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Long-term trend of PM2.5 over five Indian megacities using a new statistical approach

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Abstract

PM2.5 is one of the major contributors to air pollution in India, resulting in poor air quality and human health issues. The current study explores the applicability of TTAinterfaceTrendAnalysis (an ‘R’ language-based Graphical User Interface package) in estimating the annual and month-wise trends in PM2.5 over five Indian megacities (New Delhi, Kolkata, Mumbai, Hyderabad, and Chennai). The package offers a one-stop solution for various statistical analyses such as data imputation, outlier detection, trend analysis, data smoothing, diagnostic tests, etc. The trends are estimated using seven (2014–2020) years of PM2.5 data. All estimated month-wise trends of PM2.5 over the study cities were found to be statistically non-significant. Annual trends (both with and without applying data smoothing techniques) were found to be statistically significant with magnitudes ranging between − 0.27 and − 5.2 µg m−3 y−1 (negative sign indicates a declining trend). Autocorrelation analysis and normality tests are also conducted using the inbuilt options of TTAinterfaceTrendAnalysis. Results obtained are thoroughly discussed and compared with those of similar contemporary studies. To our knowledge, the current study is the first to apply TTAinterfaceTrendAnalysis for quantifying long-term trends in PM2.5. The sensitivity of the trend estimates due to the inclusion of COVID-19 lockdown PM2.5 data was also examined. The net impact of lockdown on the PM2.5 long-term trend ranged between − 0.13 and 2.29 µg m−3 y−1 across the study cities.

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References

  • Balakrishnan K, Dey S, Gupta T, Dhaliwal RS, Brauer M, Cohen AJ, Stanaway JD, Beig G, Joshi TK, Aggarwal AN, Sabde Y (2019) The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: the global burden of disease study 2017. Lancet Planet Health 3(1):e26–e39

    Google Scholar 

  • Chowdhury S, Dey S, Tripathi SN, Beig G, Mishra AK, Sharma S (2017) “Traffic intervention” policy fails to mitigate air pollution in megacity Delhi. Environ Sci Policy 74:8–13

    CAS  Google Scholar 

  • Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R, Feigin V (2017) Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015. The Lancet 389(10082):1907–1918

    Google Scholar 

  • Devreker D, Lefebvre A (2014) TT ainterface trend analysis: an R GUI for routine temporal trend analysis and diagnostics. J Oceanogr Res Data 7(1):1–18

    Google Scholar 

  • Friendly M (2002) Corrgrams: exploratory displays for correlation matrices. Am Stat 56(4):316–324

    MathSciNet  Google Scholar 

  • Gogikar P, Tripathy MR, Rajagopal M, Paul KK, Tyagi B (2021) PM 2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India. J Ambient Intell Hum Comput 12:2975–2991

    Google Scholar 

  • Guo Y, Li S, Tian Z, Pan X, Zhang J, Williams G (2013) The burden of air pollution on years of life lost in Beijing, China, 2004–08: retrospective regression analysis of daily deaths. Br Med J 6:347

    Google Scholar 

  • Gurjar BR, Butler TM, Lawrence MG, Lelieveld J (2008) Evaluation of emissions and air quality in megacities. Atmos Environ 42(7):1593–1606

    ADS  CAS  Google Scholar 

  • Gurjar BR, Ravindra K, Nagpure AS (2016) Air pollution trends over Indian megacities and their local-to-global implications. Atmos Environ 142:475–495

    ADS  CAS  Google Scholar 

  • Guttikunda SK, Carmichael GR, Calori G, Eck C, Woo JH (2003) The contribution of megacities to regional sulfur pollution in Asia. Atmos Environ 37(1):11–22

    ADS  CAS  Google Scholar 

  • Guttikunda SK, Dammalapati SK, Pradhan G, Krishna B, Jethva HT, Jawahar P (2023) What is polluting delhi’s air? a review from 1990 to 2022. Sustainability 15(5):4209

    CAS  Google Scholar 

  • Hoek G, Beelen R, De Hoogh K, Vienneau D, Gulliver J, Fischer P, Briggs D (2008) A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ 42(33):7561–7578

    ADS  CAS  Google Scholar 

  • Hu X, Waller LA, Lyapustin A, Wang Y, Liu Y (2014) 10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data. Atmos Chem Phys 14(12):6301

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Jacoby WG (2000) Loess: a non-parametric, graphical tool for depicting relationships between variables. Elect Stud 19(4):577–613

    Google Scholar 

  • Kendall MG (1975) Rank correlation methods. Griffin Ltd, Tokyo

    Google Scholar 

  • Landrigan PJ, Fuller R, Acosta NJ, Adeyi O, Arnold R, Baldé AB, Bertollini R, Bose-O’Reilly S, Boufford JI, Breysse PN, Chiles T (2018) The lancet commission on pollution and health. Lancet 391(10119):462–512

    PubMed  Google Scholar 

  • Lefebvre A, Devreker D (2023) How to learn more about hydrological conditions and phytoplankton dynamics and diversity in the eastern english channel and the Southern Bight of the North Sea: the suivi régional des nutriments data set (1992–2021). Earth Syst Sci Data 15(3):1077–1092

    ADS  Google Scholar 

  • Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y (2016) Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environ Health Perspect 124(2):184–192

    PubMed  Google Scholar 

  • Mahato S, Pal S, Ghosh KG (2020) Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi. Science of the Total Environment, India, p 139086

    Google Scholar 

  • Mahesh B, Rama BV, Spandana B, Sarma MSSRKN, Niranjan K, Sreekanth V (2019) Evaluation of MERRAero PM2.5 over Indian cities. Adv Space Res 64(2):328–334

    ADS  CAS  Google Scholar 

  • Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259. https://doi.org/10.2307/1907187

    Article  MathSciNet  Google Scholar 

  • Manoj MR, Satheesh SK, Moorthy KK, Gogoi MM, Babu SS (2019) Decreasing trend in black carbon aerosols over the Indian region. Geophys Res Lett 46(5):2903–2910

    ADS  CAS  Google Scholar 

  • Mor S, Kumar S, Singh T, Dogra S, Pandey V, Ravindra K (2021) Impact of COVID-19 lockdown on air quality in Chandigarh, India: understanding the emission sources during controlled anthropogenic activities. Chemosphere 263:127978

  • Moustris KP, Nastos PT, Larissi IK, Paliatsos AG (2012) Application of multiple linear regression models and artificial neural networks on the surface ozone forecast in the greater Athens area, Greece. Adv Meteorol 2012:96596

    Google Scholar 

  • Mukherjee A, Toohey DW (2016) A study of aerosol properties based on observations of particulate matter from the US Embassy in Beijing. China Earth’s Futur 4(8):381–395

    ADS  CAS  Google Scholar 

  • Munir S, Habeebullah TM, Mohammed AMF, Morsy EA, Rehan M, Ali K (2017) Analysing PM2.5 and its association with PM10 and meteorology in the arid climate of Makkah, Saudi Arabia. Aerosol Air Qual Res 17(2):453–464. https://doi.org/10.4209/aaqr.2016.03.0117

    Article  CAS  Google Scholar 

  • Ní Longphuirt S, O’Boyle S, Stengel DB (2015) Environmental response of an Irish estuary to changing land management practices. Sci Total Environ 521–522:388–399. https://doi.org/10.1016/j.scitotenv.2015.03.076

    Article  ADS  CAS  PubMed  Google Scholar 

  • Oliva F, Vegas E, Civit S, Garrido T, Fraile J and Munné A (2016) Trend assessment for groundwater pollutants: a brief review and some remarks In: Munné A, Ginebreda A, Prat N (eds) Experiences from ground, coastal and transitional water quality monitoring: The EU water framework directive implementation in the Catalan River Basin District Part II, pp 25–62 DOI: https://doi.org/10.1007/698_2015_407

  • Pye K (1995) The nature, origin and accumulation of loess. Quatern Sci Rev 14(7–8):653–667

    ADS  Google Scholar 

  • Ravindra K, Sidhu MK, Mor S, John S, Pyne S (2016) Air pollution in India: bridging the gap between science and policy. J Hazard Toxic Radioactive Waste 20(4):A4015003

    Google Scholar 

  • Ravindra K, Rattan P, Mor S, Aggarwal AN (2019) Generalized additive models: Building evidence of air pollution, climate change and human health. Environ Int 132:104987

    CAS  PubMed  Google Scholar 

  • Ravindra K, Singh T, Pandey V, Mor S (2020) Air pollution trend in Chandigarh city situated in Indo-Gangetic Plains: understanding seasonality and impact of mitigation strategies. Sci Total Environ 23:138717

    ADS  Google Scholar 

  • Ravindra K, Singh T, Biswal A, Singh V, Mor S (2021) Impact of COVID-19 lockdown on ambient air quality in megacities of India and implication for air pollution control strategies. Environ Sci Pollut Res 28:21621–21632

    CAS  Google Scholar 

  • Ravindra K, Bahadur SS, Katoch V, Bhardwaj S, Kaur-Sidhu M, Gupta M, Mor S (2023) Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections. Sci Total Environ 858:159509

    ADS  CAS  PubMed  Google Scholar 

  • Rupp DL, Larsen AS (2022) Surface water area in a changing climate: differential responses of Alaska’s subarctic lakes. PLOS Clim 1(6):e0000036

    Google Scholar 

  • Salvador P, Artíñano B, Viana MM, Querol X, Alastuey A, González-Fernández I, Alonso R (2011) Spatial and temporal variations in PM10 and PM2.5 across Madrid metropolitan area in 1999–2008. Proc Environ Sci 4:198–208. https://doi.org/10.1016/j.proenv.2011.03.024

    Article  CAS  Google Scholar 

  • Sanchez M, Ambros A, Milà C, Salmon M, Balakrishnan K, Sambandam S, Sreekanth V, Marshall JD, Tonne C (2018) Development of land-use regression models for fine particles and black carbon in peri-urban South India. Sci Total Environ 634:77–86

    ADS  CAS  PubMed  Google Scholar 

  • Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52(3/4):591–611

    MathSciNet  Google Scholar 

  • Singh V, Ravindra K, Sahu L, Sokhi R (2018) Trends of atmospheric black carbon concentration over the United Kingdom. Atmos Environ 178:148–157

    ADS  CAS  Google Scholar 

  • Singh T, Ravindra K, Sreekanth V, Gupta P, Sembhi H, Tripathi SN, Mor S (2020a) Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: rural-urban contrast. Sci Total Environ 748:140963

    ADS  CAS  PubMed  Google Scholar 

  • Singh V, Singh S, Biswal A, Kesarkar AP, Mor S, Ravindra K (2020b) Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India. Environ Pollut 266:115368

    CAS  PubMed  PubMed Central  Google Scholar 

  • Singh V, Singh S, Biswal A (2021) Exceedances and trends of particulate matter (PM2.5) in five Indian megacities. Sci Total Environ 750:141461

    ADS  CAS  PubMed  Google Scholar 

  • Singh T, Matsumi Y, Nakayama T, Hayashida S, Patra PK, Yasutomi N, Kajino M, Yamaji K, Khatri P, Takigawa M, Araki H (2023) Very high particulate pollution over northwest India captured by a high-density in situ sensor network. Sci Reports 13(1):13201

    ADS  CAS  Google Scholar 

  • Sreekanth V (2013) Satellite derived aerosol optical depth climatology over Bangalore. India Adv Space Res 51(12):2297–2308

    ADS  Google Scholar 

  • Sreekanth V (2016) Discussion on linear long-term trends in aerosol and cloud properties over India and its surrounding waters. Adv Space Res 57(10):2104–2114

    ADS  CAS  Google Scholar 

  • Sreekanth V, Mahesh B, Niranjan K (2017) Satellite remote sensing of fine particulate air pollutants over Indian mega cities. Adv Space Res 60(10):2268–2276

    ADS  CAS  Google Scholar 

  • Sreekanth V, Mahesh B, Niranjan K (2018) Gradients in PM2.5 over India: five city study. Urban Clim 25:99–108

    Google Scholar 

  • Tonne C, Salmon M, Sanchez M, Sreekanth V, Bhogadi S, Sambandam S, Balakrishnan K, Kinra S, Marshall JD (2017) Integrated assessment of exposure to PM2.5 in South India and its relation with cardiovascular risk: design of the CHAI observational cohort study. Int J Hyg Environ health 220(6):1081–1088

    CAS  PubMed  Google Scholar 

  • Upadhyay A, Dey S, Chowdhury S, Goyal P (2018) Expected health benefits from mitigation of emissions from major anthropogenic PM2.5 sources in India: statistics at state level. Environ Pollut 242:1817–1826

    CAS  PubMed  Google Scholar 

  • WHO (2016) Ambient air pollution: a global assessment of exposure and burden of disease. World Health Organization, Geneva

    Google Scholar 

  • Xu B, Luo L, Lin B (2016) A dynamic analysis of air pollution emissions in China: evidence from non-parametric additive regression models. Ecol Ind 63:346–358

    CAS  Google Scholar 

  • Yu HL, Lin YC, Kuo YM (2015) A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions. Chemosphere 134:571–580

    ADS  CAS  PubMed  Google Scholar 

  • Zhai L, Li S, Zou B, Sang H, Fang X, Xu S (2018) An improved geographically weighted regression model for PM2.5 concentration estimation in large areas. Atmos Environ 181:145–154

    ADS  CAS  Google Scholar 

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Acknowledgements

KR and SM would like to acknowledge the Health Care Without Harm for the Climate, Health and Air Monitoring Project (CHAMP) project. KR would like to thank the National Programme on Climate Change and Human Health (NPCCHH) under the Ministry of Health and Family Welfare (MoHFW) for designating his institute as a Center of Excellence (CoE) on Climate Change and Air Pollution Related Illness. KR and SM also acknowledge the Ministry of Environment, Forest & Climate Change, for identifying their institute as an Institute of Repute (IoR) under the National Clean Air Program (NCAP).

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No specific funding was received for this research work.

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Authors and Affiliations

  1. Department of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India

    Khaiwal Ravindra & Preety Rattan

  2. Center for Study of Science, Technology and Policy, Bengaluru, 560094, India

    Sreekanth Vakacherla

  3. S.C.V.B. Government College, Himachal Pradesh, Palampur, 176061, India

    Tanbir Singh

  4. Institute of Environment Education and Research, Bharati Vidyapeeth University, Pune, 411403, India

    Adithi R. Upadhya

  5. Department of Environment Studies, Panjab University, Chandigarh, 160014, India

    Suman Mor

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  1. Khaiwal Ravindra
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Contributions

K.R.: Conceptualization, methodology, formal analysis, writing—review & editing. S.V.: Methodology, formal analysis, validation, writing—review & editing. T.S.: Formal analysis, writing—review & editing. A.R.U.: Formal analysis, writing—review & editing. P.R.: Formal analysis, writing—review & editing. S.M.: Methodology, formal analysis, validation, writing—review & editing.

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Correspondence to Khaiwal Ravindra.

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Ravindra, K., Vakacherla, S., Singh, T. et al. Long-term trend of PM2.5 over five Indian megacities using a new statistical approach. Stoch Environ Res Risk Assess 38, 715–725 (2024). https://doi.org/10.1007/s00477-023-02595-x

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