Ecosystem change can profoundly affect human well‐being and health, including through changes in exposure to vector‐borne diseases. Deforestation has increased human exposure to mosquito vectors and malaria risk in Africa, but there is little understanding of how socioeconomic and ecological factors influence the relationship between deforestation and malaria risk. We examined these interrelationships in six sub‐Saharan African countries using demographic and health survey data linked to remotely sensed environmental variables for 11,746 children under 5 years old. We found that the relationship between deforestation and malaria prevalence varies by wealth levels. Deforestation is associated with increased malaria prevalence in the poorest households, but there was not significantly increased malaria prevalence in the richest households, suggesting that deforestation has disproportionate negative health impacts on the poor. In poorer households, malaria prevalence was 27%–33% larger for one standard deviation increase in deforestation across urban and rural populations. Deforestation is also associated with increased malaria prevalence in regions where Anopheles gambiae and Anopheles funestus are dominant vectors, but not in areas of Anopheles arabiensis. These findings indicate that deforestation is an important driver of malaria risk among the world's most vulnerable children, and its impact depends critically on often‐overlooked social and biological factors. An in‐depth understanding of the links between ecosystems and human health is crucial in designing conservation policies that benefit people and the environment.

Nontuberculous mycobacteria (NTM) are environmentally acquired opportunistic pathogens that can cause chronic lung disease. Within the U.S., Hawai'i shows the highest prevalence rates of NTM lung infections. Here, we investigated a potential role for active volcanism at the Kīlauea Volcano located on Hawai'i Island in promoting NTM growth and diversity. We recovered NTM that are known to cause lung disease from plumbing biofilms and soils collected from the Kīlauea environment. We also discovered viable Mycobacterium avium, Mycobacterium abscessus, and Mycobacterium intracellulare subsp. chimaera on volcanic ash collected during the 2018 Kīlauea eruption. Analysis of soil samples showed that NTM prevalence is positively associated with bulk content of phosphorus, sulfur, and total organic carbon. In growth assays, we showed that phosphorus utilization is essential for proliferation of Kīlauea‐derived NTM, and demonstrate that NTM cultured with volcanic ash adhere to ash surfaces and remain viable. Ambient dust collected on O'ahu concurrent with the 2018 eruption contained abundant fresh volcanic glass, suggestive of inter‐island ash transport. Phylogenomic analyses using whole genome sequencing revealed that Kīlauea‐derived NTM are genetically similar to respiratory isolates identified on other Hawaiian Islands. Consequently, we posit that volcanic eruptions could redistribute environmental microorganisms over large scales. While additional studies are needed to confirm a direct role of ash in NTM dispersal, our results suggest that volcanic particulates harbor and can redistribute NTM and should therefore be studied as a fomite for these burgeoning, environmentally acquired respiratory infections.

Key Points Different factors result in an underestimation of the social and environmental impact of dust events, making them receive less attention There is a lack of consistency in dust definitions and sampling methods Sampling frequency and schedules of dust events can lead to missed dust events or underestimation of dust particle concentrations

Wildfires constitute a growing source of extremely high levels of particulate matter that is less than 2.5 microns in diameter (PM2.5). Recently, toxicologic and epidemiologic studies have shown that PM2.5 generated from wildfires may have a greater health burden than PM2.5 generated from other pollutant sources. This study examined the impact of PM2.5 on hospitalizations for respiratory diseases in California between 2006 and 2019 using a health impact assessment approach that considers differential concentration‐response functions (CRF) for PM2.5 from wildfire and non‐wildfire sources of emissions. We quantified the burden of respiratory hospitalizations related to PM2.5 exposure at the zip code level through two different approaches: (a) naïve (considering the same CRF for all PM2.5 emissions) and (b) nuanced (considering different CRFs for PM2.5 from wildfires and from other sources). We conducted a Geographically Weighted Regression to analyze spatially varying relationships between the delta (i.e., the difference between the naïve and nuanced approaches) and the Centers for Disease Control and Prevention's Social Vulnerability Index (SVI). A higher attributable number of respiratory hospitalizations was found when accounting for the larger health burden of wildfire PM2.5. We found that, between 2006 and 2019, the number of hospitalizations attributable to PM2.5 may have been underestimated by approximately 13% as a result of not accounting for the higher CRF of wildfire‐related PM2.5 throughout California. This underestimation was higher in northern California and areas with higher SVI rankings. The relationship between delta and SVI varied spatially across California. These findings can be useful for updating future air pollution guideline recommendations.

This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011-2020 wildfire season (June-July-August-September-October) with a burned area greater than 1,000ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (p-value<0.05) correlation (r) between RSDxPBI (r RSD=0.58) and PNEUxPBI (r PNEU=0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (r RSD=0.48), PNEUxPBI (r PNEU=0.47), COPDxPBI (r COPD=0.45), CSDxAPI (r CSD=0.70), RSDxAPI (r CSD=0.71), PNEUxAPI (r PNEU=0.49), and COPDxAPI (r PNEU=0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.

Wastewater‐based epidemiology (WBE) has been proven to be a useful tool in monitoring public health‐related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater‐detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under‐represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically‐based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS‐CoV‐2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time‐resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back‐tracing of human‐excreted biomarkers based on only sampling at the outlet or other key points, but would require high‐frequency, contaminant‐specific sensor systems that are not available currently.

Multiple studies have shown that exposure to pollutants can increase genotoxic damage in different taxa. However, to our knowledge, the effects of environmental stress have been explored little. In certain stressful ecosystems, such as seasonally dry tropical forests, the combined effects of anthropogenic activities and ongoing global changes can cause an increase in environmental stresses, in turn, may trigger physiological and genetic effects on biodiversity. The present aims to assess changes in the prevalence of genotoxic damage in birds within three states of forest degradation in the Tumbesian Region of Western Ecuador. We used blood samples from 50 bird species to determine the frequency of micronucleus and nuclear abnormalities in erythrocytes. Our results revealed a significant impact of forest degradation on the occurrence probability of micronucleus and nuclear abnormalities at the community level. Localities with higher levels of degradation exhibited higher levels of abnormalities. However, when analyzing the dominant species, we found contrasting responses. While Lepidocolaptes souleyetii showed a reduction in the proportion of nuclear abnormalities from the natural to shrub‐dominated localities Troglodytes aedon and Polioptila plumbea showed an increase for semi‐natural and shrub‐dominated respectively. We concluded that the degradation process of these tropical forests increases the stress of bird community generating genotoxic damage. Bird responses seem to be species‐specific, which could explain the differences in changes in bird composition reported in other studies.

Past emission controls in the UK have substantially reduced precursor emissions of health‐hazardous fine particles (PM2.5) and nitrogen pollution detrimental to ecosystems. Still, 79% of the UK exceeds the World Health Organization (WHO) guideline for annual mean PM2.5 of 5 μg m−3 and there is no enforcement of controls on agricultural sources of ammonia (NH3). NH3 is a phytotoxin and an increasingly large contributor to PM2.5 and nitrogen deposited to sensitive habitats. Here we use emissions projections, the GEOS‐Chem model, high‐resolution data sets, and contemporary exposure‐risk relationships to assess potential human and ecosystem health co‐benefits in 2030 relative to the present day of adopting legislated or best available emission control measures. We estimate that present‐day annual adult premature mortality attributable to exposure to PM2.5 is 48,625 (95% confidence interval: 45,188–52,595), that harmful amounts of reactive nitrogen deposit to almost all (95%) sensitive habitat areas, and that 75% of ambient NH3 exceeds levels safe for bryophytes and lichens. Legal measures decrease the extent of the UK above the WHO guideline to 58% and avoid 6,800 premature deaths by 2030. This improves with best available measures to 36% of the UK and 13,300 avoided deaths. Both legal and best available measures are insufficient at reducing the extent of damage of nitrogen pollution to sensitive habitats. Far more ambitious reductions in nitrogen emissions (>80%) than is achievable with best available measures (34%) are required to halve the amount of excess nitrogen deposited to sensitive habitats.

Growing evidence indicates that extreme environmental conditions in summer months have an adverse impact on mental and behavioral disorders (MBD), but there is limited research looking at youth populations. The objective of this study was to apply machine learning approaches to identify key variables that predict MBD‐related emergency room (ER) visits in youths in select North Carolina cities among adolescent populations. Daily MBD‐related ER visits, which totaled over 42,000 records, were paired with daily environmental conditions, as well as sociodemographic variables to determine if certain conditions lead to higher vulnerability to exacerbated mental health disorders. Four machine learning models (i.e., generalized linear model, generalized additive model, extreme gradient boosting, random forest) were used to assess the predictive performance of multiple environmental and sociodemographic variables on MBD‐related ER visits for all cities. The best‐performing machine learning model was then applied to each of the six individual cities. As a subanalysis, a distributed lag nonlinear model was used to confirm results. In the all cities scenario, sociodemographic variables contributed the greatest to the overall MBD prediction. In the individual cities scenario, four cities had a 24‐hr difference in the maximum temperature, and two of the cities had a 24‐hr difference in the minimum temperature, maximum temperature, or Normalized Difference Vegetation Index as a leading predictor of MBD ER visits. Results can inform the use of machine learning models for predicting MBD during high‐temperature events and identify variables that affect youth MBD responses during these events.

Human populations and ecosystems are extensively exposed to pesticides. Most nations lack the capacity to control pesticide contamination and have limited availability of pesticide use information. Ecuador is a country with intense pesticide use with high exposure risks to humans and the environment, although relative or combined risks are not well understood. Here, we analyzed the distribution of application rates in Ecuador and identified regions of concern because of high potential exposure. We used a geospatial analysis to identify grid cells (∼8km×8km) where the highest pesticide application rates and density of human populations overlap. Furthermore, we identified other regions of concern based on the number of amphibian species as an indicator of ecosystem integrity and the location of natural protected areas. We found that 28% of Ecuador's population dwelled in areas with high pesticide application rate. We identified an area of ∼512km2 in the Amazon region where high application rates, large human settlements, and a high number of amphibian species overlapped. Additionally, we distinguished clusters of pesticide application rates and human populations that intersected with natural protected areas. Ecuador exemplifies how pesticides are disproportionately applied in areas with the potential to affect human health and ecosystems' integrity. Global estimates of population dwelling, pesticide application rates, and environmental factors are key in prioritizing locations to conduct further exposure assessments. The modular and scalable nature of the geospatial tools we developed can be expanded and adapted to other regions of the world where data on pesticide use are limited.