Comparison of deforestation and forest land use factors for malaria elimination in Myanmar.

Objectives: Within the remote region of Ann Township in Myanmar's Rakhine State, malaria prevalence has remained steady at ∼10% of the population from 2016-2019. Previous studies have linked areas of higher malaria prevalence in the region to heavily forested areas, however, little is known about how people live, work, and move through these areas. This work aims to disentangle landscape from land use in regard to malaria exposure. Methods: We investigated the roles of forest cover, forest loss, and land use activities with malaria prevalence through the combined use of land use surveys, malaria surveillance, and satellite earth observations. Results: Our results confirm previous research that linked areas of high forest cover with high malaria prevalence. However, areas experiencing high levels of deforestation were not associated with malaria prevalence. The land use factors that contribute most significantly to increased malaria risk remained those which put people in direct contact with forests, including conducting forest chores, having an outdoor job, and having a primary occupation in the logging and/or plantation industry. Conclusion: Malaria prevention methods in Myanmar should focus on anyone who lives near forests or engages in land use activities that bring them within proximity of forested landscapes, whether through occupation or chores.

Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires.

Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.