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COVID 19 Pandemic in India had demanded an imposition of lockdown for three weeks initially and was extended further. This has drastic effect on air quality making it better because of control of vehicle emissions. This study analyzed the air quality in Chennai city using the parameters of pollution (NH3, PM2.5, NO2, SO2, O3 and CO) for air quality data for monitoring stations (three) spread over the city. National Air Quality Index (NAQI) has been used to present the quality of air spatially during lockdown and before lockdown. The concentrations of PM2.5 among the pollutants selected showed a maximum reduction (-61%) compared to the pre-lockdown process. NO2 (-40%) and CO (-32%) have also gone down when lockdown was in place, among other contaminants. In the different sections of the city, about 53% reduction in NAQI has been observed. Deep learning short-term predictions of various air pollutants are made in this study. The correlation between AQI and Pollutants (NH3, PM2.5, NO2, SO2, O3 and CO) in the study area modelled in deep learning using PYTHON. The classification of AQI class has been created in python with AQI values of Good (0-50), Satisfactory (51-100), Moderate (101-200), Poor (201-300), Very poor (301-400) and severe (>401). The study shows the level of co-relation of PM2.5 being the highest. A linear regression model was performed and metrics such mean absolute error, r2 to check the model performance for training and testing data are calculated. These results can be coupled with social, economic and Cultural factors that could have common emission patterns and air quality especially in metropolitan cities. The present study would aid authorities as it clearly shows that the quality can be made better if sources of emission can be diminished. This will pave way to protect and make the surroundings and environment better. [ABSTRACT FROM AUTHOR]

Land-use/land-cover (LU/LC) change is an important component of global environmental change. The need to understand LU/LC change is essential for regional development and land use management towards sustainable development. To understand LU/LC change, the different LU/LC categories and their spatial as well as temporal variability in Tiruchirappalli city has been studied over a period of eight years (1998–2006), using the analysis of Indian Remote Sensing Satellite (IRS) images. In this, an attempt was made to adopt Markov model for obtaining and understanding LU/LC dynamics. Model performance was evaluated between the empirical LU/LC map extracted from CARTOSAT-1 PAN image and the simulated LU/LC map obtained from the Markov model. The future landscape distribution in the year 2014 and 2022 was derived using a Markov model. This result shows that Markov model and geospatial technology together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization in this region. [ABSTRACT FROM PUBLISHER]

Land use/land cover mapping serves as a basic inventory of land resources throughout the world. Detecting and analyzing the quantitative changes along the earth's surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, and economic and industrial growth. This paper examines the changes in land use/land cover types in Tiruchirappalli city, Tamil Nadu from 1998-2006, using remote sensing technology and Geographic Information Systems (GIS). The spatial and temporal changes of land use/land cover were analyzed through visual interpretation and image classification of IRS remote sensing data collected for years 1998, 2002 and 2006. The results of the study indicated an overall growth in built-up land by 80% over a span of eight years, with an incredible boost (58%) in development from 1998-2002. The area under agricultural wet land was found to have decreased from 3,776 ha to 2,689 ha, with an increase in agricultural dry land from 814 ha to 1,058 ha during this period, suggesting the wastage of land due to excessive use of fertilizers. [ABSTRACT FROM AUTHOR]