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Chew X, Khaw KW, Alnoor A, Ferasso M, Al Halbusi H, Muhsen YR. Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:60473-60499. [PMID: 37036648 PMCID: PMC10088637 DOI: 10.1007/s11356-023-26677-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/23/2023] [Indexed: 04/11/2023]
Abstract
Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.
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Affiliation(s)
- XinYing Chew
- School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Khai Wah Khaw
- School of Management, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Alhamzah Alnoor
- Management Technical College, Southern Technical University, Basrah, Iraq.
| | - Marcos Ferasso
- Economics and Business Sciences Department, Universidade Autónoma de Lisboa, 1169-023, Lisbon, Portugal
| | - Hussam Al Halbusi
- Department of Management, Ahmed Bin Mohammad Military College, Doha, Qatar
| | - Yousif Raad Muhsen
- Faculty of Engineering, Universiti Putra Malaysia, Seri Kembangan, Selangor, Malaysia
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Spatial Analysis on the Role of Multi-Dimensional Urbanizations in Carbon Emissions: Evidence from China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095315. [PMID: 35564712 PMCID: PMC9103709 DOI: 10.3390/ijerph19095315] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 12/10/2022]
Abstract
Using the panel data of 30 provinces in China from 1997 to 2015, this paper studies the impacts of urbanization on carbon emission. We use the entropy weight method to measure the weight of the indicator to evaluate four-dimensional urbanizations, including population, economic, consumption and living urbanization. In addition, we investigated the spatial correlation of carbon emissions, taking the spatial differences into consideration. The spatial Durbin model is finally selected to analyze the impacts of urbanizations on carbon emission. The conclusions are: Firstly, from the results of the panel data model, the four dimensions of urbanization all play a significant role in promoting carbon emissions in the whole regions. However, in eastern China, central China and western China, four dimensions of urbanization have different impacts on carbon emissions. Secondly, from Moran's I of carbon emissions from 1997 to 2015 in China, we conclude that carbon emissions in China present a significant spatial aggregation. Thirdly, from the results of spatial econometrics model, population urbanization only promotes local carbon emissions. Economic urbanization and consumption urbanization promote local carbon emissions and reduce carbon emissions in its neighboring provinces. Living urbanization promotes both local carbon emissions and its neighboring provinces' carbon emissions. This paper proposes some recommendations for the carbon emission decreasing during urbanization. First, establishment and improvement of coordination mechanisms and information sharing mechanisms across regions should also be considered. Second, control population growth reasonably and optimize population structure in order to achieve an orderly flow and rational distribution of the population. Third, the assessment mechanism of the local government should include not only economic indicators but also other indicators.
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How Does New-Type Urbanization Affect the Subjective Well-Being of Urban and Rural Residents? Evidence from 28 Provinces of China. SUSTAINABILITY 2021. [DOI: 10.3390/su132313098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The 2030 Agenda for Sustainable Development and the National New-type Urbanization Plan (2014–2020) put forward the novel principle of “people-oriented” policy. Has the Chinese government’s plan achieved the expected results? To answer this question, the present study evaluates the impacts of New-type Urbanization on the subjective well-being (SWB) of residents. Based on the IV Ordered Probit Model and the data of Chinese General Social Survey (CGSS), we determined that (1) living in cities has a positive impact on SWB, and this impact is significant at a statistical level of 1%; (2) urban household registration (hukou) has no significant impact on SWB; and (3) “Gender”, “Income”, “Job”, “Politics”, “Education”, “Marriage”, and “Health” have significant effects on SWB. To improve residents’ SWB, the government should enhance the attractiveness of the city and significantly reform the household registration system.
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Abstract
This article traces the development process of Beijing’s First Green Belt from its origins in the 1950s, to its reinterpretation in the 1980s/1990s and its implementation in the 1990s/2000s. We identify three-time phases and important milestones, which kept the green belt idea alive, developed it and contextualized it in relation to the historical background. This article shows that the first green belt project in Beijing was a continuing process of changing functions and ranges. Its adaptability to variations followed the political changes and reflected the socio-economic dynamics, which secured its longevity. Different ideas shaped the plan and its link to mega events like the Olympic Games and environmental problems accelerated the implementation, but the shortage of funding and absence of legislation led to a compromised result. The case is an interesting example of how a long-term project transforms over and with time, but also for the gap between planning ambitions and actual urban development, illustrating past and contemporary urban planning in the context of a fast-developing country.
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Dynamic Changes of Local Climate Zones in the Guangdong–Hong Kong–Macao Greater Bay Area and Their Spatio-Temporal Impacts on the Surface Urban Heat Island Effect between 2005 and 2015. SUSTAINABILITY 2021. [DOI: 10.3390/su13116374] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Local climate zones (LCZs) emphasize the influence of representative geometric properties and surface cover characteristics on the local climate. In this paper, we propose a multi-temporal LCZ mapping method, which was used to obtain LCZ maps for 2005 and 2015 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), and we analyze the effects of LCZ changes in the GBA on land surface temperature (LST) changes. The results reveal that: (1) The accuracy of the LCZ mapping of the GBA for 2005 and 2015 is 85.03% and 85.28%, respectively. (2) The built type category showing the largest increase in area from 2005 to 2015 is LCZ8 (large low-rise), with a 1.01% increase. The changes of the LCZs also vary among the cities due to the different factors, such as the economic development level and local policies. (3) The area showing a warming trend is larger than the area showing a cooling trend in all the cities in the GBA study area. The main reasons for the warming are the increase of built types, the enhancement of human activities, and the heat radiation from surrounding high-temperature areas. (4) The spatial morphology changes of the built type categories are positively correlated with the LST changes, and the morphological changes of the LCZ4 (open high-rise) and LCZ5 (open midrise) built types exert the most significant influence. These findings will provide important insights for urban heat mitigation via rational landscape design in urban planning management.
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Jiang S, Wang K, Mao Y. Rapid Local Urbanization around Most Meteorological Stations Explains the Observed Daily Asymmetric Warming Rates across China from 1985 to 2017. JOURNAL OF CLIMATE 2020; 33:9045-9061. [PMID: 0 DOI: 10.1175/jcli-d-20-0118.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
AbstractThe increasing rate of the observed daily minimum temperatureTminhas been much higher than that of the observed daily maximum temperatureTmaxduring the past six decades across China. In this study, the local urbanization impact on these observed asymmetric warming rates was investigated. The latest released land-cover data with a 30-m spatial resolution and annual temporal resolution from 1985 to 2017 were used to quantify the urbanization ratios around weather stations. Although urbanized areas occupied only 2.25% of the landmass in China, the percentage of stations with an urbanization ratio over 20% increased from 22.1% to 68.2% during the period 1985–2017. Significant asymmetric warming rates at urban stations were identified, which were approximately 3 times larger compared to the average asymmetry observed at all 2454 stations in China. However, this asymmetry disappeared at rural stations. These differences are mainly due to the rapid local urbanization around most meteorological stations in China since 1985, which affected the spatial representation of observations and led to the observed asymmetry warming rates. The results reported here indicate that the observed asymmetric warming rate over China from 1985 to 2017 is an observational bias due to local urbanization around most stations rather than large-scale climate change. The results also explain the phenomenon that the observed warming rate ofTminremains higher than that ofTmaxafter 1990 when the surface solar radiation stops decreasing in China.
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Affiliation(s)
- Shaojing Jiang
- Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, and State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Kaicun Wang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
| | - Yuna Mao
- State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
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Huang X, Wang Y, Li J, Chang X, Cao Y, Xie J, Gong J. High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images. Sci Bull (Beijing) 2020; 65:1039-1048. [PMID: 36659019 DOI: 10.1016/j.scib.2020.03.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 01/16/2020] [Accepted: 01/17/2020] [Indexed: 01/21/2023]
Abstract
Detailed and precise urban land-cover maps are crucial for urban-related studies. However, there are limited ways of mapping high-resolution urban land cover over large areas. In this paper, we propose an operational framework to map urban land cover on the basis of Ziyuan-3 satellite images. Based on this framework, we produced the first high-resolution (2 m) urban land-cover map (Hi-ULCM) covering the 42 major cities of China. The overall accuracy of the Hi-ULCM dataset is 88.55%, of which 14 cities have an overall accuracy of over 90%. Most of the producer's accuracies and user's accuracies of the land-cover classes exceed 85%. We further conducted a landscape pattern analysis in the 42 cities based on Hi-ULCM. In terms of the comparison between the 42 cities in China, we found that the difference in the land-cover composition of urban areas is related to the climatic characteristics and urbanization levels, e.g., cities with warm climates generally have higher proportions of green spaces. It is also interesting to find that cities with higher urbanization levels are more habitable, in general. From the landscape viewpoint, the geometric complexity of the landscape increases with the urbanization level. Compared with the existing medium-resolution land-cover/use datasets (at a 30-m resolution), Hi-ULCM represents a significant advance in accurately depicting the detailed land-cover footprint within the urban areas of China, and will be of great use for studies of urban ecosystems.
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Affiliation(s)
- Xin Huang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
| | - Ying Wang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Jiayi Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Xiaoyu Chang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Yinxia Cao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Junfeng Xie
- Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China, Beijing 100048, China
| | - Jianya Gong
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data. SUSTAINABILITY 2019. [DOI: 10.3390/su11236541] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.
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Zhang M, Chen W, Cai K, Gao X, Zhang X, Liu J, Wang Z, Li D. Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E4442. [PMID: 31726787 PMCID: PMC6888390 DOI: 10.3390/ijerph16224442] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/26/2019] [Accepted: 11/06/2019] [Indexed: 11/17/2022]
Abstract
The healthy development of the city has received widespread attention in the world, and urban resilience is an important issue in the study of urban development. In order to better provide a useful reference for urban resilience and urban health development, this paper takes 56 cities in China as the research object, and selects 29 indicators from urban infrastructure, economy, ecology and society. The combination weight method, exploratory spatial data analysis (ESDA) and spatial measurement model are used to explore the spatial distribution of urban resilience and its influencing factors. From 2006 to 2017, the urban resilience of prefecture-level cities in the four provinces showed a wave-like rise. During the study period, the urban resilience values, measured as Moran's Is, were greater than 0.3300, showing a significantly positive correlation in regard to their spatial distribution. Regarding the local spatial correlation, the urban resilience of the study area had spatial agglomeration characteristics within the province, with a significant distribution of "cold hot spots" in the spatial distribution. From the perspective of the factors that affected urban resilience, the proportion of the actual use of foreign capital in GDP and carbon emissions per 10,000 CNY of GDP had a negative impact and GDP per square kilometer, the proportion of urban pension insurance coverage, the proportion of the population with higher education, and expenditure to maintain and build cities had a positive impact. The development strategy of urban resilience must be combined with the actual situation of the region, and the rational resilience performance evaluation system and the top-level design of urban resilience improvement should be formulated to comprehensively improve urban resilience.
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Affiliation(s)
- Maomao Zhang
- Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
- College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
| | - Weigang Chen
- School of Architecture, University of South China, Hengyang 421001, China
| | - Kui Cai
- Institute of Geological Survey, Hebei GEO University, Shijiazhuang 050031, China
| | - Xin Gao
- Business School, Hohai University, Nanjing 211100, China
| | - Xuesong Zhang
- Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
- College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
| | - Jinxiang Liu
- School of Civil Engineering, University of South China, Hengyang 421001, China
| | - Zhiyuan Wang
- School of Architecture, University of South China, Hengyang 421001, China
| | - Deshou Li
- Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
- College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
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Gong P, Li X, Zhang W. 40-Year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Sci Bull (Beijing) 2019; 64:756-763. [PMID: 36659545 DOI: 10.1016/j.scib.2019.04.024] [Citation(s) in RCA: 115] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 03/03/2019] [Accepted: 03/04/2019] [Indexed: 01/21/2023]
Abstract
Impervious surfaces are the most significant feature of human settlements. Timely, accurate, and frequent information on impervious surfaces is critical in both social-economic and natural environment applications. Over the past 40 years, impervious surface areas in China have grown rapidly. However, annual maps of impervious areas in China with high spatial details do not exist during this period. In this paper, we made use of reliable impervious surface mapping algorithms that we published before and the Google Earth Engine (GEE) platform to address this data gap. With available data in GEE, we were able to map impervious surfaces over the entire country circa 1978, and during 1985-2017 at an annual frequency. The 1978 data were at 60-m resolution, while the 1985-2017 data were in 30-m resolution. For the 30-m resolution data, we evaluated the accuracies for 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Overall accuracies reached more than 90%. Our results indicate that the growth of impervious surface in China was not only fast but also considerably exceeding the per capita impervious surface area in developed countries like Japan. The 40-year continuous and consistent impervious surface distribution data in China would generate widespread interests in the research and policy-making community. The impervious surface data can be freely downloaded from http://data.ess.tsinghua.edu.cn.
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Affiliation(s)
- Peng Gong
- Ministry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China; Tsinghua Urban Institute, Tsinghua University, Beijing 100084, China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing 100084, China.
| | - Xuecao Li
- Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA
| | - Wei Zhang
- Ministry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
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Identifying the Conditions for Rural Sustainability through Place-Based Culture: Applying the CIPM and CDPM Models into Meibei Ancient Village. SUSTAINABILITY 2017. [DOI: 10.3390/su9081334] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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