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Lei Y, Yin Z, Lu X, Zhang Q, Gong J, Cai B, Cai C, Chai Q, Chen H, Chen R, Chen S, Chen W, Cheng J, Chi X, Dai H, Feng X, Geng G, Hu J, Hu S, Huang C, Li T, Li W, Li X, Liu J, Liu X, Liu Z, Ma J, Qin Y, Tong D, Wang X, Wang X, Wu R, Xiao Q, Xie Y, Xu X, Xue T, Yu H, Zhang D, Zhang N, Zhang S, Zhang S, Zhang X, Zhang X, Zhang Z, Zheng B, Zheng Y, Zhou J, Zhu T, Wang J, He K. The 2022 report of synergetic roadmap on carbon neutrality and clean air for China: Accelerating transition in key sectors. Environ Sci Ecotechnol 2024; 19:100335. [PMID: 37965046 PMCID: PMC10641488 DOI: 10.1016/j.ese.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/16/2023]
Abstract
China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.
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Affiliation(s)
- Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jicheng Gong
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Qimin Chai
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Huopo Chen
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health, Fudan University, Shanghai, 200032, China
| | - Shi Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Wenhui Chen
- School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Jing Cheng
- Department of Earth System Science, University of California, Irvine, CA, 92697, USA
| | - Xiyuan Chi
- National Meteorological Center, China Meteorological Administration, Beijing, 100081, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Xiangzhao Feng
- Policy Research Center for Environment and Economy, Ministry of Ecology and Environment of the People's Republic of China, Beijing, 100029, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Shan Hu
- Building Energy Research Center, School of Architecture, Tsinghua University, Beijing, 100084, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Wei Li
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xiaomei Li
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Jun Liu
- Department of Environmental Engineering, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xin Liu
- Energy Foundation China, Beijing, 100004, China
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jinghui Ma
- Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai, 200030, China
| | - Yue Qin
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xuhui Wang
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xuying Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Rui Wu
- Transport Planning and Research Institute (TPRI) of the Ministry of Transport, Beijing, 100028, China
| | - Qingyang Xiao
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Xiaolong Xu
- China Association of Building Energy Efficiency, Beijing, 100029, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100080, China
| | - Haipeng Yu
- Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Da Zhang
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Ning Zhang
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xian Zhang
- The Administrative Centre for China's Agenda 21 (ACCA21), Ministry of Science and Technology (MOST), Beijing, 100038, China
| | - Xin Zhang
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Zengkai Zhang
- State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, Xiamen, 361102, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Jian Zhou
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Tong Zhu
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Jinnan Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
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Zhang Q, Yin Z, Lu X, Gong J, Lei Y, Cai B, Cai C, Chai Q, Chen H, Dai H, Dong Z, Geng G, Guan D, Hu J, Huang C, Kang J, Li T, Li W, Lin Y, Liu J, Liu X, Liu Z, Ma J, Shen G, Tong D, Wang X, Wang X, Wang Z, Xie Y, Xu H, Xue T, Zhang B, Zhang D, Zhang S, Zhang S, Zhang X, Zheng B, Zheng Y, Zhu T, Wang J, He K. Synergetic roadmap of carbon neutrality and clean air for China. Environ Sci Ecotechnol 2023; 16:100280. [PMID: 37273886 PMCID: PMC10236195 DOI: 10.1016/j.ese.2023.100280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 06/06/2023]
Abstract
It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.
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Affiliation(s)
- Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Zhicong Yin
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Jicheng Gong
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Cilan Cai
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Qimin Chai
- National Center for Climate Change, Strategy and International Cooperation, Beijing, 100035, China
| | - Huopo Chen
- Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Zhanfeng Dong
- Institute of Environmental Policy Management, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Dabo Guan
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Jianing Kang
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Wei Li
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yongsheng Lin
- School of Economics and Resource Management, Beijing Normal University, Beijing, 100875, China
| | - Jun Liu
- Department of Environmental Engineering, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xin Liu
- Energy Foundation China, Beijing, 100004, China
| | - Zhu Liu
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Jinghui Ma
- Shanghai Typhoon Institute, Shanghai Meteorological Service, Shanghai, 200030, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xuhui Wang
- College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Xuying Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Zhili Wang
- State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Honglei Xu
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport of the People's Republic of China, Beijing, 100028, China
| | - Tao Xue
- Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100080, China
| | - Bing Zhang
- State Key Laboratory of Pollution Control & Resource Reuse School of Environment, Nanjing University, Nanjing, 210008, China
| | - Da Zhang
- Institute of Energy, Environment, and Economy, Tsinghua University, Beijing, 100084, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xian Zhang
- The Administrative Centre for China's Agenda 21 (ACCA21), Ministry of Science and Technology (MOST), Beijing, 100038, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Tong Zhu
- State Key Joint Laboratory for Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering and Center for Environment and Health, Peking University, Beijing, 100871, China
| | - Jinnan Wang
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100012, China
- Institute of Environmental Policy Management, Chinese Academy of Environmental Planning, Beijing, 100012, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
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Viana M, Rizza V, Tobías A, Carr E, Corbett J, Sofiev M, Karanasiou A, Buonanno G, Fann N. Estimated health impacts from maritime transport in the Mediterranean region and benefits from the use of cleaner fuels. Environ Int 2020; 138:105670. [PMID: 32203802 PMCID: PMC8314305 DOI: 10.1016/j.envint.2020.105670] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 03/05/2020] [Accepted: 03/16/2020] [Indexed: 05/19/2023]
Abstract
Ship traffic emissions degrade air quality in coastal areas and contribute to climate impacts globally. The estimated health burden of exposure to shipping emissions in coastal areas may inform policy makers as they seek to reduce exposure and associated potential health impacts. This work estimates the PM2.5-attributable impacts in the form of premature mortality and cardiovascular and respiratory hospital admissions, from long-term exposure to shipping emissions. Health impact assessment (HIA) was performed in 8 Mediterranean coastal cities, using a baseline conditions from the literature and a policy case accounting for the MARPOL Annex VI rules requiring cleaner fuels in 2020. Input data were (a) shipping contributions to ambient PM2.5 concentrations based on receptor modelling studies found in the literature, (b) population and health incidence data from national statistical registries, and (c) geographically-relevant concentration-response functions from the literature. Long-term exposure to ship-sourced PM2.5 accounted for 430 (95% CI: 220-650) premature deaths per year, in the 8 cities, distributed between groups of cities: Barcelona and Athens, with >100 premature deaths/year, and Nicosia, Brindisi, Genoa, Venice, Msida and Melilla, with tens of premature deaths/year. The more stringent standards in 2020 would reduce the number of PM2.5-attributable premature deaths by 15% on average. HIA provided a comparative assessment of the health burden of shipping emissions across Mediterranean coastal cities, which may provide decision support for urban planning with a special focus on harbour areas, and in view of the reduction in sulphur content of marine fuels due to MARPOL Annex VI in 2020.
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Affiliation(s)
- M Viana
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain.
| | - V Rizza
- Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino (FR), Italy
| | - A Tobías
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain
| | - E Carr
- Energy and Environmental Research Associates, LLC, Pittsford, NY, United States
| | - J Corbett
- College of Earth, Ocean, and Environment, University of Delaware, Newark, DE, United States
| | - M Sofiev
- Finnish Meteorological Institute (FMI), Helsinki, Finland
| | - A Karanasiou
- Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain
| | - G Buonanno
- Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Cassino (FR), Italy; Queensland University of Technology, Brisbane, Australia
| | - N Fann
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Washington, DC, United States
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Abstract
PURPOSE OF REVIEW By 2050, 70% of the global population will live in urban areas, exposing a greater number of people to specific city-related health risks that will only be exacerbated by climate change. Two prominent health risks are poor air quality and physical inactivity. We aim to review the literature and state the best practices for clean air and active transportation in urban areas. RECENT FINDINGS Cities have been targeting reductions in air pollution and physical inactivity to improve population health. Oslo, Paris, and Madrid plan on banning cars from their city centers to mitigate climate change, reduce vehicle emissions, and increase walking and cycling. Urban streets are being redesigned to accommodate and integrate various modes of transportation to ensure individuals can become actively mobile and healthy. Investments in pedestrian, cycling, and public transport infrastructure and services can both improve air quality and support active transportation. Emerging technologies like electric and autonomous vehicles are being developed and may reduce air pollution but have limited impact on physical activity. Green spaces too can mitigate air pollution and encourage physical activity. Clean air and active transportation overlap considerably as they are both functions of mobility. The best practices of clean air and active transportation have produced impressive results, which are improved when enacted simultaneously in integrated policy packages. Further research is needed in middle- and low-income countries, using measurements from real-world interventions, tracing air pollution back to the sources responsible, and holistically addressing the entire spectrum of exposures and health outcomes related to transportation.
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Cao Q, Shen L, Chen SC, Pui DYH. WRF modeling of PM 2.5 remediation by SALSCS and its clean air flow over Beijing terrain. Sci Total Environ 2018; 626:134-146. [PMID: 29335168 DOI: 10.1016/j.scitotenv.2018.01.062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 01/07/2018] [Accepted: 01/07/2018] [Indexed: 06/07/2023]
Abstract
Atmospheric simulations were carried out over the terrain of entire Beijing, China, to investigate the effectiveness of an air-pollution cleaning system named Solar-Assisted Large-Scale Cleaning System (SALSCS) for PM2.5 mitigation by using the Weather Research and Forecasting (WRF) model. SALSCS was proposed to utilize solar energy to generate airflow therefrom the airborne particulate pollution of atmosphere was separated by filtration elements. Our model used a derived tendency term in the potential temperature equation to simulate the buoyancy effect of SALSCS created with solar radiation on its nearby atmosphere. PM2.5 pollutant and SALSCS clean air were simulated in the model domain by passive tracer scalars. Simulation conditions with two system flow rates of 2.64 × 105 m3/s and 3.80 × 105 m3/s were tested for seven air pollution episodes of Beijing during the winters of 2015-2017. The numerical results showed that with eight SALSCSs installed along the 6th Ring Road of the city, 11.2% and 14.6% of PM2.5 concentrations were reduced under the two flow-rate simulation conditions, respectively.
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Affiliation(s)
- Qingfeng Cao
- Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, MN 55455, USA
| | - Lian Shen
- Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, MN 55455, USA
| | - Sheng-Chieh Chen
- Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, MN 55455, USA
| | - David Y H Pui
- Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, MN 55455, USA.
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Wernham AG, Cain OL, Thomas AM. Effect of an exfoliating skincare regimen on the numbers of epithelial squames on the skin of operating theatre staff, studied by surface microscopy. J Hosp Infect 2018; 100:190-4. [PMID: 29577991 DOI: 10.1016/j.jhin.2018.03.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 03/13/2018] [Indexed: 11/21/2022]
Abstract
BACKGROUND The shedding of epithelial squames (skin scales) by staff in operating theatre air is an important source of deep infection following joint replacement surgery. This is a serious complication, resulting in significant morbidity for the patient and substantial cost implications for healthcare systems. Much effort has been put into providing clean air in operating theatres, yet little attention has been given to reducing the shedding of surface skin scales at source. AIM To develop a novel method for calculating surface skin scale density using surface microscopy, and to use it to evaluate the effect of a skincare regimen on operating theatre staff. METHODS Surface microscopy with Z-stacked imaging was used to visualize the effect of a skincare regimen involving three stages: washing with soap; exfoliation; and application of emollient. A USB microscope was then used in a field study to take images of the skin of operating theatre staff who applied the regimen to one lower limb the night before testing. The other limb was used as a control. Two blinded assessors analysed scale density. RESULTS Z-stack images from the surface microscope enabled observations of the skincare regimen. The USB microscope also provided adequate images that enabled assessment of skin scale density. In the operating theatre staff, a 72.1% reduction in visible skin scales was observed following application of the skincare regimen. CONCLUSIONS Further work is required to demonstrate how this effect correlates with dispersion of skin particles in a cleanroom, and subsequently in live operating theatre studies.
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Abstract
Surgical-site infections (SSIs) due to intra-operative contamination are chiefly ascribable to airborne particles carrying microorganisms, mainly Staphylococcus aureus, which settle on the surgeon's hands and instruments. SSI prevention therefore rests on minimisation of airborne contaminated particle counts, although these have not been demonstrated to correlate significantly with SSI rates. Maintaining clear air in the operating room classically involves the use of ultra clean ventilation systems combining laminar airflow and high-efficiency particulate air filters to create a physical barrier around the surgical table; in addition to a stringent patient preparation protocol, appropriate equipment, and strict operating room discipline on the part of the surgeon and other staff members. SSI rates in clean surgery, although influenced by the type of procedure and by patient-related factors, are consistently very low, of about 1% to 2%. These low rates, together with the effectiveness of prophylactic antibiotic therapy and the multiplicity of parameters influencing the SSI risk, are major obstacles to the demonstration that a specific measure is effective in decreasing SSIs. As a result, controversy surrounds the usefulness of many measures, including laminar airflow, body exhaust suits, patient preparation techniques, and specific surgical instruments. Impeccable surgical technique and operating room behaviour, in contrast, are clearly essential.
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Affiliation(s)
- D Chauveaux
- CHU Pellegrin, place Amélie-Raba-Léon, 33076 Bordeaux cedex, France.
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