Understanding carbon sequestration trends using model and satellite data under different ecosystems in India.

This study discusses carbon sequestration variability in different ecosystems of India. Four different biosphere regions, each over 0.5° × 0.5° area, have been selected considering the geospatial and climatic variability of these regions expanding from Central India (CI), the Northeast region (NER), the Western Ghats (WG), and the Western Himalayan region (WHNI). The climatic conditions of these four regions are different so are the biosphere constituents of these regions. We expect the Gross Primary Productivity (GPP) to enhance during the all India summer monsoon rainfall season but in varied magnitudes suggesting a role of climatic parameters and flora in these regions. The GPP from FLUXCOM for the duration of 2001 to 2019 (19 years) and satellite-derived vegetation indices like the Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI) are used in this study to understand the response of regional vegetation to this variability. EVI seems to be better related to GPP in comparison to NDVI in the preliminary analysis. Further analysis suggests LAI correlates better to GPP than EVI and NDVI in different seasons in these four regions. Also, meteorological parameters like surface temperature, rainfall, soil water, and other derived parameters like Vapor Pressure Deficit (VPD) are studied. It is also observed that the year-to-year variability in the climatic conditions could also have a role to play in the observed features. It is proven that the climate around the world is experiencing changes. Vegetation is one of the potent markers to monitor the impact of climate change. These long-term data and trends were studied to understand if there is any significant impact of the changing climatic conditions on the vegetation in these regions. Our study shows that there is an increasing (positive) trend in GPP at these locations though at different rates. WG and WHNI have shown a significant high rate of increase (6.44 and 5.36 gCm-2 y-1, respectively) in GPP over the last two decades.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning.

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 µg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Classification of MODIS fire emission data based on aerosol absorption Angstrom exponent retrieved from AERONET data.

Biomass burning emits a large quantity of gaseous pollutants and aerosols into the atmosphere, which perturbs the regional and global climate and has significant impacts on air quality and human health. In order to understand the temporal and spatial distributions of biomass burning and its contribution to aerosol optical and radiative impacts, we examined fire emission data and its contribution to aerosol optical and radiative impacts over six major hot-spot continents/sub-continents across the globe, namely North-Central (NC) Africa, South America, US-Hawaii, South Asia, South East Asia, and Australia-New Zealand, using long-term satellites, ground-based and re-analysis data during 2000-2021. The selected six sites contributed ∼70% of total global fire data. The classification of biomass burning, such as pre, active, and post burning phases, was performed based on the Absorption Angstrom Exponent (AAE) estimated from 55 AERONET (AErosol RObotic NETwork) stations. The study found the highest contribution of fire count (55 %) during the active burning phase followed by post (36 %) and pre (8 %) burning phases. Such high fire counts were associated with high absorption aerosol optical depth (AAOD) during the active fire event. Strong dominance of fine and coarse mode mixed aerosols were also observed during active and post fire regimes. High AAOD and low Extinction Angstrom Exponent (EAE) over NC Africa during the fire events suggested presence of mineral dust mixed with biomass burning aerosols. Brightness temperature, fire radiative power and fire count were also dominated by the active burning followed by post and pre burning phases. The maximum heating rate of 3.15 K day-1 was observed during the active fire events. The heating rate profile shows clear variations for three different fire regimes with the highest value of 1.80 K day-1 at ∼750 hPa altitude during the active fire event.