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Prediction of Particulate Matter (PM2.5) Concentrations over an Urban Region using Different Satellite

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Title Prediction of Particulate Matter (PM2.5) Concentrations over an Urban Region using Different Satellite
 
Creator Kumar, Ajay
Singh, Sumit
Singh, Amarendra
Srivastava, A K
Pathak, Virendra
 
Subject Linear regression
Multiple linear regression
Artificial neural network
AOD
MODIS
ERA5
CALIPSO
 
Description 350-362
The accurate estimation of ground-level particulate matter concentrations (PM2.5) is essential for assessing air quality and
its impact on human health and the environment. This study focused on estimating PM2.5 concentrations from January 2021
to June 2023 in the city of Lucknow, India. Various models, including Bivariate Linear Regression (LR), Multiple Linear
Regression (MLR), and Artificial Neural Network (ANN) predicted PM2.5 concentrations at the station. Additionally,
CALIPSO observations successfully demonstrated the vertical aerosol layer profile in the region. To improve accuracy, we
incorporated Aerosol Optical Depth (AOD) data from both MODIS and VIIRS, along with meteorological parameters. The
dataset was divided into two periods: 2017-2020 for estimation and January 2021-June 2023 for model training. Our
findings revealed a positive correlation between model outputs, observed ground data, and meteorological parameters. For
MODIS, LR, MLR, and ANN models had correlation coefficients (R) of 0.41, 0.57, and 0.66. Similarly, for VIIRS, the
R-values were 0.33, 0.55, and 0.64, indicating promising agreement between model predictions and actual PM2.5
concentrations. These findings contribute to a better understanding of air quality dynamics and can support policymakers in
implementing effective measures to mitigate the adverse effects of particulate matter pollution on public health and the
environment. Data sets underwent three divisions: 80% for training, and 10% each for validation and testing. ANN
displayed strong correlation coefficients (R) across datasets, achieving MODIS R-values of 0.74 and 0.73 for training and
overall sets, and VIIRS R- values of 0.74 and 0.72. This study highlights the significant accuracy improvement in PM2.5
estimation by integrating meteorological, land use data, and satellite AOD. While LR and MLR methods yielded
comparable outcomes, ANN emerged as a superior technique for long-term PM2.5 estimation, holding promise for air quality
monitoring and guideline adherence in diverse regions.
 
Date 2024-04-08T07:19:11Z
2024-04-08T07:19:11Z
2024-05
 
Type Article
 
Identifier 0975-0959 (Online); 0301-1208 (Print)
http://nopr.niscpr.res.in/handle/123456789/63703
https://doi.org/10.56042/ijpap.v62i5.7587
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJPAP Vol.62(05) [May 2024]