A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Dynamic quantification of on-road emissions in Hong Kong: Impact from traffic congestion and fleet composition variation
https://orcid.org/0000-0002-5663-1675
https://orcid.org/0000-0002-4372-8750
https://orcid.org/0000-0002-7071-1137
https://orcid.org/0000-0002-7859-8511
https://orcid.org/0000-0002-5400-3405
Abstract Policymakers need a good understanding of on-road air pollutant emission patterns to manage air quality effectively, but it can be challenging to dynamically capture emission patterns with high spatial and temporal resolutions in cities. In this study, we used traffic congestion index extracted from Google Map, traffic density model and local emission factors to develop a dynamic on-road emission inventory for Hong Kong. The pollutants include NOx, CO, NMVOC, CO2, PM2.5, PM10, and CH4. On weekdays, exhaust emissions increased by up to 50.0% compared with those on Sunday due to higher traffic congestion index. High emission rates were observed on highways during weekday evening peak hours and shifted to regional centers on Sunday evenings. Public light buses, goods vehicles, and taxis were the most significant contributors of NMVOC, NOx, and CO emissions, respectively. Control policy testing showed that replacing diesel-fueled buses with electric buses could significantly reduce NOx and PM exhaust emissions, whereas replacing liquefied petroleum gas fueled public light buses with electric buses could efficiently control NMVOC emissions. Eliminating private cars older than 15 years could effectively reduce CO emissions. This study provides a dynamic on-road emission inventory for Hong Kong with high temporal and spatial resolutions. The results can be extended to real-time air quality simulations and human exposure research in the future.
Graphical abstract Display Omitted
Highlights A dynamic on-road emission inventory was established for Hong Kong. Traffic congestions lead up to 50% more exhaust emissions. Emission hot spots and major sources during various time were revealed. Diurnal patterns vary remarkably during different time. Control policies effectiveness for emissions were quantified.
Dynamic quantification of on-road emissions in Hong Kong: Impact from traffic congestion and fleet composition variation
https://orcid.org/0000-0002-5663-1675
https://orcid.org/0000-0002-4372-8750
https://orcid.org/0000-0002-7071-1137
https://orcid.org/0000-0002-7859-8511
https://orcid.org/0000-0002-5400-3405
Abstract Policymakers need a good understanding of on-road air pollutant emission patterns to manage air quality effectively, but it can be challenging to dynamically capture emission patterns with high spatial and temporal resolutions in cities. In this study, we used traffic congestion index extracted from Google Map, traffic density model and local emission factors to develop a dynamic on-road emission inventory for Hong Kong. The pollutants include NOx, CO, NMVOC, CO2, PM2.5, PM10, and CH4. On weekdays, exhaust emissions increased by up to 50.0% compared with those on Sunday due to higher traffic congestion index. High emission rates were observed on highways during weekday evening peak hours and shifted to regional centers on Sunday evenings. Public light buses, goods vehicles, and taxis were the most significant contributors of NMVOC, NOx, and CO emissions, respectively. Control policy testing showed that replacing diesel-fueled buses with electric buses could significantly reduce NOx and PM exhaust emissions, whereas replacing liquefied petroleum gas fueled public light buses with electric buses could efficiently control NMVOC emissions. Eliminating private cars older than 15 years could effectively reduce CO emissions. This study provides a dynamic on-road emission inventory for Hong Kong with high temporal and spatial resolutions. The results can be extended to real-time air quality simulations and human exposure research in the future.
Graphical abstract Display Omitted
Highlights A dynamic on-road emission inventory was established for Hong Kong. Traffic congestions lead up to 50% more exhaust emissions. Emission hot spots and major sources during various time were revealed. Diurnal patterns vary remarkably during different time. Control policies effectiveness for emissions were quantified.
Dynamic quantification of on-road emissions in Hong Kong: Impact from traffic congestion and fleet composition variation
https://orcid.org/0000-0002-5663-1675
https://orcid.org/0000-0002-4372-8750
https://orcid.org/0000-0002-7071-1137
https://orcid.org/0000-0002-7859-8511
https://orcid.org/0000-0002-5400-3405
Abstract Policymakers need a good understanding of on-road air pollutant emission patterns to manage air quality effectively, but it can be challenging to dynamically capture emission patterns with high spatial and temporal resolutions in cities. In this study, we used traffic congestion index extracted from Google Map, traffic density model and local emission factors to develop a dynamic on-road emission inventory for Hong Kong. The pollutants include NOx, CO, NMVOC, CO2, PM2.5, PM10, and CH4. On weekdays, exhaust emissions increased by up to 50.0% compared with those on Sunday due to higher traffic congestion index. High emission rates were observed on highways during weekday evening peak hours and shifted to regional centers on Sunday evenings. Public light buses, goods vehicles, and taxis were the most significant contributors of NMVOC, NOx, and CO emissions, respectively. Control policy testing showed that replacing diesel-fueled buses with electric buses could significantly reduce NOx and PM exhaust emissions, whereas replacing liquefied petroleum gas fueled public light buses with electric buses could efficiently control NMVOC emissions. Eliminating private cars older than 15 years could effectively reduce CO emissions. This study provides a dynamic on-road emission inventory for Hong Kong with high temporal and spatial resolutions. The results can be extended to real-time air quality simulations and human exposure research in the future.
Graphical abstract Display Omitted
Highlights A dynamic on-road emission inventory was established for Hong Kong. Traffic congestions lead up to 50% more exhaust emissions. Emission hot spots and major sources during various time were revealed. Diurnal patterns vary remarkably during different time. Control policies effectiveness for emissions were quantified.
Dynamic quantification of on-road emissions in Hong Kong: Impact from traffic congestion and fleet composition variation
Li, Xin (author) / Gu, Dasa (author) / Hohenberger, Tilman Leo (author) / Fung, Yik Him (author) / Fung, Jimmy C.H. (author) / Lau, Alexis K.H. (author) / Liang, Zhenxing (author)
Atmospheric Environment ; 313
2023-08-29
Article (Journal)
Electronic Resource
English
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