气候变化领域集成服务门户(CCPortal): Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research

CCPortal

DOI	10.1016/j.apr.2019.11.018
	Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research
	Pinto J.A.; Kumar P.; Alonso M.F.; Andreão W.L.; Pedruzzi R.; dos Santos F.S.; Moreira D.M.; Albuquerque T.T.D.A.
发表日期	2020
ISSN	13091042
卷号	11 期号:3
英文摘要	Outdoor air pollution was responsible for approximately 4.2 million deaths around the world in 2016, with the emissions from road vehicles being the main source of air pollution in urban areas. To fulfill the need to identify the contribution of pollutants emitted by on-road vehicles and examine the limitations of various air quality models (boundary conditions, wind behavior representations, chemical mechanisms and reactions), a systematic review of the main traffic variables used in emissions and air quality modeling was performed. The discussion of their relationships, connections, and relevance showed a consistent sequence to generate traffic data using different traffic models. A list of key traffic variables to use as input data for vehicle emissions modeling and consequently to improve the accuracy of air quality modeling was proposed. A revision over 125 published articles was realized approaching methods to integrate traffic, emissions, air quality models, and detailing how these data can improve the results generated by the air quality model. Traffic models (macroscopic, mesoscopic, and microscopic) require variables at different levels of detail, such as traffic flow, speed, fuel consumption, and fleet composition. The emissions models (static and dynamic) are the key inputs to regional air quality models, but there is a tradeoff between the accuracy in emission estimates and the level of detail in model inputs. Meteorological data also influence the results. The conclusions showed that gaps remain on consistent emissions factors, spatial and temporal distributions, allocations of emissions on grid cells, and performance of the meteorological models. The average link-based traffic parameters are a persistent limitation. The proposed key traffic variables list point to flow per vehicle type as the most important variable. There is a need for scientific efforts to integrate traffic engineering data into emissions models to improve air quality modeling results using better traffic flow representations. Uncertainties in traffic data must first be analyzed, and accordingly a guidance with an accuracy reference for distinctive applications in different regions should be proposed. © 2020 Turkish National Committee for Air Pollution Research and Control
英文关键词	Air quality modeling; Emission inventory; Emission model; Traffic model; Urban area
语种	英语
来源期刊	Atmospheric Pollution Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120559
作者单位	Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Belo Horizonte, 31270-010, Brazil; Institute of Integrated Engineering, Federal University of Itajubá, Itabira, 35903-087, Brazil; Department of Environmental Engineering, Federal University of Espírito Santo, Vitória, 29060-970, Brazil; Department of Meteorology, Federal University of Pelotas, Pelotas, 96001-970, Brazil; Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom; Center of Integrated Manufacturing and Technology (CIMATEC/SENAI), Salvador, 41650-010, Brazil
推荐引用方式 GB/T 7714	Pinto J.A.,Kumar P.,Alonso M.F.,et al. Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research[J],2020,11(3).
APA	Pinto J.A..,Kumar P..,Alonso M.F..,Andreão W.L..,Pedruzzi R..,...&Albuquerque T.T.D.A..(2020).Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research.Atmospheric Pollution Research,11(3).
MLA	Pinto J.A.,et al."Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research".Atmospheric Pollution Research 11.3(2020).