A study conducted by the University of Eastern Finland and Finnish Meteorological Institute has assessed the impacts of future aerosol emission reductions over the Indian subcontinent with a machine-learning method developed for correcting global model-based fine particulate matter estimates.
The aim of the study was to analyze the effects of aerosol emission mitigation on both regional radiative forcing and city-level air quality with a global-scale climate model, with a special focus on India and the surrounding area.
According to the organizations, global-scale models do not necessarily provide accurate enough estimates for city-level air quality. In order to tackle this issue, a machine learning downscaling method was developed. The method combined global model data and ground station-measured fine particulate matter concentration data from New Delhi in India. The results indicated that future aerosol mitigation could result in both improved city-level air quality and less radiative heating for India.
In the study’s abstract, the researchers said, “The aim was to simultaneously analyze both city-level air quality and regional- and global-scale radiative forcing values for anthropogenic aerosols. As the city-level air pollution values are typically underestimated in global-scale models, we used a machine learning approach to downscale fine particulate (PM2.5) concentrations toward measured values. We first simulated the global climate with the aerosol–climate model ECHAM-HAMMOZ and corrected the PM2.5 values for the Indian megacity New Delhi. Our results demonstrate that downscaling and bias correction allow more versatile utilization of global-scale climate models. With the help of downscaling, global climate models can be used in applications where one aims to analyze both global and regional effects of policies related to mitigating anthropogenic emissions.”