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Xu W, Zhou Y, Taubenböck H, Stokes EC, Zhu Z, Lai F, Li X, Zhao X. Spatially explicit downscaling and projection of population in mainland China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 941:173623. [PMID: 38815823 DOI: 10.1016/j.scitotenv.2024.173623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/09/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024]
Abstract
Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.
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Affiliation(s)
- Wenru Xu
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Yuyu Zhou
- Department of Geography, The University of Hong Kong, Hong Kong.
| | - Hannes Taubenböck
- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, 82234 Weßling, Germany
| | | | - Zhengyuan Zhu
- Department of Statistics, Iowa State University50011, Ames, IA, USA
| | - Feilin Lai
- Department of Geography and Planning, St. Cloud State University, MN 56301, USA
| | - Xuecao Li
- College of Land Science and Technology, China Agricultural University, Beijing 100083, China
| | - Xia Zhao
- Institute of Land and Urban-Rural Development, Zhejiang University of Finance & Economics, Hangzhou 310018, China
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2
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Wang Y, Li C, Zhao S, Wei Y, Li K, Jiang X, Ho J, Ran J, Han L, Zee BCY, Chong KC. Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries. PLoS Negl Trop Dis 2024; 18:e0012158. [PMID: 38683870 PMCID: PMC11081495 DOI: 10.1371/journal.pntd.0012158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 05/09/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024] Open
Abstract
Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.
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Affiliation(s)
- Yawen Wang
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Conglu Li
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Shi Zhao
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
- School of Public Health, Tianjin Medical University, Tianjin, China
| | - Yuchen Wei
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kehang Li
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiaoting Jiang
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Janice Ho
- Division of Landscape Architecture, Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China
| | - Jinjun Ran
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lefei Han
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Benny Chung-ying Zee
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Ka Chun Chong
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Clinical Trials and Biostatistics Laboratory, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
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Trancoso R, Syktus J, Allan RP, Croke J, Hoegh-Guldberg O, Chadwick R. Significantly wetter or drier future conditions for one to two thirds of the world's population. Nat Commun 2024; 15:483. [PMID: 38212324 PMCID: PMC10784476 DOI: 10.1038/s41467-023-44513-3] [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: 06/13/2023] [Accepted: 12/15/2023] [Indexed: 01/13/2024] Open
Abstract
Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world's current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century's end (or 35-61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections.
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Affiliation(s)
- Ralph Trancoso
- School of The Environment, The University of Queensland, Brisbane, QLD, Australia.
- Climate Projections and Services, Department of Environment and Science, Queensland Government, Brisbane, QLD, Australia.
| | - Jozef Syktus
- School of The Environment, The University of Queensland, Brisbane, QLD, Australia
| | - Richard P Allan
- Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading, UK
| | - Jacky Croke
- Centre for Climate, Environment and Sustainability, School of Earth and Atmospheric Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ove Hoegh-Guldberg
- School of The Environment, The University of Queensland, Brisbane, QLD, Australia
| | - Robin Chadwick
- Met Office Hadley Centre, Exeter, UK
- Global Systems Institute, Department of Mathematics, University of Exeter, Exeter, UK
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4
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Bai Y, Liu M. Multi-scale spatiotemporal trends and corresponding disparities of PM 2.5 exposure in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 340:122857. [PMID: 37925009 DOI: 10.1016/j.envpol.2023.122857] [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: 05/26/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 11/06/2023]
Abstract
Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.
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Affiliation(s)
- Yu Bai
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Menghang Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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5
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Niu Q, Wang G, Liu B, Zhang R, Lei J, Wang H, Liu M. Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China. Sci Rep 2023; 13:22542. [PMID: 38110563 PMCID: PMC10728089 DOI: 10.1038/s41598-023-49877-6] [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: 05/11/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023] Open
Abstract
Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness. According to the study's findings: (1) K-fold cross-validation was applied to test the random forest model that was built using the built metro stations and particular factors. The average accuracy of the tests and out-of-bag data (OOB) of tenfold cross-validation were 89.62% and 91.285%, respectively. Additionally, the AUC area under the ROC curve was 0.9823, indicating that this time, from the perspective of the natural environment, traffic location, and social factors The 19 elements selected from the views of the urban function structure, social economics, and natural environment are closely associated to the locations of the metro station in the research region, and the prediction the findings are more reliable; (2) It becomes apparent that more than half of the built station sites display excellent agreement with the predicted sites in terms of geographical location by superimposing the built metro station sites with the prediction results and tally up their cumulative prediction probability values within the 300 m buffering zone; (3) Based on the contribution rate of each indicator to the model, transport facilities, companies, population density, night lighting, science, education and culture, residential communities, and road network density are identified as the primary influential factors, each accounting for over 6.6%. Subsequently, land use, elevation, and slope are found to have relatively lower contributions. The results of the research provided important information for the local metro's best location selection and planning.
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Affiliation(s)
- Quanfu Niu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China.
- Emergency Mapping Engineering Research Center of Gansu Province, Lanzhou, 730050, China.
- Academician Expert Workstation of Gansu Dayu Jiu Zhou Space Information Technology Co., Ltd, Lanzhou, 730050, China.
| | - Gang Wang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Bo Liu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Ruizhen Zhang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Jiaojiao Lei
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Hao Wang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Mingzhi Liu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
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Zhang S, Zhao M, Liu Z, Yang F, Lu B, Zhao Z, Gu K, Zhang S, Lei M, Zhang C, Wang C, Cai W. City-level population projection for China under different pathways from 2010 to 2100. Sci Data 2023; 10:809. [PMID: 37978198 PMCID: PMC10656476 DOI: 10.1038/s41597-023-02735-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023] Open
Abstract
Cities play a fundamental role in policy decision-making processes, necessitating the availability of city-level population projections to better understand future population dynamics and facilitate research across various domains, including urban planning, shrinking cities, GHG emission projections, GDP projections, disaster risk mitigation, and public health risk assessment. However, the current absence of city-level population projections for China is a significant gap in knowledge. Moreover, aggregating grid-level projections to the city level introduces substantial errors of approximately 30%, leading to discrepancies with actual population trends. The unique circumstances of China, characterized by comprehensive poverty reduction, compulsory education policies, and carbon neutrality goals, render scenarios like SSP4(Shared Socioeconomic Pathways) and SSP5 less applicable. To address the aforementioned limitations, this study made three key enhancements, which significantly refines and augments our previous investigation. Firstly, we refined the model, incorporating granular demographic data at the city level. Secondly, we redesigned the migration module to consider both regional and city-level population attractiveness. Lastly, we explored diverse fertility and migration scenarios.
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Affiliation(s)
- Shangchen Zhang
- Ministry of Education Ecological Field Station for East Asian Migratory Birds, Institute for Global Change Studies, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Mengzhen Zhao
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
| | - Zhao Liu
- School of Linkong Economics and Management, Beijing Institute of Economics and Management, Beijing, 100102, China
| | - Fan Yang
- Center for Population and Development Studies, Renmin University of China, Beijing, 100872, China
| | - Bo Lu
- National Climate Center, China Meteorological Administration, NO. 46, Zhongguancun Nandajie, Haidian District, Beijing, China
| | - Zhenping Zhao
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, No. 27 Nanwei Road, Xicheng District, Beijing, 100050, China
| | - Kuiying Gu
- Vanke School of Public Health, Tsinghua University, Beijing, 100084, China
| | - Shihui Zhang
- Ministry of Education Ecological Field Station for East Asian Migratory Birds, Institute for Global Change Studies, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Mingyu Lei
- Ministry of Education Ecological Field Station for East Asian Migratory Birds, Institute for Global Change Studies, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Chi Zhang
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
| | - Can Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, 100084, China
| | - Wenjia Cai
- Ministry of Education Ecological Field Station for East Asian Migratory Birds, Institute for Global Change Studies, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
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7
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Namgyal T, Thakur DA, D S R, Mohanty MP. Are open-source hydrodynamic models efficient in quantifying flood risks over mountainous terrains? An exhaustive analysis over the Hindu-Kush-Himalayan region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 897:165357. [PMID: 37419355 DOI: 10.1016/j.scitotenv.2023.165357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/14/2023] [Accepted: 07/04/2023] [Indexed: 07/09/2023]
Abstract
The Hindu-Kush-Himalaya is abode to numerous severely flood-prone mountainous stretches that distress vulnerable communities and cause massive destruction to physical entities such as hydropower projects. Adopting commercial flood models for replicating the dynamics of flood wave propagation over such regions is a major constraint due to the financial economics threaded to flood management. For the first instance, the present study attempts to investigate whether advanced open-source models are skillful in quantifying flood hazards and population exposure over mountainous terrains. While doing so, the performance of 1D-2D coupled HEC-RAS v6.3 (the most recent version developed by the U.S. Army Corps of Engineers) is reconnoitred for the first time in flood management literature. The most flood-prone region in Bhutan, the Chamkhar Chhu River Basin, housing large groups of communities and airports near its floodplains, is considered. HEC-RAS v6.3 setups are corroborated by comparing them with 2010 flood imagery derived from MODIS through performance metrics. The results indicate a sizable portion of the central part of the basin experiences very-high flood hazards with depth and velocities exceeding 3 m, and 1.6 m/s, respectively, during 50, 100, and 200-year return periods of floods. To affirm HEC-RAS, the flood hazards are compared with TUFLOW at 1D and 1D-2D coupled levels. The hydrological similarity within the channel is reflected at river cross-sections (NSE and KGE > 0.98), while overland inundation and hazard statistics differ, however, very less significant (<10 %). Later, flood hazards extracted from HEC-RAS are fused with the World-Pop population to estimate the degree of population exposure. The study ascertains that HEC-RAS v6.3 is an efficacious option for flood risk mapping over geographically arduous regions and can be preferred in resource-constrained environments ensuring a minimal degree of anomaly.
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Affiliation(s)
- Trashi Namgyal
- Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee 247667, India; National Centre for Hydrology and Meteorology, Royal Government of Bhutan, Bhutan
| | - Dev Anand Thakur
- Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee 247667, India
| | - Rishi D S
- TUFLOW India - SRA Consultants, Telangana 500080, India
| | - Mohit Prakash Mohanty
- Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee 247667, India.
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Wang M, Fu X, Zhang D, Chen F, Liu M, Zhou S, Su J, Tan SK. Assessing urban flooding risk in response to climate change and urbanization based on shared socio-economic pathways. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163470. [PMID: 37076008 DOI: 10.1016/j.scitotenv.2023.163470] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/08/2023] [Accepted: 04/08/2023] [Indexed: 05/03/2023]
Abstract
Global climate change and rapid urbanization, mainly driven by anthropogenic activities, lead to urban flood vulnerability and uncertainty in sustainable stormwater management. This study projected the temporal and spatial variation in urban flood susceptibility during the period 2020-2050 on the basis of shared socioeconomic pathways (SSPs). A case study in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was conducted for verifying the feasibility and applicability of this approach. GBA is predicted to encounter the increase in extreme precipitation with high intensity and frequency, along with rapid expansion of constructed areas, resulting in exacerbating of urban flood susceptibility. The areas with medium and high flood susceptibility will be expected to increase continuously from 2020 to 2050, by 9.5 %, 12.0 %, and 14.4 % under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. In terms of the assessment of spatial-temporal flooding pattern, the areas with high flood susceptibility are overlapped with that in the populated urban center in GBA, surrounding the existing risk areas, which is consistent with the tendency of construction land expansion. The approach in the present study will provide comprehensive insights into the reliable and accurate assessment of urban flooding susceptibility in response to climate change and urbanization.
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Affiliation(s)
- Mo Wang
- School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China.
| | - Xiaoping Fu
- School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
| | - Dongqing Zhang
- Guangdong Provincial Key Laboratory of Petrochemical Pollution Processes and Control, School of Environmental Science and Engineering, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China.
| | - Furong Chen
- School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
| | - Ming Liu
- School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
| | - Shiqi Zhou
- College of Design and Innovation, Tongji University, Shanghai 200093, China
| | - Jin Su
- Faculty of Civil Engineering and Built Environment, University Tun Hussein Onn, 86400 Parit Raja, Batu Pahat, Johor, Malaysia.
| | - Soon Keat Tan
- School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore.
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Boguslavsky DV, Sharov KS, Sharova NP. Using Alternative Sources of Energy for Decarbonization: A Piece of Cake, but How to Cook This Cake? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16286. [PMID: 36498366 PMCID: PMC9735948 DOI: 10.3390/ijerph192316286] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/27/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Few analytical or research works claim that the negative impact of improper use of ASEs may be comparable with that of hydrocarbons and sometimes even greater. It has become a common view that "green" energy (ASE) is clean, safe and environmentally friendly (eco-friendly) in contrast with "black" energy (hydrocarbons). We analyzed 144 works on systemic and/or comparative research of the modern and prospective ASE: biofuels, hydrogen, hydropower, nuclear power, wind power, solar power, geothermal power, oceanic thermal power, tidal power, wind wave power and nuclear fusion power. We performed our analysis within the Spaceship Earth paradigm. We conclude that there is no perfect ASE that is always eco-friendly. All ASEs may be dangerous to the planet considered as a closed and isolated unit ("spaceship") if they are used in an inconsistent manner. This is not in the least a reason to deny them as prospective sources of energy. Using all ASEs in different proportions in various regions of the planet, where their harm to the planet and humanity can be minimized and, on the contrary, their efficiency maximized, would give humanity the opportunity to decarbonize the Earth, and make the energy transition in the most effective way.
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