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Xu Y, Wang Z, Pei C, Wu C, Huang B, Cheng C, Zhou Z, Li M. Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 930:172822. [PMID: 38688364 DOI: 10.1016/j.scitotenv.2024.172822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024]
Abstract
With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.
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Affiliation(s)
- Yongjiang Xu
- College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-, Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Zaihua Wang
- Institute of Resources Utilization and Rare Earth Development, Guangdong Academy of Sciences, Guangzhou 510650, Guangdong, China
| | - Chenglei Pei
- Guangzhou Ecological and Environmental Monitoring Center of Guangdong Province, Guangzhou 510030, China
| | - Cheng Wu
- College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-, Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Bo Huang
- Guangzhou Hexin Instrument Co., Ltd., Guangzhou 510530, Guangdong, China
| | - Chunlei Cheng
- College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-, Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Zhen Zhou
- College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-, Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Mei Li
- College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Jinan University, Guangzhou 510632, China; Guangdong-Hongkong-, Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China.
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Xu L, Zou Z, Chen J, Fu S. Effects of emission control areas on sulfur-oxides concentrations--Evidence from the coastal ports in China. MARINE POLLUTION BULLETIN 2024; 200:116039. [PMID: 38244359 DOI: 10.1016/j.marpolbul.2024.116039] [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: 10/22/2023] [Revised: 01/01/2024] [Accepted: 01/08/2024] [Indexed: 01/22/2024]
Abstract
The setting of Sulfur limitations in Emission Control Areas (ECAs) is a crucial action of marine environmental governance at the international regulatory levels. In this study, the overall and structural impacts of the two rounds of ECA policies on SOx concentrations were quantified using synthetic control method (SCM) based on time-series data from Chinese coastal ports from 2005 to 2020. From the outcomes, the 1st round of ECA policy announced in 2015 intensified the competition between ECA and non-ECA ports and provided strong support for ECA expansion and enhanced regulation in 2019. In addition, the restrictions on the Sulfur content of marine fuels under the 1st round of ECA policy has only effectively reduced the SOx concentration in the Bohai Rim and the Yangtze River Delta region, whereas the impact on the Pearl River Delta region isn't significant. However, the 2nd round of ECA policy has only effectively impacted the Bohai Rim. In general, the effect of the 1st round of ECA policy is better than that of the 2nd round, which is mainly because the favorable effect of the further expansion of ECA policy is offset by a significant increase in vessel activity in Chinese coastal ports.
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Affiliation(s)
- Lang Xu
- College of Transport & Communications, Shanghai Maritime University, Shanghai, China.
| | - Zeyuan Zou
- College of Transport & Communications, Shanghai Maritime University, Shanghai, China
| | - Jihong Chen
- College of Management, Shenzhen University, Shenzhen, Guangdong, China.
| | - Shanshan Fu
- College of Transport & Communications, Shanghai Maritime University, Shanghai, China.
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Zhang Y, Li W, Li L, Li M, Zhou Z, Yu J, Zhou Y. Source apportionment of PM 2.5 using PMF combined online bulk and single-particle measurements: Contribution of fireworks and biomass burning. J Environ Sci (China) 2024; 136:325-336. [PMID: 37923442 DOI: 10.1016/j.jes.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/21/2022] [Accepted: 12/12/2022] [Indexed: 11/07/2023]
Abstract
Fireworks (FW) could significantly worsen air quality in short term during celebrations. Due to similar tracers with biomass burning (BB), the fast and precise qualification of FW and BB is still challenging. In this study, online bulk and single-particle measurements were combined to investigate the contributions of FW and BB to the overall mass concentrations of PM2.5 and specific chemical species by positive matrix factorization (PMF) during the Chinese New Year in Hong Kong in February 2013. With combined information, fresh/aged FW (abundant 140K2NO3+ and 213K3SO4+ formed from 113K2Cl+ discharged by fresh FW) can be extracted from the fresh/aged BB sources, in addition to the Second Aerosol, Vehicles + Road Dust, and Sea Salt factors. The contributions of FW and BB were investigated during three high particle matter episodes influenced by the pollution transported from the Pearl River Delta region. The fresh BB/FW contributed 39.2% and 19.6% to PM2.5 during the Lunar Chinese New Year case. However, the contributions of aged FW/BB enhanced in the last two episodes due to the aging process, evidenced by high contributions from secondary aerosols. Generally, the fresh BB/FW showed more significant contributions to nitrate (35.1% and 15.0%, respectively) compared with sulfate (25.1% and 5.9%, respectively) and OC (14.8% and 11.1%, respectively) on average. In comparison, the aged FW contributed more to sulfate (13.4%). Overall, combining online bulk and single-particle measurement data can combine both instruments' advantages and provide a new perspective for applying source apportionment of aerosols using PMF.
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Affiliation(s)
- Yanjing Zhang
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao, Shandong 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, Shandong 266100, China
| | - Wenshuai Li
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao, Shandong 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, Shandong 266100, China
| | - Lei Li
- Institute of Atmospheric Environment Safety and Pollution Control, Jinan University, Guangdong 510632, China
| | - Mei Li
- Institute of Atmospheric Environment Safety and Pollution Control, Jinan University, Guangdong 510632, China
| | - Zhen Zhou
- Institute of Atmospheric Environment Safety and Pollution Control, Jinan University, Guangdong 510632, China
| | - Jianzhen Yu
- Institute of Environment, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China; Department of Chemistry, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China; Division of Environment, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Yang Zhou
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao, Shandong 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, Shandong 266100, China.
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Feng X, Ma Y, Lin H, Fu TM, Zhang Y, Wang X, Zhang A, Yuan Y, Han Z, Mao J, Wang D, Zhu L, Wu Y, Li Y, Yang X. Impacts of Ship Emissions on Air Quality in Southern China: Opportunistic Insights from the Abrupt Emission Changes in Early 2020. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:16999-17010. [PMID: 37856868 DOI: 10.1021/acs.est.3c04155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
In early 2020, two unique events perturbed ship emissions of pollutants around Southern China, proffering insights into the impacts of ship emissions on regional air quality: the decline of ship activities due to COVID-19 and the global enforcement of low-sulfur (<0.5%) fuel oil for ships. In January and February 2020, estimated ship emissions of NOx, SO2, and primary PM2.5 over Southern China dropped by 19, 71, and 58%, respectively, relative to the same period in 2019. The decline of ship NOx emissions was mostly over the coastal waters and inland waterways of Southern China due to reduced ship activities. The decline of ship SO2 and primary PM2.5 emissions was most pronounced outside the Chinese Domestic Emission Control Area due to the switch to low-sulfur fuel oil there. Ship emission reductions in early 2020 drove 16 to 18% decreases in surface NO2 levels but 3.8 to 4.9% increases in surface ozone over Southern China. We estimated that ship emissions contributed 40% of surface NO2 concentrations over Guangdong in winter. Our results indicated that future abatements of ship emissions should be implemented synergistically with reductions of land-borne anthropogenic emissions of nonmethane volatile organic compounds to effectively alleviate regional ozone pollution.
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Affiliation(s)
- Xu Feng
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Yaping Ma
- National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
| | - Haipeng Lin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Shenzhen National Center for Applied Mathematics, Shenzhen 518055, Guangdong, China
- Center for Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yan Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xiaolin Wang
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Aoxing Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yupeng Yuan
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Zimin Han
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jingbo Mao
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Dakang Wang
- School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, Guangdong, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yujie Wu
- School of Public and International Affairs, Princeton University, Princeton, New Jersey 08544, United States
| | - Ying Li
- Center for Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Department of Ocean Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
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Xiong X, Wang Z, Cheng C, Li M, Yun L, Liu S, Mao L, Zhou Z. Long-Term Observation of Mixing States and Sources of Vanadium-Containing Single Particles from 2020 to 2021 in Guangzhou, China. TOXICS 2023; 11:339. [PMID: 37112565 PMCID: PMC10144789 DOI: 10.3390/toxics11040339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
The distribution of vanadium (V) in aerosols is commonly used to track ship exhaust emissions, yet the atmospheric abundance of V has been greatly reduced due to the implementation of a clean fuel policy. Recent research mainly discussed the chemical compositions of ship-related particles during specific events, yet few studies focus on the long-term changes of V in the atmosphere. In this study, a single-particle aerosol mass spectrometer was used to measure V-containing particles from 2020 to 2021 in Huangpu Port in Guangzhou, China. The long-term trend of the particle counts of V-containing particles declined annually, but the relative abundance of V-containing particles in the total single particles increased in summer due to the influence of ship emissions. Positive matrix factorization revealed that in June and July 2020, 35.7% of the V-containing particles were from ship emissions, followed by dust and industrial emissions. Furthermore, more than 80% of the V-containing particles were found mixing with sulfate and 60% of the V-containing particles were found mixing with nitrate, suggesting that the majority of the V-containing particles were secondary particles processed during the transport of ship emissions to urban areas. Compared with the small changes in the relative abundance of sulfate in the V-containing particles, the relative abundance of nitrate exhibited clear seasonal variations, with a high abundance in winter. This may have been due to the increased production of nitrate from high concentrations of precursors and a suitable chemical environment. For the first time, the long-term trends of V-containing particles in two years are investigated to demonstrate changes in their mixing states and sources after the clean fuel policy, and to suggest the cautious application of V as an indicator of ship emissions.
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Affiliation(s)
- Xin Xiong
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Zaihua Wang
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
- Institute of Resources Utilization and Rare Earth Development, Guangdong Academy of Sciences, Guangzhou 510650, China
| | - Chunlei Cheng
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy Science, Xi’an 710061, China
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Mei Li
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Lijun Yun
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Sulin Liu
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Liyuan Mao
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Zhen Zhou
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for Online Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
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Wang N, Zhang Y, Li L, Wang H, Zhao Y, Wu G, Li M, Zhou Z, Wang X, Yu JZ, Zhou Y. Ambient particle characteristics by single particle aerosol mass spectrometry at a coastal site in Hong Kong: a case study affected by the sea-land breeze. PeerJ 2022; 10:e14116. [PMID: 36325180 PMCID: PMC9620973 DOI: 10.7717/peerj.14116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/04/2022] [Indexed: 01/21/2023] Open
Abstract
The sea-land breeze (SLB) circulation plays a vital role in the transport of atmospheric pollutants in coastal cities. In this study, a single particle aerosol mass spectrometer (SPAMS) and combined bulk aerosol instruments were deployed to investigate the ambient particle characteristic at a suburban coastal site in Hong Kong from February 22 to March 10, 2013. Significant SLB circulations were captured from March 6-10, 2013, during the campaign. During the SLB periods, air quality worsened, with PM2.5 concentrations reaching a peak of 55.6 μg m-3 and an average value of 42.8 ± 4.5 μg m-3. A total of 235,894 particles were measured during the SLB stage. Eight major sources were identified by investigating the mixing states of the total particles, including the coal-burning related particles (48.1%), biomass burning particles (6.7%), vehicle emission-related particles (16.4%), sea salt (9.2%), ship emission particles (2.7%), dust/steeling industries (3.7%), waste incineration (6.3%), and road dust (3.9%). It was noteworthy that the PM2.5 concentrations and particle numbers increased sharply during the transition of land wind to the sea breeze. Meanwhile, the continental sourced pollutants recirculated back to land resulting in a cumulative increase in pollutants. Both individual and bulk measurements support the above results, with high contributions from coal burning, biomass burning, bulk K+, and NO3 -, which were probably from the regional transportation from the nearby area. In contrast, the ship and vehicle emissions increased during the SLB period, with a high sulfate concentration partially originating from the ship emission. In this study, field evidence of continental-source pollutants backflow to land with the evolution of sea breeze was observed and helped our current understanding of the effect of SLB on air quality in the coastal city.
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Affiliation(s)
- Nana Wang
- College of Oceanic and Atmospheric Sciences, Ocean University of Qingdao, Qingdao, China
| | - Yanjing Zhang
- College of Oceanic and Atmospheric Sciences, Ocean University of Qingdao, Qingdao, China
| | - Lei Li
- Institute of Atmospheric Environment Safety and Pollution Control, Jinan University, Guangdong, China
| | - Houwen Wang
- College of Oceanic and Atmospheric Sciences, Ocean University of Qingdao, Qingdao, China
| | - Yunhui Zhao
- College of Oceanic and Atmospheric Sciences, Ocean University of Qingdao, Qingdao, China
| | - Guanru Wu
- College of Oceanic and Atmospheric Sciences, Ocean University of Qingdao, Qingdao, China
| | - Mei Li
- Institute of Atmospheric Environment Safety and Pollution Control, Jinan University, Guangdong, China
| | - Zhen Zhou
- Institute of Atmospheric Environment Safety and Pollution Control, Jinan University, Guangdong, China
| | - Xinfeng Wang
- Environment Research Institute, Shandong University, Qingdao, China
| | - Jian Zhen Yu
- Division of Environment, Hong Kong University of Science and Technology, Kowloon, Hong Kong,Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong
| | - Yang Zhou
- College of Oceanic and Atmospheric Sciences, Ocean University of Qingdao, Qingdao, China
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Influence of Ambient Atmospheric Environments on the Mixing State and Source of Oxalate-Containing Particles at Coastal and Suburban Sites in North China. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Photodegradation is a key process impacting the lifetime of oxalate in the atmosphere, but few studies investigated this process in the field due to the complex mixing and sources of oxalate. Oxalate-containing particles were measured via single-particle aerosol mass spectrometry at coastal and suburban sites in Qingdao, a coastal city in North China in the summer of 2016. The mixing state and influence of different ambient conditions on the source and photodegradation of oxalate were investigated. Generally, 6.3% and 12.3% of the total particles (by number) contained oxalate at coastal and suburban sites, respectively. Twelve major types of oxalate-containing particles were identified, and they were classified into three groups. Biomass burning (BB)-related oxalate–K and oxalate–carbonaceous particles were the dominant groups, respectively, accounting for 68.9% and 13.6% at the coastal site and 72.0% and 16.8% at the suburban site. Oxalate–Heavy metals (HM)-related particles represented 14.6% and 9.3% of the oxalate particles at coastal and suburban sites, respectively, which were mainly from industrial emissions (Cu-rich, Fe-rich, Pb-rich), BB (Zn-rich), and residual fuel oil combustion (V-rich). The peak area of oxalate at the coastal site decreased immediately after sunrise, while it increased during the daytime at the suburban site. However, the oxalate peak area of Fe-rich particles at both sites decreased after sunrise, indicating that iron plays an important role in oxalate degradation in both environments. The decay rates (k) of Fe-rich and BB-Fe particles at the coastal site (−0.978 and −0.859 h−1, respectively), were greater than those at the suburban site (−0.512 and −0.178 h−1, respectively), owing to the high-water content of particles and fewer oxalate precursors. The estimated k values of oxalate peak area for different ambient conditions were in the same order of magnitude, which can help establish or validate the future atmospheric models.
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