Spatial Distribution and Inter-City Transport of PM2.5 Concentrations from Vehicles in the Guanzhong Plain in Winter
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Method for Calculating Vehicle Emissions
2.3. Method for Calculating the Dispersion and Inter-City Transport of PM2.5
3. Results and Discussion
3.1. Emission Inventory of Vehicle Pollutants
3.2. Evaluation of Meteorological Simulations
3.3. Spatial Distribution of PM2.5 Concentrations
3.4. Inter-City Transport of PM2.5 Concentrations
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Chen, L.; Zhang, J.; You, Y. Air pollution, environmental perceptions, and citizen satisfaction: A mediation analysis. Environ. Res. 2020, 184, 109287. [Google Scholar] [CrossRef] [PubMed]
- Pu, S.; Shao, Z.; Fang, M.; Yang, L.; Liu, R.; Bi, J.; Ma, Z. Spatial distribution of the public’s risk perception for air pollution: A nationwide study in China. Sci. Total Environ. 2019, 655, 454–462. [Google Scholar] [CrossRef] [PubMed]
- Gao, S.; Yu, J.; Yang, W.; Qu, F.; Chen, L.; Sun, Y.; Zhang, H.; Mao, J.; Zhao, H.; Azzi, M.; et al. Background concentration of atmospheric PM2.5 in the Beijing-Tianjin-Hebei urban agglomeration: Levels, variation trends, and influences of meteorology and emission. Atmos. Pollut. Res. 2022, 13, 101583. [Google Scholar] [CrossRef]
- Ding, Y.; Zhang, M.; Chen, S.; Wang, W.; Nie, R. The environmental Kuznets curve for PM2.5 pollution in Beijing-Tianjin-Hebei region of China: A spatial panel data approach. J. Cleaner Prod. 2019, 220, 984–994. [Google Scholar] [CrossRef]
- He, Y.; Lin, K.; Liao, N.; Chen, Z.; Rao, J. Exploring the spatial effects and influencing factors of PM2.5 concentration in the Yangtze River Delta Urban Agglomerations of China. Atmos. Environ. 2022, 268, 118805. [Google Scholar] [CrossRef]
- Ming, L.; Jin, L.; Li, J.; Fu, P.; Yang, W.; Liu, D.; Zhang, G.; Wang, Z.; Li, X. PM2.5 in the Yangtze River Delta, China: Chemical compositions, seasonal variations, and regional pollution events. Environ. Pollut. 2017, 223, 200–212. [Google Scholar] [CrossRef] [PubMed]
- Feng, T.; Bei, N.; Zhao, S.; Wu, J.; Li, X.; Zhang, T.; Cao, J.; Zhou, W.; Li, G. Wintertime nitrate formation during haze days in the Guanzhong basin, China: A case study. Environ. Pollut. 2018, 243, 1057–1067. [Google Scholar] [CrossRef]
- Wei, N.; Wang, N.; Huang, X.; Liu, P.; Chen, L. The effects of terrain and atmospheric dynamics on cold season heavy haze in the Guanzhong Basin of China. Atmos. Pollut. Res. 2020, 11, 1805–1819. [Google Scholar] [CrossRef]
- Niu, X.; Cao, J.; Shen, Z.; Ho, S.S.H.; Tie, X.; Zhao, S.; Xu, H.; Zhang, T.; Huang, R. PM2.5 from the Guanzhong Plain: Chemical composition and implications for emission reductions. Atmos. Environ. 2016, 147, 458–469. [Google Scholar] [CrossRef]
- World Health Organization (WHO). Ambient (Outdoor) Air Pollution. Available online: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health (accessed on 26 September 2023).
- Chinese State Council (CSC) of the People’s Republic of China. Air Pollution Prevention and Control Action Plan. Available online: https://www.gov.cn/gongbao/content/2013/content_2496394.htm (accessed on 2 October 2023).
- Cai, S.; Wang, Y.; Zhao, B.; Wang, S.; Chang, X.; Hao, J. The impact of the “air pollution prevention and control action plan” on PM2.5 concentrations in Jing-Jin-Ji region during 2012–2020. Sci. Total Environ. 2017, 580, 197–209. [Google Scholar] [CrossRef]
- Ministry of Ecology and Environment (MEE) of the People’s Republic of China. Work Plan for Air Pollution Prevention and Control in Beijing, Tianjin and Hebei and Surrounding Areas in. 2017. Available online: https://www.mee.gov.cn/gkml/hbb/bwj/201708/t20170824_420330.htm (accessed on 5 October 2023).
- Ministry of Ecology and Environment (MEE) of the People’s Republic of China. China Mobile Source Environmental Management Annual Report. Available online: https://www.mee.gov.cn/hjzl/sthjzk/ydyhjgl/202212/W020221207387013521948.pdf (accessed on 15 September 2023).
- Luo, Z.; Wang, Y.; Lv, Z.; He, T.; Zhao, J.; Wang, Y.; Gao, F.; Zhang, Z.; Liu, H. Impacts of vehicle emission on air quality and human health in China. Sci. Total Environ. 2022, 813, 152655. [Google Scholar] [CrossRef] [PubMed]
- Goel, R.; Guttikunda, S.K. Evolution of on-road vehicle exhaust emissions in Delhi. Atmos. Environ. 2015, 105, 78–90. [Google Scholar] [CrossRef]
- Liu, J.; Zheng, B. A Simulation Study on the Influence of Street Tree Configuration on Fine Particulate Matter (PM2.5) Concentration in Street Canyons. Forests 2023, 14, 1550. [Google Scholar] [CrossRef]
- Craig, K.J.; Baringer, L.M.; Chang, S.; McCarthy, M.C.; Bai, S.; Seagram, A.F.; Ravi, V.; Landsberg, K.; Eisinger, D.S. Modeled and measured near-road PM2.5 concentrations: Indianapolis and Providence cases. Atmos. Environ. 2020, 240, 117775. [Google Scholar] [CrossRef]
- Lu, D.; He, H.; Wang, Z.; Zhao, H.; Peng, Z. Impact of urban viaducts on the vertical distribution of fine particles in street canyons. Atmos. Pollut. Res. 2023, 14, 101726. [Google Scholar] [CrossRef]
- Nagendra, S.S.; Diya, M.; Chithra, V.S.; Menon, J.S.; Peter, A.E. Characteristics of air pollutants at near and far field regions of a national highway located at an industrial complex. Transp. Res. Part D 2016, 48, 1–13. [Google Scholar] [CrossRef]
- Salva, J.; Vanek, M.; Schwarz, M.; Gajtanska, M.; Tonhauzer, P.; Ďuricová, A. An Assessment of the On-Road Mobile Sources Contribution to Particulate Matter Air Pollution by AERMOD Dispersion Model. Sustainability 2021, 13, 12748. [Google Scholar] [CrossRef]
- Wang, B.; Wu, J.; Xu, X.; Li, Y. Hourly population exposure index for PM2.5 in urban street canyons. Urban Clim. 2022, 45, 101242. [Google Scholar] [CrossRef]
- Tong, R.; Liu, J.; Wang, W.; Fang, Y. Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China. Atmos. Environ. 2020, 223, 117258. [Google Scholar] [CrossRef]
- Samuelsen, S.; Zhu, S.; Kinnon, M.M.; Yang, O.K.; Dabdub, D.; Brouwer, J. An episodic assessment of vehicle emission regulations on saving lives in California. Environ. Sci. Technol. 2020, 55, 547–552. [Google Scholar] [CrossRef]
- Camilleri, S.F.; Montgomery, A.; Visa, M.A.; Schnell, J.L.; Adelman, Z.E.; Janssen, M.; Grubert, E.A.; Anenberg, S.C.; Horton, D.E. Air quality, health and equity implications of electrifying heavy-duty vehicles. Nat. Sustain. 2023. [Google Scholar] [CrossRef]
- Joo, S.; Oh, C.; Lee, S.; Lee, G. Assessing the impact of traffic crashes on near freeway air quality. Transp. Res. Part D 2017, 57, 64–73. [Google Scholar] [CrossRef]
- Filigrana, P.; Milando, C.; Batterman, S.; Levy, J.I.; Mukherjee, B.; Adar, S.D. Spatiotemporal variations in traffic activity and their influence on air pollution levels in communities near highways. Atmos. Environ. 2020, 242, 117758. [Google Scholar] [CrossRef]
- Lu, P.; Deng, S.; Li, G.; Li, J.; Xu, K.; Lu, Z. Spatial Distribution of Primary and Secondary PM2.5 Concentrations Emitted by Vehicles in the Guanzhong Plain, China. Atmosphere 2022, 13, 347. [Google Scholar] [CrossRef]
- Charabi, Y.; Abdul-Wahab, S.; Al-Rawas, G.; Al-Wardy, M.; Fadlallah, S. Investigating the impact of monsoon season on the dispersion of pollutants emitted from vehicles: A case study of Salalah City, Sultanate of Oman. Transp. Res. Part D 2018, 59, 108–120. [Google Scholar] [CrossRef]
- Lee, H.D.; Yoo, J.W.; Kang, M.K.; Kang, J.S.; Jung, J.H.; Oh, K.J. Evaluation of concentrations and source contribution of PM10 and SO2 emitted from industrial complexes in Ulsan, Korea: Interfacing of the WRF–CALPUFF modeling tools. Atmos. Pollut. Res. 2014, 5, 664–676. [Google Scholar] [CrossRef]
- Guo, D.; Wang, R.; Zhao, P. Spatial distribution and source contributions of PM2.5 concentrations in Jincheng, China. Atmos. Pollut. Res. 2020, 11, 1281–1289. [Google Scholar] [CrossRef]
- Hooftman, N.; Oliveira, L.; Messagie, M.; Coosemans, T.; Van Mierlo, J. Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting. Energies 2016, 9, 84. [Google Scholar] [CrossRef]
- Zhang, N.N.; Ma, F.; Qin, C.B.; Li, Y.F. Spatiotemporal trends in PM2.5 levels from 2013 to 2017 and regional demarcations for joint prevention and control of atmospheric pollution in China. Chemosphere 2018, 210, 1176–1184. [Google Scholar] [CrossRef]
- Khuzestani, R.B.; Schauer, J.J.; Wei, Y.; Zhang, L.; Cai, T.; Zhang, Y.; Zhang, Y. Quantification of the sources of long-range transport of PM2.5 pollution in the Ordos region, Inner Mongolia, China. Environ. Pollut. 2017, 229, 1019–1031. [Google Scholar] [CrossRef]
- Song, Y.; Li, Z.; Yang, T.; Xia, Q. Does the expansion of the joint prevention and control area improve the air quality?—Evidence from China’s Jing-Jin-Ji region and surrounding areas. Sci. Total Environ. 2020, 706, 136034. [Google Scholar] [CrossRef] [PubMed]
- Chang, X.; Wang, S.; Zhao, B.; Xing, J.; Liu, X.; Wei, L.; Song, Y.; Wu, W.; Cai, S.; Zheng, H.; et al. Contributions of inter-city and regional transport to PM2.5 concentrations in the Beijing-Tianjin-Hebei region and its implications on regional joint air pollution control. Sci. Total Environ. 2019, 660, 1191–1200. [Google Scholar] [CrossRef] [PubMed]
- He, J.; Zhang, L.; Yao, Z.; Che, H.; Gong, S.; Wang, M.; Zhao, M.; Jing, B. Source apportionment of particulate matter based on 448 numerical simulation during a severe pollution period in Tangshan, North China. Environ. Pollut. 2020, 266, 115133. [Google Scholar] [CrossRef] [PubMed]
- Ministry of Civil Affairs (MCA) of the People’s Republic of China. National Administrative Division Information Query Platform. Available online: http://xzqh.mca.gov.cn/map (accessed on 25 September 2023).
- Liu, R.; He, H.D.; Zhang, Z.; Wu, C.L.; Yang, J.M.; Zhu, X.H.; Peng, Z.R. Integrated MOVES model and machine learning method for prediction of CO2 and NO from light-duty gasoline vehicle. J. Clean Prod. 2023, 422, 138612. [Google Scholar] [CrossRef]
- Perugu, H. Emission modelling of light-duty vehicles in India using the revamped VSP-based MOVES model: The case study of Hyderabad. Transp. Res. Part D 2018, 68, 150–163. [Google Scholar] [CrossRef]
- Hong, Z.L.; Zimmerman, N. Air quality and greenhouse gas implications of autonomous vehicles in Vancouver, Canada. Transp. Res. Part D 2021, 90, 102676. [Google Scholar] [CrossRef]
- Li, X.; Hussain, S.A.; Sobri, S.; Said, M.S.M. Overviewing the air quality models on air pollution in Sichuan Basin, China. Chemosphere 2021, 271, 129502. [Google Scholar] [CrossRef]
- Abdul-Wahab, S.; Sappurd, A.; Al-Damkhi, A. Application of California Puff (CALPUFF) model: A case study for Oman. Clean Technol. Environ. Policy 2011, 13, 177–189. [Google Scholar] [CrossRef]
- Holnicki, P.; Kałuszko, A.; Trapp, W. An urban scale application and validation of the CALPUFF model. Atmos. Pollut. Res. 2016, 7, 393–402. [Google Scholar] [CrossRef]
- Huang, D.; Guo, H. Dispersion modeling of odour, gases, and respirable dust using AERMOD for poultry and dairy barns in the Canadian Prairies. Sci. Total Environ. 2019, 690, 620–628. [Google Scholar] [CrossRef]
- Foley, K.M.; Pouliot, G.A.; Eyth, A.; Aldridge, M.F.; Allen, C.; Appel, K.W.; Bash, J.O.; Beardsley, M.; Beidler, J.; Choi, D.; et al. 2002–2017 anthropogenic emissions data for air quality modeling over the United States. Data Brief 2023, 47, 109022. [Google Scholar] [CrossRef]
- Meroni, A.; Pirovano, G.; Gilardoni, S.; Lonati, G.; Colombi, C.; Gianelle, V.; Paglione, M.; Polluzzi, V.; Riva, G.M.; Toppetti, A. Investigating the role of chemical and physical processes on organic aerosol modelling with CAMx in the Po Valley during a winter episode. Atmos. Environ. 2017, 171, 126–142. [Google Scholar] [CrossRef]
- Bezyk, Y.; Oshurok, D.; Dorodnikov, M.; Sówka, I. Evaluation of the CALPUFF model performance for the estimation of the urban ecosystem CO2 flux. Atmos. Pollut. Res. 2020, 12, 260–277. [Google Scholar] [CrossRef]
- Otero-Pregigueiro, D.; Fernández-Olmo, I. Use of CALPUFF to predict airborne Mn levels at schools in an urban area impacted by a nearby manganese alloy plant. Environ. Int. 2018, 119, 455–465. [Google Scholar] [CrossRef]
- Scire, J.S.; Strimaitis, D.G.; Yamartino, R.J. A User’s Guide for the CALPUFF Dispersion Model; Version 7.3.2; Exponent Inc.: Concord, MA, USA, 2019; Available online: https://www.src.com/calpuff/download/CALPUFF_v7_UserGuide_Addendum.pdf (accessed on 7 September 2023).
- Kia, S.; Flesch, T.K.; Freeman, B.S.; Aliabadi, A.A. Calculating gas emissions from open-pit mines using inverse dispersion modelling: A numerical evaluation using CALPUFF and CFD-LS. J. Wind Eng. Ind. Aero. 2022, 226, 105046. [Google Scholar] [CrossRef]
- Tan, R.; Guo, S.; Lu, S.; Wang, H.; Zhu, W.; Yu, Y.; Tang, R.; Shen, R.; Song, K.; Lv, D.; et al. Characteristics and Secondary Organic Aerosol Formation of Volatile Organic Compounds from Vehicle and Cooking Emissions. Atmosphere 2023, 14, 806. [Google Scholar] [CrossRef]
- Huang, C.; Wang, H.L.; Li, L.; Wang, Q.; Lu, Q.; de Gouw, J.A.; Zhou, M.; Jing, S.A.; Lu, J.; Chen, C.H. VOC species and emission inventory from vehicles and their SOA formation potentials estimation in Shanghai, China. Atmos. Chem. Phys. 2015, 15, 7977–8015. [Google Scholar] [CrossRef]
- Wu, C.; Wang, G.; Li, J.; Li, J.; Cao, C.; Ge, S.; Xie, Y.; Chen, J.; Liu, S.; Du, W.; et al. Non-agricultural sources dominate the atmospheric NH3 in Xi’an, a megacity in the semi-arid region of China. Sci. Total Environ. 2020, 722, 137756. [Google Scholar] [CrossRef]
- Wu, H.; Zhang, Y.; Yu, Q.; Ma, W. Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi, China. J. Air Waste Manag. Assoc. 2018, 68, 347–368. [Google Scholar] [CrossRef]
- Levy, I.; Mahrer, Y.; Dayan, U. Coastal and synoptic recirculation affecting air pollutants dispersion: A numerical study. Atmos. Environ. 2009, 43, 1991–1999. [Google Scholar] [CrossRef]
- Xu, J.S.; Xu, M.X.; Snape, C.; He, J.; Behera, S.N.; Xu, H.H.; Ji, D.S.; Wang, C.J.; Yu, H.; Xiao, H.; et al. Temporal and spatial variation in major ion chemistry and source identification of secondary inorganic aerosols in Northern Zhejiang Province, China. Chemosphere 2017, 179, 316–330. [Google Scholar] [CrossRef] [PubMed]
- Feng, J.; Yu, H.; Mi, K.; Su, X.; Li, Y.; Li, Q.; Sun, J. One year study of PM2.5 in Xinxiang city, North China: Seasonal characteristics, climate impact and source. Ecotox. Environ. Saf. 2018, 154, 75–83. [Google Scholar] [CrossRef] [PubMed]
- Cao, J.J.; Cui, L. Current status, characteristics and causes of particulate air pollution in the Fenwei Plain, China: A review. J. Geophys. Res. Atmos. 2021, 126, e2020JD034472. [Google Scholar] [CrossRef]
Parameterization Scheme | Scheme Name |
---|---|
Solar radiation scheme | Dudhia |
Longwave and shortwave radiation scheme | RRTM |
Microphysics process scheme | WSM6 |
Boundary layer scheme | ACM2 |
Surface layer scheme | Obukhov |
Land surface scheme | Noah-MP |
Cumulus convection scheme | Kain-Fritsch |
Model Parameter | Parameter Settings |
---|---|
Map projection | Lambert Conic Conformal |
Domain size | 500 km × 250 km |
Horizontal and vertical diffusion | Gaussian |
Terrain adjustment | ISC terrain adjustment scheme |
Plume rise | Briggs Plume Rise |
Building downwash | ISC method |
Dispersion option | Turbulence computed from micrometeorology |
Chemical transformation | RIVAD + ISORROPIA + CalTechSOA |
Deposition | Vertical Structure and Mass Depletion/Resistance Deposition Model |
Initial and boundary conditions | Default |
City | SO2 | NOx | VOCs | Primary PM2.5 | ||
---|---|---|---|---|---|---|
Exhaust Emission | Brake Wear | Tire Wear | ||||
XA | 10.03 | 3292.73 | 1111.49 | 125.44 | 30.02 | 6.07 |
WN | 3.48 | 1318.70 | 499.66 | 90.31 | 7.35 | 1.41 |
XY | 1.96 | 752.14 | 262.05 | 28.58 | 5.31 | 0.97 |
BJ | 1.75 | 612.59 | 434.01 | 23.70 | 2.99 | 0.63 |
TC | 0.70 | 306.66 | 107.17 | 20.33 | 1.89 | 0.35 |
GZP | 17.92 | 6282.82 | 2414.38 | 288.36 | 47.56 | 9.43 |
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Lu, P.; Tuheti, A.; Deng, S.; Li, G.; Liu, J. Spatial Distribution and Inter-City Transport of PM2.5 Concentrations from Vehicles in the Guanzhong Plain in Winter. Atmosphere 2023, 14, 1748. https://doi.org/10.3390/atmos14121748
Lu P, Tuheti A, Deng S, Li G, Liu J. Spatial Distribution and Inter-City Transport of PM2.5 Concentrations from Vehicles in the Guanzhong Plain in Winter. Atmosphere. 2023; 14(12):1748. https://doi.org/10.3390/atmos14121748
Chicago/Turabian StyleLu, Pan, Abula Tuheti, Shunxi Deng, Guanghua Li, and Jiayao Liu. 2023. "Spatial Distribution and Inter-City Transport of PM2.5 Concentrations from Vehicles in the Guanzhong Plain in Winter" Atmosphere 14, no. 12: 1748. https://doi.org/10.3390/atmos14121748