PMID- 36532156 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20221222 IS - 2193-2697 (Electronic) IS - 2193-2697 (Linking) VI - 11 IP - 1 DP - 2022 TI - Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track. PG - 28 LID - 10.1186/s40068-022-00276-2 [doi] LID - 28 AB - One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Given the same vehicle in a different timeframe, the drivers' reactions to similar situations vary, which has a significant influence on the emissions and fuel consumption as their use of acceleration and speed differ. Because the driving patterns of automated vehicles are programmable and provide a platform for smooth driving situations, it is predicted that deploying them might potentially reduce fuel consumption, particularly in urban areas with given traffic situations. This study's goal is to examine how different degrees of automated vehicles behave when it comes to emissions and how accelerations affect that behavior. Furthermore, the total aggregated emissions on the synthesized urban network are evaluated and compared to legacy vehicles. The emission measuring model is based on the Handbook Emission Factors for Road Transport (HBEFA)3 and is utilized with the Simulation of Urban Mobility (SUMO) microscopic simulation software. The results demonstrate that acceleration value is strongly correlated with individual vehicle emissions. Although the ability of automated vehicles (AVs) to swiftly achieve higher acceleration values has an adverse effect on emissions reduction, it was compensated by the rate of accelerations, which decreases as the automation level increases. According to the simulation results, automated vehicles can reduce carbon monoxide (CO) emissions by 38.56%, carbon dioxide (CO(2)) emissions by 17.09%, hydrocarbons (HC) emissions by 36.3%, particulate matter (PM(x)) emissions by 28.12%, nitrogen oxides (NO(x)) emissions by 19.78% in the most optimistic scenario (that is, when all vehicles are replaced by the upper bound automated vehicles) in the network level. CI - (c) The Author(s) 2022. FAU - Biramo, Zelalem Birhanu AU - Biramo ZB AD - Faculty of Civil Engineering, Arba Minch Institute of Technology, Arba Minch University, P.O.Box 21, Arba Minch, Ethiopia. GRID: grid.442844.a. ISNI: 0000 0000 9126 7261 FAU - Mekonnen, Anteneh Afework AU - Mekonnen AA AD - Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Muegyetem Rkp. 3, Budapest, 1111 Hungary. GRID: grid.6759.d. ISNI: 0000 0001 2180 0451 AD - School of Civil and Environmental Engineering, Addis Ababa Institute of Technology, Addis Ababa University, P.O. Box 385, Addis Ababa, Ethiopia. GRID: grid.7123.7. ISNI: 0000 0001 1250 5688 LA - eng PT - Journal Article DEP - 20221212 PL - Germany TA - Environ Syst Res (Heidelb) JT - Environmental systems research JID - 9918284166506676 PMC - PMC9742016 OTO - NOTNLM OT - Air pollution OT - Automated vehicles OT - Fuel consumption OT - Pollutant emissions OT - SUMO COIS- Competing interestsThe authors declare that they have no competing interests. EDAT- 2022/12/20 06:00 MHDA- 2022/12/20 06:01 PMCR- 2022/12/12 CRDT- 2022/12/19 04:08 PHST- 2022/10/01 00:00 [received] PHST- 2022/11/23 00:00 [accepted] PHST- 2022/12/19 04:08 [entrez] PHST- 2022/12/20 06:00 [pubmed] PHST- 2022/12/20 06:01 [medline] PHST- 2022/12/12 00:00 [pmc-release] AID - 276 [pii] AID - 10.1186/s40068-022-00276-2 [doi] PST - ppublish SO - Environ Syst Res (Heidelb). 2022;11(1):28. doi: 10.1186/s40068-022-00276-2. Epub 2022 Dec 12.