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Article

Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches

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Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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Department of Geography, The University of Hong Kong, Hong Kong
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Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong
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Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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Author to whom correspondence should be addressed.
Academic Editor: Ho Kim
Int. J. Environ. Res. Public Health 2021, 18(12), 6532; https://doi.org/10.3390/ijerph18126532
Received: 11 April 2021 / Revised: 8 June 2021 / Accepted: 14 June 2021 / Published: 17 June 2021
Excessive traffic pollutant emissions in high-density cities result in thermal discomfort and are associated with devastating health impacts. In this study, an improved data analytic framework that combines geo-processing techniques, social habits of local citizens like traffic patterns and working schedule and district-wise building morphologies was established to retrieve street-level traffic NOx and PM2.5 emissions in all 18 districts of Hong Kong. The identification of possible human activity regions further visualizes the intersection between emission sources and human mobility. The updated spatial distribution of traffic emission could serve as good indicators for better air quality management, as well as the planning of social infrastructures in the neighborhood environment. Further, geo-processed traffic emission figures can systematically be distributed to respective districts via mathematical means, while the correlations of NOx and mortality within different case studies range from 0.371 to 0.783, while varying from 0.509 to 0.754 for PM2.5, with some assumptions imposed in our study. Outlying districts and good practices of maintaining an environmentally friendly transportation network were also identified and analyzed via statistical means. This newly developed data-driven framework of allocating and quantifying traffic emission could possibly be extended to other dense and heavily polluted cities, with the aim of enhancing health monitoring campaigns and relevant policy implementations. View Full-Text
Keywords: traffic NOx and PM2.5 emissions; spatial and socio-classification in high density cities; mortality rates and health; urban environment and air quality management; data analytic framework traffic NOx and PM2.5 emissions; spatial and socio-classification in high density cities; mortality rates and health; urban environment and air quality management; data analytic framework
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MDPI and ACS Style

Mak, H.W.L.; Ng, D.C.Y. Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches. Int. J. Environ. Res. Public Health 2021, 18, 6532. https://doi.org/10.3390/ijerph18126532

AMA Style

Mak HWL, Ng DCY. Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches. International Journal of Environmental Research and Public Health. 2021; 18(12):6532. https://doi.org/10.3390/ijerph18126532

Chicago/Turabian Style

Mak, Hugo W.L., and Daisy C.Y. Ng. 2021. "Spatial and Socio-Classification of Traffic Pollutant Emissions and Associated Mortality Rates in High-Density Hong Kong via Improved Data Analytic Approaches" International Journal of Environmental Research and Public Health 18, no. 12: 6532. https://doi.org/10.3390/ijerph18126532

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