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1.
Science ; 381(6664): 1295, 2023 Sep 22.
Article in English | MEDLINE | ID: mdl-37733867
2.
Sci Data ; 9(1): 624, 2022 10 14.
Article in English | MEDLINE | ID: mdl-36241886

ABSTRACT

Accurate location-based big data has a high resolution and a direct interaction with human activities, allowing for fine-scale population spatial data to be realized. We take the average of Tencent user location big data as a measure of ambient population. The county-level statistical population data in 2018 was used as the assigned input data. The log linear spatially weighted regression model was used to establish the relationship between location data and statistical data to allocate the latter to a 0.01° grid, and the ambient population data of mainland China was obtained. Extracting street-level (lower than county-level) statistics for accuracy testing, we found that POP2018 has the best fit with the actual permanent population (R2 = 0.91), and the error is the smallest (MSEPOP2018 = 22.48

Subject(s)
Big Data , Humans , China , Population Dynamics
3.
Environ Monit Assess ; 194(8): 523, 2022 Jun 23.
Article in English | MEDLINE | ID: mdl-35737175

ABSTRACT

Water scarcity, which refers to a deficit of freshwater resources availability in meeting anthropogenic and environmental water needs, is nowadays a growing concern in many countries around the world. Because water scarcity is often poor management induced, research is critical to advance knowledge and provide technical and policy support for water scarcity adaptation and solutions. Here, we address global water scarcity research pattern and underlying drivers, using the bibliometric analysis combined with geographic detector. The results indicate that water scarcity research exhibits great temporal and spatial variations. Predominant factors that control the numbers of water scarcity publications are gross domestic products (GDP) and population, which altogether explain 30-52% of the variance of the number of publications in different countries. Water scarcity research is biased in a few populated and affluent countries. Other factors, including physical water scarcity, research and development expenditure, and governance indicators can also be linked to water scarcity research. Keywords mining reveals that hotspots of research domains on causes, approaches, types, and effects of water scarcity show continental difference. The results have policy implications for guiding future water scarcity research. Research in developing countries suffering from physical and economic water scarcity should be enhanced to improve adaptive capacity and reduce vulnerability to water scarcity.


Subject(s)
Water Insecurity , Water Supply , Bibliometrics , Environmental Monitoring , Fresh Water
4.
Article in English | MEDLINE | ID: mdl-34200515

ABSTRACT

This paper systematically summarizes the hierarchical cross-regional multi-directional linkage in terms of air pollution control models implemented in the Beijing-Tianjin-Hebei urban agglomeration, including the hierarchical linkage structure of national-urban agglomeration-city, the cross-regional linkage governance of multiple provinces and municipalities, the multi-directional linkage mechanism mainly involving industry access, energy structure, green transportation, cross-regional assistance, monitoring and warning, consultation, and accountability. The concentration data of six air pollutants were used to analyze spatiotemporal characteristics. The concentrations of SO2, NO2, PM10, PM2.5, CO decreased, and the concentration of O3 increased from 2014 to 2017; the air pollution control has achieved good effect. The concentration of O3 was the highest in summer and lowest in winter, while those of other pollutants were the highest in winter and lowest in summer. The high pollution ranges of O3 diffused from south to north, and those of other pollutants decreased significantly from north to south. Finally, we suggest strengthening the traceability and process research of heavy pollution, increasing the traceability and process research of O3 pollution, promoting the joint legislation of different regions in urban agglomeration, create innovative pollution discharge supervision mechanisms, in order to provide significant reference for the joint prevention and control of air pollution in urban agglomerations.


Subject(s)
Air Pollutants , Air Pollution , Air Pollutants/analysis , Air Pollution/analysis , Beijing , China , Cities , Environmental Monitoring , Particulate Matter/analysis
5.
PLoS One ; 15(9): e0237863, 2020.
Article in English | MEDLINE | ID: mdl-32986700

ABSTRACT

The green development of coastal urban agglomerations, which are strategic core areas of national economic growth in China, has become a major focus of both academics and government agencies. In this paper, China's coastal urban agglomeration is taken as the research area, aiming at the serious air pollution problem of coastal urban agglomeration, geographic information system (ArcGIS10.2) spatial analysis and the spatial Dubin model were applied to National Aeronautics and Space Administration atmospheric remote sensing image inversion fine particulate matter (PM2.5) data from 2010-2016 to reveal the temporal and spatial evolution characteristics and Influence mechanism of PM2.5 in China's coastal urban agglomerations, with a view to providing a reference value for coordinating air pollution in the coastal cities of the world. From 2010-2016, the PM2.5 concentration in China's coastal urban agglomerations decreased as a whole, and large spatial differences in PM2.5 concentration were observed in China's coastal urban agglomerations; the core high-pollution areas were the Beijing-Tianjin-Hebei, Shandong Peninsula, and Yangtze River Delta urban agglomerations. Large spatial differences in PM2.5 concentration were also observed within individual urban agglomerations, with higher PM2.5 concentrations found in the northern parts of the urban agglomerations. Significant spatial autocorrelation and spatial heterogeneity were observed among PM2.5-polluted cities in China's coastal urban agglomerations. The northern coastal urban agglomerations formed a relatively stable and continuous high-pollution zone. The spatial Dubin model was used to analyze the driving factors of PM2.5 pollution in coastal urban agglomerations. Together, meteorological, socioeconomic, pollution source, and ecological factors affected the spatial characteristics of PM2.5 pollution during the study period, and the overall effect was a mixed effect with significant spatial variation. Among them, meteorological factors were the greatest driver of PM2.5 pollution. In the short term, the rapid increase in population density, industrial emissions, industrial energy consumption, and total traffic emissions were the important driving factors of PM2.5 pollution in the coastal urban agglomerations of China.


Subject(s)
Air Pollution/analysis , Ecosystem , Geographic Information Systems , Urbanization , Air Pollutants/analysis , Algorithms , China , Factor Analysis, Statistical , Gross Domestic Product , Models, Theoretical , Particle Size , Particulate Matter/analysis , Time Factors
6.
Sci Total Environ ; 739: 140197, 2020 Oct 15.
Article in English | MEDLINE | ID: mdl-32758959

ABSTRACT

Improving energy efficiency and building a low-carbon economy are the important ways to resolve the current contradiction between economic growth and the environment in China. In this paper, we use the super-efficiency Slack-Based Measure model (super-efficiency SBM model) to measure the energy efficiency of 30 provinces in China, and then conduct Empirical Orthogonal Function (EOF) to analyze its spatial-temporal evolution. Moreover, we use the Geographically and Temporally Weighted Regression (GTWR) to analyze the spatial-temporal heterogeneity of its driving factors. The results show that: (i) during the sample period, China's energy efficiency shows a rapidly upward trend, accompanied by the gradually strengthening spatial pattern of the "eastern>central>western"; (ii) the spatial pattern of the "southern>northern" exhibited by the annual growth rate of energy efficiency experienced a process of weakening first and then gradually strengthening; (iii) the influencing effects of market openness, relative energy price and industry structure on energy efficiency have no significant heterogeneity as a whole; (iv) the effects of environmental regulation intensity, the marketization level, the technical level, energy consumption structure and economic development level have significant spatial heterogeneity, and the effects of energy conservation and emission reduction policies has significant temporal heterogeneity.

7.
PLoS One ; 15(5): e0231335, 2020.
Article in English | MEDLINE | ID: mdl-32433695

ABSTRACT

The Chinese government adheres to the innovation driven strategy and emphasizes that technological innovation is the strategic support for improving social productivity and comprehensive national strength. This paper discusses the mechanism of technological innovation and regional economic co-evolution, and constructs an index system to assess them based on the principles of synergy and systematics. The authors use a dynamic coupling model to study the law of the cooperative evolution of composite systems and geo-detector methods to reveal the main factors controlling the degree of coordination among them. The results show that the total factor productivity of China's high-tech industry showed a "W"-type trend of change from 2006 to 2016, and the other indices exhibited a volatile trend. The total factor productivity, technical efficiency, scale efficiency, pure technical efficiency, and technological progress increased by 37%, 13.3%, 3.9%, 9%, and 20.8%, respectively. There was a significant spatial difference in changes in total factor productivity, forming a core-edge spatial pattern with the middle and upper reaches of the Yangtze River as the center of concentration. Most of China's technological innovation and regional economic complex systems were in a state of interactive development from 2007 to 2016, except in the three northeastern provinces of Zhejiang, Shanghai, and the western part of the country. The degree of coupling of the other provinces showed an increasing trend, and the overall degree of coupling exhibited the spatial pattern of Central > Eastern > Western > Northeastern. The three most influential factors for the degree of coupling of China's provincial complex system were the gross domestic product, efficiency of technological innovation, and expenditure on research and development, whereas the three most important factors affecting the degree of coupling of complex systems were the efficiency of technological innovation, gross domestic product, and number of high-tech enterprises as well as research and development personnel, respectively, in the eastern, central, western, and northeastern regions. Finally, the paper puts forward the suggestions of regional innovation driven coordinated development, technology innovation and regional economic coordinated development, in order to provide reference for the high-quality economic development of developing countries.


Subject(s)
Economic Development , Industry/economics , Inventions/statistics & numerical data , Models, Theoretical , Technology/statistics & numerical data , Geography , Humans
8.
Environ Pollut ; 256: 113419, 2020 Jan.
Article in English | MEDLINE | ID: mdl-31706769

ABSTRACT

Ozone has become a major atmospheric pollutant in China as the pattern of urban energy usage has changed and the number of motor vehicles has grown rapidly. The Beijing-Tianjin-Hebei Urban Agglomeration, also known as the Jing-Jin-Ji Urban Agglomeration (hereafter, JJJUA), with a precarious balance between protecting the ecological environment and sustaining economic development, is challenged by high levels of ozone pollution. Based on ozone observation data from 13 cities in the JJJUA from 2014 to 2017, the spatio-temporal trends in the evolution of ozone pollution and its associated influencing factors were analyzed using Moran's I Index, hot-spot analysis, and Geodetector using ArcGIS and SPSS software. Five key results were obtained. 1) There was an increase in the annual average ozone concentration, for the period 2014-2017. Comparing the 13 prefecture-level cities, ozone pollution in Chengde and Hengshui decreased, while it worsened in the remaining 11 cities. 2) Ozone pollution was worse in spring and summer than in autumn and winter; the peak ozone pollution season was from May to September; the average ozone concentration on workdays was higher than that on non-workdays, showing a counter-weekend effect. 3) Annual average concentrations were high in the central and southern parts of the study region but low in the north. 4) Prominent positive spatial correlations were observed in ozone concentration, with the best correlations shown in summer and autumn; concentrations were high in Baoding and Xingtai but low in Beijing and Chengde. 5) Concentrations of PM10, NO2, CO, SO2, and PM2.5, as well as average wind speed, sunshine duration, evaporation, precipitation, and temperature, all had significant effects on ozone pollution, and interactions between these influencing factors increased it.


Subject(s)
Air Pollutants/analysis , Environmental Monitoring , Ozone/analysis , Air Pollution/analysis , Air Pollution/statistics & numerical data , Beijing , China , Cities , Economic Development , Particulate Matter/analysis , Seasons , Wind
9.
J Environ Manage ; 243: 227-239, 2019 Aug 01.
Article in English | MEDLINE | ID: mdl-31096175

ABSTRACT

Numerous environmental problems have been seen due to the "high energy consumption, high pollution, high emissions" economic model in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA). The coupling coordination degree model is applied to provide a coordination of urbanization and ecological environment composite system (CUECS) value while a geographic detector is applied to explore the dominant factors controlling it. This study reached the following conclusions. (1)The CUECS types are mainly low coordination, but which generally exhibit positive evolutionary trend. The change trends can be characterized as urbanization lags followed by system equilibrium followed by ecological environmental lags. (2)The CUECS conforms to a core-edge distributional pattern that comprises plain high mountain low, inland high coastal low. Industrialization played a key role in the development of BTHUA, the landform type was the important factor controlling CUECS. (3) Social consumer goods, gross domestic product, the disposable income of urban residents (all per capita) are the core factors controlling CUECS within different spatial units. Urbanization rate, per capita social consumer goods, the proportion of tertiary industrial population are the core factors controlling CUECS during different urbanization development stages. (4)The relative impacts of urbanization and ecological environmental subsystems on CUECS are (in decreasing order of importance) population urbanization, economic urbanization, social urbanization, ecological environment subsystem. Therefore, green urbanization remains the primary path for sustainable development within the urban agglomeration. It is unsuitable for rapid urbanization development model in the mountainous areas that encapsulate ecological and environmental security as their main functions, so the government urgently needs to amend its 'one size fits all' policy system.


Subject(s)
Environment , Urbanization , Beijing , China , Ecology , Ecosystem , Urban Population
10.
Huan Jing Ke Xue ; 38(10): 4005-4014, 2017 Oct 08.
Article in Chinese | MEDLINE | ID: mdl-29965182

ABSTRACT

Controlling air pollution in the Jing-jin-ji urban agglomeration (JJJUA), the most seriously polluted area in China, is related to the integrated development strategy for the region. Based on the national and regional implementation of air pollution control measures in recent years, the hierarchical cross-regional multi-directional linkage (HCML) air pollution prevention and control model was applied in this study. The effect of air pollution control was evaluated by monitoring the pollutants, SO2, NO2, PM10, PM2.5, O3, and CO, at 112 monitoring sites in 13 cities in 2014-2015. The results can be summarized as follows:① The HCML model is an interrelated framework at the horizontal and vertical level. Under the efforts provided by the central, urban agglomeration, and city governments, this multi-level governance model serves as an effective tool to resolve the issues related to air pollution beyond the borders of municipalities. Environmental regulations on certain industries, energy consumption structure, car ownership and usage, and air quality supervision and warning systems are well established under this governance model. ② The air quality of the JJJUA has improved significantly in the past two years. The concentrations of air pollutants significantly decreased, with the exception of O3, and high pollution ranges significantly reduced from north to south. The annual average concentrations of PM2.5, PM10, SO2, NO2, and CO decreased by 17.84%, 14.61%, 37.55%, 4.82%, and 16.18%, respectively. The number of days meeting the air quality standards increased for all pollutants except NO2. Based on the current situation and unsolved problems of air pollution, the JJJUA area needs certain measures including diversifying the governance subjects, joint legislation (beyond municipalities) on air pollution to regulate pollution discharge, enhancing public awareness on air pollution and its health impacts, carefully examining sources of air pollution in winter to reduce pollution, and to better understand the sources of ozone and adopt effective control measures.

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