The HadGEM2 earth system climate model was used to assess the impact of biomass burning on surface ozone concentrations over the Amazon forest and its impact on vegetation, under present-day climate conditions. Here the researchers consider biomass burning emissions from wildfires, deforestation fires, agricultural forest burning, and residential and commercial combustion.

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Snow depth is an important parameter for avalanche forecast and hydrological studies. In the Himalaya, manual snow depth data collection is difficult due to remote and rugged terrain and the severe weather conditions. However, microwave-based sensors in various satellites have the capability to estimate snow depth in all weather conditions. In the present study, experiments were performed to establish an algorithm for snow depth estimation using ground-based passive microwave radiometer with 6.9, 18.7 and 37 GHz antenna frequencies at Dhundhi and Patseo, Himachal Pradesh, India.

The livestock sector is estimated to account for 15% of global greenhouse gas (GHG) emissions, 80% of which originate from ruminant animal systems due to high emissions of methane (CH4) from enteric fermentation and manure management.

Large-scale deforestation could cause monsoon rains to shift south, cutting rainfall in India by nearly a fifth warns this new study by Indian Institute of Science published in Proceedings of the National Academy of Sciences (PNAS) 

In this paper, we investigate the coagulation of interstitial aerosol particles (particles too small to activate to cloud droplets) with cloud drops, a process often ignored in

Biomass burning (BB) largely modifies the chemical composition of atmospheric aerosols on the globe. We collected aerosol samples (TSP) at Cape Hedo, on subtropical
Okinawa Island, from October 2009 to February 2012 to study anhydrosugars as BB tracers. Levoglucosan was detected as the dominant anhydrosugar followed by its isomers, mannosan and galactosan.

While previous research helped to identify and prioritize the sources of error in air quality modeling due to anthropogenic emissions and spatial scale effects our knowledge
is limited on how these uncertainties affect climate forced air-quality assessments. Using as reference a 10 yr model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5