DOCSLIB.ORG

A Land Use Regression Application Into Simulating Spatial Distribution Characteristics of Particulate

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Land Use Regression Application Into Simulating Spatial Distribution Characteristics of Particulate Pol. J. Environ. Stud. Vol. 29, No. 6 (2020), 4065-4076 DOI: 10.15244/pjoes/118426 ONLINE PUBLICATION DATE: 2020-05-22 Original Research A Land Use Regression Application into Simulating Spatial Distribution Characteristics of Particulate Matter (PM2.5) Concentration in City of Xi’an, China Bin Guo1*, Xiaoxia Wang1, Donghai Zhang1, Lin Pei2, Dingming Zhang1, Xiaofeng Wang3,4 1College of Geomatics, Xi’an University of Science and Technology, Xi’an, China 2School of Public Health, Xi’an JiaoTong University, Xi’an, China 3Shaanxi Key Laboratory of Land Consolidation, Xi’an, China 4School of Earth Science and Resources, Chang’an University, Xi’an, China Received: 25 December 2019 Accepted: 24 February 2020 Abstract Decreasing of PM2.5 concentration in the heating season was not significant in Xi’an. This article determined a land use regression (LUR) model and researched the distribution characteristics of PM2.5 in heating and non-heating seasons in Xi'an. The results showed that: (1) The R2 of LUR was larger than 0.9, and the simulation results were better than previous studies. (2) The PM2.5 concentration in the heating season was larger than in the non-heating season. In Xi’an, the distribution of PM2.5 concentration was low in the southeast and high in the northwest in the non-heating season and was low in the southeast and high in the main urban region in the heating season. (3) The PM2.5 concentration was affected by temperature, average air pressure, altitude, humidity and precipitation in non-heating season and was influenced by precipitation, altitude, average air pressure, vegetation, and density of roads in heating season. (4) This paper showed some improvements in selection of potential variables for LUR model, and the conclusion can provide a scientific basis for PM2.5 pollution control and a reliable method for simulating PM2.5 concentration in other cities. Keywords: ambient air pollution, geographic information system (GIS), uapping, modelling, urban areas Introduction opening-up policy and has become the second-largest economy in the world. However, economic development China has been making tremendous achievements has also led to a series of environmental issues, in economic development since the reform and and China is currently facing serious air pollution challenges [1]. It is unhealthy to be exposed to ambient air pollution, and it increases the probability of mortality, and morbidity decreases life expectancy. *e-mail: [email protected] 4066 Guo B., et al. Therefore, the report in Global Burden of Disease Study small-scale concentration of PM2.5 [10], nighttime (GBD) identified air pollution as a leading cause of lighting data simulation method [20], and a model global diseases, especially in low-income and middle- between other pollutants concentration and PM2.5 income countries [2]. At the same time, some of research concentration [12]. Although field sampling method results revealed that PM2.5 was the fifth largest death could acquire high precision data, it was costly, time- risk factor in 2015 [3]. Xi’an, a famed tourist destination consuming and inefficient. The nighttime lighting as well as the largest national central city in northwest data simulation method could be used for obtaining China, has been experiencing severe air pollution which the spatial distribution of PM2.5 concentration on a has become an urgent issue that threatens people's large scale. However, the accuracy was low for PM2.5 health and quality of life. Xi’an has taken several concentration simulation in a small scale. Therefore, measures to improve air quality since 2013, such as we should find a new method to simulate PM2.5 coal reduction, vehicle control, dust suppression, source concentration and analyze the probable influences from of contamination control and green area enhancement. nature and human beings. At the same time, Xi’an is As a result, a statement released by the Bureau of Xi’an undergoing unprecedented urbanization process that ecological environment in 2017 indicated the air quality leads to obvious landscape change [21]. In addition, achieved “one increase”, “two decreases” and “four Xi’an appears some unreasonable land use cases, and declines” compared with 2013, those were the number the urban planning of Xi’an may neglect the effects of of excellent days increased from 138 days to 180 days, natural factors and human activities on the landscape the number of heavy pollution days decreased from [22]. Land use change is due to human activities and 67 days to 39 days, the number of extraordinary natural factors [23]. Some of previous studies used land pollution days decreased from 8 days to 0 days, and the cover maps derived from remote sensing images to average annual PM2.5 concentration declined by 30.5% evaluate the changes in urban development and green from 2013 to 2017, respectively. However, the number areas [23-33]. Furthermore, some recent studies are of excellent days in the heating season was only 68 of to develop a landscape plan according to the aims of 180 excellent days, the improvement of air quality was sustaining the natural and cultural landscape values of not very ideal in the heating season, and the air quality an area by considering landscape variables such as the was still inferior and showed an obvious difference number of potential visitors, vegetation cover, cultural between the heating season and the non-heating season. values and the topographic structure. ArcGIS was used At the same time, the conclusion that the concentrations to evaluate the landscape variables, and the study data of multiple air pollutants during the heating season were obtained through a land survey, questionnaires were higher than those during the non-heating season and maps [23-35]. Besides, some of the previous studies resembled results from different regions, such as Czech have revealed that urban planning including land use, Republic [4], Italy [5] and China [6-7]. urban spatial structure, spatial form, transportation Most of the previous studies in Xi’an focused on and green space may affect concentrations of air the chemical composition of PM2.5 in a small scale pollutants [36-38]. Moreover, particulate matter can be [8-15]. However, the current studies seldom simulate purified and absorbed by urban green spaces [39], and PM2.5 concentration on a small grid-scale, so the the landscape pattern of green spaces (such as edge existing results could hardly meet the needs of density and patch density) can also influence PM2.5 precision for further study. At present, there were two concentration [40]. Hence, the PM2.5 concentration is main methods for spatial distribution simulation of not only affected by natural factors but also influenced PM2.5 concentration. One was the spatial interpolation by human activities especially land use and urban method, and the other was remote sensing inversion planning, so it is feasible to control PM2.5 concentration through aerosol optical depth (AOD) products. Firstly, through optimizing land use structure, urban planning, the spatial interpolation method used limited in-situ spatial form, transportation and green space [37]. sampling data to establish an approximation function Therefore, it is possible to simulate PM2.5 concentration to simulate the PM2.5 concentration outside the in-situ through land use and urban planning perspective, sites. However, the precision of spatial interpolation and the LUR model supply probability for simulating method had some uncertainties [16]. Moreover, the PM2.5 concentration. The LUR model was often used spatial interpolation method could not be implemented to predict pollutant distribution, including NO2, NOX in some places if in-situ sampling sites data was sparse and PM2.5 [41-44]. The LUR model is based on or missing, so the results would not be reasonable monitoring data and land use information within a [17]. Secondly, remote sensing inversion method took certain radius of the monitoring sites, road traffic advantage of the relationship between AOD and PM2.5 characteristics and other relevant geographic variables concentration to simulate PM2.5 concentration [18]. to construct regression model [45]. The LUR model is However, the spatial resolution of PM2.5 concentration able to generalize regression relationship to simulate products based on remote sensing inversion were also the spatial distribution of pollutants in unmonitored coarse and were inadequate to satisfy simulation for areas [46]. In short, LUR model can simulate PM2.5 PM2.5 concentration on a small-scale [19]. Besides, concentration with higher simulation precision for there were field sampling methods for measuring further study. A Land Use Regression Application... 4067 The purposes of this study were: (1) to establish the Data Sources LUR model for PM2.5 in a heating season and a non- heating season in Xi’an. (2) to map the distribution of PM2.5 Data PM2.5 on a grid scale. (3) to analyze the influence factors for PM2.5. (4) to supply some suggestions for improving PM2.5 concentration data with a period from air quality in Xi’an. November 15, 2016 to November 14, 2017 was collected from the Xi’an Air Quality Daily Reporting System (http://www.xianemc.gov.cn/). There are 13 air quality Materials and Methods monitoring stations in Xi’an (Fig. 1, Table 1). The mean concentration during a heating season (From November Study Area 15, 2016 to March 15, 2017) and a non-heating season (From March 16, 2017 to November 14, 2017) was Xi’an is located in the center of the Guanzhong calculated respectively. Basin. The overall terrain is elevated in the north and south and low in the center. It has a warm, temperate, Potential Independent Variable Data semi-humid, continental monsoon climate with four distinct seasons. The annual average temperature (1) Land use data with 30m resolution was collected is 15.6ºC and the average annual precipitation is from the Department of Earth System Science of 649.0 mm.
Load more

Recommended publications
  • Day 2 Day 3 Day 1
    Xi’AN Back to the heyday of the Tang Dynasty Location of Xi’an Xi’an is known as Chang’an in ancient times. Having served as the capital of thirteen dynasties, this city is one of the most important places to study and review the history of China. The Tang Dynasty was the pinnacle of China’s history, a period marked by great urbanism and cultural magnificence. As the capital of the Tang Dynasty, Xi’an was the centre of Eastern civilisation. Its importance was comparable to that of Rome in the West. Xi’an’s Tang Dynasty monuments are the most famous of all, and beyond that, the city is committed to recreating the prosperity of the Tang Dynasty. Nowadays, in Xi’an, it is no longer an unattainable dream to travel back in time to the Tang Dynasty. What’s hot Shaanxi History Museum The Shaanxi History Museum is one of the four major museums in China. Its extensive collection of artefacts showcase 1.5 million years of Shaanxi’s history. The third gallery features the culture of the Tang Dynasty as well as artefacts from both the Sui and Tang Dynasties, while the fourth gallery displays a collection of gold and silver artefacts from the Tang Dynasty unearthed in Hejiacun Village. The “Treasure of the Museum”—Agate Cup with Beast’s Head Carving is a superbly crafted jade carving with chic colour. One can also visit the Treasures Gallery and the Tang Dynasty Mural Treasures Gallery of the museum. 91 Xiaozhai East Road, Xiaozhai Commercial Street, Yanta District, Xi’an City, Shaanxi Province, China Take Xi’an Metro Line 1 or 2 and get off at Xiaozhai Station, the museum will be reach from north-east exit.
    [Show full text]
  • 2019 Form 10-K/A
    UNITED STATES SECURITIES AND EXCHANGE COMMISSION Washington, D.C. 20549 FORM 10-K/A (Amendment No. 1) ☒ ANNUAL REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 For the fiscal year ended December 31, 2019 OR ☐ TRANSITION REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 For the Transition Period from _________to _________ Commission File Number 001-34502 Future FinTech Group Inc. (Exact name of registrant as specified in its charter) Florida 98-0222013 (State or other jurisdiction of (I.R.S. Employer incorporation or organization) Identification Number) Room 2103, 21st Floor, SK Tower 6A Jianguomenwai Avenue, Chaoyang District Beijing, PRC 100022 (Address of principal executive offices) (Zip Code) Registrant’s Telephone Number: 86-10- 8589-9303 Securities registered pursuant to Section 12(b) of the Act: Title of each class Name of each exchange on which registered Common Stock, $0.001 par value Nasdaq Capital Market Securities registered pursuant to Section 12(g) of the Act: None (Title of class) Indicate by check mark if the registrant is a well-known seasoned issuer, as defined in rule 405 of the Securities Act. Yes ☐ No ☒ Indicate by check mark if the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. Yes ☐ No ☒ Indicate by check mark whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange Act of 1934 during the preceding 12 months (or for such shorter period that the registrant was required to file such reports), and (2) has been subject to such filing requirements for the past 90 days.
    [Show full text]
  • Pre-Primary Education
    Detailed Sector Assessment: Pre-Primary Education Project Number: 53060-001 March 2021 People's Republic of China: Shaanxi Xi'an Preschool Education Development Program CONTENTS Page A. EARLY CHILDHOOD EDUCATION: CHALLENGES AND OPPORTUNITIES ............... 1 B. SECTOR PROFILE: EARLY CHILDHOOD EDUCATION IN XI'AN ............................... 5 1. Access to Early Childhood Education ....................................................................... 5 2. Quality of Early Childhood Education ..................................................................... 11 3. Early Childhood Education Management, Supervision, and Financing ..................... 19 4. Early Childhood Education Innovation .................................................................... 20 C. INTERNATIONAL AND NATIONAL LESSONS AND GOOD PRACTICE..................... 21 D. ADB COUNTRY AND SECTOR EXPERIENCE AND ASSISTANCE ........................... 27 E. RECOMMENDATIONS FOR ADB TO SUPPORT EARLY CHILDHOOD EDUCATION DEVELOPMENT IN XI'AN .................................................................................................... 28 APPENDIXES 1. Bilingual Glossary ............................................................................................................ 30 2. Problem Tree ................................................................................................................... 35 3. Xi'an Preschool Education Development Plan, 2019–2025 ................................................ 36 ABBREVIATIONS ADB – Asian Development Bank
    [Show full text]
  • Quantifying Urban Vegetation Coverage Change with a Linear Spectral Mixing Model: a Case Study in Xi’An, China
    DOI: 10.2478/eces-2021-0008 ECOL CHEM ENG S. 2021;28(1):87-100 Xuan ZHAO 1 and Jianjun LIU 1* QUANTIFYING URBAN VEGETATION COVERAGE CHANGE WITH A LINEAR SPECTRAL MIXING MODEL: A CASE STUDY IN XI’AN, CHINA Abstract: With the rapid development of urban area of Xi’an in recent years, the contradiction between ecological environmental protection and urban development has become prominent. The traditional remote sensing classification method has been unable to meet the accuracy requirements of urban vegetation monitoring. Therefore, how to quickly and accurately conduct dynamic monitoring of urban vegetation based on the spectral component characteristics of vegetation is urgent. This study used the data of Landsat 5 TM and Landsat 8 OLI in 2011, 2014 and 2017 as main information source and LSMM, region of variation grid analysis and other methods to analyse the law of spatial-temporal change of vegetation components in Xi’an urban area and its influencing factors. The result shows that: (1) The average vegetation coverage of the study area from 2011 to 2017 reached more than 50 %, meeting the standard of National Garden City (great than 40 %). The overall vegetation coverage grade was high, but it had a decreasing trend during this period. (2) The vegetation in urban area of Xi’an experienced a significant change. From 2011 to 2017, only 30 % of the low-covered vegetation, 24.39 % of the medium-covered vegetation and 20.15 % of the high-covered vegetation remained unchanged, while the vegetation in the northwest, northeast, southwest and southeast of the edge of the city’s third ring changed significantly.
    [Show full text]
  • Preparing the Shaanxi-Qinling Mountains Integrated Ecosystem Management Project (Cofinanced by the Global Environment Facility)
    Technical Assistance Consultant’s Report Project Number: 39321 June 2008 PRC: Preparing the Shaanxi-Qinling Mountains Integrated Ecosystem Management Project (Cofinanced by the Global Environment Facility) Prepared by: ANZDEC Limited Australia For Shaanxi Province Development and Reform Commission This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents. (For project preparatory technical assistance: All the views expressed herein may not be incorporated into the proposed project’s design. FINAL REPORT SHAANXI QINLING BIODIVERSITY CONSERVATION AND DEMONSTRATION PROJECT PREPARED FOR Shaanxi Provincial Government And the Asian Development Bank ANZDEC LIMITED September 2007 CURRENCY EQUIVALENTS (as at 1 June 2007) Currency Unit – Chinese Yuan {CNY}1.00 = US $0.1308 $1.00 = CNY 7.64 ABBREVIATIONS ADB – Asian Development Bank BAP – Biodiversity Action Plan (of the PRC Government) CAS – Chinese Academy of Sciences CASS – Chinese Academy of Social Sciences CBD – Convention on Biological Diversity CBRC – China Bank Regulatory Commission CDA - Conservation Demonstration Area CNY – Chinese Yuan CO – company CPF – country programming framework CTF – Conservation Trust Fund EA – Executing Agency EFCAs – Ecosystem Function Conservation Areas EIRR – economic internal rate of return EPB – Environmental Protection Bureau EU – European Union FIRR – financial internal rate of return FDI – Foreign Direct Investment FYP – Five-Year Plan FS – Feasibility
    [Show full text]
  • Research on PM2.5 Concentration Based on Dissipative Structure Theory
    www.nature.com/scientificreports OPEN Research on ­PM2.5 concentration based on dissipative structure theory: a case study of Xi’an, China Xiaoke Sun, Hong Chen*, Zhizhen Liu & Hengrui Chen PM2.5 pollution has become a serious urban environmental problem, especially in developing countries with increasing urbanization. Understanding the proportion of PM2.5 generation sources has laid a foundation for better ­PM2.5 concentration reduction This paper used Point of Interesting (POI)data, building profle data of Xi’an, PM2.5 concentration and wind monitoring data of fve provinces near Xi’an as the basic data. And this paper studied the spatial distribution of various buildings in Xi’an, the temporal and spatial distribution of ­PM2.5 in Xi’an and the fve provinces, and found that the spatial distribution of ­PM2.5 concentration in Xi’an and the building distribution in Xi’an does not match. Based on this, a quantitative model of ­PM2.5 concentration in Xi’an, energy consumption, wind, and other factors is established through the qualitative and quantitative analysis of ­PM2.5 concentration in Xi’an. Entropy theory and dissipative structure theory are applied to analyze this phenomenon. The results show ­PM2.5 in Xi’an mainly comes from the spread of ­PM2.5 in the fve provinces. The PM2.5 generated by energy consumption in Xi’an is not enough to cause serious ­PM2.5 pollution. And further suggestions on how to reduce ­PM2.5 concentration in Xi’an are put forward. With the rapid economic growth and development of infrastructure in developing countries, urbanization and motorization are accelerating.
    [Show full text]
  • & the 19 National Academic Symposium of Red Beds And
    THE 4TH INTERNATIONAL SYMPOSIUM ON DANXIA LANDFORM & The 19th National Academic Symposium of Red Beds and Danxia Landform Yan’an Tourism Development Conference (First Announcement) August 18th – 22nd, 2019 Yan’an, Shaanxi, China Hosted by IAG Working Group on Red Beds and Danxia Geomorphology Asia Pacific Geoparks Network (APGN) Red Beds and Danxia Working Group, Geographical Society of China Department of Natural Resources of Shaanxi Province Xi'an Center of the China Geological Survey. Yan’an Municipal People's Government Shaanxi Institute of Geological Survey Organized by Yan'an Municipal Natural Resources Bureau Shaanxi Provincial Mineral Geological Survey Center Bureau of Land and Resources of Yan’an City Co-organized by SunYat-Sen University Shaanxi Normal University Chang’an University Northwestern university, Yan’an University Yulin College Geographical Society of Shaanxi Province Northwest Geological Science and Technology Innovation Center 1. About the Conference After consulting with the Yan’an Municipal People's Government of Shaanxi Province, the 4th International Symposium on Danxia Landforms & the 19th National Academic Symposium of Red Beds and Danxia Landforms & Yan’an Tourism Development Conference is decided to be held from August 18th to 22nd, 2019 in the Yan’an City of Shaanxi Province. We welcome scholars from diverse fields to participate in the conference, to prompt the scientific understanding, protection and utilization of Danxia Landform resources in Northern Shaanxi and offer suggestive advice on Yan’an tourism
    [Show full text]
  • 4.2 Tianzifang Art District, Shanghai
    CREATING A SOCIAL AND CULTURAL SPACE --A CONTINUATION OF URBAN MEMORY: OPPORTUNITIES AND OBSTACLES FOR DEVELOPING ART DISTRICTS IN THE PROCESS OF CHINESE URBAN REVITALIZATION --THREE CASE STUDIES IN SHANGHAI, BEIJING AND XI’AN A RESEARCH PAPER SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE MASTER OF URBAN AND REGIONAL PLANNING BY JIONGZHI LI PROF. BRUCE RACE-ADVISOR BALL STATE UNIVERSITY MUNCIE, INDIANA DECEMBER 2013 ACKNOWLEDGEMENTS I would like to express my deepest gratitude to my advisor, Professor Bruce Race for accepting me as his advisee even in the face of his tight schedule. I greatly appreciate his encouragement and guidance pushed me to structure this research project. I would also like to thank Professor Geralyn Strecker for her patience and assistance. She helped me with the writing process and improved my English. My sincere thanks to the faculty of the College of Architecture and Planning, friends, and classmates, from whom I learned so much, and their positive spirit and encouragement through out the past two years. Finally, I would like to thank my family. Your everlasting love have supported and motivated me throughout my graduate school career. 1 ABSTRACT RESEARCH PAPER: Creating a Social and Cultural Space--A Continuation of Urban Memory: Opportunities and Obstacles for Developing Art Districts in the Process of Chinese Urban Revitalization--Three Case Studies in Shanghai, Beijing and Xi’an STUDENT: Jiongzhi Li DEGREE: Master of Urban and Regional Planning COLLEGE: Architecture and Planning DATE: December, 2013 PAGES: 84 This paper investigates the development situation of Chinese art districts that are a continuation of urban memory.
    [Show full text]
  • Multi-Disciplinary Determination of the Rural/Urban Boundary: a Case Study in Xi’An, China
    sustainability Article Multi-Disciplinary Determination of the Rural/Urban Boundary: A Case Study in Xi’an, China Lei Fang 1,2 and Yingjie Wang 1,2,* 1 State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China; [email protected] 2 University of Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China * Correspondence: [email protected]; Tel.: +86-010-6488-9077 Received: 19 June 2018; Accepted: 23 July 2018; Published: 26 July 2018 Abstract: Rapid urbanization in China has blurred the boundaries between rural and urban areas in both geographic and conceptual terms. Accurately identifying this boundary in a given area is an important prerequisite for studies of these areas, but previous research has used fairly simplistic factors to distinguish the two areas (such as population density). In this study, we built a model combining multi-layer conditions and cumulative percentage methods based on five indicators linking spatial, economic, and demographic factors to produce a more comprehensive and quantitative method for identifying rural and urban areas. Using Xi’an, China as a case study, our methods produced a more accurate determination of the rural-urban divide when compared to data from the National Bureau of Statistics of the People’s Republic of China. Specifically, the urbanization level was 3.24% lower in the new model, with a total urban area that was 621.87 km2 lower. These results were checked by field survey and satellite imagery for accuracy.
    [Show full text]
  • Minimum Wage Standards in China August 11, 2020
    Minimum Wage Standards in China August 11, 2020 Contents Heilongjiang ................................................................................................................................................. 3 Jilin ............................................................................................................................................................... 3 Liaoning ........................................................................................................................................................ 4 Inner Mongolia Autonomous Region ........................................................................................................... 7 Beijing......................................................................................................................................................... 10 Hebei ........................................................................................................................................................... 11 Henan .......................................................................................................................................................... 13 Shandong .................................................................................................................................................... 14 Shanxi ......................................................................................................................................................... 16 Shaanxi ......................................................................................................................................................
    [Show full text]
  • Evaluation of the Equity and Regional Management of Some Urban Green Space Ecosystem Services: a Case Study of Main Urban Area of Xi’An City
    Article Evaluation of the Equity and Regional Management of Some Urban Green Space Ecosystem Services: A Case Study of Main Urban Area of Xi’an City Hui Dang 1, Jing Li 1,*, Yumeng Zhang 1 and Zixiang Zhou 2 1 School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China; [email protected] (H.D.); [email protected] (Y.Z.) 2 College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China; [email protected] * Correspondence: [email protected]; Tel.: +86-137-2061-8191 Abstract: Urban green spaces can provide many types of ecosystem services for residents. An imbalance in the pattern of green spaces leads to an inequality of the benefits of such spaces. Given the current situation of environmental problems and the basic geographical conditions of Xi’an City, this study evaluated and mapped four kinds of ecosystem services from the perspective of equity: biodiversity, carbon sequestration, air purification, and climate regulation. Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) was used to obtain the partition groups of ecosystem services. The results indicate that first, the complexity of the urban green space community is low, and the level of biodiversity needs to be improved. The dry deposition flux of particulate matter (PM2.5) decreases from north to south, and green spaces enhance the adsorption of PM2.5. Carbon sequestration in the south and east is higher than that in the north and west, respectively. The average surface temperature in green spaces is lower than that in other urban areas.
    [Show full text]
  • Study on Temporal and Spatial Variation Characteristics and Influencing Factors of Land Use Efficiency in Xi’An, China
    sustainability Article Study on Temporal and Spatial Variation Characteristics and Influencing Factors of Land Use Efficiency in Xi’an, China Jing Huang and Dongqian Xue * School of Geographical Science and Tourism, Shaanxi Normal University, Xi’an 710062, China; [email protected] * Correspondence: [email protected] Received: 17 October 2019; Accepted: 22 November 2019; Published: 25 November 2019 Abstract: China’s urban land use has shifted from incremental expansion to inventory eradication. The traditional extensive management mode is difficult to maintain, and the fundamental solution is to improve land use efficiency. Xi’an, the largest central city in Western China, was selected as the research area. The super-efficiency data envelopment analysis (DEA) model and Malmquist index method were used to measure the land use efficiency of each district and county in the city from the micro perspective, and the spatial-temporal change characteristics and main influencing factors of land use efficiency were analyzed, which not only made up for the research content of urban land use efficiency in China’s underdeveloped areas, but also pointed out the emphasis and direction for the improvement of urban land use efficiency. The results showed that: (1) The land use efficiency of Xi’an reflected the land use intensive level of the underdeveloped areas in Western China, that is, the overall intensive level was not high, the gap between the urban internal land use efficiency was large, the land use efficiency of the old urban area and the mature built-up area was relatively high, and the land use efficiency of the emerging expansion area and the edge area was relatively low.
    [Show full text]