Dataset Information

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

ABSTRACT: 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 NO_x and PM_2.5 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 NO_x and mortality within different case studies range from 0.371 to 0.783, while varying from 0.509 to 0.754 for PM_2.5, 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.

SUBMITTER: Mak HWL

PROVIDER: S-EPMC8296480 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe social unrest in the second half of 2019 in Hong Kong came with conflicts, confrontations, and violence which affected almost everyone in the city. The destruction and disruption of the urban facilities have undoubtedly had a significant impact on the lives and mental well-being of the public, and the older people are even more vulnerable. This study examined the impacts of the social unrest on the wellbeing of older people, an area that was seldomly addressed in the public discourse and literature.MethodsNarrative interviews were conducted to capture older people's lived experiences and ways of making sense of the unrest in Hong Kong. A total of 63 participants aged 60 and above was recruited through personal networks of the research team, and referrals by participants who took part in the interviews. Qualitative semi-structure interviews was conducted one on one via telephone.ResultsThirty-three male and 30 female participants took part in the interview. The number of participants from different risk zones affected by political unrest was comparable. Three themes were generated. Participants experienced challenges during the social unrest, including disturbance to family and social life, reduced incomes which affect quality of life, and difficulties in socializing with friends and accessing medical services. The social unrest caused emotional disturbance, giving rise to feelings of panic, fear, insomnia, depression, annoyance, and anger. Participants reported different coping strategies, ranging from moving to other places, to avoiding going to risky areas and watching news.ConclusionSocial unrest brings emotional distress to older people. In many cases, older people cope with challenges in different ways, whether active or passive. Social workers and other professionals should give more support to older people to encourage them to overcome their difficulties. The stakeholders' awareness of the problem and mental health promotion is required to alleviate the multiple layers of negative impacts.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets