Achieving Brazil's Deforestation Target Will Reduce Fire and Deliver Air Quality and Public Health Benefits.

Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.

Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020.

Anthropogenic emissions and ambient fine particulate matter (PM) concentrations have declined in recent years across China. However, PM exposure remains high, ozone (O) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010-2020.

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.

Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual-mean fine particulate matter (PM) and ozone (O) concentrations and their associated chronic health impacts from changes in five major emission sectors (residential, industrial, land transport, agriculture, and power generation) in China. The emulators predicted 99.

Assessing costs of Indonesian fires and the benefits of restoring peatland.

Deforestation and drainage has made Indonesian peatlands susceptible to burning. Large fires occur regularly, destroying agricultural crops and forest, emitting large amounts of CO and air pollutants, resulting in adverse health effects. In order to reduce fire, the Indonesian government has committed to restore 2.