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Shen X, Che H, Yao Z, Wu B, Lv T, Yu W, Cao X, Hao X, Li X, Zhang H, Yao X. Real-World Emission Characteristics of Full-Volatility Organics Originating from Nonroad Agricultural Machinery during Agricultural Activities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37419883 DOI: 10.1021/acs.est.3c02619] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Nonroad agricultural machinery (NRAM) emissions constitute a significant source of air pollution in China. Full-volatility organics originating from 19 machines under 6 agricultural activities were measured synchronously. The diesel-based emission factors (EFs) for full-volatility organics were 4.71 ± 2.78 g/kg fuel (average ± standard deviation), including 91.58 ± 8.42% volatile organic compounds (VOCs), 7.94 ± 8.16% intermediate-volatility organic compounds (IVOCs), 0.28 ± 0.20% semivolatile organic compounds (SVOCs), and 0.20 ± 0.16% low-volatility organic compounds (LVOCs). Full-volatility organic EFs were significantly reduced by stricter emission standards and were the highest under pesticide spraying activity. Our results also demonstrated that combustion efficiency was a potential factor influencing full-volatility organic emissions. Gas-particle partitioning in full-volatility organics could be affected by multiple factors. Furthermore, the estimated secondary organic aerosol formation potential based on measured full-volatility organics was 143.79 ± 216.80 mg/kg fuel and could be primarily attributed to higher-volatility-interval IVOCs (bin12-bin16 contributed 52.81 ± 11.58%). Finally, the estimated emissions of full-volatility organics from NRAM in China (2021) were 94.23 Gg. This study provides first-hand data on full-volatility organic EFs originating from NRAM to facilitate the improvement of emission inventories and atmospheric chemistry models.
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Affiliation(s)
- Xianbao Shen
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Hongqian Che
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Bobo Wu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Tiantian Lv
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
| | - Wenhan Yu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
| | - Xinyue Cao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xuewei Hao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xin Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Hanyu Zhang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xiaolong Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
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Wen C, Lang J, Zhou Y, Fan X, Bian Z, Chen D, Tian J, Wang P. Emission and influences of non-road mobile sources on air quality in China, 2000-2019. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 324:121404. [PMID: 36893973 DOI: 10.1016/j.envpol.2023.121404] [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: 12/14/2022] [Revised: 02/16/2023] [Accepted: 03/05/2023] [Indexed: 06/18/2023]
Abstract
Non-road mobile sources (NRMS) are potential important contributors to air pollution in China. However, their extreme impact on air quality had been seldom studied. In this study, the emission inventory of NRMS in mainland China during 2000-2019 was established. Then, the validated WRF-CAMx-PSAT model was applied to simulate the contribution to the atmospheric PM2.5, NO3-, and NOx. Results showed that emissions increased rapidly since 2000 and reached a peak in 2014-2015, with an annual average change rate (AACR) of 8.7-10.0%; after then, the emissions were relatively stable (AACR, -1.4-1.5%). The modeling results indicated that NRMS has become a crucial contributor to the air quality in China: from 2000 to 2019, the contribution to PM2.5, NOx, and NO3- significantly increased by 131.1%, 43.9%, and 61.7%; and for NOx, the contribution ratio in 2019 reached 24.1%. Further analysis showed that the reduction (-0.8% and -0.5%) of the NOx and NO3- contribution ratios was much lower than that (-4.8%) of NOx emissions from 2015 to 2019, implying that the control of NRMS lagged behind the national overall pollution control level. The contribution ratio of agricultural machinery (AM) and construction machinery (CM) to PM2.5, NOx, NO3- in 2019 was 2.6%, 11.3%, 8.3% and 2.5%, 12.6%, 6.8%, respectively. Although the contribution was much lower, the contribution ratio of civil aircraft had the fastest growth (202-447%). Moreover, an interesting phenomenon was that AM and CM had opposite contribution sensitivity characteristics for air pollutants: CM had a higher Contribution Sensitivity Index (CSI) for primary pollutants (e.g., NOx), ∼1.1 times that of AM; while AM had a higher CSI for secondary pollutants (e.g., NO3-), ∼1.5 times that of CM. This work can provide a deeper understanding for the environmental impact of NRMS emissions and for the control strategy formulation of NRMS.
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Affiliation(s)
- Chaoyu Wen
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
| | - Jianlei Lang
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China; Beijing Laboratory for Intelligent Environmental Protection, Beijing University of Technology, Beijing, 100124, China.
| | - Ying Zhou
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
| | - Xiaohan Fan
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
| | - Zejun Bian
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
| | - Dongsheng Chen
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
| | - Jingjing Tian
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
| | - Peiruo Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
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Bao H, Liu X, Xu X, Shan L, Ma Y, Qu X, He X. Spatial-temporal evolution and convergence analysis of agricultural green total factor productivity-evidence from the Yangtze River Delta Region of China. PLoS One 2023; 18:e0271642. [PMID: 36940226 PMCID: PMC10027226 DOI: 10.1371/journal.pone.0271642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 03/01/2023] [Indexed: 03/21/2023] Open
Abstract
Measuring regional differences in agricultural green total factor productivity (AGTFP) provides a basis for policy guidance on agricultural green development in the Yangtze River Delta (YRD) region. By constructing a two-period Malmquist-Luenberger index under the carbon emission constraint, we measure the AGTFP of cities in the YRD region from 2001 to 2019. Furthermore, adopting the Moran index method and the hot spot analysis method, this paper analyzes the global spatial correlation and local spatial correlation of AGTFP in this region. Moreover, we investigate its spatial convergence. The results show that the AGTFP of 41 cities in the YRD region is on an increasing trend; the growth of AGTFP in the eastern cities is mainly driven by green technical efficiency, while this growth in the southern cities is mainly stimulated by green technical efficiency and green technological progress. We also find a significant spatial correlation between cities' AGTFP in the YRD region from 2001 to 2019, but with certain fluctuations, showing a U-shaped trend of "strong-weak-strong". In addition, absolute β convergence of the AGTFP exists in the YRD region, and this convergence speed is accelerated with the addition of spatial factors. This evidence provides support for implementing the regional integration development strategy and optimizing the regional agricultural spatial layout. Our findings offer implications for promoting the transfer of green agricultural technology to the southwest of the YRD region, strengthening the construction of agricultural economic belts and agricultural economic circles, and improving the efficiency of agricultural resource use.
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Affiliation(s)
- Hongjie Bao
- School of Management, Northwest Minzu University, Lanzhou, China
| | - Xiaoqian Liu
- Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu, China
| | - Xiaoyong Xu
- Department of Logistics, LanZhou University, Lanzhou, China
| | - Ling Shan
- School of Business Administration, Zhongnan University of Economics and Law, Wuhan, China
| | - Yongteng Ma
- School of Economic, Northwest Minzu University, Lanzhou, China
| | - Xiaoshuang Qu
- Business School, Zhengzhou University of Aeronautics, Zhengzhou, China
| | - Xiangyu He
- Cantoese Merchants Business School, Guangdong University of Finance and Economics, Guangzhou, China
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Jiang H, Zhang H, Fu M, Huang Z, Ni H, Yin H, Ding Y. Recent advances and perspectives towards emission inventories of mobile sources: Compilation approaches, data acquisition methods, and case studies. J Environ Sci (China) 2023; 123:460-475. [PMID: 36522006 DOI: 10.1016/j.jes.2022.09.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 09/09/2022] [Accepted: 09/09/2022] [Indexed: 06/17/2023]
Abstract
In recent years, great efforts have been devoted to reducing emissions from mobile sources with the dramatic growth of motor vehicle and nonroad mobile source populations. Compilation of a mobile source emission inventory is conducive to the analysis of pollution emission characteristics and the formulation of emission reduction policies. This study summarizes the latest compilation approaches and data acquisition methods for mobile source emission inventories. For motor vehicles, a high-resolution emission inventory can be developed based on a bottom-up approach with a refined traffic flow model and real-world speed-coupled emission factors. The top-down approach has advantages when dealing with macroscale vehicle emission estimation without substantial traffic flow infrastructure. For nonroad mobile sources, nonroad machinery, inland river ships, locomotives, and civil aviation aircraft, a top-down approach based on fuel consumption or power is adopted. For ocean-going ships, a bottom-up approach based on automatic identification system (AIS) data is adopted. Three typical cases are studied, including emission reduction potential, a cost-benefit model, and marine shipping emission control. Outlooks and suggestions are given on future research directions for emission inventories for mobile sources: building localized emission models and factor databases, improving the dynamic updating capability of emission inventories, establishing a database of emission factors of unconventional pollutants and greenhouse gas from mobile sources, and establishing an urban high temporal-spatial resolution volatile organic compound (VOC) evaporation emission inventory.
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Affiliation(s)
- Han Jiang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Hefeng Zhang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Mingliang Fu
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Zhihui Huang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Hong Ni
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Hang Yin
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yan Ding
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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Wu B, Wu Z, Yao Z, Li J, Wang W, Shen X, Hao X. Multi-type emission factors quantification of black carbon from agricultural machinery based on the whole tillage processes in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 314:120280. [PMID: 36167170 DOI: 10.1016/j.envpol.2022.120280] [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: 08/10/2022] [Revised: 09/19/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Black carbon (BC), as one of the short-lived climate pollutants, is becoming more prominent contribution from non-road mobile source, especially for agricultural machinery (AM) in China. However, the understanding of BC emissions from AM is still not clear, and the BC emission factors (EFs) are also limited. In this study, we conducted real-world measurements on twenty AM to investigate the instantaneous BC emission characteristics and quantify BC EFs under the whole tillage processes. We find the instantaneous BC emissions and fuel consumptions are obvious differences and present good synchronization under different tillage processes. Multi-type (CO2-, fuel-, distance-, time-, and area-based) EFs of BC are developed, which are significantly affected by different tillage processes and emission standards of the used AM. While AM conducting rotary tillage, ploughing, harvest corn and harvest wheat on the same area of land, total BC emissions by using the China III emission standard AM will be reduced by 56%, 36%, 88%, and 87% than those by using China II emission standard AM, respectively. Furthermore, for corn and wheat production under the whole tillage processes, BC EFs are 16.90 (6.03-39.12) g/hm2 and 18.18 (5.91-38.69) g/hm2, CO2 EFs are 112.64 (72.07-195.98) g/hm2 and 103.72 (71.47-167.02) g/hm2, respectively. We estimate the BC and CO2 emissions from wheat and corn productions based on the average area-based EFs. The large fluctuation ranges of BC and CO2 emissions in different tillage processes and the whole processes can reflect that the use of AM in China is uneven. It also indicates that there is a large space for BC and CO2 emission reduction and optimization. Therefore, more attention should be paid to the control of BC and CO2 emissions from AM. We believe that the recommended multi-type EFs are applicable for the quantification of BC emissions from AM in China and other countries.
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Affiliation(s)
- Bobo Wu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Zichun Wu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China.
| | - Jiahan Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Weijun Wang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Xianbao Shen
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
| | - Xuewei Hao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing, 100048, China
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Ji J, Zhang H, Peng D, Fu M, He C, Yi F, Yin H, Ding Y. Estimation of typical agricultural machinery emissions in China: Real-world emission factors and inventories. CHEMOSPHERE 2022; 307:136052. [PMID: 35977564 DOI: 10.1016/j.chemosphere.2022.136052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Jianglin Ji
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; School of Mechanical and Traffic Engineering, Southwest Forestry University, Kunming, 650224, China; Key Lab of Vehicle Emission and Safety on Plateau Mountain, Yunnan Provincial Department of Education, Kunming, 650224, China
| | - Hefeng Zhang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Di Peng
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Mingliang Fu
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Chao He
- School of Mechanical and Traffic Engineering, Southwest Forestry University, Kunming, 650224, China; Key Lab of Vehicle Emission and Safety on Plateau Mountain, Yunnan Provincial Department of Education, Kunming, 650224, China
| | - Fei Yi
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Hang Yin
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Yan Ding
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
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Liu Z, Ding X, Haider MS, Ali F, Yu H, Chen X, Tan S, Zu Y, Liu W, Ding B, Zheng A, Zheng J, Qian Z, Ashfaq H, Yu D, Li K. A metagenomic insight into the Yangtze finless porpoise virome. Front Vet Sci 2022; 9:922623. [PMID: 36118360 PMCID: PMC9478467 DOI: 10.3389/fvets.2022.922623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The Yangtze finless porpoise (Neophocaena phocaenoides asiaeorientalis) inhabiting the Yantze River, China is critically endangered because of the influences of infectious disease, human activity, and water contamination. Viral diseases are one of the crucial factors that threatening the health of Yangtze finless porpoise. However, there are few studies which elaborate the viral diversity of Yangtze finless. Therefore, this study was performed to investigate the viral diversity of Yangtze finless by metagenomics. Results indicated that a total of 12,686,252 high-quality valid sequences were acquired and 2,172 virus reads were recognized. Additionally, we also obtained a total of 10,600 contigs. Phages was the most abundant virus in the samples and the ratio of DNA and RNA viruses were 69.75 and 30.25%, respectively. Arenaviridae, Ackermannviridae and Siphoviridae were the three most predominant families in all the samples. Moreover, the majority of viral genus were Mammarenavirus, Limestonevirus and Lambdavirus. The results of gene prediction indicated that these viruses play vital roles in biological process, cellular component, molecular function, and disease. To the best of our knowledge, this is the first report on the viral diversity of Yangtze finless porpoise, which filled the gaps in its viral information. Meanwhile, this study can also provide a theoretical basis for the establishment of the prevention and protection system for virus disease of Yangtze finless porpoise.
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Affiliation(s)
- Zhigang Liu
- College of Life Science, Anqing Normal University, Anqing, China
- Research Center of Aquatic Organism Conservation and Water Ecosystem Restoration in Anhui Province, Anqing Normal University, Anqing, China
- Zhigang Liu
| | - Xin Ding
- College of Life Science, Anqing Normal University, Anqing, China
| | | | - Farah Ali
- Department of Theriogenology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Han Yu
- College of Life Science, Anqing Normal University, Anqing, China
| | - Xin Chen
- College of Life Science, Anqing Normal University, Anqing, China
| | - Shuaishuai Tan
- College of Life Science, Anqing Normal University, Anqing, China
| | - Yuan Zu
- College of Life Science, Anqing Normal University, Anqing, China
| | - Wenlong Liu
- College of Life Science, Anqing Normal University, Anqing, China
| | - Bangzhi Ding
- College of Life Science, Anqing Normal University, Anqing, China
| | - Aifang Zheng
- College of Life Science, Anqing Normal University, Anqing, China
| | - Jinsong Zheng
- Institute of Hydrobiology, Chinese Academy of Sciences, Beijing, China
| | - Zhengyi Qian
- Hubei Yangtze River Ecological Protection Foundation, Wuhan, China
| | - Hassan Ashfaq
- Institute of Continuing Education and Extension, University of Veterinary Animal Sciences, Lahore, Pakistan
| | - Daoping Yu
- College of Life Science, Anqing Normal University, Anqing, China
- Research Center of Aquatic Organism Conservation and Water Ecosystem Restoration in Anhui Province, Anqing Normal University, Anqing, China
| | - Kun Li
- Institute of Traditional Chinese Veterinary Medicine, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, China
- MOE Joint International Research Laboratory of Animal Health and Food Safety, Nanjing Agricultural University, Nanjing, China
- *Correspondence: Kun Li
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Tu R, Li T, Meng C, Chen J, Sheng Z, Xie Y, Xie F, Yang F, Chen H, Li Y, Gao J, Liu Y. Real-world emissions of construction mobile machines and comparison to a non-road emission model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 771:145365. [PMID: 33736176 DOI: 10.1016/j.scitotenv.2021.145365] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/25/2020] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
This study implemented real-world tests in Nanjing, China for measuring emission factors (EFs) of air pollutants, including Carbon Monoxide (CO), Hydrocarbon (HC), Nitrogen Oxides (NOx), and Particulate Matter (PM) from ten construction machines in three operational modes (idling, moving, and working) with a Portable Emission Measurement System. The idling mode shows the least variation of EFs, and its average CO EFs can be higher than the moving and working modes by 43% and 34%, respectively. The working mode generates the highest emission for all other pollutants with the highest variation. The EFs suggested by the Guide (an official guidebook for developing emission inventory in China) are in general lower than the measured EFs, and the gap becomes larger for older machines. The EFs of CO, NOx, and PM of China Stage II machines are 24%, 120%, and 66% higher than those of the Guide, respectively. The differences go up as high as 126%, 1066%, and 559% for China Stage I machines, indicating the upgrade of engine technology from Stage I to Stage II, as well as the effect of machine deterioration. The result of this study reveals the effectiveness of stringent emission standards in controlling emissions from construction machines. High emissions from older machines emphasize the importance of a more rigorous machine replacement policy and a regulated maintenance strategy. The result also stresses the need to update the Guide with differentiated activity modes, region variations, and machine deterioration effects.
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Affiliation(s)
- Ran Tu
- School of Transportation, Southeast University, Nanjing, China.
| | - Tiezhu Li
- School of Transportation, Southeast University, Nanjing, China.
| | - Chunsheng Meng
- School of Transportation, Southeast University, Nanjing, China.
| | - Jinyi Chen
- School of Transportation, Southeast University, Nanjing, China.
| | - Zhen Sheng
- School of Medicine, Southeast University, Nanjing, China.
| | - Yisong Xie
- Nanjing Institute of Ecological Environmental Protection, Nanjing, China.
| | - Fangjian Xie
- Nanjing Institute of Ecological Environmental Protection, Nanjing, China.
| | - Feng Yang
- Nanjing Institute of Ecological Environmental Protection, Nanjing, China.
| | - Haibo Chen
- Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
| | - Ying Li
- Dynnoteq, 61 Bridge Street, Kington HR5 3DJ, UK.
| | - Jianbing Gao
- Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
| | - Ye Liu
- Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
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The Research Progress of the Influence of Agricultural Activities on Atmospheric Environment in Recent Ten Years: A Review. ATMOSPHERE 2021. [DOI: 10.3390/atmos12050635] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
In recent years, the industrial emission of air pollution has been reduced via a series of measures. However, with the rapid development of modern agriculture, air pollution caused by agricultural activities is becoming more and more serious. Agricultural activities can generate a large amount of air pollutants, such as ammonia, methane, nitrogen oxides, volatile organic compounds, and persistent organic pollutants, the sources of which mainly include farmland fertilization, livestock breeding, pesticide use, agricultural residue burning, agricultural machinery, and agricultural irrigation. Greenhouse gases emitted by agricultural activities can affect regional climate change, while atmospheric particulates and persistent organic pollutants can even seriously harm the health of surrounding residents. With the increasing threat of agricultural air pollution, more and more relevant studies have been carried out, as well as some recommendations for reducing emissions. The emissions of ammonia and greenhouse gases can be significantly reduced by adopting reasonable fertilization methods, scientific soil management, and advanced manure treatment systems. Regarding pesticide use and agricultural residues burning, emission reduction are more dependent on the restriction and support of government regulations, such as banning certain pesticides, prohibiting open burning of straw, and supporting the recycling and reuse of residues. This review, summarizing the relevant research in the past decade, discusses the current situation, health effects, and emission reduction measures of agricultural air pollutants from different sources, in order to provide some help for follow-up research.
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