Browsing by Author "Nandan, A K"

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    Sustainable data-driven insights: Statistical analysis and artificial intelligence-driven modelling of aerosol concentrations in Hyderabad district, India
    (Elsevier Ltd., 2024-04-29) Nandan, A K; Mathew, Aneesh
    Air pollution stands as a pressing issue in contemporary times, leading to the loss of millions of lives and exerting detrimental effects on the economy. The aerosols especially particulate matter, which are dispersions of matter in air medium play an important role in manipulating the climatological variables in an area. The current study was developed in response to the need to study aerosols and particulates on annual levels using 20-year (2002–2021) daily mean Aerosol Optical Depth (AOD) product released by Moderate Resolution Imaging Spectrometer (MODIS) sensors, and to generate prediction models for AOD using artificial intelligence (AI) techniques for Hyderabad district in India. The results of daily mean analysis revealed a rising trend in the number of days with severe AOD (> 1). Yearly mean AOD distribution showed a percentage increase of 45.31 % from 2002 to 2021. Furthermore, factor analysis was carried out to check for correlations of AOD and PM2.5 with various meteorological and pollutant variables. It was observed that both PM2.5 and AOD had significant weak to moderate (p < 0.05; r < 0.5) correlations with both pollutants and meteorological variables. The hybrid deep learning-based CNN-LSTM was identified as the best-fit model to predict AOD, outperforming MLP – ARIMA and MLP models. CNN – LSTM showed an R2 of 0.70, MAE of 0.08, MSE of 0.02 and RMSE of 0.14.
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    Trend Analysis of Aerosol Concentrations over Last Two Decades from MODIS Retrievals over Hyderabad District of India
    (AGH University of Science and Technology Press, 2024-01-31) Nandan, A K; Mathew, Aneesh; Shekar, Padala Raja
    Air pollution is one of the grave concerns of the modern era, claiming millions of lives and adversely impacting the economy. Aerosols have been observed to play a significant role in negatively influencing climatological variables and human health in given areas. The current study aimed to study the trend of aerosols and particulates on daily, monthly, seasonal, and annual levels using a 20-year (2002–2021) daily mean aerosol optical depth (AOD) product released by moderate resolution imaging spectrometer (MODIS) sensors for the Hyderabad district in India. The results of the daily mean analysis revealed a rising trend in the number of days with severe AOD (>1), whereas examinations of the seasonal and monthly mean data from 2017 through 2022 showed that peak AOD values alternated between the summer, autumn, and winter seasons over the years. Trend analysis using Mann–Kendall, modified Mann–Kendall, and innovative trend analysis (ITA) tests revealed that AOD increased significantly from 2002 through 2021 (p < 0.05; Z > 0). Furthermore, correlation analysis was performed to check for correlations between AOD levels and certain meteorological factors for the Charminar and Secunderabad regions; it was noticed that temperature had a weak positive correlation with AOD (p < 0.05; r = 0.283 [Secunderabad] – p < 0.05; r = 0.301 [Charminar]), whereas relative humidity developed a very weak negative correlation with AOD (p < 0.05; r = −0.079 [Secunderabad] – p < 0.05; r = −0.109 [Charminar]).