Effect of Seasonal and Meteorological Variability of Air Pollution in Singrauli Coalfield

Yadav, Akhilesh Kumar; Shirin, Saba; Emmanouil, Christina; Jamal, Aarif

doi:10.1007/s41810-021-00124-3

Cited by 8 publications

(4 citation statements)

References 52 publications

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“…During 2015–2020, CH 4 increased from 1853.75 ppbv to 1865.33 ppbv, and SO 2 from 0.17 DU to 0.20 DU. Similarly, it was observed that in the year 2016–2017, PM 2.5 , PM 10 , Total Suspended Particles (TSP), NO 2, and SO 2 concentrations present in the study area were higher than the National Ambient Air Quality Standards of India [ 38 ].…”

Section: Discussionmentioning

confidence: 83%

“…Singrauli, also known as the energy hub of India, with a number of coal-based TPPs and extensive coal mining activities, is one of the highly polluted regions [ 20 , 22 , 37 ]. As a result, high concentrations of PM, NO 2 , and SO 2 are severely affecting air quality of the region [ 38 ]. Black carbon (BC), capable of direct impact on solar radiation absorption is also found in the area with peaks during early morning and evening hours [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Air and Soil Quality around Thermal Power Plants and Coal Mines of Singrauli Region, India

Romana

Singh

Dubey

et al. 2022

IJERPH

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Singrauli region is known as the energy capital of India, as it generates nearly 21 GW of electricity, supplied to various parts of the northern India. Many coal-based Thermal Power Plants (TPPs) using coal from several nearby coal mines, and numerous industries are set up in this region which has made it as one of the highly polluted regions of India. In the present study, detailed temporal analysis and forecast of carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and methane (CH4) concentrations retrieved from satellite data have been carried out for the periods 2005–2020. Based on the classical multiplicative model and using linear regression, the maximum concentration of CO2, NO2, SO2, and CH4 in the year 2025 is found to be 422.59 ppm, 29.28 ppm, 0.23 DU, and 1901.35 ppbv, respectively. Detailed analysis shows that carbon dioxide has a 95% correlation with all other trace gases. We have also carried out the geo-accumulation index for the presence of various contaminants in the soil of this region. The geo-accumulation index shows that soil in and around thermal power plants and coal mines is contaminated by heavy metals. The cumulative index shows that soil around Hindalco industries, Bina coal mines, Khadia coal mines, and coal-based TPPs (Anpara and Vindhayachal) are highly polluted and a threat to human population living in the region.

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Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Analysis of Air and Soil Quality around Thermal Power Plants and Coal Mines of Singrauli Region, India

Romana

Singh

Dubey

et al. 2022

IJERPH

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show abstract

“…The prediction of air pollutant concentrations has long been a focal point [ 41 ]. However, the variation in atmospheric pollutant concentrations is intricate and dynamic, spanning multiple sectors, regions, and dimensions of analysis [ 42 ]. Delving into the dynamic patterns of air pollutant concentration changes necessitates handling substantial volumes of data on air pollutant concentrations alongside pertinent meteorological information.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Xia,

Chen,

Wang

et al. 2024

Entropy

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The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction’s mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.

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“…However, the change of air pollutant concentration is dynamic, and the production of air pollutants is complicated. The analysis and prediction work involves multiple departments, regions, and fields [7]. It needs to process a large amount of air pollutant concentration data and related meteorological information.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Correlation Prediction Model of Urban Pm2.5 Concentration Based on Deconvolution and Lstm

Zhang

Liu

et al. 2023

Preprint

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Effect of Seasonal and Meteorological Variability of Air Pollution in Singrauli Coalfield

Cited by 8 publications

References 52 publications

Analysis of Air and Soil Quality around Thermal Power Plants and Coal Mines of Singrauli Region, India

Analysis of Air and Soil Quality around Thermal Power Plants and Coal Mines of Singrauli Region, India

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

A Spatial Correlation Prediction Model of Urban Pm2.5 Concentration Based on Deconvolution and Lstm

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