trace gases and aerosols for the year 2015 from three different global emission inventories, the Community Emissions Data System (CEDS), the Copernicus Atmosphere Monitoring Service Global Anthropogenic Emissions (CAMS-GLOB-ANT), and Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants version 6b (ECLIPSEv6b). We then employ the Community Atmosphere Model with chemistry version 6.0 within the Community Earth System Model version 2.2.0 to quantify the atmospheric chemistry and air quality impacts from the above three anthropogenic emission inventories, with a focus on PM (particulate matter with aerodynamic diameters equal or less than 2.5 μm) and ozone (O). Our results indicate that differences between emission inventories are largest for black carbon, organic carbon, ammonia and sulfur dioxide, in terms of global annual total emissions. These differences in emissions across CEDS, CAMS, and ECLIPSEv6b lead to substantial variations in global annual totals and spatial distribution patterns. This study shows that the global annual total PM-induced premature mortality is three times higher than that from O mortality, indicating that PM is the primary contributor compared with O. An inter-comparison of global human health impacts from CEDS, CAMS and ECLIPSEv6b indicates that 80% (CEDS), 81.2% (CAMS), and 77.6% (ECLIPSEv6b) of premature deaths due to anthropogenic activities are associated with Asia and Africa continents.

Warming temperatures are expanding the potential for malaria transmission into higher altitudes, with important implications for malaria control planning. In Papua New Guinea (PNG), malaria is widespread in lowland areas but rarely transmitted above 1,600 m. This study assessed changes in malaria transmission suitability across PNG from 1960 to 2019 and projected shifts through 2040, using satellite-derived temperature data and climate models. We applied a temperature-dependent basic reproduction number ( ) to identify shifts in geographic suitability, estimate the population at risk, and evaluate the effectiveness of interventions. Malaria temperature suitability ranges have subtly changed between 1960 and 2019, with the proportion of people living in suitable areas increasing from 58% to 61% (equivalent to an additional 249,125 people). Under a conservative climate change model, this proportion is expected increase to 74% by 2040 (equivalent to an additional 2,802,709 people). Interventions had a larger impact on malaria incidence in areas with < 0.3, mitigating the current and future impact of climate change. Nevertheless, the number of people requiring access to malaria control is expected to double by 2040, to 13.4 million with 2.8 million attributed to climate change alone. The impacted areas are densely populated highlands regions with a more susceptible population and an increased potential for epidemics and clinical disease. These findings underscore the challenges of climate change for malaria elimination in PNG and highlight the need to accurately guide preparedness and forecast the additional resource requirements.

This study investigated the socioeconomic disparities of asthma incidence attributable to ambient particulate matter in aerodynamic diameter ≤2.5 μm (PM) exposures among schoolchildren in California, U.S. We found that schoolchildren attending public schools in more vulnerable communities, characterized by higher proportions of people of color, low educational attainment, and poverty, experienced elevated PM exposures by 2.07-2.96 μg/m. The disproportionate PM exposures were likely driven by higher traffic-related emissions and point-source facility emissions in these communities. Using school-specific PM concentrations, student enrollment numbers, and model-estimated (not directly observed) baseline age-specific asthma incidence rates, we calculated that the asthma incidence rate attributable to 2016 PM exposures was 562 new cases per 100,000 schoolchildren [95% confidence interval (CI) = 311-854]. In absolute terms (i.e., asthma incidence), it was equivalent to 34,537 PM-related new asthma cases (95% CI = 19,090-52,493) among all schoolchildren. On average, more vulnerable communities experienced 140 excess new asthma cases per 100,000 schoolchildren (i.e., the difference in average asthma cases per 100,000 schoolchildren between more and less vulnerable groups) across all demographic factors considered. Examining health disparities separately by each demographic factor revealed that race/ethnicity was associated with the largest disparities (209 new cases per 100,000 schoolchildren), followed by educational attainment (128) and poverty (85). Our findings indicate the substantial socioeconomic disparities of asthma incidence attributable to PM among schoolchildren in California. Addressing these health disparities could benefit from sustained and long-term emission reduction strategies, such as adopting zero-emission vehicles, which contribute to lower PM levels.

PM is a critical air pollutant that significantly impacts human health and the environment. To develop effective air quality management and mitigation strategies, understanding PM source attribution and associated health risks is essential. This study investigates the source attribution and health burden of PM focusing on Mainland Thailand (MT), North Thailand (NT), and the Bangkok Metropolitan Region (BMR), using the WRF-Chem model and a brute-force method for source attribution. PM contributions from biomass burning including both crop and non-crop burning are quantified, along with contributions from transportation, industry, energy, residential, and other anthropogenic sectors. This study focuses on the haze season (February-April) in 2019. Our research shows that in-domain foreign country's biomass burning is a major contributor to PM, accounting for 23%-38% of PM concentrations in MT. In NT, non-crop burning within MT contributes the most (21%-36%) to PM levels, while crop burning within MT has a minimal impact (less than 6%). In the BMR, PM is strongly impacted by sources outside the model domain. Overall, industrial and transportation emissions are the most impactful anthropogenic sources. We further estimate the total health burden, associated with long-term PM exposure during the haze season contributes to 46% of this PM health burden in MT in 2019, 66% in NT, and 37% in the BMR. These findings suggest that reducing biomass burning within MT and from in-domain foreign countries during February-April could reduce the annual health burden in MT by up to 20%.

Dengue is an arboviral fever. Weather modulates dengue transmission by influencing the life cycles of vector mosquitoes and the virus. Three teleconnections are known to affect the weather in Sri Lanka. Those are El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and ENSO Modoki. We studied correlations between dengue incidence (DI) in the Western Province (WP) of Sri Lanka as a whole and three districts of the province and indices of ENSO, IOD and ENSO Modoki. We used four indices of ENSO and one index each of IOD and ENSO Modoki. We acquired notified dengue cases in WP, population data and monthly indices of three teleconnections for the 2005-2019 period. We used wavelet time series analysis to determine correlations between indices of teleconnections and DI. Two indices of ENSO were correlated with the DI of the WP and all three districts of the WP individually. The other two indices were correlated with the DI of two districts. The index of IOD was correlated with DI of two districts. The index of ENSO Modoki was correlated with the DI of WP and one district of it. Both positive and negative extremes of at least one teleconnection index were followed by the rise of DI in all districts. We concluded that three teleconnections modulate DI of different districts of WP in different ways. Monitoring of indices of these teleconnections and escalating dengue preventive work after extremes of indices can potentially blunt impending dengue peaks.

Eighty-four percent of sub-Saharan African households rely on polluting fuels (e.g., wood, charcoal) for cooking, leading to high levels of household air pollution (HAP). While switching to modern fuels/stoves could decrease HAP levels, they are not always available or affordable. Improved biomass cookstoves could provide an intermediate step supporting transitions from traditional biomass to clean burning fuels/stoves. We conducted two stove intervention trials in Lusaka, Zambia using targeted marketing/incentives to motivate participants to use improved biomass stoves, either the Mimi Moto (pellet) or the EcoZoom (charcoal). Before the intervention, 65% of participants exclusively used charcoal, while 27% relied on electricity to some extent for cooking. We measured 24-hr personal exposure to CO ( = 747) and PM ( = 90) of primary cooks. We implemented several statistical approaches to estimate the effects of interventions on exposure: household-specific endline minus baseline exposure, ranksum testing, difference-in-differences analyses, and cross-sectional analyses. We found that switching from traditional charcoal stoves to either intervention stove was not associated with significantly reduced exposures. However, cooks using electric stoves independent of the intervention did have significantly lower CO exposures than those using traditional charcoal, with greater electric stove use corresponding to greater exposure reductions. Variability in exposure was dominated by seasonal, regional, and neighborhood differences rather than household stove/fuel choices. A focus on HAP exposure from cooking in urban settings is unlikely to yield expected exposure reductions. Policy makers should consider pollution reduction policies/interventions that target ambient air quality in tandem with HAP-mitigating strategies to address air pollution health burden.

Exposure to wildfire smoke is a well-known concern for public health and is anticipated to worsen with an increase in wildfire activity related to climate change. This study uses satellite and ground-based carbon monoxide (CO) measurements from 2004 to 2019 to evaluate a change in its seasonal cycle due to wildfire emissions. Monthly average CO total columns from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument over Alberta and Ontario, and from a ground-based Fourier transform infrared spectrometer in downtown Toronto are compared before and after 1 January 2012, following previous literature. Between the two time periods, a new peak emerges in the seasonal cycle of CO, centered around August. Monthly emergency room admissions from Alberta and Ontario for nine cardiovascular and respiratory diseases are assessed with a difference in difference analysis, using MOPITT CO as the exposure metric. This analysis was used to calculate the change in monthly hospital admissions per 100,000 people, given a 1 ppb increase in XCO post-2012 compared to pre-2012, along with the 95% confidence interval (CI). For Ontario, this term is positive and significant for hypertension (change = 1.88, CI = 1.18-2.57), ischemic heart disease (0.50, CI = 0.12-0.88), arrhythmia (0.12, CI = 0.03-0.22), and asthma (0.31, CI = 0.05-0.57). For Alberta, there is a significant and positive interaction for arrhythmia (0.48, CI = 0.12-0.85). These results indicate that there was a statistically significant increase in adverse health outcomes for five of the eighteen disease-province pairings associated with the increase in atmospheric CO after 2011 coinciding with enhanced wildfire emissions.