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Trend Analysis of Aerosol Concentrations over Last Two Decades from MODIS Retrievals over Hyderabad District of India
Corresponding Author(s) : Aneesh Mathew
Geomatics and Environmental Engineering,
Vol. 18 No. 1 (2024): Geomatics and Environmental Engineering
Abstract
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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- Singh T., Ravindra K., Sreekanth V., Gupta P., Sembhi H., Tripathi S.N., Mor S.: Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: Rural-urban contrast. Science of the Total Environment, vol. 748, 2020, 140963. https://doi.org/10.1016/j.scitotenv.2020.140963.
- Gurjar B.R.: Air pollution in India: Major issues and challenges. MAGZTER: Energy Future, January – March 2021. https://www.magzter.com/stories/Education/Energy-Future/Air-Pollution-In-India-Major-Issues-And-Challenges [access: 5.04.2021].
- Census of India: Registrar General and Census Commissioner of India. 2011. https://censusindia.gov.in/census.website/ [access: 4.05.2019].
- Lyapustin A., Wang Y.: MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data User’s Guide. 2018. https://lpdaac.usgs.gov/documents/110/MCD19_User_Guide_V6.pdf [access: 1.06.2018].
- Ross M.S.: Introductory Statistics. 4th ed. Academic Press, London 2017.
- Sen P.K.: Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, vol. 63(324), 1968, pp. 1379–1389. https://doi.org/10.1080/01621459.1968.10480934.
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- Hamed K.H., Rao A.R.: A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology, vol. 204(1–4), pp. 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X.
- Şen Z.: Innovative trend analysis methodology. Journal of Hydrologic Engineering, vol. 17(9), 2012, pp. 1042–1046. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556.
- Das J., Mandal T., Rahman A.T.M.S., Saha P.: Spatio-temporal characterization of rainfall in Bangladesh: an innovative trend and discrete wavelet transformation approaches. Theoretical and Applied Climatology, vol. 143(6), 2021, pp. 1557–1579. https://doi.org/10.1007/s00704-020-03508-6.
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- Kharol S.K., Badarinath K.V.S., Sharma A.R., Kaskaoutis D.G., Kambezidis H.D.: Multiyear analysis of Terra/Aqua MODIS aerosol optical depth and ground observations over tropical urban region of Hyderabad, India. Atmospheric Environment, vol. 45(8), 2011, pp. 1532–1542. https://doi.org/10.1016/j.atmosenv.2010.12.047.
- David L.M., Ravishankara A.R., Kodros J.K., Venkataraman C., Sadavarte P., Pierce J.R.: Aerosol optical depth over India. Journal of Geophysical Research. Atmospheres, vol. 123(7), 2018, pp. 3688–3703. https://doi.org/10.1002%2F2017JD027719.
- Attri P., Sarkar S., Mani D.: Classification and transformation of aerosols over selected Indian cities during reduced emissions under Covid-19 lockdown. Journal of Earth System Science, vol. 131(3), 2022, 190. https://doi.org/10.1007/s12040-022-01916-y.
- Ramachandran S., Srivastava R., Sumita Kedia, Rajesh T.A.: Contribution of natural and anthropogenic aerosols to optical properties and radiative effects over an urban location. Environmental Research Letters, vol. 7(3), 2012, 034028. https://doi.org/10.1088/1748-9326/7/3/034028.
- Sinha P.R., Kaskaoutis D.G., Manchanda R.K., Sreenivasan S.: Characteristics of aerosols over Hyderabad in southern Peninsular India: Synergy in classification techniques. Annales Geophysicae, vol. 30(9), 2012, pp. 1393–1410. https://doi.org/10.5194/angeo-30-1393-2012.
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References
Pandey A., Brauer M., Cropper M.L., Balakrisnan K., Mathur P., Dey S. Turgulu B. et al.: Health and economic impact of air pollution in the states of India: The Global Burden of Disease Study 2019. The Lancet Planetary Health, vol. 5(10), 2021, pp. e25–e38. https://doi.org/10.1016/S2542-5196(20)30298-9.
He Y., Gao Z., Guo T., Qu F., Liang D., Li D., Shi J., Shan B.: Fine particulate matter associated mortality burden of lung cancer in Hebei Province, China. Thoracic Cancer, vol. 9(7), 2018, pp. 820–826. https://doi.org/10.1111/1759-7714.12653.
Mathew A., Gokul P.R., Raja Shekar P., Arunab K.S., Ghassan Abdo H., Almohamad H., Abdullah Al Dughairi A.: Air quality analysis and PM2.5 modelling using machine learning techniques: A study of Hyderabad city in India. Cogent Engineering, vol. 10(1), 2023, 2243743. https://doi.org/10.1080/23311916.2023.2243743.
Raju L., Gandhimathi R., Mathew A., Ramesh S.T.: Spatio-temporal modelling of particulate matter concentrations using satellite derived aerosol optical depth over coastal region of Chennai in India. Ecological Informatics, vol. 69, 2022, 101681. https://doi.org/10.1016/j.ecoinf.2022.101681.
Li L., Zhang J., Meng X., Fang Y., Ge Y., Wang J., Wang C., Wu J., Kan H.: Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth. Remote Sensing of Environment, vol. 217, 2018, pp. 573–586. https://doi.org/10.1016/j.rse.2018.09.001.
Van Donkelaar A., Martin R.V., Brauer M.: Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environmental Health Perspectives, vol. 118(6), 2010, pp. 847–855. https://doi.org/10.1289/ehp.0901623.
Ranjan A.K., Patra A.K., Gorai A.K.: A review on estimation of particulate matter from satellite-based aerosol optical depth: Data, methods, and challenges. AsiaPacific Journal of Atmospheric Sciences, vol. 57, 2020, pp. 679–699. https://doi.org/10.1007/s13143-020-00215-0.
Mohammad L., Mondal I., Bandyopadhyay J., Pham Q.B., Nguyen X.C., Dinh C.D., Al-Quraishi A.M.F.: Assessment of spatio-temporal trends of satellite based aerosol optical depth using Mann–Kendall test and Sen’s slope estimator model. Geomatics, Natural Hazards and Risk, vol. 13(1), 2022, pp. 1270–1298. https://doi.org/10.1080/19475705.2022.2070552.
Calvo A.I., Alves C., Castro A., Pont V., Vicente A.M., Fraile R.: Research on aerosol sources and chemical composition: Past, current and emerging issues. Atmospheric Research, vol. 120–121, 2013, pp. 1–28. https://doi.org/10.1016/j.atmosres.2012.09.021.
Gokul P.R., Mathew A., Bhosale A., Nair A.T.: Spatio-temporal air quality analysis and PM2.5 prediction over Hyderabad City, India using artificial intelligence techniques. Ecological Informatics, vol. 76, 2023, 102067. https://doi.org/10.1016/j.ecoinf.2023.102067.
Taneja K., Ahmad S., Ahmad K., Attri S.: Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach. Atmospheric Pollution Research, vol. 7(4), 2016, pp. 585–596. https://doi.org/10.1016/j.apr.2016.02.004.
Myhre G., Lund Myhre C.E., Samset B.H., Storelvmo T.: Aerosols and their relation to global climate and climate sensitivity. Nature Education Knowledge, vol. 4(5), 2013, 7.
Rosenfeld D., Lohmann U., Raga G.B., O’Dowd C.D., Kulmala M., Fuzzi S., Reisell A., Andrae M.O.: Flood or drought: How do aerosols affect precipitation? Science, vol. 321(5894), 2008, pp. 1309–1313. https://doi.org/10.1126/science.1160606.
Zhang Q., Quan J., Tie X., Huang M., Ma X.: Impact of aerosol particles on cloud formation: Aircraft measurements in China. Atmospheric Environment, vol. 45(3), 2011, pp. 665-672. https://doi.org/10.1016/j.atmosenv.2010.10.025.
Yang Q., Yuan Q., Yue L., Li T., Shen H., Zhang L.: The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind spatio-temporal variations. Environmental Pollution, vol. 248, 2019, pp. 526–535. https://doi.org/10.1016/j.envpol.2019.02.071.
Tariq S., Qayyum F., Ul-Haq Z., Mehmood U.: Long-term spatiotemporal trends in aerosol optical depth and its relationship with enhanced vegetation index and meteorological parameters over South Asia. Environmental Science and Pollution Research, vol. 29, 2020, pp. 30638–30655. https://doi.org/10.1007/s11356-021-17887-4.
Wang W., He Q., Zhang M., Zhang W., Zhu H.: Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019. Atmospheric Pollution Research, vol. 13(11), 2022, 101579. https://doi.org/10.1016/j.apr.2022.101579.
Khalid B., Khalid A., Muslim S., Habib A., Khan K., Alvim D.S., Shakoor S., Mustafa S., Zaheer S., Zoon M., Khan A.H., Ilyas S., Chen B.: Estimation of aerosol optical depth in relation to meteorological parameters over eastern and western routes of China Pakistan economic corridor. Journal of Environmental Sciences, vol. 99, pp. 28–39. https://doi.org/10.1016/j.jes.2020.04.045.
Tan Y., Wang Q., Zhang Z.: Assessing spatiotemporal variations of AOD in Japan based on Himawari-8 L3 V31 aerosol products: Validations and applications. Atmospheric Pollution Research, vol. 13(6), 2022, 101439. https://doi.org/10.1016/j.apr.2022.101439.
Luong N.D., Hieu B.T., Hiep N.H.: Contrasting seasonal pattern between ground-based PM2.5 and MODIS satellite-based aerosol optical depth (AOD) at an urban site in Hanoi, Vietnam. Environmental Science and Pollution Research, vol. 29(6), 2022, pp. 41971–41982. https://doi.org/10.1007/s11356-021-16464-z.
Abuelgasim A., Bilal M., Alfaki I.A.: Spatiotemporal variations and long term trends analysis of aerosol optical depth over the United Arab Emirates. Remote Sensing Applications: Society and Environment, vol. 23, 2021,100532. https://doi.org/10.1016/j.rsase.2021.100532.
Yousefi R., Wang F., Ge Q., Shaheen A.: Long term aerosol optical depth trend over Iran and identification of dominant aerosol types. Science of The Total Environment, vol. 722, 2020, 137906. https://doi.org/10.1016/j.scitotenv.2020.137906.
Gouda K.C., Gogeri I., Thippareddy A.S.: Assessment of aerosol optical depth over Indian subcontinent during COVID-19 lockdown (March–May 2020). Environment Monitoring and Assessment, vol. 194(3), 2022, 195. https://doi.org/10.1007/s10661-022-09855-3.
Kumar A.: Spatio-temporal variations in satellite based aerosol optical depths & aerosol index over Indian subcontinent: Impact of urbanization and climate change. Urban Climate, vol. 32, 2020, 100598. https://doi.org/10.1016/j.uclim.2020.100598.
Singh T., Ravindra K., Sreekanth V., Gupta P., Sembhi H., Tripathi S.N., Mor S.: Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: Rural-urban contrast. Science of the Total Environment, vol. 748, 2020, 140963. https://doi.org/10.1016/j.scitotenv.2020.140963.
Gurjar B.R.: Air pollution in India: Major issues and challenges. MAGZTER: Energy Future, January – March 2021. https://www.magzter.com/stories/Education/Energy-Future/Air-Pollution-In-India-Major-Issues-And-Challenges [access: 5.04.2021].
Census of India: Registrar General and Census Commissioner of India. 2011. https://censusindia.gov.in/census.website/ [access: 4.05.2019].
Lyapustin A., Wang Y.: MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data User’s Guide. 2018. https://lpdaac.usgs.gov/documents/110/MCD19_User_Guide_V6.pdf [access: 1.06.2018].
Ross M.S.: Introductory Statistics. 4th ed. Academic Press, London 2017.
Sen P.K.: Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, vol. 63(324), 1968, pp. 1379–1389. https://doi.org/10.1080/01621459.1968.10480934.
Yue S., Wang C.: The Mann–Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management, vol. 18(3), 2004, pp. 201–218. https://doi.org/10.1023/B:WARM.0000043140.61082.60.
Hamed K.H., Rao A.R.: A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology, vol. 204(1–4), pp. 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X.
Şen Z.: Innovative trend analysis methodology. Journal of Hydrologic Engineering, vol. 17(9), 2012, pp. 1042–1046. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000556.
Das J., Mandal T., Rahman A.T.M.S., Saha P.: Spatio-temporal characterization of rainfall in Bangladesh: an innovative trend and discrete wavelet transformation approaches. Theoretical and Applied Climatology, vol. 143(6), 2021, pp. 1557–1579. https://doi.org/10.1007/s00704-020-03508-6.
Rousseau R., Egghe L., Guns R.: Statistics. [in:] Rousseau R., Egghe L., Guns R., Becoming Metric-Wise: A Bibliometric Guide for Researchers, Elsevier, 2008, pp. 67–97. https://doi.org/10.1016/B978-0-08-102474-4.00004-2.
Lord D., Qin X., Geedipally S.R.: Exploratory analyses of safety data. [in:] Lord D., Qin X., Geedipally R., Highway Safety Analytics and Modeling, Elsevier, 2021, pp. 135–177. https://doi.org/10.1016/B978-0-12-816818-9.00015-9.
Liu H., Chen C., Li Y., Duan Z., Li Y.: Monitoring and spatial prediction of multidimensional air pollutants. [in:] Liu H., Chen C., Li Y., Duan Z., Li Y., Smart Metro Station Systems: Data Science and Engineering, Elsevier, 2022, pp. 171–200. https://doi.org/10.1016/B978-0-323-90588-6.00007-X.
Kharol S.K., Badarinath K.V.S., Sharma A.R., Kaskaoutis D.G., Kambezidis H.D.: Multiyear analysis of Terra/Aqua MODIS aerosol optical depth and ground observations over tropical urban region of Hyderabad, India. Atmospheric Environment, vol. 45(8), 2011, pp. 1532–1542. https://doi.org/10.1016/j.atmosenv.2010.12.047.
David L.M., Ravishankara A.R., Kodros J.K., Venkataraman C., Sadavarte P., Pierce J.R.: Aerosol optical depth over India. Journal of Geophysical Research. Atmospheres, vol. 123(7), 2018, pp. 3688–3703. https://doi.org/10.1002%2F2017JD027719.
Attri P., Sarkar S., Mani D.: Classification and transformation of aerosols over selected Indian cities during reduced emissions under Covid-19 lockdown. Journal of Earth System Science, vol. 131(3), 2022, 190. https://doi.org/10.1007/s12040-022-01916-y.
Ramachandran S., Srivastava R., Sumita Kedia, Rajesh T.A.: Contribution of natural and anthropogenic aerosols to optical properties and radiative effects over an urban location. Environmental Research Letters, vol. 7(3), 2012, 034028. https://doi.org/10.1088/1748-9326/7/3/034028.
Sinha P.R., Kaskaoutis D.G., Manchanda R.K., Sreenivasan S.: Characteristics of aerosols over Hyderabad in southern Peninsular India: Synergy in classification techniques. Annales Geophysicae, vol. 30(9), 2012, pp. 1393–1410. https://doi.org/10.5194/angeo-30-1393-2012.
Badarinath K.V.S., Kharol S.K., Chand T.R.K., Parvathi Y.G., Anasuya T., Jyothsna A.N.: Variations in black carbon aerosol, carbon monoxide and ozone over an urban area in Hyderabad, India during the forest fire season. Atmospheric Research, vol. 85(1), 2007, pp. 18–26. https://doi.org/10.1016/j.atmosres.2006.10.004.