The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.

Center for Environmental Sciences & Engineering

Fulltext

ISPRS International Journal of Geo-Information

Spatio-Temporal Monitoring of Atmospheric Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of Stubble Burning a Case Study

We propose a novel video sampling scheme for human action recognition in videos, using Gaussian Weighing Function. Traditionally in deep learning-based human activity recognition approaches, either a few random frames or every $k^{th}$ frame of the video is considered for training the 3D CNN, where $k$ is a small positive integer, like $4$, $5$, or $6$. This kind of sampling reduces the volume of the input data, which speeds-up the training network and also avoids overfitting to some extent, thus enhancing the performance of the 3D CNN model. In the proposed video sampling technique, consecutive $k$ frames of a video are aggregated into a single frame by computing a Gaussian-weighted summation of the $k$ frames. The resulting frame preserves the information in a better way than the conventional approaches and experimentally shown to perform better. In this paper, a 3-Dimensional deep CNN is proposed to extract the spatio-temporal features and follows Long Short-Term Memory (LSTM) to recognize human actions. The proposed 3D CNN architecture is capable of handling the videos where the camera is placed at a distance from the performer. Experiments are performed with KTH, WEIZMANN, and CASIA-B Human Activity and Gait datasets, whereby it is shown to outperform state-of-the-art deep learning based techniques. We achieve 95.78%, 95.27%, and 95.27% over the KTH, WEIZMANN, and CASIA-B human action and gait recognition datasets, respectively. &nbsp;&nbsp;

World University root node

Computer Science and Engineering

(SoE) School of Engineering

Multimedia Tools and Applications

An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos

The Indian Himalayan Region (IHR) forms the northern boundary of India covering an area of about 5.3 lakh km2, which constitutes 16.2% of the total geographical area. The region is remarkable for its diversity of plants and animals and provides abundant ecosystem services to people. The Indian Himalayas (IH) is richly endowed with forest resources and supports different types of forest ecosystems along with varied topography. In addition to anthropogenic factors, the forests of IH are under pressure due to changing climate. There are several pieces of evidence of significant changes in temperature, precipitation, and vegetation phenology in IHR. Climate change (CC) has a significant impact on forest ecosystems of IH which results in the upward movement of several plant species, and further changes are expected to cause the extinction of species. Due to changing climatic conditions in IHR, possibly some of the locations may become more favorable to alien invasive species causing negative impacts on native plants. Changing temperature and rainfall in the region have caused phenological changes in many economically and ecologically important plant species like Rhododendron sp., Myrica esculenta, Pinus wallichiana, etc. As a result of which these species are shifting towards higher elevations to cope up with altered climate. A significant reduction in suitable habitat and the massive decline in the population of animal species due to climate change is also predicted. The studies carried out in IHR indicated shifts in vegetation types across IHR from moderate to large scale. Higher elevation Himalayan temperate forests, subalpine forests, and alpine forests are more vulnerable to the adverse effects of climate change while Sal forests are predicted to shift towards the north-eastern region. Western Himalayan forests are highly susceptible to changing climate while eastern Himalayan forests are estimated to be more resilient.

Springer Climate. Springer

Climate Change and Its Impact on Forest of Indian Himalayan Region: A Review

Chemistry

Shiv Nadar University

(SoNS) School of Natural Sciences

Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.