Characteristics of Trace Metal Elements in Ambient Sub-Micron Particulate Matter in a Coastal Megacity of Northern China Influenced by Shipping Emissions from 2018 to 2022
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Collection
2.1.1. Sampling Site and Time
2.1.2. Sample Analysis
2.1.3. Quality Assurance/Quality Control (QA/QC)
2.2. Pollutant and Meteorological Data
2.3. Analysis Method
2.3.1. K-Means Clustering
2.3.2. Enrichment Factor
2.3.3. Backward Trajectory and Clustering Analysis
2.3.4. Potential Source Contribution Factor Analysis
3. Results
3.1. Meteorological Parameters and Pollutant Concentrations
3.2. Source Analysis Based on the Ratio Method
3.3. Airflow Backward Trajectory
3.4. Impact of Ship Emissions on Coastal SO2
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Manshausen, P.; Watson-Parris, D.; Christensen, M.W.; Jalkanen, J.P.; Stier, P. Invisible ship tracks show large cloud sensitivity to aerosol. Nature 2022, 610, 101–106. [Google Scholar] [CrossRef]
- Jang, E.; Choi, S.; Yoo, E.; Hyun, S.; An, J. Impact of shipping emissions regulation on urban aerosol composition changes revealed by receptor and numerical modelling. NPJ Clim. Atmos. Sci. 2023, 6, 52. [Google Scholar] [CrossRef]
- Toscano, D. The impact of shipping on air quality in the port cities of the Mediterranean Area: A review. Atmosphere 2023, 14, 1180. [Google Scholar] [CrossRef]
- Liu, H.; Fu, M.; Jin, X.; Shang, Y.; Shindell, D.; Faluvegi, G.; Shindell, C.; He, K. Health and climate impacts of ocean-going vessels in East Asia. Nat. Clim. Chang. 2016, 6, 1037–1041. [Google Scholar] [CrossRef]
- Tian, L.; Ho, K.-f.; Louie, P.K.K.; Qiu, H.; Pun, V.C.; Kan, H.; Yu, I.T.S.; Wong, T.W. Shipping emissions associated with increased cardiovascular hospitalizations. Atmos. Environ. 2013, 74, 320–325. [Google Scholar] [CrossRef]
- Fan, Q.; Zhang, Y.; Ma, W.; Ma, H.; Feng, J.; Yu, Q.; Yang, X.; Ng, S.K.W.; Fu, Q.; Chen, L. Spatial and seasonal dynamics of ship emissions over the Yangtze River Delta and East China Sea and their potential environmental influence. Environ. Sci. Technol. 2016, 50, 1322–1329. [Google Scholar] [CrossRef]
- Wang, X.; Yi, W.; Lv, Z.; Deng, F.; Zheng, S.; Xu, H.; Zhao, J.; Liu, H.; He, K. Ship emissions around China under gradually promoted control policies from 2016 to 2019. Atmos. Chem. Phys. 2021, 21, 13835–13853. [Google Scholar] [CrossRef]
- Lv, Z.; Liu, H.; Ying, Q.; Fu, M.; Meng, Z.; Wang, Y.; Wei, W.; Gong, H.; He, K. Impacts of shipping emissions on PM2.5 pollution in China. Atmos. Chem. Phys. 2018, 18, 15811–15824. [Google Scholar] [CrossRef]
- Feng, X.; Ma, Y.; Lin, H.; Fu, T.; Zhang, Y.; Wang, X.; Zhang, A.; Yuan, Y.; Han, Z.; Mao, J.; et al. Impacts of ship emissions on air quality in southern China: Opportunistic insights from the abrupt emission changes in early 2020. Environ. Sci. Technol. 2023, 57, 16999–17010. [Google Scholar] [CrossRef]
- Zhai, J.; Yu, G.; Zhang, J.; Shi, S.; Yuan, Y.; Jiang, S.; Xing, C.; Cai, B.; Zeng, Y.; Wang, Y.; et al. Impact of ship emissions on air quality in the greater bay area in China under the latest global marine fuel regulation. Environ. Sci. Technol. 2023, 57, 12341–12350. [Google Scholar] [CrossRef]
- Yuan, C.; Wong, K.W.; Tseng, Y.; Ceng, J.; Lee, C.; Lin, C. Chemical significance and source apportionment of fine particles (PM2.5) in an industrial port area in East Asia. Atmos. Pollut. Res. 2022, 13, 101349. [Google Scholar] [CrossRef]
- Deng, M.; Peng, S.; Xie, X.; Jiang, Z.; Hu, J.; Qi, Z.; Sun, J. SO2 compliance monitoring and emission characteristics analysis of navigating ships: A case study of Shanghai waters in emission control areas, China. Atmos. Pollut. Res. 2022, 13, 101560. [Google Scholar] [CrossRef]
- Mandal, A.; Biswas, J.; Farooqui, Z.; Roychowdhury, S. A detailed perspective of marine emissions and their environmental impact in a representative Indian port. Atmos. Pollut. Res. 2021, 12, 101194. [Google Scholar] [CrossRef]
- Van, T.C.; Ramirez, J.; Rainey, T.; Ristovski, Z.; Brown, R.J. Global impacts of recent IMO regulations on marine fuel oil refining processes and ship emissions. Transp. Res. D Transp. Environ. 2019, 70, 123–134. [Google Scholar] [CrossRef]
- Carr, E.W.; Corbett, J.J. Ship Compliance in Emission Control Areas: Technology Costs and Policy Instruments. Environ. Sci. Technol. 2015, 49, 9584–9591. [Google Scholar] [CrossRef]
- Spada, N.J.; Cheng, X.; White, W.H.; Hyslop, N.P. Decreasing Vanadium Footprint of Bunker Fuel Emissions. Environ. Sci. Technol. 2018, 52, 11528–11534. [Google Scholar] [CrossRef]
- Yang, L.; Zhang, Q.; Lv, Z.; Zhang, Y.; Yang, Z.; Fu, F.; Lv, J.; Wu, L.; Mao, H. Efficiency of DECA on ship emission and urban air quality: A case study of China port. J. Clean. Prod. 2022, 362, 132556. [Google Scholar] [CrossRef]
- Viana, M.; Amato, F.; Alastuey, A.; Querol, X.; Moreno, T.; García Dos Santos, S.; Herce, M.D.; Fernández-Patier, R. Chemical tracers of particulate emissions from commercial shipping. Environ. Sci. Technol. 2009, 43, 7472–7477. [Google Scholar] [CrossRef] [PubMed]
- Liu, H.; Jin, X.; Wu, L.; Wang, X.; Fu, M.; Lv, Z.; Morawska, L.; Huang, F.; He, K. The impact of marine shipping and its DECA control on air quality in the Pearl River Delta, China. Sci. Total Environ. 2018, 625, 1476–1485. [Google Scholar] [CrossRef]
- Zhang, Y.; Deng, F.; Man, H.; Fu, M.; Lv, Z.; Xiao, Q.; Jin, X.; Liu, S.; He, K.; Liu, H. Compliance and port air quality features with respect to ship fuel switching regulation: A field observation campaign, SEISO-Bohai. Atmos. Chem. Phys. 2019, 19, 4899–4916. [Google Scholar] [CrossRef]
- Zhang, X.; Zhang, Y.; Liu, Y.; Zhao, J.; Zhou, Y.; Wang, X.; Yang, X.; Zou, Z.; Zhang, C.; Fu, Q.; et al. Changes in the SO2 level and PM2.5 components in Shanghai driven by Implementing the ship emission control policy. Environ. Sci. Technol. 2019, 53, 11580–11587. [Google Scholar] [CrossRef]
- Zou, Z.; Zhao, J.; Zhang, C.; Zhang, Y.; Yang, X.; Chen, J.; Xu, J.; Xue, R.; Zhou, B. Effects of cleaner ship fuels on air quality and implications for future policy: A case study of Chongming Ecological Island in China. J. Clean. Prod. 2020, 267, 122088. [Google Scholar] [CrossRef]
- Zhao, S.; Tian, H.; Luo, L.; Liu, H.; Wu, B.; Liu, S.; Bai, X.; Liu, W.; Liu, X.; Wu, Y.; et al. Temporal variation characteristics and source apportionment of metal elements in PM2.5 in urban Beijing during 2018–2019. Environ. Pollut. 2021, 268, 115856. [Google Scholar] [CrossRef] [PubMed]
- Agrawal, H.; Welch, W.A.; Miller, J.W.; Cocker, D.R. Emission measurements from a crude oil tanker at sea. Environ. Sci. Technol. 2008, 42, 7098–7103. [Google Scholar] [CrossRef] [PubMed]
- Agrawal, H.; Eden, R.; Zhang, X.; Fine, P.M.; Katzenstein, A.; Miller, J.W.; Ospital, J.; Teffera, S.; Cocker, D.R. Primary particulate matter from ocean-going engines in the Southern California air basin. Environ. Sci. Technol. 2009, 43, 5398–5402. [Google Scholar] [CrossRef]
- Celo, V.; Dabek-Zlotorzynska, E.; McCurdy, M. Chemical characterization of exhaust emissions from selected Canadian marine vessels: The case of trace metals and lanthanoids. Environ. Sci. Technol. 2015, 49, 5220–5226. [Google Scholar] [CrossRef]
- Corbin, J.C.; Mensah, A.A.; Pieber, S.M.; Orasche, J.; Michalke, B.; Zanatta, M.; Czech, H.; Massabò, D.; Buatier de Mongeot, F.; Mennucci, C.; et al. Trace metals in soot and PM2.5 from heavy-fuel-oil combustion in a marine engine. Environ. Sci. Technol. 2018, 52, 6714–6722. [Google Scholar] [CrossRef]
- John, S.G.; Kelly, R.L.; Bian, X.; Fu, F.; Smith, I.; Lanning, N.T.; Liang, H.; Pasquier, B.; Seelen, E.; Holzer, M.; et al. The biogeochemical balance of oceanic nickel cycling. Nat. Geosci. 2022, 15, 906–912. [Google Scholar] [CrossRef]
- Zhang, B.; Zhang, H.; He, J.; Zhou, S.; Dong, H.; Rinklebe, J.; Ok, Y.S. Vanadium in the Environment: Biogeochemistry and Bioremediation. Environ. Sci. Technol. 2023, 57, 14770–14786. [Google Scholar] [CrossRef] [PubMed]
- Yu, G.; Zhang, Y.; Yang, F.; He, B.; Zhang, C.; Zou, Z.; Yang, X.; Li, N.; Chen, J. Dynamic Ni/V ratio in the ship-emitted particles driven by multiphase fuel oil regulations in coastal China. Environ. Sci. Technol. 2021, 55, 15031–15039. [Google Scholar] [CrossRef]
- Liu, Z.; Zhang, H.; Zhang, Y.; Liu, X.; Ma, Z.; Xue, L.; Peng, X.; Zhao, J.; Gong, W.; Peng, Q.; et al. Characterization and sources of trace elements in PM1 during autumn and winter in Qingdao, Northern China. Sci. Total Environ. 2022, 811, 151319. [Google Scholar] [CrossRef] [PubMed]
- Zhao, M.; Zhang, Y.; Ma, W.; Fu, Q.; Yang, X.; Li, C.; Zhou, B.; Yu, Q.; Chen, L. Characteristics and ship traffic source identification of air pollutants in China’s largest port. Atmos. Environ. 2013, 64, 277–286. [Google Scholar] [CrossRef]
- Wang, Z.; Xu, H.; Gu, Y.; Feng, R.; Zhang, N.; Wang, Q.; Cao, J.; Liu, S.; Zhang, Q.; Liu, P.; et al. Chemical characterization of PM2.5 in heavy polluted industrial zones in the Guanzhong Plain, northwest China: Determination of fingerprint source profiles. Sci. Total Environ. 2022, 840, 156729. [Google Scholar] [CrossRef] [PubMed]
- 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]
- Qin, S.; Li, B.; Wang, X.; Huang, H.; Zeng, M.; Xiao, F.; Xu, X. Metal element detection and carcinogenicity risk assessment of PM2.5 Samples. Environ. Toxicol. Chem. 2020, 39, 1273–1276. [Google Scholar] [CrossRef]
- Liu, H.; Song, D.; Zhang, X.; Huang, F.; Hu, X. A study on characteristics of heavy metal elements in atmospheric PM2.5 during winter in Chengdu. IOP Conf. Ser. Earth Environ. Sci. 2020, 514, 032046. [Google Scholar] [CrossRef]
- Bie, S.; Yang, L.; Zhang, Y.; Huang, Q.; Li, J.; Zhao, T.; Zhang, X.; Wang, P.; Wang, W. Source appointment of PM2.5 in Qingdao Port, East of China. Sci. Total Environ. 2021, 755, 142456. [Google Scholar] [CrossRef]
- Chen, D.; Wang, X.; Nelson, P.; Li, Y.; Zhao, N.; Zhao, Y.; Lang, J.; Zhou, Y.; Guo, X. Ship emission inventory and its impact on the PM2.5 air pollution in Qingdao Port, North China. Atmos. Environ. 2017, 166, 351–361. [Google Scholar] [CrossRef]
- MOT, Ministry of Transport of China. Available online: https://www.mot.gov.cn/tongjishuju/gangkouhuowulvkettl (accessed on 20 November 2023).
- Luo, H.; Wang, Q.; Guan, Q.; Ma, Y.; Ni, F.; Yang, E.; Zhang, J. Heavy metal pollution levels, source apportionment and risk assessment in dust storms in key cities in Northwest China. J. Hazard. Mater. 2022, 422, 126878. [Google Scholar] [CrossRef] [PubMed]
- Xu, H.; Wang, Y.; Liu, R.; Wang, M.; Zhang, Y. Spatial distribution, chemical speciation and health risk of heavy metals from settled dust in Qingdao urban area. Atmosphere 2019, 10, 73. [Google Scholar] [CrossRef]
- Cesari, D.; De Benedetto, G.E.; Bonasoni, P.; Busetto, M.; Dinoi, A.; Merico, E.; Chirizzi, D.; Cristofanelli, P.; Donateo, A.; Grasso, F.M.; et al. Seasonal variability of PM2.5 and PM10 composition and sources in an urban background site in Southern Italy. Sci. Total Environ. 2018, 612, 202–213. [Google Scholar] [CrossRef]
- Islam, N.; Dihingia, A.; Khare, P.; Saikia, B.K. Atmospheric particulate matters in an Indian urban area: Health implications from potentially hazardous elements, cytotoxicity, and genotoxicity studies. J. Hazard. Mater. 2020, 384, 121472. [Google Scholar] [CrossRef] [PubMed]
- Wu, R.; Zhou, X.; Wang, L.; Wang, Z.; Zhou, Y.; Zhang, J.; Wang, W. PM2.5 characteristics in Qingdao and across coastal cities in China. Atmosphere 2017, 8, 77. [Google Scholar] [CrossRef]
- Zhang, Y.; Lang, J.; Cheng, S.; Li, S.; Zhou, Y.; Chen, D.; Zhang, H.; Wang, H. Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn. Sci. Total Environ. 2018, 630, 72–82. [Google Scholar] [CrossRef]
- Zeng, P.; Huang, X.; Yan, M.; Zheng, Z.; Qiu, Z.; Yun, L.; Lin, C.; Zhang, L. Ambient Ozone and Fine Particular Matter Pollution in a Megacity in South China: Trends, Concurrent Pollution, and Health Risk Assessment. Atmosphere 2023, 14, 1806. [Google Scholar] [CrossRef]
- Khan, J.Z.; Sun, L.; Tian, Y.; Shi, G.; Feng, Y. Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources. J. Environ. Sci. 2021, 99, 196–209. [Google Scholar] [CrossRef] [PubMed]
- Squizzato, S.; Masiol, M.; Agostini, C.; Visin, F.; Formenton, G.; Harrison, R.M.; Rampazzo, G. Factors, origin and sources affecting PM1 concentrations and composition at an urban background site. Atmos. Res. 2016, 180, 262–273. [Google Scholar] [CrossRef]
- Sarti, E.; Pasti, L.; Rossi, M.; Ascanelli, M.; Pagnoni, A.; Trombini, M.; Remelli, M. The composition of PM1 and PM2.5 samples, metals and their water soluble fractions in the Bologna area (Italy). Atmos. Pollut. Res. 2015, 6, 708–718. [Google Scholar] [CrossRef]
- Perrone, M.R.; Becagli, S.; Garcia Orza, J.A.; Vecchi, R.; Dinoi, A.; Udisti, R.; Cabello, M. The impact of long-range-transport on PM1 and PM2.5 at a Central Mediterranean site. Atmos. Environ. 2013, 71, 176–186. [Google Scholar] [CrossRef]
- Shen, Z.; Cao, J.; Arimoto, R.; Han, Y.; Zhu, C.; Tian, J.; Liu, S. Chemical characteristics of fine particles (PM1) from Xi’an, China. Aerosol Sci. Technol. 2010, 44, 461–472. [Google Scholar] [CrossRef]
- Cheng, H.; Gong, W.; Wang, Z.; Zhang, F.; Wang, X.; Lv, X.; Liu, J.; Fu, X.; Zhang, G. Ionic composition of submicron particles (PM1.0) during the long-lasting haze period in January 2013 in Wuhan, central China. J. Environ. Sci. 2014, 26, 810–817. [Google Scholar] [CrossRef] [PubMed]
- Jeong, J.H.; Shon, Z.H.; Kang, M.; Song, S.K.; Kim, Y.K.; Park, J.; Kim, H. Comparison of source apportionment of PM2.5 using receptor models in the main hub port city of East Asia: Busan. Atmos. Environ. 2017, 148, 115–127. [Google Scholar] [CrossRef]
- Mamoudou, I.; Zhang, F.; Chen, Q.; Wang, P.; Chen, Y. Characteristics of PM2.5 from ship emissions and their impacts on the ambient air: A case study in Yangshan Harbor, Shanghai. Sci. Total Environ. 2018, 640–641, 207–216. [Google Scholar] [CrossRef] [PubMed]
- Tao, J.; Zhang, L.; Cao, J.; Zhong, L.; Chen, D.; Yang, Y.; Chen, D.; Chen, L.; Zhang, Z.; Wu, Y.; et al. Source apportionment of PM2.5 at urban and suburban areas of the Pearl River Delta region, south China—With emphasis on ship emissions. Sci. Total Environ. 2017, 574, 1559–1570. [Google Scholar] [CrossRef] [PubMed]
- Zhang, F.; Chen, Y.; Tian, C.; Wang, X.; Huang, G.; Fang, Y.; Zong, Z. Identification and quantification of shipping emissions in Bohai Rim, China. Sci. Total Environ. 2014, 497–498, 570–577. [Google Scholar] [CrossRef]
- Cheng, K.; Chang, Y.; Kuang, Y.; Ling, Q.; Zou, Z.; Huang, R.-J. Multiple-Year Changes (2014–2018) in Particulate Vanadium Linked to Shipping Regulations in the World’s Largest Port Region. ACS Earth Space Chem. 2022, 6, 415–420. [Google Scholar] [CrossRef]
- MEPC. 2019 Report of Fuel Oil Consumption Data Submitted to the IMO Ship Fuel Oil Consumption Database in GISIS; MEPC: London, UK, 2020; pp. 7–8. Available online: https://www.imo.org/ (accessed on 20 November 2023).
- MEPC. 2020 Report of Fuel Oil Consumption Data Submitted to the IMO Ship Fuel Oil Consumption Database in GISIS; MEPC: London, UK, 2021; pp. 7–8. Available online: https://www.imo.org/ (accessed on 20 November 2023).
- Zhou, L.; Li, M.; Cheng, C.; Zhou, Z.; Nian, H.; Tang, R.; Chan, C.K. Real-time chemical characterization of single ambient particles at a port city in Chinese domestic emission control area—Impacts of ship emissions on urban air quality. Sci. Total Environ. 2022, 819, 153117. [Google Scholar] [CrossRef]
- Ray, I.; Das, R.; Chua, S.L.; Wang, X. Seasonal variation of atmospheric Pb sources in Singapore—Elemental and lead isotopic compositions of PM10 as source tracer. Chemosphere 2022, 307, 136029. [Google Scholar] [CrossRef]
- Liu, L.; Liu, Y.; Wen, W.; Liang, L.; Ma, X.; Jiao, J.; Guo, K. Source Identification of trace elements in PM2.5 at a rural site in the North China Plain. Atmosphere 2020, 11, 179. [Google Scholar] [CrossRef]
- Liu, J.; Chen, Y.; Chao, S.; Cao, H.; Zhang, A.; Yang, Y. Emission control priority of PM2.5-bound heavy metals in different seasons: A comprehensive analysis from health risk perspective. Sci. Total Environ. 2018, 644, 20–30. [Google Scholar] [CrossRef] [PubMed]
- Si, R.; Xin, J.; Zhang, W.; Wen, T.; Li, S.; Ma, Y.; Wu, X.; Cao, Y.; Xu, X.; Tang, H.; et al. Environmental and health benefits of establishing a coal banning area in the Beijing-Tianjin-Hebei region of China. Atmos. Environ. 2021, 247, 118191. [Google Scholar] [CrossRef]
- Cheng, Y.; Zou, S.; Lee, S.C.; Chow, J.; Ho, K.; Watson, J.; Han, Y.; Zhang, R.; Zhang, F.; Yau, P.; et al. Characteristics and source apportionment of PM1 emissions at a roadside station. J. Hazard. Mater. 2011, 195, 82–91. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.; Wei, E.; Wu, L.; Fang, X.; Li, F.; Yang, Z.; Wang, T.; Mao, H. Elemental composition and health risk assessment of PM10 and PM2.5 in the roadside microenvironment in Tianjin, China. Air. Qual. Res. 2018, 18, 1817–1827. [Google Scholar] [CrossRef]
- Si, R.; Xin, J.; Zhang, W.; Li, S.; Wen, T.; Wang, Y.; Ma, Y.; Liu, Z.; Xu, X.; Li, M.; et al. Source apportionment and health risk assessment of trace elements in the heavy industry areas of Tangshan, China. Air Qual. Atmos. Health. 2019, 12, 1303–1315. [Google Scholar] [CrossRef]
- Win, M.S.; Zeng, J.; Yao, C.; Zhao, M.; Xiu, G.; Xie, T.; Rao, L.; Zhang, L.; Lu, H.; Liu, X.; et al. Sources of HULIS-C and its relationships with trace metals, ionic species in PM2.5 in suburban Shanghai during haze and non-haze days. J. Atmos. Chem. 2020, 77, 63–81. [Google Scholar] [CrossRef]
- Luo, Y.; Zhou, X.; Zhang, J.; Xiao, Y.; Wang, Z.; Zhou, Y.; Wang, W. PM2.5 pollution in a petrochemical industry city of northern China: Seasonal variation and source apportionment. Atmos. Res. 2018, 212, 285–295. [Google Scholar] [CrossRef]
- Tao, J.; Gao, J.; Zhang, L.; Zhang, R.; Che, H.; Zhang, Z.; Lin, Z.; Jing, J.; Cao, J.; Hsu, S.C. PM2.5 pollution in a megacity of southwest China: Source apportionment and implication. Atmos. Chem. Phys. 2014, 14, 8679–8699. [Google Scholar] [CrossRef]
- Gao, J.; Wang, K.; Wang, Y.; Liu, S.; Zhu, C.; Hao, J.; Liu, H.; Hua, S.; Tian, H. Temporal-spatial characteristics and source apportionment of PM2.5 as well as its associated chemical species in the Beijing-Tianjin-Hebei region of China. Environ. Pollut. 2018, 233, 714–724. [Google Scholar] [CrossRef]
- Yu, Y.; He, S.; Wu, X.; Zhang, C.; Yao, Y.; Liao, H.; Wang, Q.G.; Xie, M. PM2.5 elements at an urban site in Yangtze River Delta, China: High time-resolved measurement and the application in source apportionment. Environ. Pollut. 2019, 253, 1089–1099. [Google Scholar] [CrossRef]
- Wang, Q.; Fang, J.; Shi, W.; Dong, X. Distribution characteristics and policy-related improvements of PM2.5 and its components in six Chinese cities. Environ. Pollut. 2020, 266, 115299. [Google Scholar] [CrossRef]
- Qu, Y.; Liu, X.Q.; Liu, H.K.; Wang, Q.Y.; Zhu, C.S.; Zhou, Y.; Zhang, R.J.; Cao, J.J. PM2.5 Elements in the rural area of Jing-Jin-Ji Region in China: Source identification and health risk assessment. Aerosol Sci. Eng. 2021, 5, 429–439. [Google Scholar] [CrossRef]
- Li, X.; Yan, C.; Wang, C.; Ma, J.; Li, W.; Liu, J.; Liu, Y. PM2.5-bound elements in Hebei Province, China: Pollution levels, source apportionment and health risks. Sci. Total Environ. 2022, 806, 150440. [Google Scholar] [CrossRef]
- Diao, L.; Zhang, H.; Liu, B.; Dai, C.; Zhang, Y.; Dai, Q.; Bi, X.; Zhang, L.; Song, C.; Feng, Y. Health risks of inhaled selected toxic elements during the haze episodes in Shijiazhuang, China: Insight into critical risk sources. Environ. Pollut. 2021, 276, 116664. [Google Scholar] [CrossRef] [PubMed]
- Valotto, G.; Squizzato, S.; Masiol, M.; Zannoni, D.; Visin, F.; Rampazzo, G. Elemental characterization, sources and wind dependence of PM1 near Venice, Italy. Atmos. Res. 2014, 143, 371–379. [Google Scholar] [CrossRef]
- Salameh, D.; Detournay, A.; Pey, J.; Pérez, N.; Liguori, F.; Saraga, D.; Bove, M.C.; Brotto, P.; Cassola, F.; Massabò, D.; et al. PM2.5 chemical composition in five European Mediterranean cities: A 1-year study. Atmos. Res. 2015, 155, 102–117. [Google Scholar] [CrossRef]
- Miranda, R.M.; de Fatima Andrade, M.; Dutra Ribeiro, F.N.; Mendonça Francisco, K.J.; Pérez-Martínez, P.J. Source apportionment of fine particulate matter by positive matrix factorization in the metropolitan area of São Paulo, Brazil. J. Clean. Prod. 2018, 202, 253–263. [Google Scholar] [CrossRef]
- Nakatsubo, R.; Oshita, Y.; Aikawa, M.; Takimoto, M.; Kubo, T.; Matsumura, C.; Takaishi, Y.; Hiraki, T. Influence of marine vessel emissions on the atmospheric PM2.5 in Japan’s around the congested sea areas. Sci. Total Environ. 2020, 702, 134744. [Google Scholar] [CrossRef] [PubMed]
- Alves, C.; Evtyugina, M.; Vicente, E.; Vicente, A.; Rienda, I.C.; de la Campa, A.S.; Tomé, M.; Duarte, I. PM2.5 chemical composition and health risks by inhalation near a chemical complex. J. Environ. Sci. 2023, 124, 860–874. [Google Scholar] [CrossRef]
- Wiseman, C.L.S.; Zereini, F. Characterizing metal(loid) solubility in airborne PM10, PM2.5 and PM1 in Frankfurt, Germany using simulated lung fluids. Atmos. Environ. 2014, 89, 282–289. [Google Scholar] [CrossRef]
- Hassanvand, M.S.; Naddafi, K.; Faridi, S.; Nabizadeh, R.; Sowlat, M.H.; Momeniha, F.; Gholampour, A.; Arhami, M.; Kashani, H.; Zare, A.; et al. Characterization of PAHs and metals in indoor/outdoor PM10/PM2.5/PM1 in a retirement home and a school dormitory. Sci. Total Environ. 2015, 527–528, 100–110. [Google Scholar] [CrossRef]
- Visser, S.; Slowik, J.G.; Furger, M.; Zotter, P.; Bukowiecki, N.; Canonaco, F.; Flechsig, U.; Appel, K.; Green, D.C.; Tremper, A.H.; et al. Advanced source apportionment of size-resolved trace elements at multiple sites in London during winter. Atmos. Chem. Phys. 2015, 15, 11291–11309. [Google Scholar] [CrossRef]
- Shaltout, A.A.; Boman, J.; Alsulimane, M.E. Identification of elemental composition of PM2.5 collected in Makkah, Saudi Arabia, using EDXRF. X-ray Spectrom. 2017, 46, 151–163. [Google Scholar] [CrossRef]
- Bi, X.; Dai, Q.; Wu, J.; Zhang, Q.; Zhang, W.; Luo, R.; Cheng, Y.; Zhang, J.; Wang, L.; Yu, Z.; et al. Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017. Atmos. Chem. Phys. 2019, 19, 3223–3243. [Google Scholar] [CrossRef]
- Li, L.; Pan, Y.; Gao, S.; Yang, W. An innovative model to design extreme emission control areas (ECAs) by considering ship’s evasion strategy. Ocean Coast. Manag. 2022, 227, 106289. [Google Scholar] [CrossRef]
- Ramacher, M.O.; Tang, L.; Moldanová, J.; Matthias, V.; Karl, M.; Fridell, E.; Johansson, L. The impact of ship emissions on air quality and human health in the Gothenburg area–Part II: Scenarios for 2040. Atmos. Chem. Phys. 2020, 20, 10667–10686. [Google Scholar] [CrossRef]
- Ni, N.; Yuan, H.; Zhang, Z.; Bai, Y.; Bai, Y.; Zhu, M. Theoretical research on ship desulfurization wastewater freezing desalination system driven by waste heat. Desalination 2023, 549, 116363. [Google Scholar] [CrossRef]
- Sofiev, M.; Winebrake, J.J.; Johansson, L.; Carr, E.W.; Prank, M.; Soares, J.; Corbett, J.J. Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nat. Commun. 2018, 9, 406. [Google Scholar] [CrossRef]
- Li, M.; Shao, M.; Li, L.-Y.; Lu, S.-H.; Chen, W.-T.; Wang, C. Quantifying the ambient formaldehyde sources utilizing tracers. Chin. Chem. Lett. 2014, 25, 1489–1491. [Google Scholar] [CrossRef]
- Park, E.H.; Heo, J.; Kim, H.; Yi, S.M. Long term trends of chemical constituents and source contributions of PM2.5 in Seoul. Chemosphere 2020, 251, 126371. [Google Scholar] [CrossRef]
Shipping Policy Stage | Implementation Time | Observation Time | Number of Samples |
---|---|---|---|
DECA 1.0 | 1 January 2016–31 December 2018 | 1 November 2018–31 December 2018 | 61 |
DECA 2.0 | Since 1 January 2019 | 1 January 2019–20 January 2019 1 November 2019–31 December 2019 | 80 |
IMO 2020 | Since 1 January 2020 | 1 January 2020–20 January 2020 1 November 2020–31 December 2020 1 January 2021–20 January 2021 | 101 |
Pre-OWG Beijing 2022 | October 2021–January 2022 | 1 November 2021–31 December 2021 1 January 2022–20 January 2022 | 81 |
Types | DECA 1.0 (n = 61) | DECA 2.0 (n = 80) | IMO 2020 (n = 101) | Pre-OWG Beijing 2022 (n = 81) | ||||
---|---|---|---|---|---|---|---|---|
Range | Average | Range | Average | Range | Average | Range | Average | |
T (°C) | −6.22–16.1 | 6.50 ± 6.04 | −3.79–17.5 | 5.60 ± 5.60 | −10.9–17.3 | 3.98 ± 5.96 | −7.10–17.8 | 6.24 ± 5.55 |
VIS 1 (km) | 1.03–29.6 | 13.2 ± 8.08 | 0.74–28.6 | 13.8 ± 8.65 | 1.88–30.0 | 15.2 ± 8.49 | 1.50–30.0 | 15.9 ± 8.98 |
WS (m/s) | 1.13–7.93 | 3.29 ± 1.68 | 1.39–7.96 | 3.60 ± 1.53 | 1.17–8.77 | 3.52 ± 1.54 | 1.28–8.43 | 3.44 ± 1.58 |
RH (%) | 39.8–93.9 | 63.7 ± 11.5 | 29.3–97.3 | 62.9 ± 14.5 | 36.4–91.8 | 60.1 ± 14.6 | 27.5–91.2 | 59.2 ± 14.6 |
SO2 (μg/m3) | 2.96–34.9 | 12.4 ± 7.03 | 4.00–31.2 | 13.4 ± 6.08 | 5.00–39.4 | 15.1 ± 6.71 | 4.23–22.0 | 9.68 ± 3.41 |
NO2 (μg/m3) | 21.8–118 | 55.8 ± 21.6 | 14.2–106 | 58.2 ± 21.8 | 5.62–115 | 47.4 ± 20.6 | 11.8–84.5 | 45.8 ± 18.2 |
PM10 (μg/m3) | 30.3–493 | 114 ± 78.7 | 18.6–375 | 109 ± 64.4 | 25.0–325 | 96.0 ± 59.4 | 14.5–205 | 77.5 ± 44.3 |
PM2.5 (μg/m3) | 12.5–186 | 59.4 ± 41.4 | 10.6–286 | 67.1 ± 54.8 | 7.86–257 | 60.8 ± 49.2 | 8.04–161 | 47.3 ± 32.5 |
Types | DECA 1.0 | DECA 2.0 | IMO 2020 | Pre-OWG Beijing 2022 | ||||
---|---|---|---|---|---|---|---|---|
Range | Mean ± SD | Range | Mean ± SD | Range | Mean ± SD | Range | Mean ± SD | |
PM1 (μg/m³) | 9.36–108 | 37.9 ± 22.2 | 7.74–123 | 42.2 ± 29.3 | 6.29–154 | 41.9 ± 28.8 | 4.99–95.8 | 34.3 ± 22.4 |
V (ng/m³) | 0.03–11.1 | 3.45 ± 3.01 | 0.19–5.39 | 1.07 ± 1.04 | 0.00–3.02 | 0.84 ± 0.62 | 0.00–3.27 | 0.68 ± 0.61 |
Ni (ng/m³) | 0.25–10.9 | 2.86 ± 2.27 | 0.11–5.83 | 1.90 ± 1.20 | 0.71–15.6 | 2.90 ± 2.41 | 0.21–13.8 | 3.57 ± 2.43 |
Zn (ng/m³) | 12.32–298 | 64.4 ± 50.8 | 3.75–262 | 59.6 ± 49.9 | 3.26–193 | 64.6 ± 38.4 | 6.31–323 | 65.3 ± 46.8 |
Pb (ng/m³) | 3.92–60.7 | 19.8 ± 13.4 | 0.96–54.8 | 16.0 ± 12.3 | 2.38–68.0 | 21.0 ± 11.7 | 2.15–34.8 | 12.7 ± 6.54 |
V/Ni | 0.05–2.98 | 1.14 ± 0.79 | 0.10–7.65 | 0.93 ± 1.24 | 0.00–1.43 | 0.35 ± 0.24 | 0.00–0.95 | 0.22 ± 0.18 |
V/Zn | 0.00–0.48 | 0.07 ± 0.09 | 0.00–0.48 | 0.05 ± 0.10 | 0.00–0.70 | 0.03 ± 0.08 | 0.00–0.17 | 0.02 ± 0.03 |
V/Pb | 0.00–0.90 | 0.20 ± 0.20 | 0.01–1.77 | 0.17 ± 0.35 | 0.00–0.96 | 0.07 ± 0.12 | 0.00–0.50 | 0.07 ± 0.08 |
Observation Time | Location | Particle Size | V | Ni | Zn | Pb | V/Ni | V/Zn | V/Pb | References | |
---|---|---|---|---|---|---|---|---|---|---|---|
Before DECA 1.0 | October 2014–September 2005 | Hong Kong | PM1 | 16 | 5.2 | 210 | 47 | 3.08 | 0.08 | 0.34 | [65] |
March 2006–February 2007 | Qingdao | PM2.5 | 10 | 6 | 271 | 128 | 1.67 | 0.04 | 0.08 | [44] | |
January 2012–December 2012 | Yantai | PM2.5 | 6 | 4 | 94.3 | 91.7 | 1.50 | 0.06 | 0.07 | [56] | |
September 2013–August 2014 | Ningbo | PM2.5 | 7.36 | 8.25 | 190 | 56.3 | 0.89 | 0.04 | 0.13 | [34] | |
September 2013–August 2014 | Shanghai | PM2.5 | 16.5 | 14.9 | 215 | 69.7 | 1.11 | 0.08 | 0.24 | [34] | |
January 2015–December 2015 | Tianjin | PM2.5 | 17.1 | 10.8 | 80.5 | 19.8 | 1.58 | 0.21 | 0.86 | [66] | |
DECA 1.0 | January 2016–December 2016 | Tangshan | PM2.5 | 5.6 | 7.1 | 186 | 183 | 0.79 | 0.03 | 0.03 | [67] |
January 2017–December 2017 | Shanghai | PM2.5 | 10.98 | 5.33 | / | / | 2.06 | / | / | [30] | |
January 2018–December 2018 | Shanghai | PM2.5 | 9.44 | 5 | / | / | 1.89 | / | / | [30] | |
January 2017–December 2017 | Tangshan | PM2.5 | 3.2 | 4.6 | 198 | 146 | 0.70 | 0.02 | 0.02 | [67] | |
January 2018–December 2018 | Shenzhen | PM2.5 | 11.6 | 4.76 | / | 31 | 2.44 | 0.37 | [35] | ||
May 2018–July 2018 | Tianjin | PM2.5 | 2.2 | 12.1 | 86 | 19.4 | 0.18 | 0.03 | 0.11 | [47] | |
August 2018–May 2019 | Qingdao | PM2.5 | 10.68 | 22.88 | 130 | 355.2 | 0.47 | 0.08 | 0.03 | [37] | |
DECA 2.0 | January 2019–December 2019 | Shanghai | PM2.5 | 4.6 | 3.87 | / | / | 1.19 | / | / | [30] |
January 2018–December 2019 | Shanghai | PM2.5 | 9.57 | 9.57 | 31.2 | 8.64 | 1 | 0.31 | 1.11 | [68] | |
August 2019 | Tangshan | PM2.5 | 2.3 | 1.53 | 41.6 | 14.2 | 1.5 | 0.06 | 0.16 | [62] | |
IMO 2020 | January 2020–December 2020 | Shanghai | PM2.5 | 1.19 | 3.18 | / | / | 0.37 | / | / | [30] |
Observation Time | Location | Particle Size | V | Ni | Zn | Pb | V/Ni | V/Zn | V/Pb | References | |
---|---|---|---|---|---|---|---|---|---|---|---|
Before DECA 1.0 | March 2006–February 2007 | Zibo | PM2.5 | 8.23 | 10.55 | 1070 | 1630 | 0.78 | 0.01 | 0.01 | [69] |
November 2011 | Chengdu | PM2.5 | 1.7 | 2.5 | 350 | 172 | 0.5 | 0 | 0.01 | [70] | |
September 2013–August 2014 | Nanjing | PM2.5 | 9.88 | 9.3 | 247 | 90.9 | 1.06 | 0.04 | 0.11 | [34] | |
September 2013–August 2014 | Hangzhou | PM2.5 | 15.3 | 10.4 | 495 | 122 | 1.47 | 0.03 | 0.13 | [34] | |
April 2013–June 2013 | Beijing | PM2.5 | 3.1 | 2.6 | 117 | 90 | 1.19 | 0.03 | 0.03 | [51] | |
December 2014–February 2015 | Baoding | PM2.5 | 10 | 40 | 1170 | 460 | 0.25 | 0.01 | 0.02 | [71] | |
March 2014–June 2015 | Langfang | PM2.5 | 10 | 40 | 330 | 140 | 0.25 | 0.03 | 0.07 | [71] | |
DECA 1.0 | October 2016–November 2016 | Shijiazhuang | PM2.5 | 8.85 | 17.7 | 398 | 185 | 0.5 | 0.02 | 0.05 | [62] |
January 2016–December 2016 | Beijing | PM2.5 | 3.22 | 3.01 | 291 | 53.6 | 1.07 | 0.01 | 0.06 | [63] | |
September 2016–November 2016 | Baoding | PM2.5 | 8.85 | 17.7 | 398 | 185 | 0.5 | 0.02 | 0.05 | [62] | |
September 2016–January 2017 | Baoding | PM2.5 | 1.8 | 6.2 | 340 | 192 | 0.29 | 0.01 | 0.01 | [64] | |
September 2016–January 2017 | Shijiazhuang | PM2.5 | 2.8 | 6.1 | 288 | 124 | 0.46 | 0.01 | 0.02 | [64] | |
September 2016–January 2017 | Langfang | PM2.5 | 3.6 | 5.8 | 228 | 150 | 0.62 | 0.02 | 0.02 | [64] | |
January 2016–December 2017 | Chengdu | PM2.5 | 1.24 | 3.06 | 357 | 87.2 | 0.41 | 0 | 0.01 | [36] | |
January 2016–December 2017 | Nanjing | PM2.5 | 6.05 | 3.62 | 199 | 50.8 | 1.67 | 0.03 | 0.12 | [72] | |
January 2017 | Shijiazhuang | PM2.5 | 1.1 | 14.2 | 212 | 51.2 | 0.08 | 0.01 | 0.02 | [73] | |
September 2017–January 2018 | Baoding | PM2.5 | 1.2 | 4.4 | 188 | 80 | 0.27 | 0.01 | 0.02 | [64] | |
September 2017–January 2018 | Langfang | PM2.5 | 2.1 | 4.8 | 123 | 56 | 0.44 | 0.02 | 0.04 | [64] | |
October 2017–January 2018 | Langfang | PM2.5 | 4 | 6 | 198 | 68 | 0.67 | 0.02 | 0.06 | [74] | |
September 2017–January 2018 | Shijiazhuang | PM2.5 | 1.5 | 4.9 | 161 | 59 | 0.31 | 0.01 | 0.03 | [64] | |
June 2018 | Shijiazhuang | PM2.5 | 1.7 | 1.26 | 114 | 63.8 | 1.35 | 0.01 | 0.03 | [75] | |
July 2018 | Shijiazhuang | PM2.5 | 0.25 | 1.1 | 115 | 32.6 | 0.23 | 0 | 0.01 | [75] | |
November 2018–December 2018 | Baoding | PM2.5 | 0.51 | 3.12 | 979 | 279 | 0.16 | 0 | 0 | [75] | |
January 2018–December 2018 | Taiyuan | PM2.5 | 4.46 | 10.4 | / | 63.3 | 0.43 | / | 0.07 | [35] | |
DECA 2.0 | October 2018–January 2019 | Shijiazhuang | PM2.5 | 1.2 | 8 | 183 | 51 | 0.15 | 0.01 | 0.02 | [76] |
December 2018–January 2019 | Langfang | PM2.5 | 0.16 | 2.2 | 184 | 67.5 | 0.07 | 0 | 0 | [75] | |
January 2019 | Baoding | PM2.5 | 0.7 | 1.44 | 123 | 38 | 0.49 | 0.01 | 0.02 | [75] | |
March 2019 | Baoding | PM2.5 | 4.66 | 2.96 | 698 | 156 | 1.57 | 0.01 | 0.03 | [75] |
Location | Country | Observation Time | Particle Size | V | Ni | Zn | Pb | V/Ni | V/Zn | V/Pb | References |
---|---|---|---|---|---|---|---|---|---|---|---|
Salento Island | Italy | July 2008–May 2010 | PM1 | 3 | 3 | / | 4 | 1 | 0.75 | 3.00 | [50] |
Venice | Italy | November 2010–July 2011 | PM1 | 8.0 | 1.7 | 22.1 | / | 4.7 | / | 1.70 | [77] |
Venice | Italy | December 2013–February 2014 | PM1 | 2.4 | 2.5 | 28 | 7 | 0.96 | 0.34 | 2.50 | [48] |
Salento Island | Italy | July 2008–May 2010 | PM2.5 | 4 | 5 | / | 7 | 0.80 | 0.57 | 5.00 | [50] |
Venice | Italy | January 2011–December 2011 | PM2.5 | 7 | 4 | 81 | 12 | 1.75 | 0.58 | 4.00 | [78] |
Genoa | Italy | January 2011–December 2011 | PM2.5 | 14 | 7 | 19 | 6 | 2.00 | 2.33 | 7.00 | [78] |
Barcelona | Spain | January 2011–December 2011 | PM2.5 | 6 | 3 | 42 | 6 | 2.00 | 1.00 | 3.00 | [68] |
Marseille | France | January 2011–December 2012 | PM2.5 | 6 | 4 | 24 | 8 | 1.50 | 0.75 | 4.00 | [78] |
Busan | South Korea | January 2013–December 2013 | PM2.5 | 8.3 | 4.4 | 92 | 30 | 1.89 | 0.28 | 4.39 | [53] |
Sao Paulo | Brazil | January 2014–December 2015 | PM2.5 | 2 | 17 | 320 | 44 | 0.12 | 0.05 | 16.67 | [79] |
Hayashizaki | Japan | January 2016–December 2017 | PM2.5 | 8.96 | 3.12 | 25.3 | 6.98 | 2.89 | 1.28 | / | [80] |
Tarumi | Japan | January 2016–December 2017 | PM2.5 | 10.2 | 3.35 | 22.5 | 7.61 | 3.04 | 1.34 | 3.36 | [80] |
Suma | Japan | January 2016–December 2017 | PM2.5 | 8.34 | 2.7 | 14.3 | 4.77 | 3.09 | 1.75 | 2.70 | [80] |
Estarreja | Portugal | September 2019–November 2019 | PM2.5 | 2.13 | 0.534 | 3.99 | 21.2 | 3.78 | 0.10 | 0.56 | [81] |
Frankfurt | Germany | June 2009–November 2010 | PM1 | 1.2 | 2.2 | / | 4.8 | 0.54 | 0.25 | 2.22 | [82] |
Tehran | Iran | May 2012–May 2013 | PM1 | 3.04 | 4.1 | 84.4 | 54.46 | 0.74 | 0.06 | 4.11 | [83] |
Frankfurt | Germany | June 2009–November 2010 | PM2.5 | 2.5 | 5.0 | / | 13 | 0.5 | 0.19 | 5.00 | [82] |
London | UK | January 2012–December 2012 | PM2.5 | 1.3 | 0.5 | 8.9 | 2.3 | 2.60 | 0.57 | 0.50 | [84] |
Tehran | Iran | May 2012–May 2013 | PM2.5 | 4.19 | 4.91 | 133.97 | 72.65 | 0.85 | 0.06 | 4.93 | [83] |
Makkah | Saudi Arabia | January 2012–December 2014 | PM2.5 | 3.2 | 20 | 19 | 15 | 0.16 | 0.21 | 20.00 | [85] |
Cluster Types | Policy Stages | Samples | V (ng/m³) | Ni (ng/m³) | Zn (ng/m³) | Pb (ng/m³) | V/Ni | V/Zn | V/Pb |
---|---|---|---|---|---|---|---|---|---|
Cluster 1 | DECA 1.0 | 29 | 4.01 ± 2.80 | 3.03 ± 1.78 | 77.5 ± 60.6 | 23.3 ± 14.2 | 1.29 ± 0.73 | 0.08 ± 0.10 | 0.21 ± 0.18 |
DECA 2.0 | 22 | 1.50 ± 1.40 | 2.13 ± 1.26 | 63.7 ± 56.2 | 18.4 ± 15.6 | 1.17 ± 1.38 | 0.10 ± 0.16 | 0.38 ± 0.61 | |
IMO 2020 | 46 | 1.07 ± 0.75 | 3.55 ± 2.70 | 71.0 ± 47.2 | 22.8 ± 14.6 | 0.36 ± 0.25 | 0.04 ± 0.11 | 0.10 ± 0.17 | |
Pre-OWG Beijing 2022 | 34 | 0.85 ± 0.72 | 4.21 ± 2.42 | 78.7 ± 58.2 | 15.0 ± 6.85 | 0.23 ± 0.21 | 0.02 ± 0.10 | 0.08 ± 0.04 | |
Cluster 2 | DECA 1.0 | 17 | 2.96 ± 3.00 | 3.70 ± 3.30 | 58.8 ± 33.5 | 18.1 ± 9.95 | 0.86 ± 0.66 | 0.20 ± 0.24 | 0.06 ± 0.08 |
DECA 2.0 | 22 | 1.11 ± 1.04 | 2.35 ± 1.15 | 68.4 ± 40.7 | 20.0 ± 11.9 | 0.66 ± 0.58 | 0.02 ± 0.03 | 0.09 ± 0.11 | |
IMO 2020 | 26 | 0.71 ± 0.43 | 2.53 ± 1.42 | 68.2 ± 29.6 | 22.4 ± 8.82 | 0.31 ± 0.16 | 0.01 ± 0.01 | 0.03 ± 0.02 | |
Pre-OWG Beijing 2022 | 14 | 0.53 ± 0.54 | 3.73 ± 3.18 | 64.5 ± 27.4 | 12.9 ± 5.12 | 0.17 ± 0.17 | 0.01 ± 0.06 | 0.05 ± 0.01 | |
Cluster 3 | DECA 1.0 | 15 | 2.92 ± 3.35 | 1.89 ± 1.62 | 48.2 ± 41.4 | 15.6 ± 13.8 | 1.15 ± 0.96 | 0.18 ± 0.19 | 0.06 ± 0.08 |
DECA 2.0 | 26 | 0.68 ± 0.31 | 1.37 ± 1.00 | 48.6 ± 51.2 | 10.6 ± 6.65 | 0.91 ± 1.45 | 0.02 ± 0.03 | 0.10 ± 0.11 | |
IMO 2020 | 29 | 0.61 ± 0.41 | 2.23 ± 2.41 | 51.7 ± 26.3 | 16.9 ± 7.45 | 0.37 ± 0.27 | 0.02 ± 0.03 | 0.05 ± 0.07 | |
Pre-OWG Beijing 2022 | 33 | 0.55 ± 0.47 | 2.83 ± 1.90 | 51.8 ± 36.2 | 10.3 ± 6.04 | 0.22 ± 0.15 | 0.01 ± 0.05 | 0.06 ± 0.01 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Du, J.; Liu, Z.; Tao, W.; Wang, T.; Zhao, J.; Gong, W.; Li, Y.; Xue, L.; Yang, J.; Wang, C.; et al. Characteristics of Trace Metal Elements in Ambient Sub-Micron Particulate Matter in a Coastal Megacity of Northern China Influenced by Shipping Emissions from 2018 to 2022. Atmosphere 2024, 15, 264. https://doi.org/10.3390/atmos15030264
Du J, Liu Z, Tao W, Wang T, Zhao J, Gong W, Li Y, Xue L, Yang J, Wang C, et al. Characteristics of Trace Metal Elements in Ambient Sub-Micron Particulate Matter in a Coastal Megacity of Northern China Influenced by Shipping Emissions from 2018 to 2022. Atmosphere. 2024; 15(3):264. https://doi.org/10.3390/atmos15030264
Chicago/Turabian StyleDu, Jinhua, Ziyang Liu, Wenxin Tao, Ting Wang, Jiaojiao Zhao, Weiwei Gong, Yue Li, Lian Xue, Jianli Yang, Chaolong Wang, and et al. 2024. "Characteristics of Trace Metal Elements in Ambient Sub-Micron Particulate Matter in a Coastal Megacity of Northern China Influenced by Shipping Emissions from 2018 to 2022" Atmosphere 15, no. 3: 264. https://doi.org/10.3390/atmos15030264