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Trend Analysis of Aerosol Concentrations over Last Two Decades from MODIS Retrievals over Hyderabad District of India

Nandan A.K., Mathew Aneesh, Shekar Padala Raja

Geomatics and Environmental Engineering

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2024

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Vol. 18, no. 1

83--116

EN

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]).

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Prediction of aerosol optical depth over Pakistan using novel hybrid machine learning model

Zaheer Komal, Saeed Sana, Tariq Salman

Acta Geophysica

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2023

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Vol. 71, no. 4

2009--2029

EN

Impact of aerosols on health includes both long-term chronic irritation and inflammation of the respiratory tract. Aerosol optical depth (AOD), a crucial optical parameter that assesses the extinction effect of atmospheric aerosols, is frequently used to estimate the extent of air pollution on large scales. So, the better prediction of AOD is crucial for understanding the health impacts of aerosols. The accurate prediction of AOD is difficult due to its nonlinear relationships with other climatic variables, uncertainties, and time series variable characteristics. In this paper, a machine learning (ML) model such as support vector regression (SVR), novel hybrid SVR-GWO model (SVR integrated with gray wolf optimizer (GWO)), and statistical model multi-linear regression (MLR) are used to predict AOD. Also, for SVR-GWO model, SVR hyper-parameters are optimized using meta-heuristic GWO algorithm. Satellite-based data of Pakistan is used for the prediction of AOD on monthly bases. In addition, preprocessing techniques of forward feature selection (FFS) is utilized to select the optimal input features for the SVR-GWO, SVR and MLR models to predict AOD. The performance of the novel hybrid SVR-GWO, SVR, and MLR model is analyzed using RMSE, MAE, RRMSE, R2 and Taylor diagram, and it is found that hybrid SVR-GWO model (RMSE = 0.07, MAE = 0.06, RRMSE = 0.22 and R2=0.60) is better than ordinary SVR model (RMSE = 0.10, MAE = 0.07, RRMSE = 0.29 and R2=0.18) and MLR model (RMSE = 0.11, MAE = 0.07, RRMSE = 0.32 and R2=0.03). Keynotes: (a) The study demonstrates the potential of ML models such as SVR-GWO for accurate prediction of AOD, which can aid in better understanding of the health impacts of aerosols. (b) The use of preprocessing techniques like FFS and optimization algorithms like GWO can significantly improve the performance of the ML (SVR-GWO) model in predicting AOD. (c) The findings of this study can be useful for policymakers and healthcare professionals in identifying regions and populations at risk of aerosol-induced respiratory health issues and designing effective interventions to mitigate them.

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Characteristics of aerosol optical depth and Ångström parameters over Mohal in the Kullu Valley of Northwest Himalayan Region, India

Sharma N.L., Kuniyal J.C., Singh M., Sharma M., Guleria R.P.

Acta Geophysica

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2011

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Vol. 59, no. 2

334-360

EN

The measurements using a ground based multi wavelength radiometer (MWR) at Mohal (31°54’N, 77°07’E, 1154 m AMSL) in the Kullu valley of Northwestern Himalayan region show that the spectral aerosol optical depth (AOD) and turbidity coefficient, ß, are high in summer, moderate in monsoon season, low in winter and lowest in autumn, while wavelength exponent, α, has an opposite trend. Average annual value of AOD at 500 nm is 0.24±0.01, 0.43±0.02, and 0.28±0.02; that of β is 0.14±0.01, 0.22±0.02, and 0.17±0.03; and that of α is 1.06±0.09, 1.16±0.10, and 0.86±0.13, respectively, for clear, hazy and partially clear sky days. The considerably greater value of β on hazy days indicates more coarse particles in mountain haze. The fractional asymmetry factor (AF) is more negative in summer and autumn months. The AOD and β have significantly positive correlation with temperature and wind speed, suggesting high AODs and turbidity on hot and windy days.