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Over the past few years, we have built a team to work on the biosphere-atmosphere interaction. The changes happening in these two systems as a result of global climate change are our primary focus of research. We mostly used observational data from remotely sensed images and on-site monitoring stations to pursue these issues. Presently, we are working on the following areas:
Application of machine learning and deep learning for mapping air pollution.
Characterization of atmospheric aerosols through in-situ AERONET observations.
Spatial modelling of aerosol-cloud-precipitation interaction
Interaction between atmospheric methane and meteorology
Effect of atmospheric aerosols on vegetation
Crop mapping using the moderate and finer resolution satellite images
Snippets of current research
Snippets of current research
Mapping high-resolution aerosol optical depth over South Asia
A computationally efficient model has been built considering the surface reflection to map high-resolution aerosol optical depth using AERONET and Landsat images. The accuracy depends on the precise estimation of the surface reflection. As the model is tuned with the AERONET data, a proximity effect is obtained on the accuracy level.
Energy inequality and air pollution in India
The access to the clean fuel is less among the poorest community. However, clean fuel usage penetration is highest among the wealthy community. This study take a deep dive into the access to the clean fuel usage patterns and explored the its connection with the ambient air quality.
Effect of wetlands on air pollution load
The effect of wetlands is modelled in this study for estimating the PM2.5 load in atmosphere. A range of meteorological parameters and surface parameters were selected including the wetland surface area and proximity were used. The complexity of the data space led to the selection of random forest machine learning model to estimate the PM2.5 at grid space. We obtained a significant reducing effect of wetlands on the PM level. The proximity effect and surface area parameters of wetlands have an impulsive control on the PM level.
Global pollution during COVID-19
In this study we looked for the global pollution level during the travel restriction due to COVID-19. About 20% reduction of pollution was estimated over south and south east Asian countries. NO2 reduction was measured up to 20–40%, while SO2 increased up to 30% for majority of the areas during the lockdown. A complex interaction among SO2, WS and RH leads to high AOD by forming new particles.
Characterization of aerosol optical depth over India using time series satellite based observation
The columnar aerosol load over the Indian region has been characterized using a combination of linear and wavelet models. The trend test suggests that AOD increases in eastern Indian region. Presence of 32–128 days cycle in the time series is evident of fine mode aerosols. The longer periodicity of AOD support the theoretical proposition of EHP over India
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