Investigating factors influencing PM2.5 threshold exceedance in Nigeria using multivariate logistic regression analysis

Authors

  • Tertsea Igbawua
    Department of Physics, Joseph Sarwuan Tarka University, Makurdi, Benue State, Nigeria
  • Aondongu Alexander Tyovenda
    Department of Physics, Joseph Sarwuan Tarka University, Makurdi, Benue State, Nigeria
  • Terver Sombo
    Department of Physics, Joseph Sarwuan Tarka University, Makurdi, Benue State, Nigeria
  • Idugba Mathias Echi
    Department of Physics, Joseph Sarwuan Tarka University, Makurdi, Benue State, Nigeria
  • Fanan Ujoh
    Centre for the Integrated Delivery of the Built Environment (IDoBE), School of the Built Environment and Architecture, London South Bank University, United Kingdom

Keywords:

PM2.5 threshold exceedance, Logistic Regression Model, Meteorological Variables , Aerosol Emission Rate Variables, Nigeria

Abstract

The understanding of the drivers of fine particulate matter of diameter less than or equal to 2.5 micrometers (PM2.5) in Nigeria will greatly assist policymakers and other stakeholders in developing appropriate air quality management strategies to protect the health of the public. This research analyzed the drivers of PM2.5 threshold exceedance (PTE) in Nigeria from 2000 to 2022, employing a multivariate logistic regression (MLR) model. The work assessed aerosol emission variables (AEVs) and meteorology variables (MEVs) to understand their impact on PM2.5 concentrations. Results showed a significant spatial heterogeneity in the trend of PM2.5 concentrations, with increasing values in all the study areas. The estimated coefficients of black carbon emission (BCEM) and sulfate emission (SUEM) showed a significant positive response, exacerbating the threshold exceedance in Abuja and Lagos, while dust emission (DUEM) had a significant positive response in Kano and Lagos. On the other hand, OCEM had a negative association in all the study locations. The MEV variables of sea level pressure (SLP), relative humidity (RH), planetary boundary layer height (PBLH), and wind speed (WS) had significant negative responses in Abuja, while RH and WS were significant in Kano, and RH, PBLH, and precipitation (P) were significant in Lagos, indicating that the PM2.5 concentrations decrease with increasing values of these factors; however, temperature (TMP) had a significant positive response in Lagos but was not significant in Abuja and Kano. The output of the study revealed complex relationships between aerosol emissions, climate, and other environmental variables driving the PTE in Nigeria.

Dimensions

R. Sun, Y. Zhou, J. Wu & Z. Gong, “Influencing factors of PM2.5 pollution: Disaster points of meteorological factors”, International Journal of Environmental Research and Public Health 16 (2019) 3891. https://doi.org/10.3390/ijerph16203891.

T. Amnuaylojaroen & N. Parasin, “Future health risk assessment of exposure to PM2.5 in different age groups of children in Northern Thailand”, Toxics 11 (2023) 291. https://doi.org/10.3390/toxics11030291.

A. U. Abidin, F. B. Maziya, S. H. Susetyo, M. Yoneda & Y. Matsui, “Exposure particulate matter (PM2.5) and health risk assessment on informal workers in landfill site, Indonesia”, Environmental Challenges 13 (2023) 100795. https://doi.org/10.1016/j.envc.2023.100795.

S. Gao, Z. Cong, H. Yu, Y. Sun, J. Mao, H. Zhang & Z. Bai, “Estimation of background concentration of PM in Beijing using a statistical integrated approach”, Atmospheric Pollution Research 10 (2018) 858. https://doi.org/10.1016/j.apr.2018.12.014.

I. D. Sulaymon, X. Mei, S. Yang, S. Chen, Y. Zhang, P. K. Hopke, J. J. Schauer & Y. Zhang, “PM2.5 in Abuja, Nigeria: Chemical characterization, source apportionment, temporal variations, transport pathways and the health risks assessment”, Atmospheric Research 273 (2019) 104833. https://doi.org/10.1016/j.atmosres.2019.104833.

J. Liu, G. Kiesewetter, Z. Klimont, J. Cofala, C. Heyes, W. Schopp, T. Zhu, G. Cao, A. G. Sanabria, R. Sander, F. Guo, Q. Zhang, B. Nguyen,¨ I. Bertok, P. Rafaj & M. Amann, “Mitigation pathways of air pollution from residential emissions in the Beijing-Tianjin-Hebei region in China”, Environmental International 125 (2019) 236. https://doi.org/10.1016/j.envint.2018.09.059.

T. Ayua, A. Tyovenda, T. Sombo, E. V. Tikyaa & T. Igbawua, “Fine particulate matter and heavy metals pollution status in ambient air of some selected industrial sites in Northern Nigeria”, Journal of Geoscience and Environment Protection 8 (2020) 1. https://doi.org/10.4236/gep.2020.88001.

J. Soares, A. Gonzalez Ortiz, A. Gsella, J. Hor´ alek, D. Plass & S. Kienzler, “Health risk assessment of air pollution and the impact of the´ new WHO guidelines (Eionet Report – ETC HE 2022/10)”, European Topic Centre on Human Health and Environment. Retrieved from https://iris.who.int/bitstream/handle/10665/345329/9789240034228-eng.pdfonMarch30,2024.

E. M. M. Zahran, “Calibration of background concentrations versus the use of grid sources in air pollution dispersion modelling”, Asian Journal of Natural and Applied Sciences 4 (2015) 1. http://www.ajsc.leena-luna.co.jp/AJSCPDFs/Vol.4(1)/AJSC2015(4.1-01).pdf.

Y. Zhang & W. Jiang, “Pollution characteristics and influencing factors of atmospheric particulate matter (PM2.5) in Chang-Zhu-Tan area”, IOP Conference Series: Earth and Environmental Science 108 (2018) 042047. https://doi.org/10.1088/1755-1315/108/4/042047.

H. Yang, Q. Peng, J. Zhou, G. Song & X. Gong, “The unidirectional causality influence of factors on PM2.5 in Shenyang city of China”, Scientific Reports 10 (2020) 1. https://doi.org/10.1038/s41598-020-65391-5.

X. Xu, K. Shi, Z. Huang & J. Shen, “What factors dominate the change of PM2.5 in the world from 2000 to 2019? A study from multi-source data”, International Journal of Environmental Research and Public Health 3 (2023) 2282. https://doi.org/10.3390/ijerph20032282.

I. D. Sulaymon, Y. Zhang, P. K. Hopke, F. Ye, K. Gong, J. Mao, & J. Hu, “Modeling PM2.5 during severe atmospheric pollution episode in Lagos, Nigeria: Spatiotemporal variations, source apportionment, and meteorological influences”, Journal of Geophysical Research: Atmospheres 128 (2023) 2022JD038360. https://doi.org/10.1029/2022JD038360.

J. M. Angulo & A. E. Madrid, “Structural analysis of Spatio-temporal threshold exceedances”, Environmetrics 21 (2009) 415. https://doi.org/10.1002/env.1018.

A. E. Madrid, J. M. Angulo & J. Mateu, ”Spatial threshold exceedance analysis through marked point processes.” Environmetrics 23 (2011) 108. https://doi.org/10.1002/env.1141.

R. L. Smith, J. A. Tawn & S. G. Coles, “Markov chain models for threshold exceedances”, Biometrika 84 (1997) 249. http://www.jstor.org/stable/2337455.

S. E. Grineski, R. A. Renteria, T. W. Collins, A. V. Bakian, D. Bilder, J. A. VanDerslice, A. Frase, J. Gomez & K. D. Ramos, “PM2.5 threshold exceedances during the prenatal period and risk of intellectual disability”, Journal of Exposure Science & Environmental Epidemiology 34 (2024) s41370-023-00634-w. https://doi.org/10.1038/s41370-024-00647-0.

G. Rincon, G. Morantes Quintana, A. Gonzalez, Y. Buitrago, J. C. Gonzalez, C. Molina & B. Jones, “PM2.5 exceedances and source appointment as inputs for an early warning system”, Environmental Geochemistry and Health 44 (2022) 4569. https://doi.org/10.1007/s10653-021-01189-2.

M. Idris, A. G. Muhammad, R. S. Said & D. O. Akpootu, “Effect of meteorological parameters on the dispersion of vehicular emission in some selected areas in Kano State, Nigeria”, Bayero Journal of Pure and Applied Sciences 13 (2022) 517. https://doi.org/10.1234/bjpas.2022.13.1.517.

N. M. Wambebe & X. Duan “Air quality levels and health risk assessment of particulate matters in Abuja Municipal Area, Nigeria”, Atmosphere 11 (2020) 817. https://doi.org/10.3390/atmos11080817.

R. Kanee, A. Adeyemi, D. Edokpa & P. Ede, “Particulate matter-based air quality index estimate for Abuja, Nigeria: Implications for health”, Journal of Geoscience and Environment Protection 8 (2020) 313. https://doi.org/10.4236/gep.2020.85019.

Global Modeling and Assimilation Office (GMAO), “tavgM 2d aer Nx: MERRA-2 2D, Monthly Mean, Time-Averaged, Aerosol Diagnostics (0.625x0.5, L42), version 5.12.4.”, Greenbelt, MD, USA: Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), 2015. https://doi.org/10.5067/FH9A0MLJPC7N. (Accessed February 23, 2023)

Global Modeling and Assimilation Office (GMAO), “tavgM 2d flx Nx: MERRA-2 2D, Monthly Mean, Time-Averaged, Surface Flux Diagnostics (0.625x0.5, L42), version 5.12.4.”, Greenbelt, MD, USA: Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), 2015. https://doi.org/10.5067/0JRLVL8YV2Y4. (Accessed March 5, 2023)

Global Modeling and Assimilation Office (GMAO), “instM 3d asm Np: MERRA-2 3D IAU State, Meteorology Instantaneous, Monthly Mean (p-coord, 0.5x0.625, L42), version 5.12.4.”, Greenbelt, MD, USA: Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC). https://doi.org/10.5067/2E096JV59PK7. (Accessed July 31, 2024)

H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horanyi, J. Mu´ noz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A.˜ Simmons, C. Soci, D. Dee & J. -N. Thepaut, “ERA5 Monthly Averaged Data on Single Levels from 1940 to Present”, Copernicus Climate´ Change Service (C3S) Climate Data Store (CDS), 2023. https://doi.org/10.24381/cds.f17050d7. (Accessed: Aug. 28, 2023)

I. Harris, T. J. Osborn, P. Jones & D.H. Lister, “Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset”, Sci Data 34 7 (2020) 109. https://doi.org/10.1002/joc.3711.

J. L. Hand, S. A. Copeland, D. E. Day, A. M. Dillner, H. Indresand, W. C. Malm, C. E. McDade, C. T. Moore, M. L. Pitchford, B. A. Schichtel & J. G. Watson, “Spatial and seasonal patterns and temporal variability of haze and its constituents in the United States: Report V June 2011”, Interagency Monitoring of Protected Visual Environments 35 (2011) 3119977. https://vista.cira.colostate.edu/Improve/spatial-and-seasonal-patterns-and-temporal-variability-of-haze-and-its-constituents-in-the-united-states-report-v-june-2011/.

L. M. Tesfaw, & H. M. Fenta, “Multivariate logistic regression analysis on the association between anthropometric indicators of under-five children in Nigeria: NDHS 2018”, BMC Pediatrics 21 (2021) 193. https://doi.org/10.1186/s12887-021-02657-5.

G. Bordeaux & A. Couto, “A binary logistic regression model to identify key aspects that enhance global hub airports status”, J. Air Transport Res. Soc. 3 (2024) 100035. https://doi.org/10.1016/j.jatrs.2024.100035.

G. Zeng, “A comprehensive study of coefficient signs in weighted logistic regression”, Heliyon 10 (2024) e35040. https://doi.org/10.1016/j.heliyon.2024.e35.

Study area

Published

2024-09-12

How to Cite

Investigating factors influencing PM2.5 threshold exceedance in Nigeria using multivariate logistic regression analysis. (2024). African Scientific Reports, 3(3), 212. https://doi.org/10.46481/asr.2024.3.3.212

Issue

Volume 3, Issue 3, December 2024

Section

GEOSCIENCES SECTION
Copyright & Licensing

Copyright (c) 2024 Tertsea Igbawua, Aondongu Alexander Tyovenda, Terver Sombo, Idugba Mathias Echi, Fanan Ujoh

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Investigating factors influencing PM2.5 threshold exceedance in Nigeria using multivariate logistic regression analysis. (2024). African Scientific Reports, 3(3), 212. https://doi.org/10.46481/asr.2024.3.3.212

Similar Articles

121-130 of 143

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)