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Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature

Journal of Exposure Science & Environmental Epidemiology volume 31, pages 709–726 (2021)Cite this article

Abstract

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study’s analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants’ sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study’s CBPF technique and time series signature analysis’ outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

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Fig. 1: Study area.
Fig. 2: Missing data.
Fig. 3: Summary plot.
Fig. 4: Box plots.
Fig. 5: Whisker plots.
Fig. 6: CBPF plot.
Fig. 7: Polar plots.
Fig. 8: Rose plot.
Fig. 9: Sensitivity analysis.

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Acknowledgements

The authors gratefully acknowledge the financial support from the University Teknologi PETRONAS (UTP) STIRF research grant [0153AA-F83] for this project. Also, we are very grateful to Department of Environment (DoE), Malaysia, for providing the air quality data used in this study and the Federal Department of Town and Country Planning (PLANMalaysia) for providing spatial and attribute data of the study area.

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Authors and Affiliations

  1. Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-ro Gwangjin-gu, Seoul, 05006, Republic of Korea

    Omar F. Althuwaynee, Sang-Wan Kim & Hyuck-Jin Park

  2. School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, Australia

    Badal Pokharel

  3. Department of Geology, Faculty of Sciences, Ibn Zohr University, B.P 8106, 80000, Agadir, Morocco

    Ali Aydda

  4. Geospatial Analysis and Modelling (GAM) Research Laboratory, Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, 32610, Perak, Malaysia

    Abdul-Lateef Balogun

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  1. Omar F. Althuwaynee
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Correspondence to Abdul-Lateef Balogun.

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Althuwaynee, O.F., Pokharel, B., Aydda, A. et al. Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature. J Expo Sci Environ Epidemiol 31, 709–726 (2021). https://doi.org/10.1038/s41370-020-00271-8

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