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.
This is a preview of subscription content, log in via an institution to check access.
Data Availability
The datasets generated during and analyzed during the current study are not publicly available as the data is collected from the concerned industry with their permission. The dataset is part of the Ph. D thesis but is available from the corresponding author upon reasonable request.
References
Ahmad T, Zhang D (2020) A critical review of comparative global historical energy consumption and future demand: the story told so far. Energy Rep 6:1973–1991. https://doi.org/10.1016/j.egyr.2020.07.020
Akcin H (2021) A GIS-based building risk assessment for the subsidence due to undercity coal mining activities in Zonguldak, Turkey. Arab J Geosci 14. https://doi.org/10.1007/s12517-021-06702-6
Asif Z, Chen Z, Guo J (2018) A study of meteorological effects on PM2.5 concentration in mining area. Atmospheric Pollution Res 9(6):1146–1154. https://doi.org/10.1016/j.apr.2018.01.004
Ato García M, López García JJ, Benavente Reche A (2008) Un índice de sesgo entre observadores basado en modelos mixtura [A mixture model-based rater bias index]. Psicothema 20(4):918–923
Beersma JJ, Buishand TA (2003) Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation. Climate Res 25(2):121–133. http://www.jstor.org/stable/24868391
Borrego C, Amorim JH, Tchepel O, Dias D, Rafael S, Sá E, Coelho MC (2016) Urban scale air quality modelling using detailed traffic emissions estimates. Atmos Environ 131:341–351. https://doi.org/10.1016/j.atmosenv.2016.02.017
Chang JC, Hanna SR (2004) Air quality model performance evaluation. Meteorol Atmos Phys 87(1–3):167–196. https://doi.org/10.1007/s00703-003-0070-7
Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci 7:e623. https://doi.org/10.7717/peerj-cs.623
Chinthala S, Khare M (2011) Particle Dispersion Within a Deep Open Cast Coal Mine. InTech. https://doi.org/10.5772/16326
Chowdari KK, Girisha R, Gouda KC (2015), December A study of rainfall over India using data mining. In 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) (pp. 44–47). IEEE. https://doi.org/10.1109/ERECT.2015.7498985
Cimorelli AJ, Perry SG, Venkatram A et al (2005) AERMOD: a dispersion model for industrial source applications. Part I: General model formulation and boundary layer characterization. J Appl Meteorol 44(5):682–693. https://doi.org/10.1175/JAM2227.1
Coria J, Bonilla J, Grundström M, Pleijel H (2015) Air pollution dynamics and the need for temporally differentiated road pricing. Transp Res Part A: Policy Pract 75:178–195. https://doi.org/10.1016/J.TRA.2015.03.004
CPCB F (2010) Air quality monitoring, emission inventory and source apportionment study for Indian cities. Central Pollution Control Board
de Souza DO, dos Santos Alvalá RC (2014) Observational evidence of the urban heat island of Manaus City, Brazil. Meteorol Appl 21(2):186–193. https://doi.org/10.1002/met.1340
Derakhshani R, Raoof A, Mahvi AH, Chatrouza H (2019) Similarities in the fingerprints of coal mining activities, high ground water fluoride, and dental fluorosis in Zarand district. kerman province, iran
Duveiller G, Fasbender D, Meroni M (2016) Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci Rep 6(1):19401. https://doi.org/10.1038/srep19401
Garg T, Kumar N, Chauhan T, Kango R (2016), September Estimation of Reference Evapotranspiration using the FAO Penman-Monteith Method for Climatic Conditions of Himachal Pradesh, India. In Proceedings of National Conference: Civil Engineering Conference–Innovation for Sustainability (CEC–2016) (Vol. 9, p. 10th)
Gautam S, Patra AK (2015) Dispersion of particulate matter generated at higher depths in opencast mines. Environ Technol Innov 3:11–27. https://doi.org/10.1016/j.eti.2014.11.002
Hadlocon LS, Zhao LY, Bohrer G, Kenny W, Garrity SR, Wang J, Upadhyay J (2015) Modeling of particulate matter dispersion from a poultry facility using AERMOD. J Air Waste Manag Assoc 65(2):206–217. https://doi.org/10.1080/10962247.2014.986306
Huertas JI, Huertas ME, Díaz J (2012a) Assessing precision and accuracy of atmospheric emission inventories. Int J Environ Sci Technol 9:195–202. https://doi.org/10.1007/s13762-012-0022-1
Huertas JI, Huertas ME, Izquierdo S, González ED (2012b) Air quality impact assessment of multiple open pit coal mines in northern Colombia. J Environ Manage 93(1):121–129. https://doi.org/10.1016/j.jenvman.2011.08.007
Ibrahim IA, Ötvös T, Gilmanova A, Rocca E, Ghanem C, Wanat M (2021) International energy agency. Kluwer Law International BV
World Energy Balances: Overview. IEA, IEA, Paris (2021) https://www.iea.org/reports/world-energy-balances-overview, Licence: CC BY 4.0
Iskandar M, Integration of Csr Projects in Coal Mining Activities in ACEH (2021). Prosiding Temu Profesi Tahunan PERHAPI, 0, 325–336. Retrieved from https://www.prosiding.perhapi.or.id/index.php/prosiding/article/view/164
Javed A, Ahmad R, Khan I (2021) Impact of coal mining on landuse/landcover in Singrauli Coalfield, vol GSJ. A study using Remote Sensing & GIS, Central India, 11
Jones AM, Harrison RM (2004) The effects of meteorological factors on atmospheric bioaerosol concentrations—a review. Sci Total Environ 326(1–3):151–180. https://doi.org/10.1016/j.scitotenv.2003.11.021
Kahraman MM, Erkayaoglu M (2021) A data-driven approach to control fugitive dust in mine operations. Min Metall Explor 38(1):549–558. https://doi.org/10.1007/s42461-020-00318-2
Khazini L, Dehkharghanian ME, Vaezihir A (2021) Dispersion and modeling discussion of aerosol air pollution caused during mining and processing of open-cast mines. Int J Environ Sci Technol 1–12. https://doi.org/10.1007/s13762-021-03225-1
Kundu S, Pal AK (2018) Application of AERMOD model in air quality (PM10) impact assessment of selected opencast mines in the Jharia Coalfield, Jharkhand, India. Environ Prot Eng 44(4):5–21. https://doi.org/10.5277/epe180401
Li L, Yang S, Wang Z, Zhu X, Tang H, Arctic (2010) Antarct Alp Res 42(4):449–457. https://doi.org/10.1657/1938-4246-42.4.449
Lone BA, Qayoom S, Nazir A, Ahanger SA, Basu U, Bhat TA, Fathallah El-Agamy R (2022) Climatic trends of variable temperate environment: a complete time series analysis during 1980–2020. Atmosphere 13(5):749. https://doi.org/10.3390/atmos13050749
Luo H, Zhou W, Jiskani IM, Wang Z (2021) Analyzing characteristics of particulate matter pollution in open-pit coal mines: implications for green mining. Energies 14(9):2680. https://doi.org/10.3390/en14092680
Ma CM, Dai EF, Liu YC, Wang YH, Wang F (2020) Methane fugitive emissions from coal mining and post-mining activities in China. Resour Sci 42:311–322
Mavrakou T, Philippopoulos K, Deligiorgi D (2012) The impact of sea breeze under different synoptic patterns on air pollution within Athens basin. Sci Total Environ 433:31–43. https://doi.org/10.1016/j.scitotenv.2012.06.011
Mishra A, Lal B, Kumar R (2024) Air quality monitoring and its impact on local tree species in and around mining areas of Dhanbad District, Jharkhand, India. Spatial modeling of Environmental Pollution and Ecological Risk. Woodhead Publishing, pp 9–40
MoEFCC (2018) Parivesh, EIA-EMP of Dipka Opencast Expansion Project. http://www.environmentclearance.nic.in/onlineSearch.aspx. (Accessed 8 June 2018)
Moradi M, Dyer B, Nazem A, Nambiar MK, Nahian MR, Bueno B, Aliabadi AA (2019) The vertical city weather generator (VCWG v1. 0.0). Geosci Model Dev Discuss. https://doi.org/10.5194/gmd-2019-176
Panda A, Sahu N (2019) Trend analysis of seasonal rainfall and temperature pattern in Kalahandi, Bolangir and Koraput districts of Odisha, India. Atmospheric Sci Lett 20(10):e932. https://doi.org/10.1002/asl.932
Perry SG, Cimorelli AJ, Paine RJ et al (2005) AERMOD: a dispersion model for industrial source applications. Part II: model performance against 17 field study databases. J Appl Meteorol 44(5):694–708. https://doi.org/10.1175/JAM2228.1
Peter AE, Nagendra SS (2021) Dynamics of PM 2.5 pollution in the vicinity of the old municipal solid waste dumpsite. Environ Monit Assess 193:1–16. https://doi.org/10.1007/s10661-021-09052-8
RAJA KP, Reddy SR (2019) Regression analysis between mean daily intensity, rainy days and seasonal rainfall in normal, excess and deficient years: a case study. Mausam 70(1):141–158
Reddy BR, Srinivas CV, Venkatraman B (2023) A simulation study on the recirculation effect of land–sea breeze flows on atmospheric dispersion of airborne releases in Southeast coast of India. Meteorol Atmos Phys 135(5):47. https://doi.org/10.1007/s00703-023-00983-0
Ritchie H, Rosado P, Roser M (2020) Energy Production and Consumption. OurWorldInData.org. Retrieved from: https://ourworldindata.org/energy-production-consumption
Romana HK, Singh RP, Shukla DP (2020) Long term air quality analysis in reference to thermal power plants using satellite data In Singrauli Region, India. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 829–834. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-829-2020
Romana HK, Singh RP, Dubey CS, Shukla DP (2022) Analysis of Air and Soil Quality around Thermal Power plants and Coal Mines of Singrauli Region, India. Int J Environ Res Public Health 19(18):11560. https://doi.org/10.3390/ijerph191811560
Rood AS (2014) Performance evaluation of AERMOD, CALPUFF, and legacy air dispersion models using the Winter Validation Tracer Study dataset. Atmos Environ 89:707–720. https://doi.org/10.1016/j.atmosenv.2014.02.054
Sahu SP, Yadav M, Rani N, Das AJ (2018b) Assessment of occupational health exposure to particulate matter around opencast coal mines, India: a case study. Arab J Geosci 11. https://doi.org/10.1007/s12517-018-3631-2
Scheffran J, Felkers M, Froese R (2020) Economic growth and the global energy demand. Green energy to sustainability: strategies for global industries. https://doi.org/10.1002/9781119152057.ch1
Seastedt TR, Bowman WD, Caine TN, McKnight D, Townsend A, Williams MW (2004) The landscape continuum: a model for high-elevation ecosystems. Bioscience 54(2):111–121. https://doi.org/10.1641/0006-3568(2004)054[0111:TLCAMF]2.0.CO;2
Sergeev A, Shichkin A, Baglaeva E, Buevich A, Butorova A (2024) A permutation approach to evaluating the performance of a forecasting model of methane content in the atmospheric surface layer of arctic region. Atmospheric Pollution Res 15(2):102000. https://doi.org/10.1016/j.apr.2023.102000
Shrestha DP, Saepuloh A, Van Der Meer F (2019) Land cover classification in the tropics, solving the problem of cloud covered areas using topographic parameters. Int J Appl Earth Obs 77:84–93. https://doi.org/10.1016/j.jag.2018.12.010
Snoun H, Krichen M, Chérif H (2023) A comprehensive review of gaussian atmospheric dispersion models: current usage and future perspectives. Euro-Mediterranean J Environ Integr 8(1):219–242. https://doi.org/10.1007/s41207-023-00354-6
Soler MR, Arasa R, Merino M, Olid M, Ortega S (2011) Modelling local sea-breeze flow and associated dispersion patterns over a coastal area in north-east Spain: a case study. Boundary Layer Meteorol 140:37–56. https://doi.org/10.1007/s10546-011-9599-z
Srivastava A, Elumalai SP (2021) Assessment of emission-source contribution to spatial dispersion for coal crusher agglomeration using prognostic model. Clean Eng Technol 3:100113. https://doi.org/10.1016/j.clet.2021.100113
Srivastava A, Kumar A, Elumalai SP (2021) Evaluating dispersion modeling of inhalable particulates (PM10) emissions in complex terrain of coal mines. Environ Model Assess 26:385–403. https://doi.org/10.1007/s10666-021-09762-w
Srivastava D, Xu J, Vu TV, Liu D, Li L, Fu P, Harrison RM (2021b) Insight into PM2.5 sources by applying positive matrix factorization (PMF) at urban and rural sites of Bei**g. Atmos Chem Phys 21(19):14703–14724. https://doi.org/10.5194/acp-21-14703-2021
Teggi S, Costanzini S, Ghermandi G, Malagoli C, Vinceti M (2018) A GIS-based atmospheric dispersion model for pollutants emitted by complex source areas. Sci Total Environ 610:175–190. https://doi.org/10.1016/j.scitotenv.2017.07.196
The World Counts (Accessed on 08.02.2024) Global energy consumption only going up. https://www.theworldcounts.com/challenges/climate-change/energy/global-energy-consumption
ul Haq A, Nadeem Q, Farooq A, Irfan N, Ahmad M, Ali MR (2019) Assessment of AERMOD modeling system for application in complex terrain in Pakistan. Atmospheric Pollution Res 10(5):1492–1497. https://doi.org/10.1016/j.apr.2019.04.006
USEPA (1998) Revision of emission factors for AP-42. Chapter 11: mineral products industry. Section 11.9: Western Surface Coal Mining. http://www.epa.gov/ttn/chief/ap42/index.html. Accessed 5 September 2009
Valbuena R, Hernando A, Manzanera JA, Görgens EB, Almeida DR, Silva CA, García-Abril A (2019) Evaluating observed versus predicted forest biomass: R-squared, index of agreement or maximal information coefficient? Eur J Remote Sens 52(1):345–358. https://doi.org/10.1080/22797254.2019.1605624
Varaprasad V, Kanawade VP, Narayana AC (2024) Association between sea-land breeze and particulate matter in five coastal urban locations in India. Sci Total Environ 913:169773. https://doi.org/10.1016/j.scitotenv.2023.169773
Vora J (2010) Dust Dispersion Modeling For Opencast Mines (Doctoral dissertation)
Wang L, Ting M, Kushner PJ (2017) A robust empirical seasonal prediction of winter NAO and surface climate. Sci Rep 7(1):279. https://doi.org/10.1038/s41598-017-00353-y
Webster HN, Thomson DJ (2022) Using Ensemble Meteorological Data Sets To Treat Meteorological Uncertainties in a bayesian volcanic Ash Inverse modeling system: a Case Study, Grímsvötn 2011. J Geophys Research: Atmos 127(24):e2022JD036469. https://doi.org/10.1029/2022JD036469
World Health Organization (2021) WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization
Wu Y, Liu J, Zhai J, Cong L, Wang Y, Ma W, Li C (2018) Comparison of dry and wet deposition of particulate matter in near-surface waters during summer. PLoS ONE 13(6):e0199241. https://doi.org/10.1371/journal.pone.0199241
**e D, Wang H, Kearfott KJ, Liu Z, Mo S (2014) Radon dispersion modeling and dose assessment for uranium mine ventilation shaft exhausts under neutral atmospheric stability. J Environ Radioact 134:105–116. https://doi.org/10.1016/j.jenvrad.2013.12.003
Acknowledgements
The Authors wish to thank Coal India Limited for funding this project (project code: CIL/R&D/05/02/2021) and special thanks to Northern Coalfield Limited and Central Mine Planning & Design Institute for their technical and logistic help.
Funding
The research was funded by Coal India Limited under the project code number CIL/R&D/05/02/2021.
Author information
Authors and Affiliations
Contributions
Dr. Tanushree Bhattacharya communicated and coordinated this research work, Navin Prasad and Akash Mishra carried out the tests, and data analysis and drafted the manuscript. Dr. Bindhu Lal conceived of the study and participated in research coordination. The authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Software and Data Availability
Name of software: AERMOD VIEW 10.0.1. Year first available: 1996. Developers: Lakes Environment Software. License: Licensed Version (Serial #: AER0010836). Hardware and software requirements: An Intel Pentium 4 processor (or equivalent) or higher, at least 2 GB of available hard disk space, 1 GB of RAM (2 GB recommended).
Competing Interests
The authors declare that they have no competing interests.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Prasad, N., Mishra, A., Bhattacharya, T. et al. Validation of AERMOD Prediction Accuracy for Particulate Matters (PM10, PM2.5) for a Large Coal Mine Complex: A Multisource Perspective. Aerosol Sci Eng (2024). https://doi.org/10.1007/s41810-024-00241-9
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41810-024-00241-9