Road Emissions in London: Insights from Geographically Detailed Classification and Regression Modelling

Atmosphere

Alexandros Sfyridis¹  Paolo Agnolucci¹ 

[1] Institute for Sustainable Resources, University College London, 14 Upper Woburn Place, London WC1H 0NN, UK;

关键词: greenhouse gases;    air pollution;    gradient boosting machine;    GBM;    probabilistic classification;    annual average daily traffic (AADT);   

DOI  :  10.3390/atmos12020188

来源: DOAJ

【 摘 要 】

Greenhouse gases and air pollutant emissions originating from road transport continues to rise in the UK, indicating a significant contribution to climate change and negative impacts on human health and ecosystems. However, emissions are usually estimated at aggregated levels, and on many occasions roads of minor importance are not taken into account, normally due to lack of traffic counts. This paper presents a methodology enabling estimation of air pollutants and CO₂ for each street segment in the Greater London area. This is achieved by applying a hybrid probabilistic classification–regression approach on a set of variables believed to affect traffic volumes and utilizing emission factors. The output reveals pollution hot spots and the effects of open spaces in a spatially rich dataset. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels.

【 授权许可】

Unknown