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Sadat M, Ahmad SA, Silgu MA, Bajpai S, Pandey D. A Study on Environmental Impact of Slow Moving Electric Vehicles Using Microsimulation on Lucknow Urban Road With an On-Ramp. ENVIRONMENTAL HEALTH INSIGHTS 2024; 18:11786302241231706. [PMID: 38495797 PMCID: PMC10943754 DOI: 10.1177/11786302241231706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/19/2024] [Indexed: 03/19/2024]
Abstract
Background The adoption of electric vehicles for mobility is seen as a major step towards the conservation of the environment. In India, slow-moving Electric 3-Wheelers (E3Ws) have been adopted for last-mile connectivity. The present study investigated the impact of slow-moving electric 3-wheelers on the environment in terms of emissions and traffic performance in mixed conditions. Methods Field traffic data from a section of road in the city of Lucknow was collected and used for the calibration of the traffic model. A total of 6 scenarios were tested using traffic modelling in the open-source microsimulation software SUMO. Krauss model was used to model mixed traffic and HBEFA 4 was used to calculate the emissions of fuel-driven vehicles. In each scenario, the volume of fuel-driven vehicles was kept constant and the volume of E3Ws was varied. For the last 2 scenarios, E3Ws were replaced with modified Electric 3-wheelers (ME3Ws) and Electric Buses. Results Initial findings showed that the average emission decreased as the number of slowly moving electric vehicles increased, but the average flow and harmonic mean speed decreased by 49.8% and 28.8%, respectively, despite keeping the original composition of fuel-driven vehicles the same in every scenario. Further analysis of scenarios revealed a strong correlation (R 2 = 0 . 88 ) between the reduction in the number of vehicles and the reduction in emissions like Carbon Dioxide (CO 2 ), which is responsible for global warming. Scenarios in which faster electric vehicles and electric buses replace slow-moving E3Ws also demonstrate emission reduction without noticeably affecting traffic performance parameters. Conclusion The study shows that the environmental benefits of E3Ws in a limited section of Lucknow road are offset by their low-speed capability. Hypothetical scenarios wherein Modified E3Ws and Electric Buses were introduced reported benefits both in terms of emissions and traffic performance.
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Affiliation(s)
- Mohd Sadat
- Department of Civil Engineering, Faculty of Engineering & Information Technology, Integral University, Lucknow, Uttar Pradesh, India
| | - Syed Aqeel Ahmad
- Department of Civil Engineering, Faculty of Engineering & Information Technology, Integral University, Lucknow, Uttar Pradesh, India
| | - Mehmet Ali Silgu
- Department of Civil Engineering Bartin University, Bartin, Turkey
| | - Shrish Bajpai
- Department of Electronics & Communication Engineering, Faculty of Engineering & Information Technology, Integral University, Lucknow, Uttar Pradesh, India
| | - Digvijay Pandey
- Department of Technical Education, Lucknow, Uttar Pradesh, India
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Fan L, Han X, Li L, Liu H, Ge T, Wang X, Wang Q, Du H, Su L, Yao X, Wang X. Indoor air quality of urban public transportation stations in China: Based on air quality evaluation indexes. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 349:119440. [PMID: 37939468 DOI: 10.1016/j.jenvman.2023.119440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/28/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023]
Abstract
Indoor air quality (IAQ) of urban public transportation stations (UPTS) has adverse health impacts on the station employees and commuters. However, there is a lack of comprehensively evaluations of IAQ in waiting rooms of UPTS. Therefore, it is crucial to select appropriate air quality indexes (including fuzzy synthetic index (B), comprehensive index (P), I1 index, and indoor air quality index (IAQI)) to assess air quality and potential health risks. Our study is a subsample of the CPPEHS 2019 study, which included 224 UPTS in 126 cities of China. We found that P index showed an excellent air quality rate of 95.96% in the railway stations and 83.19% in the inter-city bus stations. The P index was correlated with UPTS usage years, useable area, and per passenger useable area. Furthermore, waiting rooms in UPTS with good (OR = 1.9187, 95% CI: 1.1204, 3.2859) and bad (OR = 2.0854, 95% CI: 1.2182, 3.5698) air quality evaluated by P index had a higher risk of rhinitis compared to those with excellent air quality. Similarly, UPTS with good (OR = 2.2202, 95% CI: 1.3427, 3.6711), bad (OR = 1.7897, 95% CI: 1.0807, 2.9637), and serious (OR = 1.7478, 95% CI: 1.0098, 3.0250) air quality evaluated by P index were associated with a higher risk of pharyngitis. These findings suggested that P index is the optimal index for assessing air quality in UPTS, while IAQI may exaggerate indoor air pollution and the B index may underestimate it. Overall, this study aims to identify and evaluate the more suitable air quality index (P) in nationwide UPTS, providing valuable insights for control of IAQ and guiding the air quality management and standards.
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Affiliation(s)
- Lin Fan
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
| | - Xu Han
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Li Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Hang Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Tanxi Ge
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xinqi Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Qin Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Hang Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Liqin Su
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xiaoyuan Yao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Xianliang Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
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