1
|
Xu H, Guo S, Shi X, Wu Y, Pan J, Gao H, Tang Y, Han A. Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases. Front Public Health 2024; 12:1420608. [PMID: 39104885 PMCID: PMC11299116 DOI: 10.3389/fpubh.2024.1420608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 07/08/2024] [Indexed: 08/07/2024] Open
Abstract
Introduction Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, consciousness disorders and other organ system damage. This study aims to analyze the effect of meteorological factors on the incidence of heatstroke using machine learning, and to construct a heatstroke forecasting model to provide reference for heatstroke prevention. Methods The data of heatstroke incidence and meteorological factors in a city in South China from May to September 2014-2019 were analyzed in this study. The lagged effect of meteorological factors on heatstroke incidence was analyzed based on the distributed lag non-linear model, and the prediction model was constructed by using regression decision tree, random forest, gradient boosting trees, linear SVRs, LSTMs, and ARIMA algorithm. Results The cumulative lagged effect found that heat index, dew-point temperature, daily maximum temperature and relative humidity had the greatest influence on heatstroke. When the heat index, dew-point temperature, and daily maximum temperature exceeded certain thresholds, the risk of heatstroke was significantly increased on the same day and within the following 5 days. The lagged effect of relative humidity on the occurrence of heatstroke was different with the change of relative humidity, and both excessively high and low environmental humidity levels exhibited a longer lagged effect on the occurrence of heatstroke. With regard to the prediction model, random forest model had the best performance of 5.28 on RMSE and dropped to 3.77 after being adjusted. Discussion The incidence of heatstroke in this city is significantly correlated with heat index, heatwave, dew-point temperature, air temperature and zhongfu, among which the heat index and dew-point temperature have a significant lagged effect on heatstroke incidence. Relevant departments need to closely monitor the data of the correlated factors, and adopt heat prevention measures before the temperature peaks, calling on citizens to reduce outdoor activities.
Collapse
Affiliation(s)
- Hui Xu
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Shufang Guo
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaojun Shi
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Yanzhen Wu
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Junyi Pan
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Han Gao
- School of Humanities, Beijing University of Chinese Medicine, Beijing, China
| | - Yan Tang
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Aiqing Han
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| |
Collapse
|
2
|
Wang J, Li Y, Liu W, Gou A. Spatial and temporal evolution characteristics and factors of heat vulnerability in the Pearl River Delta urban agglomeration from 2001 to 2022. Heliyon 2024; 10:e34116. [PMID: 39091952 PMCID: PMC11292507 DOI: 10.1016/j.heliyon.2024.e34116] [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: 03/26/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024] Open
Abstract
To explore the spatiotemporal evolution characteristics of heat vulnerability in the Pearl River Delta urban agglomeration during heatwave disasters, this research employs the Entropy Weight Method (EWM) to calculate the heat vulnerability assessment results for nine cities in the region spanning from 2001 to 2022. Through the application of kernel density estimation, Moran's I, and the Geographically and Temporally Weighted Regression (GTWR) model, which is proven to be superior to traditional model such as OLS, this study analyzes the dynamic distribution patterns of heat vulnerability in the study area and dissect the trends of influencing factors. The results reveal that from 2001 to 2022, the overall heat vulnerability index in the study area demonstrates a fluctuating downward trend. Key contributors to heat vulnerability include high-frequency and long-duration heatwaves, population sensitivity, and changes in residents' consumption levels. Throughout this period of development, the disparity in heat vulnerability among cities has gradually widened, indicating an overall pattern of uneven development in the region. Future attention should be focused on formulating heat adaptation strategies in areas with high vulnerability to enhance the overall sustainability of the study area.
Collapse
Affiliation(s)
- Jiangbo Wang
- College of Architecture, Nanjing Tech University, Nanjing, 211816, China
| | - Yishu Li
- College of Architecture, Nanjing Tech University, Nanjing, 211816, China
| | - Wei Liu
- Jiangsu Provincial Planning and Design Group, Nanjing, 210019, China
| | - Aiping Gou
- School of Ecological Technology and Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
| |
Collapse
|
3
|
Chen S, Zhao J, Dou H, Yang Z, Li F, Byun J, Kim SW. A study on the monitoring of heatwaves and bivariate frequency analysis based on mortality risk assessment in Wuhan, China. Front Public Health 2024; 12:1409563. [PMID: 38962759 PMCID: PMC11220200 DOI: 10.3389/fpubh.2024.1409563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024] Open
Abstract
The increasingly frequent occurrence of urban heatwaves has become a significant threat to human health. To quantitatively analyze changes in heatwave characteristics and to investigate the return periods of future heatwaves in Wuhan City, China, this study extracted 9 heatwave definitions and divided them into 3 mortality risk levels to identify and analyze historical observations and future projections of heatwaves. The copula functions were employed to derive the joint distribution of heatwave severity and duration and to analyze the co-occurrence return periods. The results demonstrate the following. (1) As the concentration of greenhouse gas emissions increases, the severity of heatwaves intensifies, and the occurrence of heatwaves increases significantly; moreover, a longer duration of heatwaves correlated with higher risk levels in each emission scenario. (2) Increasing concentrations of greenhouse gas emissions result in significantly shorter heatwave co-occurrence return periods at each level of risk. (3) In the 3 risk levels under each emission scenario, the co-occurrence return periods for heatwaves become longer as heatwave severity intensifies and duration increases. Under the influence of climate change, regional-specific early warning systems for heatwaves are necessary and crucial for policymakers to reduce heat-related mortality risks in the population, especially among vulnerable groups.
Collapse
Affiliation(s)
- Si Chen
- School of Resources and Environmental Science, Hubei University, Wuhan, China
- Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan, China
| | - Junrui Zhao
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Haonan Dou
- School of Resources and Environmental Science, Hubei University, Wuhan, China
| | - Zhaoqian Yang
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
| | - Fei Li
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Jihye Byun
- Department of Transportation Engineering, University of Seoul, Seoul, Republic of Korea
| | - Seong Wook Kim
- Department of Applied Mathematics, Hanyang University, Ansan, Republic of Korea
| |
Collapse
|
4
|
Zhu Q, Ye P, Wang Y, Duan L, He G, Er Y, Jin Y, Ji C, Hu J, Deng X, Ma W, Liu T. Heatwaves increase road traffic injury morbidity risk and burden in China and its provinces. ENVIRONMENT INTERNATIONAL 2024; 188:108760. [PMID: 38788419 DOI: 10.1016/j.envint.2024.108760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/08/2024] [Accepted: 05/18/2024] [Indexed: 05/26/2024]
Abstract
Previous studies have demonstrated health impacts of climate change, but evidence on heatwaves' associations with road traffic injury (RTI) is limited. In this study, individual information of RTI cases in May-September during 2006-2021 in China were obtained from the National Injury Surveillance System. Daily maximum temperatures (TMmax) during 2006-2021 were collected from the ERA-5 reanalysis, and the projected daily TMmax during 2020-2099 were obtained from the latest Coupled Model Intercomparison Project Phase 6 Shared Socioeconomic Pathways scenarios (SSPs). We used a time-stratified case-crossover analysis to investigate the association between short-term exposure (lag01 days) to heatwaves (exceeding the 92.5th percentile of daily TMmax for ≥ three consecutive days) and RTI, and to project heatwave-related RTI until 2099 across China. Finally, a total of 1 031 082 RTI cases were included in the analyses. Compared with non-heatwaves, the risks of RTI increased by 3.61 % during heatwaves. Greater associations were found in people aged 15-64 years, in people with transportation occupation, for non-motor traffic vehicle injuries, for severe RTI cases, and in Western China particularly in Qinghai province. We projected substantial increases in attributable fraction (AF) of heatwave-related RTI in the future, particularly in Western and Southwest China. The national average increase in AF (per decade) during 2020s-2090s was 0.036 % for SSP1-2.6 scenario, and 0.267 % for SSP5-8.5 scenario. This study provided evidence on the associations of heatwaves with RTI, and the heatwave-related RTI will substantially increase in the future.
Collapse
Affiliation(s)
- Qijiong Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Key Laboratory of Viral Pathogenesis & Infection Prevention and Control, Jinan University, Ministry of Education, Guangzhou 510632, China
| | - Pengpeng Ye
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Yuan Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Leilei Duan
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Yuliang Er
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Ye Jin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Cuirong Ji
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Jianxiong Hu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Xiao Deng
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China.
| | - Tao Liu
- China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China; Key Laboratory of Viral Pathogenesis & Infection Prevention and Control, Jinan University, Ministry of Education, Guangzhou 510632, China.
| |
Collapse
|
5
|
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. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 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] [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.
Collapse
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
| |
Collapse
|
6
|
Xie Y, Zhou Z, Sun Q, Zhao M, Pu J, Li Q, Sun Y, Dai H, Li T. Social-economic transitions and vulnerability to extreme temperature events from 1960 to 2020 in Chinese cities. iScience 2024; 27:109066. [PMID: 38361620 PMCID: PMC10867637 DOI: 10.1016/j.isci.2024.109066] [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: 09/05/2023] [Revised: 12/13/2023] [Accepted: 01/25/2024] [Indexed: 02/17/2024] Open
Abstract
Climate change leads to more frequent and intense extreme temperature events, causing a significant number of excess deaths. Using an epidemiological approach, we analyze all-cause deaths related to heatwaves and cold spells in 2,852 Chinese counties from 1960 to 2020. Economic losses associated with these events are determined through the value of statistical life. Findings reveal that cold-related cumulative excess deaths (1,133 thousand) are approximately 2.5 times higher than heat-related deaths, despite an increase in heat-related fatalities in recent decades. Monetized mortality due to heat-related events is estimated at 1,284 billion CNY, while cold-related economic loss is 1,510 billion CNY. Notably, cities located in colder regions experience more heat-related excess deaths, and vice versa. Economic development does not significantly reduce mortality risks to heatwaves across China. This study provides insights into the spatial-temporal heterogeneity of heatwaves and cold spells mortality, essential for policymakers ensuring long-term climate adaptation and sustainability.
Collapse
Affiliation(s)
- Yang Xie
- School of Economics and Management, Beihang University, Beijing, China
| | - Ziqiao Zhou
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Qinghua Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Mengdan Zhao
- School of Economics and Management, Beihang University, Beijing, China
| | - Jinlu Pu
- School of Economics and Management, Beihang University, Beijing, China
| | - Qiutong Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yue Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hancheng Dai
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, 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, China
| |
Collapse
|
7
|
Yan L, Yin M, Yu H, Qin G, He BJ. Public responses to urban heat and payment for heat-resilient infrastructure: implications for heat action plan formulation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:120387-120399. [PMID: 37938491 DOI: 10.1007/s11356-023-30881-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/31/2023] [Indexed: 11/09/2023]
Abstract
Heat action plans should be urgently formulated to enable urban managers, planners, and designers to take appropriate actions for mitigation and adaptation. However, few studies have been conducted to investigate the societal needs and knowledge gaps regarding heat mitigation and adaptation. To address such research gaps, this paper presents an empirical study of 574 questionnaires in Chengdu, China, to explore heat-related impacts, public responses, and driving mechanisms. The results indicated that outdoor activities and work/study were more sensitive to extreme heat than transportation, sleep/rest, and diet. Heat-related impacts on physiological health were at the same level as those on psychological health, where digestive system illness and emotional irritability were the most prevalent physiological and psychological symptoms. Respondents' knowledge of heat-related threats, adaptation awareness, and adaptation knowledge were insufficient, compared with heat severity. The payment willingness among the respondents was not strong and payment amount was not high. Poorer, healthier, and the less affected in outdoor activities were positive groups in payment willingness, while the group which experienced heat-related impacts on outdoor activities could pay more compared with other groups. Overall, these results help to shape the main contents of heat action plans.
Collapse
Affiliation(s)
- Li Yan
- School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang, 621010, China
| | - Mingqiang Yin
- Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing, 400045, China
- CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing, 401147, China
- Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang, 213300, Jiangsu, China
| | - Hongmei Yu
- Mianyang Municipal Party School, Fucheng District, Mianyang, 621000, China
| | - Guojin Qin
- School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, Sichuan, China
| | - Bao-Jie He
- Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing, 400045, China.
- CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing, 401147, China.
- Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang, 213300, Jiangsu, China.
| |
Collapse
|
8
|
Cheng L, Gu K, Zhao L, Wang H, Ji JS, Liu Z, Huang J, Chen Y, Gao X, Xu Y, Wang C, Luo Y, Cai W, Gong P, Liang W, Huang C. Projecting future labor losses due to heat stress in China under climate change scenarios. Sci Bull (Beijing) 2023; 68:2827-2837. [PMID: 37858411 PMCID: PMC10694465 DOI: 10.1016/j.scib.2023.09.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 07/10/2023] [Accepted: 07/11/2023] [Indexed: 10/21/2023]
Abstract
Climate change is expected to increase occupational heat stress, which will lead to diminished work performance and labor losses worldwide. However, sub-regional analyses remain insufficient, especially for countries with a heterogeneous spatial distribution of working populations, industries and climates. Here, we projected heat-induced labor losses in China, by considering local climate simulations, working population characteristics and developing an exposure-response function suitable for Chinese workers. We showed that the annual heat-induced work hours lost (WHL), compared to the baseline of 21.3 billion hours, will increase by 121.1% (111.2%-131.1%), 10.8% (8.3%-15.3%), and -17.8% (-15.3%--20.3%) by the end of the century under RCP(Representative Concentration Pathways)8.5, RCP4.5, and RCP2.6, respectively. We observed an approximately linear upward trend of WHL under RCP8.5, despite the decrease in future working population. Notably, WHL will be most prominent in the southern, eastern and central regions, with Guangdong and Henan accounting for a quarter of national total losses; this is largely due to their higher temperature exposure, larger population size, and higher shares of vulnerable population in total employment. In addition, limiting global warming to 1.5 °C would yield substantial gains. Compared to RCP2.6, RCP4.5, and RCP8.5, all provinces can avoid an average of 11.8%, 33.7%, and 53.9% of annual WHL if the 1.5 °C target is achieved, which is equivalent to avoiding 0.1%, 0.6%, and 1.4% of annual GDP losses in China, respectively. This study revealed climate change will exacerbate future labor losses, and adverse impacts can be minimized by adopting stringent mitigation policies coupled with effective adaptation measures. Policymakers in each province should tailor occupation health protection measures to their circumstances.
Collapse
Affiliation(s)
- Liangliang Cheng
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Kuiying Gu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China; Institute of Healthy China, Tsinghua University, Beijing 100084, China
| | - Liang Zhao
- State Key Laboratory of Numerical Modelling for Atmosphere Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Huibin Wang
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Zhao Liu
- School of Linkong Economics and Management, Beijing Institute of Economics and Management, Beijing 100102, China
| | - Jianbin Huang
- Beijing Yanshan Earth Critical Zone National Research Station, University of Chinese Academy of Sciences, Beijing 101408, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yidan Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xuejie Gao
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100084, China; Climate Change Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Ying Xu
- National Climate Centre, China Meteorological Administration, Beijing 100081, China
| | - Can Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yong Luo
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
| | - Wenjia Cai
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
| | - Peng Gong
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China; Department of Earth Sciences and Department of Geography, University of Hong Kong, Hong Kong 999077, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China; Institute of Healthy China, Tsinghua University, Beijing 100084, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China; Institute of Healthy China, Tsinghua University, Beijing 100084, China.
| |
Collapse
|
9
|
Zhang G, Han L, Yao J, Yang J, Xu Z, Cai X, Huang J, Pei L. Assessing future heat stress across China: combined effects of heat and relative humidity on mortality. Front Public Health 2023; 11:1282497. [PMID: 37854241 PMCID: PMC10581210 DOI: 10.3389/fpubh.2023.1282497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 09/15/2023] [Indexed: 10/20/2023] Open
Abstract
This study utilizes China's records of non-accidental mortality along with twenty-five simulations from the NASA Earth Exchange Global Daily Downscaled Projections to evaluate forthcoming heat stress and heat-related mortality across China across four distinct scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The findings demonstrate a projected escalation in the heat stress index (HSI) throughout China from 2031 to 2100. The most substantial increments compared to the baseline (1995-2014) are observed under SSP5-8.5, indicating a rise of 7.96°C by the year 2100, while under SSP1-2.6, the increase is relatively modest at 1.54°C. Disparities in HSI growth are evident among different subregions, with South China encountering the most significant elevation, whereas Northwest China exhibits the lowest increment. Projected future temperatures align closely with HSI patterns, while relative humidity is anticipated to decrease across the majority of areas. The study's projections indicate that China's heat-related mortality is poised to surpass present levels over the forthcoming decades, spanning a range from 215% to 380% from 2031 to 2100. Notably, higher emission scenarios correspond to heightened heat-related mortality. Additionally, the investigation delves into the respective contributions of humidity and temperature to shifts in heat-related mortality. At present, humidity exerts a greater impact on fluctuations in heat-related mortality within China and its subregions. However, with the projected increase in emissions and global warming, temperature is expected to assume a dominant role in shaping these outcomes. In summary, this study underscores the anticipated escalation of heat stress and heat-related mortality across China in the future. It highlights the imperative of emission reduction as a means to mitigate these risks and underscores the variances in susceptibility to heat stress across different regions.
Collapse
Affiliation(s)
- Guwei Zhang
- Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Transforming Climate Resources to Economy, China Meteorological Administration, Chongqing, China
| | - Ling Han
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiajun Yao
- Shengzhou Meteorological Bureau, Shaoxing, China
| | - Jiaxi Yang
- Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Transforming Climate Resources to Economy, China Meteorological Administration, Chongqing, China
| | - Zhiqi Xu
- Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Transforming Climate Resources to Economy, China Meteorological Administration, Chongqing, China
| | - Xiuhua Cai
- Chinese Academy of Meteorological Sciences, Beijing, China
| | - Jin Huang
- Chifeng City Center Hospital Ningcheng County, Chifeng, China
| | - Lin Pei
- Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing, China
- Key Laboratory of Transforming Climate Resources to Economy, China Meteorological Administration, Chongqing, China
| |
Collapse
|
10
|
Zeng M, Zhang K, Xu D, Ma H, Deng X. The complex impacts of economic growth pressure on carbon emission intensity: an empirical evidence from city data in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:109135-109144. [PMID: 37770733 DOI: 10.1007/s11356-023-30040-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 09/18/2023] [Indexed: 09/30/2023]
Abstract
Excessive carbon emissions are the major challenge to global sustainable development. In the context of the coronavirus pandemic, pressure on global economic growth is gradually rising, threatening established carbon reduction targets. However, the relationship between economic growth pressures and carbon emission intensity has yet to be clearly discussed. Thus, this study quantitatively discusses the impacts of economic growth pressures from central (EGPN) and provincial (EGPP) governments on city carbon intensity. The study is based on data from China's city panels from 2005 to 2019. This study finds that (1) there is a U-shaped correlation between economic growth pressure and a city's carbon emission intensity, whether the economic growth pressure comes from the central government or the provincial government; (2) carbon emission intensity is more sensitive to economic growth pressure from the provincial government than it is to economic growth pressure from the central government. The findings of this study will help enhance the understanding of the relationship between economic growth pressure and carbon emission intensity, and can also provide a reference for global sustainable development that balances economic growth and environmental protection.
Collapse
Affiliation(s)
- Miao Zeng
- School of Economics, Sichuan University, Chengdu, 610065, China
| | - Kuan Zhang
- College of Economics, Sichuan Agricultural University, Chengdu, 611130, China
| | - Dingde Xu
- College of Management, Sichuan Agricultural University, Chengdu, 611130, China
| | - Hongju Ma
- Center for Agricultural Ecology and Resource Protection of Sichuan, Chengdu, 610041, China
| | - Xin Deng
- College of Economics, Sichuan Agricultural University, Chengdu, 611130, China.
| |
Collapse
|
11
|
Wang FL, Wang WZ, Zhang FF, Peng SY, Wang HY, Chen R, Wang JW, Li PF, Wang Y, Zhao MH, Yang C, Zhang LX. Heat exposure and hospitalizations for chronic kidney disease in China: a nationwide time series study in 261 major Chinese cities. Mil Med Res 2023; 10:41. [PMID: 37670366 PMCID: PMC10478241 DOI: 10.1186/s40779-023-00478-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/29/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Climate change profoundly shapes the population health at the global scale. However, there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease (CKD). METHODS In the present study, we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018. Standard time-series regression models and random-effects meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span, respectively. RESULTS A total of 768,129 hospitalizations for CKD was recorded during the study period. The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD, especially in sub-tropical cities. With a 1 °C increase in daily mean temperature, the cumulative relative risks (RR) over lag 0-7 d were 1.008 [95% confidence interval (CI) 1.003-1.012] for nationwide. The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%. Stronger associations were observed among younger patients and those with obstructive nephropathy. Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days (RR = 1.116, 95% CI 1.069-1.166) above the effect of daily mean temperature. CONCLUSIONS Short-term heat exposure may increase the risk of hospitalization for CKD. Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.
Collapse
Affiliation(s)
- Fu-Lin Wang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
- National Institute of Health Data Science at Peking University, Beijing, 100191, China
| | - Wan-Zhou Wang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China
| | - Fei-Fei Zhang
- National Institute of Health Data Science at Peking University, Beijing, 100191, China
| | - Su-Yuan Peng
- National Institute of Health Data Science at Peking University, Beijing, 100191, China
| | - Huai-Yu Wang
- National Institute of Health Data Science at Peking University, Beijing, 100191, China
| | - Rui Chen
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, 100034, China
| | - Jin-Wei Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, 100034, China
| | - Peng-Fei Li
- Advanced Institute of Information Technology, Peking University, Hangzhou, 311215, China
| | - Yang Wang
- National Climate Center, China Meteorological Administration, Beijing, 100081, China
| | - Ming-Hui Zhao
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, 100034, China
- Peking-Tsinghua Center for Life Sciences, Beijing, 100034, China
| | - Chao Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China.
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, 100034, China.
- Advanced Institute of Information Technology, Peking University, Hangzhou, 311215, China.
| | - Lu-Xia Zhang
- National Institute of Health Data Science at Peking University, Beijing, 100191, China.
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China.
- Advanced Institute of Information Technology, Peking University, Hangzhou, 311215, China.
| |
Collapse
|
12
|
Li Z, Fan Y, Su H, Xu Z, Ho HC, Zheng H, Tao J, Zhang Y, Hu K, Hossain MZ, Zhao Q, Huang C, Cheng J. The 2022 Summer record-breaking heatwave and health information-seeking behaviours: an infodemiology study in Mainland China. BMJ Glob Health 2023; 8:e013231. [PMID: 37730248 PMCID: PMC10510944 DOI: 10.1136/bmjgh-2023-013231] [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: 06/26/2023] [Accepted: 08/20/2023] [Indexed: 09/22/2023] Open
Abstract
INTRODUCTION Heatwave is a major global health concern. Many countries including China suffered a record-breaking heatwave during the summer of 2022, which may have a significant effect on population health or health information-seeking behaviours but is yet to be examined. METHODS We derived health information-seeking data from the Baidu search engine (similar to Google search engine). The data included city-specific daily search queries (also referred to Baidu Search Index) for heat-sensitive diseases from 2021 to 2022, including heatstroke, hospital visits, cardiovascular diseases and diabetes, respiratory diseases, mental health and urological diseases. For each city, the record-breaking heatwave days in 2022 were matched to days in the same calendar month in 2021. RESULTS The 2022 record-breaking heatwave hit most cities (83.64%) in Mainland China. The average heatwave duration was 13 days and the maximum temperature was 3.60°C higher than that in 2021 (p<0.05). We observed increased population behaviours of seeking information on respiratory diseases (RR=1.014, 95% CI: 1.008 to 1.020), urological diseases (RR=1.011, 95% CI: 1.006 to 1.016) and heatstroke (RR=1.026, 95% CI: 1.016 to 1.036) associated with the heatwave intensity in 2022 (per 1°C increase). The heatwave duration in 2022 (per 1 day increase) was also associated with an increase in seeking information on cardiovascular diseases and diabetes (RR=1.003, 95% CI: 1.002 to 1.004), urological diseases (RR=1.005, 95% CI: 1.002 to 1.008), mental health (RR=1.009, 95% CI: 1.006 to 1.012) and heatstroke (RR=1.038, 95% CI: 1.032 to 1.043). However, there were substantial geographical variations in the effect of the 2022 heatwave intensity and duration on health information-seeking behaviours. CONCLUSION This infodemiology study suggests that the 2022 summer unprecedented heatwave in Mainland China has significantly increased population demand for health-related information, especially for heatstroke, urological diseases and mental health. Population-based research of real-time disease data is urgently needed to estimate the negative health impact of the exceptional heatwave in Mainland China and elsewhere.
Collapse
Affiliation(s)
- Zhiwei Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Yinguang Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Zhiwei Xu
- School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, Australia
| | - Hung Chak Ho
- Department of Public and International Affairs, City University of Hong Kong, Hong Kong, China
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Junwen Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Kejia Hu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | | | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Major Autoimmune Disease, Anhui Medical University, Hefei, China
| |
Collapse
|
13
|
Zhang B, Chen H, Lu B. An Early Warning System for Heatwave-Induced Health Risks in China: A Sub-Seasonal to Seasonal Perspective - China, 2022. China CDC Wkly 2023; 5:647-650. [PMID: 37529144 PMCID: PMC10388180 DOI: 10.46234/ccdcw2023.124] [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: 05/29/2023] [Accepted: 06/25/2023] [Indexed: 08/03/2023] Open
Abstract
What is already known about this topic? Climate change has had a detrimental impact on global health, particularly through the rise of extreme heatwaves. Presently, the early warning system for heatwave-related health risks can forecast potential dangers several days in advance; however, long-term warnings fall short. What is added by this report? This report introduces a novel early warning system aimed at predicting heatwave-induced health risks in China at sub-seasonal to seasonal timescales. The outcomes of the assessment suggest this system holds significant potential. What are the implications for public health practices? The system facilitates advanced assessment of both the scale and dispersal of risk among various demographic groups. This allows for the proactive management of potential risks with extended lead times.
Collapse
Affiliation(s)
- Baichao Zhang
- National Climate Center, China Meteorological Administration, Beijing, China
| | - Huiqi Chen
- Sun Yat-Sen University, Guangzhou City, Guangdong Province, China
| | - Bo Lu
- National Climate Center, China Meteorological Administration, Beijing, China
| |
Collapse
|
14
|
He BJ, Yin M. Government is expected to lead the payment of heat-resilient infrastructure. iScience 2023; 26:106566. [PMID: 37250319 PMCID: PMC10214286 DOI: 10.1016/j.isci.2023.106566] [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: 06/05/2022] [Revised: 02/22/2023] [Accepted: 03/22/2023] [Indexed: 05/31/2023] Open
Abstract
Urban heat is severe in numerous cities, but the urgency of heat action and support for the development of heat-resilient infrastructure is unclear. To address these research gaps, this study investigated the perceived urgency of developing heat-resilient infrastructure and associated payment issues in eight megacities, in China using a questionnaire survey of 3758 respondents in August 2020. Overall, the respondents thought it was moderately urgent to take actions to address heat-related challenges. The development of mitigation and adaptation infrastructure is urgent. About 86.4% of the 3758 respondents expected the government to be involved in paying for heat-resilient infrastructure, but 41.2% supported cost-sharing among the government, developers, and owners. There were 1299 respondents willing to pay, resulting in an average annual payment of 44.06 RMB in a conservative scenario. This study is important for decision-makers to formulate plans on heat-resilient infrastructure and to release financial strategies for collecting investments and funds.
Collapse
Affiliation(s)
- Bao-Jie He
- Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Shapingba, Chongqing 400045, China
- Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang 213300, Jiangsu, China
- Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing 400045, China
- State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, Guangdong 510640, China
- Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, Hiroshima 739-8530, Japan
| | - Mingqiang Yin
- Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Shapingba, Chongqing 400045, China
- Institute for Smart City of Chongqing University in Liyang, Chongqing University, Liyang 213300, Jiangsu, China
| |
Collapse
|