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Reliability and stability of a statistical model to predict ground-based PM2.5 over 10 years in Karachi, Pakistan, using satellite observations

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Abstract

Understanding the complex mechanisms of climate change and its environmental consequences requires the collection and subsequent analysis of geospatial data from observations and numerical modeling. Multivariable linear regression and mixed-effects models were used to estimate daily surface fine particulate matter (PM2.5) levels in the megacity of Pakistan. The main parameters for the multivariable linear regression model were the 10-km-resolution satellite aerosol optical depth (AOD) and daily averaged meteorological parameters from ground monitoring (temperature, dew point, relative humidity, wind speed, wind direction, and planetary boundary layer height). Ground-based PM2.5 was measured in two stations in the city, Korangi (industrial/residential) and Tibet Center (commercial/residential). The initial linear regression model was modified using a stepwise selection procedure and adding interaction parameters. Finally, the modified model showed a strong correlation between the PM2.5–satellite AOD and other meteorological parameters (R2 = 0.88–0.92 and p-value = 10−7 depending on the season and station). The mixed-effect technique improved the model performance by increasing the R2 values to 0.99 and 0.93 for the Korangi and Tibet Center sites, respectively. Cross-validation methods were used to confirm the reliability of the model to predict PM2.5 after 10 years.

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References

  • Ansmann A, M Tesche, S Gross, V Freudenthaler, P Seifert, A Hiebsch, J Schmidt, U Wandinger, I Mattis, D Müller, M Wiegner (2010) The 16 April 2010 major volcanic ash plume over central Europe: EARLINET Lidar and AERONET photometer observations at Leipzig and Munich, Germany. Geophys. Res. Lett., № 37, L13810.

  • Bodor Z, Bodor K, Keresztesi Á (2020) Major air pollutants seasonal variation analysis and long-range transport of PM10 in an urban environment with specific climate condition in Transylvania (Romania). Environ Sci Pollut Res 27:38181–38199. https://doi.org/10.1007/s11356-020-09838-2

    Article  CAS  Google Scholar 

  • Brook RD, Newby DE, Rajagopalan S (2017) The global threat of outdoor ambient air pollution to cardiovascular health: time for intervention. JAMA Cardiol 2:353–354. https://doi.org/10.1001/jamacardio.2017.0032

    Article  Google Scholar 

  • Carabali G, Villanueva-Macias J, Ladino LA et al (2021) Characterization of aerosol particles during a high pollution episode over Mexico City. Sci Rep 11:22533. https://doi.org/10.1038/s41598-021-01873-4

    Article  CAS  Google Scholar 

  • Chen Chen, Jason A Warrington, Francesca Dominici, Roger D Peng, Daniel C Esty, Jennifer F Bobb, Michelle L Bell (2021) Temporal variation in association between short-term exposure to fine particulate matter and hospitalisations in older adults in the USA: a long-term time-series analysis of the US Medicare dataset, The Lancet Planetary Health, Vol 5, Issue 8 https://doi.org/10.1016/S2542-5196(21)00168-6.

  • Chiacchio M, T Ewen, M Wild, M Chin, T Diehl (2011). Decadal variability of aerosol optical depth in Europe and its relationship to the temporal shift of the North Atlantic Oscillation in the realm of dimming and brightening. J Geophys Res № 116:D02108

  • Chitranshi S, Sharma SP, Dey S (2014) Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India. Air Qual Atmos Health 8:55–56

    Article  Google Scholar 

  • Cosselman KE, Navas-Acien A, Kaufman JD (2015) Environmental factors in cardiovascular disease. Nat Rev Cardiol 12:627–642. https://doi.org/10.1038/nrcardio.2015.152

    Article  CAS  Google Scholar 

  • Dey S, Di Girolamo L, van Donkelaar A, Tripathi SN, Gupta T, Mohan M (2012) Variability of outdoor fine particulate (PM2.5) concentration in the Indian Subcontinent: a remote sensing approach. Remote Sens Environ 127:153–161

    Article  Google Scholar 

  • Di Qian, Itai Kloog, Petros Koutrakis, Alexei Lyapustin, Yujie Wang, and Joel Schwartz. (2016). Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environ Sci Technol. 50 (9): 4712–4721

  • Gu J, Wang Y, Ma J, Lu Y, Wang S, Li X (2022) An estimation method for PM2.5 based on aerosol optical depth obtained from remote sensing image and meteorological factors. Remote Sens. 14, 1617. https://doi.org/10.3390/rs14071617

  • Hamanaka RB, Mutlu GM (2018) Particulate Matter Air Pollution: Effects on the Cardiovascular System. Front Endocrinol (Lausanne) 16(9)680. https://doi.org/10.3389/fendo.2018.00680

  • Hammer MS, van Donkelaar A, Li C, Lyapustin A, Sayer AM, Hsu NC, Levy RC, Garay MJ, Kalashnikova OV, Kahn RA, Brauer M, Apte JS, Henze DK, Zhang L, Zhang Q, Ford B, Pierce JR, Martin RV (2020) Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018). Environ Sci Technol 54(13):7879–7890. https://doi.org/10.1021/acs.est.0c01764

  • Harrel Franck (2001) Regression modeling strategies ISBN: 978–3–319–19425–7 Springer Link

  • Khwaja HA, Fatmi Z, Malashock D, Aminov Z, Kazi A, Siddique A, Qureshi J, Carpenter D (2012) Effect of air pollution on daily morbidity in Karachi, Pakistan. J Local Global Health Sci 3:1–13

    Google Scholar 

  • Koukouli M E, S Kazadzis, V Amiridis, C Ichoku, D S Balis, A F Bais (2010) Signs of a negative trend in the MODIS aerosol optical depth over the Southern Balkans /Atmos. Environ., № 44, P. 1219 – 1228

  • Lee HJ, Liu Y, Coull BA, Schwartz J, Koutrakis P (2011) A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmos Chem Phys 11:9769–9795

    Article  Google Scholar 

  • Liang F, Yang X, Liu F, Li J, Xia Q, Chen J, Liu X, Cao J, Shen C, Yu L, Lu F, Wu X, Zhao L, Wu X, Hu D, Huang J, Liu Y, Lu X, Gu D (2019) Long-term exposure to ambient fine particulate matter and incidences of diabetes in China: a cohort study.https://doi.org/10.1012/j.envint.2019.02.069

  • Luo Y, Liu S, Che L, Yu Y (2021) Analysis of temporal spatial distribution characteristics of PM2.5 pollution and the influential meteorological factors using Big Data in Harbin, China. J Air Waste Manag Assoc 71:964–973. https://doi.org/10.1080/10962247.2021.1902423

    Article  CAS  Google Scholar 

  • Lurie K, Nayebare SR, Fatmi Z, Carpenter DO, Siddique A, Malashock D, Khan K, Zeb J, Hussain MM, Khatib F, Khwaja HA (2019) PM2.5 in a megacity of Asia (Karachi): source apportionment and health effects. Atmos Environ 202:223–233

    Article  CAS  Google Scholar 

  • Lv B, Cai J, Xu B et al. (2017) Understanding the rising phase of the PM2.5 concentration evolution in large China cities. Sci Rep 7, 46456. https://doi.org/10.1038/srep46456

  • Mills NL, Donaldson K, Hadoke PW, Boon NA, MacNee W, Cassee FR et al (2009) Adverse cardiovascular effects of air pollution. Nat Clin Pract Cardiovasc Med 6:36–44. https://doi.org/10.1038/ncpcardio1399

    Article  CAS  Google Scholar 

  • Mohan M, Payra S (2009) Influence of aerosol spectrum and air pollutants on fog formation in urban environment of megacity Delhi, India. Environ Monit Assess 151(1–4):265–277. https://doi.org/10.1007/s10661-008-0268-8

    Article  CAS  Google Scholar 

  • Newby DE, Mannucci PM, Tell GS, Baccarelli AA, Brook RD, Donaldson K et al (2015) Expert position paper on air pollution and cardiovascular disease. Eur Heart J 36:83–93b. https://doi.org/10.1093/eurheartj/ehu458

    Article  CAS  Google Scholar 

  • Obregón MJC, Silva AM, Serrano A (2018) Impact of aerosol and water vapour on S.W. radiation at the surface: sensitivity study and applications. Atmos Res 213:252–263

    Article  Google Scholar 

  • Qingyang X, Wang Y, Chang HH, Meng X, Geng G, Lyapustin A, Liu Y (2017) Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China. Remote Sens Environ 199:437–446

    Article  Google Scholar 

  • Sillberg C V, Rungratanaubon T, Bualert S, Choomanee P and Chueytawarit P (2021) An approach of statistical analysis and interpretation of PM2.5 concentration based on meteorological factors and temperature effects in Bangkok, Thailand. International Journal of Science and Innovative Technology Vol 4 Issue 1

  • Sinha PR, Gupta P, Kaskaoutis DG, Sahu LK, Nagendra N, Manchanda RK, Kumar YB, Sreenivasan S (2015) Estimation of particulate matter from satellite and ground- based observations over Hyderabad. India Intern J Rem Sens 36(24):6192–6213

    Article  Google Scholar 

  • Sotoudeheian S, Arhami M (2014) Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran. J. Environ. Health Sci. Eng. 12: 122. http://www.ijehse.com/content/12/1/122

  • Streets D G, F. Yan, M. Chin, T. Diehl, N. Mahowald, M. Schultz, M. Wild, Y. Wu, C. Yu (2009) Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006 J. Geophys. Res., № 114, D00d18

  • Van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, Villeneuve, (2010) Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ Health Perspect 118(6):847–855. https://doi.org/10.1289/ehp.0901623

    Article  CAS  Google Scholar 

  • World Health Organization (2018) https://www.who.int/news-room/fact-sheets/detail/ambient(outdoor)-air-quality-and-health

  • Xie Y, Wang Y, Zhanh K, Dong W, Lv B, Bai Y (2015) Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD. Environ.Sci.Tech. doi: https://doi.org/10.1021/acs.est.5b01413.

  • Yang Qianqian, Qiangqiang Yuan, Linwei Yue, Tongwen Li, Huanfeng Shen, Liangpei Zhang. (2019) The relationships between PM2.5 and meteorological factors in China: seasonal and regional variations. Environmental Pollution. https://doi.org/10.1016/j.envpol.2019.02.071

  • Yoon J, W von Hoyningen-Huene, M. Vountas, J.P. Burrows (2011). Analysis of linear long-term trend of aerosol optical thickness derived from SeaWiFS using BAER over Europe and South China / Atmos. Chem. Phys., № 11, P. 12149 – 12167.

  • Zhang J, Liu L, Wang Y, Ren Y, Wang X, Shi Z, Zhang D, Che H, Zhao H, Liu Y, Niu H, Chen J, Zhang X, Lingaswamy AP, Wang Z, Li W (2017) Chemical composition, source, and process of urban aerosols during winter haze formation in Northeast China. Environ Pollut 231(Pt 1):357–366. https://doi.org/10.1016/j.envpol.2017.07.102. (PMID: 28810205)

    Article  CAS  Google Scholar 

  • Zhang N, Huang H, Duan X, Zhao J, Su B (2018) Quantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation. Sci Rep 8:9461

  • Zhao R, Gu X, Xue B, Zhang J, Ren W (2018) Short period PM2.5 prediction based on multivariate linear regression model. PLoS One. 13(7):e0201011. https://doi.org/10.1371/journal.pone.0201011

  • Zheng YX, Zhang Q, Liu Y, Geng G, He K (2015) Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements. Atmos Environ 124:232–242. https://doi.org/10.1016/j.atmosenv.2015.06.046

    Article  CAS  Google Scholar 

  • Zuberi MJS, Torkmahalleh MA, Ali SMH (2015) A comparative study of biomass resources utilization for power generation and transportation in Pakistan. Int J Hydrog Energy 40(34):11154–11160

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Acknowledgements

We extend our thanks to Kimberly McClive-Reed for editing the manuscript.

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Authors and Affiliations

  1. Faculty of Science and Technology, University of Lorraine, Nancy, France

    Zhuldyz Darynova

  2. Department of Computer Science and Engineering, Chalmers University of Technology, 41296, Gothenburg, Sweden

    Milad Malekipirbazari

  3. Department of Production Automation and Information Technology, Atyrau Oil and Gas University, Atyrau, 060027, Kazakhstan

    Daryn Shabdirov

  4. Wadsworth Center, New York State Department of Health, Albany, NY, USA

    Haider A. Khwaja

  5. Department of Environmental Health Sciences, School of Public Health, University at Albany, Albany, NY, USA

    Haider A. Khwaja

  6. Division of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, IL, 60612, USA

    Mehdi Amouei Torkmahalleh

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  1. Zhuldyz Darynova
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Correspondence to Mehdi Amouei Torkmahalleh.

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Darynova, Z., Malekipirbazari, M., Shabdirov, D. et al. Reliability and stability of a statistical model to predict ground-based PM2.5 over 10 years in Karachi, Pakistan, using satellite observations. Air Qual Atmos Health 16, 669–679 (2023). https://doi.org/10.1007/s11869-022-01296-8

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