{"title":"Validation of AERMOD Prediction Accuracy for Particulate Matters (PM10, PM2.5) for a Large Coal Mine Complex: A Multisource Perspective","authors":"Navin Prasad, Akash Mishra, Tanushree Bhattacharya, Bindhu Lal, Prakash Chandra Jha, Abhishek Kumar","doi":"10.1007/s41810-024-00241-9","DOIUrl":null,"url":null,"abstract":"<div><p>Particulate matter (PM) emission from coal mining activities is inevitable and a significant concern worldwide. American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is one of the most widely used dispersion models for predicting air PM dispersion in coal mines. However, validation of AERMOD-predicted PM concentration in a large mine complex has not been reported. So, in this study, AERMOD predicted PM concentration was validated against the PM concentrations measured by nine continuous ambient air quality monitoring stations (CAAQMS) stationed in the Singrauli coal mining complex. The complex contains nine coal mines across 438 square kilometers, with around 129 pollution sources chiefly from the area, pit, and line categories. PM<sub>10</sub> and PM<sub>2.5</sub> concentrations peak during summer (204.58 µg/m<sup>3</sup>) and winter (67.67 µg/m<sup>3</sup>), respectively. The AERMOD model predicts peak dispersion of PM<sub>10</sub> (500–1200 µg/m<sup>3</sup>) and PM<sub>2.5</sub> (100–800 µg/m<sup>3</sup>) during the winter season. The AERMOD model reveals that the region’s wind movement caused by land and lake breezes was the predominant driver of PM surface dispersion. In the winter season, atmospheric inversion increases ground-level PM concentrations in the region. The AERMOD cannot represent the vertical dispersion of PMs in the summer, resulting in an underestimation of PM concentration. The statistical validation shows that AERMOD underestimates PM<sub>10</sub> and PM<sub>2.5</sub> concentrations across all seasons and years. The AERMOD model’s prediction accuracy for PM<sub>10</sub> (R<sup>2</sup> = 0.38) and PM<sub>2.5</sub> (R<sup>2</sup> = 0.56) is also low. Finally, it can be concluded that AERMOD-predicted PM concentrations are not accurate for large mining complexes but more suitable for individual mines.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"9 1","pages":"30 - 44"},"PeriodicalIF":1.6000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aerosol Science and Engineering","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s41810-024-00241-9","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Particulate matter (PM) emission from coal mining activities is inevitable and a significant concern worldwide. American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is one of the most widely used dispersion models for predicting air PM dispersion in coal mines. However, validation of AERMOD-predicted PM concentration in a large mine complex has not been reported. So, in this study, AERMOD predicted PM concentration was validated against the PM concentrations measured by nine continuous ambient air quality monitoring stations (CAAQMS) stationed in the Singrauli coal mining complex. The complex contains nine coal mines across 438 square kilometers, with around 129 pollution sources chiefly from the area, pit, and line categories. PM10 and PM2.5 concentrations peak during summer (204.58 µg/m3) and winter (67.67 µg/m3), respectively. The AERMOD model predicts peak dispersion of PM10 (500–1200 µg/m3) and PM2.5 (100–800 µg/m3) during the winter season. The AERMOD model reveals that the region’s wind movement caused by land and lake breezes was the predominant driver of PM surface dispersion. In the winter season, atmospheric inversion increases ground-level PM concentrations in the region. The AERMOD cannot represent the vertical dispersion of PMs in the summer, resulting in an underestimation of PM concentration. The statistical validation shows that AERMOD underestimates PM10 and PM2.5 concentrations across all seasons and years. The AERMOD model’s prediction accuracy for PM10 (R2 = 0.38) and PM2.5 (R2 = 0.56) is also low. Finally, it can be concluded that AERMOD-predicted PM concentrations are not accurate for large mining complexes but more suitable for individual mines.
期刊介绍:
ASE is an international journal that publishes high-quality papers, communications, and discussion that advance aerosol science and engineering. Acceptable article forms include original research papers, review articles, letters, commentaries, news and views, research highlights, editorials, correspondence, and new-direction columns. ASE emphasizes the application of aerosol technology to both environmental and technical issues, and it provides a platform not only for basic research but also for industrial interests. We encourage scientists and researchers to submit papers that will advance our knowledge of aerosols and highlight new approaches for aerosol studies and new technologies for pollution control. ASE promotes cutting-edge studies of aerosol science and state-of-art instrumentation, but it is not limited to academic topics and instead aims to bridge the gap between basic science and industrial applications. ASE accepts papers covering a broad range of aerosol-related topics, including aerosol physical and chemical properties, composition, formation, transport and deposition, numerical simulation of air pollution incidents, chemical processes in the atmosphere, aerosol control technologies and industrial applications. In addition, ASE welcomes papers involving new and advanced methods and technologies that focus on aerosol pollution, sampling and analysis, including the invention and development of instrumentation, nanoparticle formation, nano technology, indoor and outdoor air quality monitoring, air pollution control, and air pollution remediation and feasibility assessments.