Characterization of air pollution in urban areas of Yangtze River Delta,China

The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.     

基于中国大气环境监测站点的2015—2019年大气质量状况时空变化分析

2013年以来几次严重的雾霾污染事件引起了公众的广泛关注,此后中国实施了一系列有关大气污染防治的政策、法规和措施来改善大气质量。为了分析近年来中国大气质量的时空变化特征,本文选取2015—2019年生态环境部国控站点监测的大气污染关键参数,对比分析了空气质量指数和6种大气污染物的季均、年均浓度变化结果,并利用组合指标分析法和相关分析法探讨了不同大气污染物之间的相关性。结果表明：① PM_2.5、PM₁₀、SO₂、CO和NO₂浓度和AQI均有明显下降,2019年均浓度较2015年均浓度分别下降4.5%、3.84%、7.86%、3.74%、0.95%,AQI下降了19.31%,同时,O₃浓度则上升了0.79%;② 从空间分布来看,中国北方地区PM₁₀、PM_2.5、O₃、NO₂、SO₂、CO年均质量浓度和AQI分别比南方地区高25.2%、18.73%、4.95%、17.6%,32.74%、16.17%、28.3%;③ 从季节性变化规律来看,除了O₃呈现出夏季浓度高,冬季浓度低外,其他5种污染物和AQI都呈现相反的季节变化规律;④ 总体而言,目前中国大气污染以PM_2.5和O₃为主,PM_2.5与NO₂、SO₂、CO之间有极显著的正相关关系（r>0.85,p<0.01）,而O₃与其前体物NO₂和CO之间存在显著的负相关关系（r>0.8,p<0.01）。     

Characteristics and cause analysis of heavy haze in Changchun City in Northeast China

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze episodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complexity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index(AQI) level-1 standard(AQI 50) according to the National Ambient Air Quality Standard(NAAQS) of China. Conversely, 33.7% of the days showed a higher level with AQI 100. Extreme haze events(AQI 300) occurred frequently during agricultural harvesting period(from October 10 to November 10), intensive winter heating period(from Late-December to February) and period of spring windblown dust(April and May). Most daily concentrations of gaseous pollutants, i.e., NO_2(43.8 μg/m~3), CO(0.9 mg/m~3), SO_2(37.9 μg/m~3), and O_3(74.9 μg/m~3) were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter(PM_(2.5) and PM10) concentrations(67.3 μg/m~3 and 115.2 μg/m~3, respectively) were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions(i.e., low wind speed and inversion layer). The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. Therefore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emissions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.     

耦合卡尔曼滤波和多层次聚类的中国PM2.5时空分布分析

近年来,细颗粒物污染尤其是PM_2.5受到人们越来越多的关注,研究PM_2.5的时空分布规律也具有越来越重大的意义。传统的遥感反演方法模型复杂,且不能揭示近地表面的PM_2.5分布规律。地面监测站的建设为PM_2.5的研究提供了更实时的观测数据,但由于测量噪声的影响,观测数据存在不准确的极端异常值。为了揭示中国PM_2.5的时空分布特征,本研究采用Kalman滤波对2015年中国338个城市的空气质量监测网络大数据进行最佳估计,并分析其时空特征。同时,根据中国各城市的PM_2.5浓度的时序分布,采用基于DTW的K-Medoids聚类方法将其分为4个等级,并采用q统计量来评估PM_2.5浓度分布的空间分层异质性。结果表明,采用Kalman滤波能有效去除数据噪声,峰值信噪比（PSNR）明显增大。在时空分布上,中国PM_2.5时间分布曲线呈现“U”形,冬季PM_2.5浓度明显高于夏季,且日变化曲线呈现“W”形;秋冬季PM_2.5浓度的空间分层异质性非常显著,且空间分布呈现“双核分布”,重污染区主要分布在华北平原、新疆等地,西藏、广东、云南等地是稳定的空气质量优良区。     

成长型城市群产业生态化时空演化特征及其影响因素——以山东半岛城市群为例

该文以山东半岛城市群作为研究对象,构建产业生态化综合评价指标体系,运用熵值法与主成分分析方法,探讨了山东半岛城市群产业生态化总体水平的时空分异特征及影响因素。结果表明:2004—2016年,山东半岛城市群产业生态化呈现上升趋势,山东半岛城市群产业生态化发展水平不断提高;山东半岛城市群产业生态化空间分异现象显著,形成了以济南和青岛为核心的两极化发展特征;山东半岛城市群产业生态化发展水平的区域化现象愈加明显,呈现出NE—SW走向的不均衡发展;山东半岛城市群产业生态化发展影响因素为区域经济发展水平、政府监管水平、外资利用差异以及区域科学技术差异。     

Regional Characteristics and Causes of Haze Events in Northeast China

Northeast China experiences severe atmospheric pollution, with an increasing occurrence of heavy haze episodes. However, the underlying forces driving haze formation during different seasons are poorly understood. In this study, we explored the spatio-temporal characteristics and causes of haze events in Northeast China by combining a range of data sources(i.e., ground monitoring, satellite-based products, and meteorological products). It was found that the ‘Shenyang-Changchun-Harbin(SCH)'city belt was the most polluted area in the region on an annual scale. The spatial distribution of air quality index(AQI) values had a clear seasonality, with the worst pollution occurring in winter, an approximately oval-shaped polluted area around western Jilin Province in spring, and the best air quality occurring in summer and most of the autumn. The three periods that typically experienced intense haze events were Period I from mid-October to mid-November(i.e., late autumn and early winter), Period II from late-December to February(i.e., the coldest time in winter), and Period III from April to mid-May(i.e., spring). During Period I, strong PM_(2.5) emissions from seasonal crop residue burning and coal burning for winter heating were the dominant reasons for the occurrence of extreme haze events(AQI 300). Period II had frequent heavy haze events(200 AQI 300) in the coldest months of January and February, which were due to high PM_(2.5) emissions from coal burning and vehicle fuel consumption, a lower atmospheric boundary layer, and stagnant atmospheric conditions. Haze events in Period III, with high PM_(10) concentrations, were primarily caused by the regional transportation of windblown dust from degraded grassland in central Inner Mongolia and bare soil in western Jilin Province. Local agricultural tilling could also release PM_(10) and enhance the levels of windblown dust from tilled soil. Better control of coal burning, fuel consumption, and crop residue burning in winter and autumn is urgently needed to address the haze problem in Northeast China.     

成都市PM_(2.5)浓度时空变化特征及影响因子分析

近年来,空气质量问题日渐突出,大气污染已严重影响了人们的身体健康和日常生活。为了探究成都市近年来的大气污染状况,分析大气颗粒物浓度的时空变化特征及其控制性因子,本文利用2016年成都市逐小时PM_(2.5)监测数据,分析成都市PM_(2.5)浓度时空变化特征,探索气象因子与城市空间形态因子(建筑错落度)对PM_(2.5)浓度变化的影响。结果表明:(1)成都市PM_(2.5)浓度的日变化特征表现为城市中心具有"单峰单谷"特征,而城市郊区日变化特征不明显。且城市中心PM_(2.5)浓度空间变化特征整体表现为由西北向东南逐渐减小的趋势。(2)从全年来看,成都市PM_(2.5)浓度与气温和风速均呈显著负相关,与海平面气压呈显著正相关,而与相对湿度的相关性不明显。(3)PM_(2.5)浓度日间变化率与城市空间形态因子的相关性较强,建筑错落度越高,PM_(2.5)浓度下降越快。     

Spatio-temporal Characteristics of Atmospheric Pollution and Cause Analysis of Haze Events in Sichuan Basin,China

This study analyzed the spatio-temporal variability of air quality data for six standard air pollutants(Particulate Matter 2.5(PM_(2.5)), Particulate Matter 10(PM_(10)), SO_2, NO_2, CO, and O_3) in the Sichuan Basin(SCB), China from 2015 to 2018 in relation to the formation of haze using conventional meteorological data(temperature, wind speed, and relative humidity), satellite data(fire point data, vertical profiles of aerosol subtypes, and aerosol optical depth), planetary boundary layer height, and backward trajectories. The results indicated that the spatio-temporal evolution of the air quality index(AQI) had notable seasonality for the pollution severity in descending order: winter, spring, summer, and autumn. Autumn and winter severe haze events occurred in November and January, respectively, and were caused by higher local pollution emissions under stagnant air conditions. Spring severe haze events occurred in May and were caused by dust from Northwest China and local regions. Severe summer haze events occurred in July and were caused by local burning. Therefore, the analyses showed that local burning, stagnant meteorological conditions, air mass transport and anthropogenic pollution emissions played a key role in haze in the SCB. This study provides scientific insights for fully analyzing heavy air pollution in SCB, China, and also provides a scientific basis for pollution research in regions of complex terrain as basins and mountains.     

基于夜光遥感与POI数据空间耦合关系的南海港口城市空间结构研究

南海港口城市研究是南海资源环境监测的重要内容,目前对于南海港口城市空间结构特别是港口在城市空间结构中的地位缺乏研究。夜光遥感数据和POI数据均为城市空间结构研究的重要数据源,但对于2种数据的空间耦合关系与集成应用研究存在不足。针对上述问题,本文选取南海港口城市典型代表的三亚市为研究区,以研究区2016年NPP-VIIRS夜光遥感数据和POI数据为数据源,利用叠加分析将夜光遥感数据和POI核密度分析结果数据分别网格化。然后,利用双因素组合制图法对两种数据的空间耦合关系进行可视化,分析空间耦合关系相异区域和城市空间结构的关系,并在此基础上探讨港口在三亚城市结构中的地位。研究表明,三亚市夜光遥感和POI数据的空间分布总体趋势相一致,空间耦合关系相近的区域占比达85.6%;夜光遥感和POI数据空间耦合关系在三亚市内存在一定量的相异区域,如新城区、经济开发区、城市边缘区和乡镇中心等,结合2种数据的特点可以更显著地表征这些区域的城市空间结构特征;三亚市作为重要的南海港口城市,其城市的中心区域与港口密切关联。本研究为港口城市空间结构研究提供了新视角。     

Spatial and Temporal Pattern of Urban Smart Development in China and Its Driving Mechanism

Smart urban development is an inevitable choice, and is essential to overall strength improvement. It is important to explore an urban smart development path which unites smart growth with driving shrinkage perfectly in forming scientific and sustainable development concept and responding to new normal strategic opportunities. Based on statistic data of 294 prefecture-level cities and above in China from 2000 to 2015, we analyzed spatial and temporal evolution of urban smart development in China by constructing a dynamic fitting model of urban land expansion, population growth, and economic development as well as the coefficient of variation of urban smart development(CVSD). Further efforts were then made to consider differential distribution regularity of urban smart development so as to understand the driving mechanisms of heterogeneous classification of urban smart development in China from different scales and scale variation. Our results indicate that: 1) the disordered growth tendency of urban cities in China is overall well controlled in the middle, and late research and it mainly presented a doublet coexistence of shrinkage disordered cities and smart developing cities. It is particularly obvious that Northeast China and East China have regarded shrinkage disordered cities and smart developing cities as main development tendency separately. 2) Areas with basic stability and relative variation were relatively dispersed across the time period, but the proportion was far beyond areas with significant variation. It demonstrates a relative equilibrium spatial and temporal differential evolution pattern of prefecture-level cities and above in China, except for Tongling, Lanzhou and Chaoyang. 3) prefecture-level cities and above in China are mostly characterized by shrinkage disordered and smart development classification under the background of different scale and scale variation from 2000–2015; however, the spatial resonance relation is not obvious. 4) There are many interaction factors forming an important driving mechanism in developing the spatial and temporal pattern of urban smart development in China, including natural geographical factors, industrial structure adjustment, human capital radiation, regional traffic accessibility, and government decision-making intervention.     

中国居民预期寿命及其影响因素的空间差异分析

良好的健康和人类福祉是联合国提出的可持续发展目标之一,提高人口预期寿命是迈向此目标的重要一步。由于中国城市在自然环境和社会发展方面有所差异,因此理解不同城市居民的预期寿命主要受何种因素的影响是制定城市公共卫生策略的关键。本研究基于2015年中国286个城市的有效数据,利用探索性回归、普通最小二乘回归、地理加权回归筛选与预期寿命最相关的影响因素并探索其空间差异,再通过二阶聚类将城市分类,以针对性地提出每类城市政策建议。结果显示：① 经济发展,教育条件和医疗设施条件对预期寿命有显著的积极影响,平均海拔和环境污染则具有负面影响;② 东南地区的经济发展对当地居民的预期寿命影响程度更大;东北和西南地区的医疗设施条件对其居民预期寿命促进程度更高;北部地区的教育条件对当地居民预期寿命影响比其他地区更高;平均海拔对西部地区居民预期寿命的影响最大;西北地区居民的预期寿命则更易受到环境污染带来的负面影响;③ 根据空间差异将城市分为3类,其居民预期寿命关键影响因素依次是经济发展和环境污染、教育条件、医疗设施,每类城市的城市管理者应重点关注不同因素来提升居民的预期寿命。     

湖南省工业用地利用效率评价分析

工业用地高效利用是城市可持续发展的前提,新型工业化道路是可持续发展的必由之路,当前工业经济高速发展,土地低效利用与环境污染问题日益凸显。该研究基于DEA方法,从环境—经济、投入—产出2个角度建立评价指标体系,以湖南省为例,测度2013—2016年湖南省工业用地利用效率,并运用ArcGIS10.2分别对2013年和2016年工业用地利用效率的空间布局进行分析。得出结论:湖南省工业用地利用效率整体较高,呈上升趋势,各城市之间利用效率有较大差异。空间上,工业用地利用效率存在明显的区域差异,但逐步形成协同发展模式;大部分城市的纯技术效率与规模效率变化不同步,需要进一步探讨两者稳定的发展模式。结合产业类型进行工业结构调整是对工业用地利用效率研究内容的补充及研究趋势。     

Evolution and spatial patterns of spheres of urban influence in China

This article presents the findings of a study of the spheres of urban influence with regard to all cities in China（not including Hong Kong,Macau and Taiwan Province of China）in the years 1990,2000 and 2009.An optimized gravity model with comprehensive time distance was used to carry out a detailed analysis of the spatial patterns of Chinese spheres of urban influence and the spatial characteristics of urban agglomerations.Such urban agglomerations are characterized by high density population and a developed economy,which are also considered as the national competition unit.This paper initially identifies four spatial patterns of urban agglomerations based on the spatial layout of city groups during their evolution.Some basic characteristics of urban agglomerations are outlined,including the number of cities,the size of cities and the functions of urban centers.These characteristics are examined by using statistical methods and Geographic Information System（GIS）.The main findings from this research are that the development stages and structures of urban agglomerations in China vary significantly.It is also clear that the stages and evolution of spatial patterns are strongly affected and dominated by both policy and location factors.     

A study on regional difference of China’s paid urban landuse system and grading

At present, paid urban landuse system is one of the most important economic reform in China. In the other words, landuse right can be transferred and land users must pay the rent to the state according to the quality of land. It is necessary to apply the theory of rent and location to the economic approaisal of urban land. China is vast in territory. Is’s geographical condition and economic development vary from place to place, so does the urban land value. In order to reveal the difference of land value between different cities, the following method is used. (1) Analysing the factors and elements that affect the quality of urban land. Six factors including 17 elements were selected in this paper: macrolocation of a city, benefit of urban aggregation, infrastructure investment, output value of urban land, potential of urban land, and investment intensity. (2) Deciding the weight andvalue of each factor. (3) Appraising each element separately. (4) Accounting the value of all factors and getting the total appraisal score of each city. (5) Grouping the 430 Chinese cities into sevencategories according to the appraisal values. The result shows that all the cities in the category with the highest land output values are in the coast belt, whereas most cities in the inland and outlying areas are belong to the category with low rank. For example, 87% of the cities in the outlying regions are belong to the lowest rank. Although there are some relationship between the size of cities and urban land rank, generally speaking, the larger the city, the higher the urban land rank. In fact, the locational condition is the most important factor which influences the rank of urban land.     

城市空气质量GIS数据模型及分析系统的集成与应用

城市空气质量模拟是目前环境与健康研究的一个重要领域。本文基于GIS技术,采用准稳态高斯扩散模型建立了城市空气质量模拟系统,着重研究了三维空间城市空气质量模型系统的集成方法,并以福州市为例,采用该模型系统对城市建城区的NO₂与SO₂年平均浓度场分布进行了模拟。     

葫芦岛市空气质量分析及其与暴雨的关系

针对暴雨对空气质量的影响的问题,结合葫芦岛市环境监测中心站2010年市区空气质量逐日监测资料与葫芦岛市气象局气象观测资料,采用空气污染指数和空气质量指数法,分析了葫芦岛市空气质量时间分布特征及其与暴雨的关系。结果表明,在影响葫芦岛市空气质量的主要3种污染物——二氧化硫(SO2)、二氧化氮(NO2)和可吸入颗粒物(PM10)中PM10为首要污染物;3种污染物变化趋势基本一致,都是夏季污染最轻,冬季污染最重,春秋两季居中;暴雨与污染物浓度变化有着密切的关系,4次暴雨过程所在日3种污染物浓度都较低,且在强降水过后第一天污染物浓度继续降低。     

中国城市群臭氧污染时空分布研究

随着中国城市建设进程的加快,中国的臭氧（O₃）污染逐渐受到重视。城市群是人口大国城镇化的主要空间载体,是城镇化的主体形态。截至2017年3月底,国务院共先后批复了6个国家级城市群,并提出要优化提升东部地区城市群,培育发展中西部地区城市群。另外,新的城市规模划分标准以城区常住人口为统计口径,将城市划分为5类7档。为了研究中国O₃污染的时空分布特征以及O₃污染与城市群之间的关系,利用地理探测器（Geographical detector）和演化树模型对2014年6月到2017年5月共36个月的O₃监测数据进行时空分析。结果表明：中国O₃污染水平呈现上升趋势,并在2017年迅速增长,O₃已成为仅次于PM_2.5的第二大污染因子,且与PM_2.5在时间上呈“交错污染”的态势;O₃污染超标城市绝大多数集中在城市群区域,其中长江三角洲城市群、京津冀城市群、山东半岛城市群和中原城市群相对突出;O₃和PM_2.5均不超标的城市主要集中在北部湾城市群和海峡西岸城市群;城市群中人口规模大的城市O₃污染较为严重。     

A STUDY ON REGIONAL DIFFERENCE OF CHINA＇S PAID URBAN LANDUSE SYSTEM AND GRADING

董黎明，李向明，冯长春ＡＳＴＵＤＹＯＮＲＥＧＩＯＮＡＬＤＩＦＦＥＲＥＮＣＥＯＦＣＨＩＮＡ＇ＳＰＡＩＤＵＲＢＡＮＬＡＮＤＵＳＥＳＹＳＴＥＭＡＮＤＧＲＡＤＩＮＧ￥ＤｏｎｇＬｉｍｉｎｇ；ＬｉＸｉａｎｇｍｉｎｇ；ＦｅｎｇＣｈａｎｇｃｈｕｎ（ＰｅｋｉｎｇＵｎｉ...     

福州市地表温度热点及时空变化分析

中国作为世界第一大发展中国家,近年来城镇化发展迅速,大量自然地表转化为人工地表,从而引起了一系列环境问题,其中以城市热岛问题最为显著。因此如何缓解因城市化进程的加快引起的城市热岛效应已成为热门研究方向。为精确分析城市空间格局对热集聚的影响,本研究利用2000年5月4日的Landsat ETM+和2016年7月27日获取的Landsat OLI两期遥感影像,获取福州市的土地覆盖信息并进行精度验证。在地表温度（Land Surface Temperature, LST）反演基础上通过热点分析（Getis-Ord Gi*）,并结合不透水面（Impervious Surface Area, ISA）信息来研究城市化进程中福州市16 年来 LST的变化特性,空间集聚特性及其产生的尺度效应。热点分析结果显示：① 通过分析福州市内各地和热点中心的距离与LST的关系可较好地反映空间热聚集。2000 年在距热点中心0.97、1.03、0.95 km范围内热聚集明显;2016 年则增长到分别在距热点中心半径1.89、2.01、2.10、2.05、2.13 km范围内热集聚显著且热点区数量也从3 个增加至5 个。热集聚区（热点区和较热区）总面积在此期间从15.7%增至47.3%;② 由于热点图中的热点区和冷点区的形成不单取决于LST的高低,因此热点分析与空间自相关分析方法相比,能更直观地分析土地覆盖变化对LST的影响,了解城市内部热强度变化的细节。本研究采用的热点分析方法可用于城市环境保护与规划,将来还可作为城市土地规划与热环境影响的分析依据。同时可利用热点分析图模拟城市微气候,估算城市绿地降温程度等。此外,未来还可基于此进一步探讨更多时相以及不同城市的对比分析,特别是对不同城市类型如带状城市,多中心城市及中心城市等的研究。     

中国市级人口增长的多因素空间建模分析

本文以中国市域单元为研究对象,利用1990-2010年人口普查数据,采用探索性空间数据分析的方法,分析了过去20 年间中国市级人口增长率的空间分布特征和多变量的空间依赖关系。使用空间计量模型和空间滤波后的经典线性回归模型分别探究了经济、气候、地形、社会文化等因素对中国市级人口增长率的影响。模型对比结果显示,经过空间滤波后的经典线性回归模型能够更好的模拟中国市级人口增长率的变化。在该模型中,经济因素是影响中国城市人口增长率的主要因素,例如代表城市经济发展水平的城市夜光指数密度。气候因素对人口增长率也有着不可忽视的作用,如七月热指数随着等级的提升对人口增长率有着越来越强的负向影响。研究结果表明：人口的区域增长模式是多要素综合作用的结果,在相关建模研究和政策制定中需要重点考虑经济发展水平和气候条件因素对人口增长趋势的不同影响。