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1.
Haze pollution has become a severe environmental problem in the daily life of the people in China. PM_(2.5) makes a significant contribution to poor air quality. The spatio-temporal features of China's PM_(2.5) concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification(ISIC), reveals the spatio-temporal variations of PM_(2.5) concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM_(2.5) concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM_(2.5) concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM_(2.5) concentrations. Results:(1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China.(2) The PM_(2.5) concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change.(3) PM_(2.5) concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality. 相似文献
2.
High concentrations of PM2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM2.5 concentrations for regional air quality control and management. In this study, PM2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m3. (2) PM2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM2.5. (3) The center of gravity of PM2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM2.5 concentrations have moved eastward, while low-value PM2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM2.5 concentration in China. 相似文献
3.
ABSTRACTOne of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM 2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM 2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM 2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM 2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R 2 of 0.93 and root mean square error of 9.64 μg/ m3. The results indicated that the multi-stage PM 2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE. 相似文献
4.
ABSTRACT Recently developed urban air quality sensor networks are used to monitor air pollutant concentrations at a fine spatial and temporal resolution. The measurements are however limited to point support. To obtain areal coverage in space and time, interpolation is required. A spatio-temporal regression kriging approach was applied to predict nitrogen dioxide (NO 2) concentrations at unobserved space-time locations in the city of Eindhoven, the Netherlands. Prediction maps were created at 25 m spatial resolution and hourly temporal resolution. In regression kriging, the trend is separately modelled from autocorrelation in the residuals. The trend part of the model, consisting of a set of spatial and temporal covariates, was able to explain 49.2% of the spatio-temporal variability in NO 2 concentrations in Eindhoven in November 2016. Spatio-temporal autocorrelation in the residuals was modelled by fitting a sum-metric spatio-temporal variogram model, adding smoothness to the prediction maps. The accuracy of the predictions was assessed using leave-one-out cross-validation, resulting in a Root Mean Square Error of 9.91 μg m ?3, a Mean Error of ?0.03 μg m ?3 and a Mean Absolute Error of 7.29 μg m ?3. The method allows for easy prediction and visualization of air pollutant concentrations and can be extended to a near real-time procedure. 相似文献
5.
京津冀城市群是中国雾霾最严重的区域,在京津冀协同发展背景下,探究该地区大气污染的时空分布和影响因素具有重要意义。运用空间自相关分析和三种空间计量模型,分析了京津冀202个区县PM 2.5的时空分异特征,创新性地对自然与人文影响因素贡献及其空间溢出效应进行系统地甄别和量化。结果表明：2000-2014年来京津冀城市群PM 2.5浓度整体呈上升趋势,季节上呈秋冬高、春夏低,空间上呈东南高、西北低的特点,且城市建成区PM 2.5浓度比周围郊区和农村平均高10~20 μg/m 3;2014年仅有13.9%的区县空气质量达标,PM 2.5浓度存在显著的空间集聚性与扩散性,城市间交互影响距离平均为200 km,邻近地区的PM 2.5每升高1%,将导致本地PM 2.5至少升高0.5%;社会经济内因对PM 2.5主要是正向影响,自然外因主要是负向影响;影响因素中对本地大气污染的直接效应贡献强度依次是：年均风速>年均气温>人口密度>地形起伏度>第二产业占比>能源消费>植被覆盖度,人均GDP、年降水量和相对湿度对本地PM 2.5没有显著影响;对邻近地区大气污染具有显著空间溢出效应的因素排序是：植被覆盖度>地形起伏度>能源消费>人口密度;对于自然和人文影响因素应分别采取针对性的适应策略和调控策略,加强区域间联防联控与合作治理,在城市群规划中注重环保规划与立法。 相似文献
6.
以中国286个主要地级市为研究区域,基于2003—2016年中国地级市大气PM 2.5质量浓度栅格数据及各地级市社会经济数据,运用空间自相关和空间面板杜宾模型,揭示了中国地级市PM 2.5质量浓度的时空格局与影响因素。研究显示：(1)中国PM 2.5污染在时序演化上呈“M”型波动态势,在空间分布上以胡焕庸线为界呈现出“东高西低”的集聚型格局;(2)中国地级市PM 2.5污染在空间效应上呈现出显著的正相关性,表明区域间大气污染存在交互影响;(3)人口密度与非农产业从业人员占比对PM 2.5质量浓度升高的贡献最大,而液化石油气供气总量与第三产业占GDP的比重对PM 2.5有较为显著的负向消减作用。 相似文献
7.
GIS-based proximity models are one of the key tools for the assessment of exposure to air pollution when the density of spatial monitoring stations is sparse. Central to exposure assessment that utilizes proximity models is the ‘exposure intensity–distance’ hypothesis. A major weakness in the application of this hypothesis is that it does not account for the Gaussian processes that are at the core of the physical mechanisms inherent in the dispersion of air pollutants. Building upon the utility of spatial proximity models and the theoretical reliability of Gaussian dispersion processes of air pollutants, this study puts forward a novel Gaussian weighting function-aided proximity model (GWFPM). The study area and data set for this work consisted of transport-related emission sources of PM2.5 in the Houston-Baytown-Sugar Land metropolitan area. Performance of the GWFPM was validated by comparing on-site observed PM2.5 concentrations with results from classical ordinary kriging (OK) interpolation and a robust emission-weighted proximity model (EWPM). Results show that the fitting R2 between possible exposure intensity calculated by GWFPM and observed PM2.5 concentrations was 0.67. A variety of statistical evidence (i.e., bias, root mean square error [RMSE], mean absolute error [MAE], and correlation coefficient) confirmed that GWFPM outperformed OK and EWPM in estimating annual PM2.5 concentrations for all monitoring sites. These results indicate that a GWFPM utilizing the physical dispersing mechanisms integrated may more effectively characterize annual-scale exposure than traditional models. Using GWFPM, receptors’ exposure to air pollution can be assessed with sufficient accuracy, even in those areas with a low density of monitoring sites. These results may be of use to public health and planning officials in a more accurate assessment of the annual exposure risk to a population, especially in areas where monitoring sites are sparse. 相似文献
8.
Whether economic agglomeration can promote improvement in environmental quality is of great importance not only to China’s pollution prevention and control plans but also to its future sustainable development. Based on the COD (Chemical Oxygen Demand) and NH3-N (Ammonia Nitrogen) emissions Database of 339 Cities at the city level in China, this study explores the impact of economic agglomeration on water pollutant emissions, including the differences in magnitude of the impact in relation to city size using an econometric model. The study also examines the spillover effect of economic agglomeration, by conducting univariate and bivariate spatial autocorrelation analysis. The results show that economic agglomeration can effectively reduce water pollutant emissions, and a 1% increase in economic agglomeration could lead to a decrease in COD emissions by 0.117% and NH3-N emissions by 0.102%. Compared with large and megacities, economic agglomeration has a more prominent effect on the emission reduction of water pollution in small- and medium- sized cities. From the perspective of spatial spillover, the interaction between economic agglomeration and water pollutant emissions shows four basic patterns: high agglomeration-high emissions, high agglomeration-low emissions, low agglomeration-high emissions, and low agglomeration-low emissions. The results suggest that the high agglomeration-high emissions regions are mainly distributed in the Beijing-Tianjin-Hebei region, Shandong Peninsula, and the Harbin-Changchun urban agglomeration; thus, local governments should consider the spatial spillover effect of economic agglomeration in formulating appropriate water pollutant mitigation policies. 相似文献
9.
基于企业数据与空间计量方法,总结2005年、2010年、2015年和2020年东北地区制造业空间格局演化特征,并在分析地区空气污染同期变化的基础上,探讨制造业空间格局演化对空气污染的环境效应。结果表明：① 东北地区制造业集聚水平逐渐增高,并形成多个新兴“热点区”,31个细分行业大类均呈集聚分布,资本规模由高度集中向“T字形”铁路周边沿线城市扩散。② 东北地区产业结构清洁度与产业技术高级度均呈下降趋势,在县区尺度上体现出空间差异且有不同程度的增幅和降幅。③ 制造业空间格局对空气污染具有显著的集聚效应和技术效应,二者分别表现为拥挤效应和回弹效应,但规模效应和结构效应不明显。 相似文献
10.
长三角是中国空气污染较为严重的区域。基于地统计、探索性空间数据分析等方法,使用实时监测数据,探讨该区域41个地级以上城市PM 2.5在2013-2016年的时空格局演变与污染特征。结果显示：① PM 2.5表现出不同的年度、月度以及逐日特征。年均值逐年下降,月均值则体现出鲜明季节性,呈现冬高、夏低的“U型”特征,日均值有“脉冲型”波动特点。② 2013-2016年,长三角PM 2.5污染情况已经得到显著改善,但超过三分之二的地区仍存在不同程度超标现象,呈现“西北高、东南低”的污染格局。③ PM 2.5高值区从以合肥、扬州为中心向江苏、安徽北部边界地区转移,低值区逐步扩展形成东至舟山、西至黄山、北至上海、南至温州的低值集聚带。④ 趋势分析来看,PM 2.5年均值东西差异和南北差异均逐步减小,但东西向差异略大于南北向。同时,两个方向中部隆起现象得到改善,城市投影点趋于线性拟合。 相似文献
11.
Human health effects have been linked to airborne concentrations of fine particulate matter. One source of fine particulate matter in the atmosphere is resuspended soil dust from a variety of activities, including agricultural operations. We have established a method to measure the potential of soil to emit fugitive dust in the PM 10 or PM 2.5 size range. The method is repeatable, and provides an index of PM 10 or PM 2.5 dust that is highly correlated to the soil texture. The ratio of the PM 2.5 Index to the PM 10 Index produced by this method is similar to field observations of ambient PM 2.5 and PM 10 concentrations downwind of agricultural operations in the San Joaquin Valley of California. The PM 2.5 or PM 10 Index will be a more useful parameter to estimate the potential of a soil to emit fugitive dust than the currently used dry silt content of soil. Research is currently underway to relate the PM 10 and PM 2.5 Index to measured emission factors, accounting for soil moisture and type of agricultural operation, so that a more reliable predictive equation can be developed for agricultural practices. 相似文献
12.
As the main form of new urbanization in China,urban agglomerations are an im-portant platform to support national economic growth,promote coordinated regional devel-opment,and participate in international competition and cooperation.However,they have become core areas for air pollution.This study used PM2.5 data from NASA atmospheric re-mote sensing image inversion from 2000 to 2015 and spatial analysis including a spatial Durbin model to reveal the spatio-temporal evolution characteristics and main factors con-trolling PM2.5 in China's urban agglomerations.The main conclusions are as follows:(1)From 2000 to 2015,the PM2.5 concentrations of China's urban agglomerations showed a growing trend with some volatility.In 2007,there was an inflection point.The number of low-concentration cities decreased,while the number of high-concentration cities increased.(2)The concentrations of PM2.5 in urban agglomerations were high in the west and low in the east,with the"Hu Line"as the boundary.The spatial differences were significant and in-creasing.The concentration of PM2.5 grew faster in urban agglomerations in the eastern and northeastern regions.(3)The urban agglomeration of PM2.5 had significant spatial concentra-tions.The hot spots were concentrated to the east of the Hu Line,and the number of hot-spot cities continued to rise.The cold spots were concentrated to the west of the Hu Line,and the number of cold-spot cities continued to decline.(4)There was a significant spatial spillover effect of PM2.5 pollution among cities within urban agglomerations.The main factors control-ling PM2.5 pollution in different urban agglomerations had significant differences.Industriali-zation and energy consumption had a significant positive impact on PM2.5 pollution.Foreign direct investment had a significant negative impact on PM2.5 pollution in the southeast coastal and border urban agglomerations.Population density had a significant positive impact on PM2.5 pollution in a particular region,but this had the opposite effect in neighboring areas.Urbanization rate had a negative impact on PM2.5 pollution in national-level urban agglomer-ations,but this had the opposite effect in regional and local urban agglomerations.A high degree of industrial structure had a significant negative impact on PM2.5 pollution in a region,but this had an opposite effect in neighboring regions.Technical support level had a signifi-cant impact on PM2.5 pollution,but there were lag effects and rebound effects. 相似文献
13.
Air quality was improved considerably and the so-called "Lanzhou Blue" appeared frequently in Lanzhou due to implementation of some strict emission-control measures in recent years. To better understand whether the concentration of each air pollutant had decreased significantly and then give some suggestions as to urban air-quality improvement in the near future, the variations of the Air Quality Index (AQI) and six criterion air pollutants (PM 2.5, PM 10, CO, SO 2, NO 2, and O 3) at five state-controlled monitoring sites of Lanzhou were studied from 2013 to 2016. The AQI, PM 2.5, PM 10, and SO 2 gradually decreased from 2013 to 2016, while CO and NO 2 concentrations had slightly increasing trends, especially in urban areas, due to the large number of motor vehicles, which had an annual growth rate of 30.87%. The variations of the air pollutants in the no-domestic-heating season were more significant than those in the domestic-heating season. The increase of ozone concentration for the domestic-heating season at a background station was the most significant among the five monitoring sites. The vehicle-exhaust and ozone pollution was increasingly severe with the rapid increase in the number of motor vehicles. The particulate-matter pollution became slight in the formerly highly polluted Lanzhou City. Some synergetic measures in urban and rural areas of Lanzhou should be taken by the local government in the near future to control fine particulate-matter (PM 2.5) and ozone pollution. 相似文献
14.
基于全国城市的PM 2.5监测数据,识别PM 2.5的时空分布特征,并着重利用地理加权回归模型分析自然和社会经济因素对PM 2.5影响的空间异质性。结果显示：2015年全国PM 2.5的年均浓度为50.3 μg/m3,浓度变化呈现冬高夏低,春秋居中的“U型”特征;PM 2.5的空间集聚状态明显,其中京津冀城市群是全国PM 2.5的污染重心。地理加权回归结果显示：影响因素除高程外,其余指标均呈现正负两种效应,且影响程度具有显著的空间差异性特征。从回归系数的贡献均值来看,自然因素对城市PM 2.5浓度影响强度由高到低依次是高程、相对湿度、温度、降雨量、风速、植被覆盖指数;各类社会经济指标对城市PM 2.5浓度影响强度排名依次是人口密度、研发经费、建设用地比例、产业结构、外商直接投资、人均GDP。由于各指标对城市PM 2.5浓度变化的影响程度存在着空间异质性,因此在制定大气治理对策时可以考虑不同指标影响程度的空间差异,从而使得治霾对策更具针对性。 相似文献
15.
Urban agglomeration is caused by the continuous acceleration of the urbanization process in China. Studying the expansion of construction land can not only know the changes and development of urban agglomeration in time, but also obtain the great significance of the future management. In this study, taking Changsha-Zhuzhou-Xiangtan (Chang-Zhu-Tan) urban agglomeration in Hunan province as a study area, Landsat images from 1995 to 2014 and Autologistic-CLUE-S model simulation data were used. Moreover, several factors including gravity center, direction, distance and landscape index were considered in the analysis of the expansion. The results revealed that the construction area increased by 132.18%, from 372.28 km2 in 1995 to 864.37 km2 in 2014. And it might even reach 1327.23 km2 in 2023. Before 2014, three cities had their own respective and discrete development directions. However, because of the integration policy implementation in 2008, the Chang-Zhu-Tan began to gather, the gravity center moved southward after 2014, and the distance between cities decreased, which was in line with the development plan of urban expansion. The research methods and results were relatively reliable, and these results could provide some reference for the future land use planning and spatial allocation in the urbanization process of Chang-Zhu-Tan urban agglomeration. 相似文献
16.
以“十三五”提及的19个城市群为研究对象,采用第五、第六次全国人口普查的常住人口数据,引入重心模型、空间自相关、泰尔指数等研究方法,从城市群视角分析中国人口分布的演变特征。结果表明：① 2000-2010年中国城市人口密度、自然增长率、流动人口、常住人口的空间演变与城市群的分布、发展密切关联;全国常住人口的空间分布重心朝东南方向移动;城市群是全国人口密度和人口总量增长的高值区,是人口自然增长率的低值区;全国集聚或扩散的人口流量较大的城市主要位于城市群内。② 10年间城市群内外地区的人口密度差异均呈扩大趋势,城市群的发展使人口大量流入城市群或其核心城市,城市群内的人口分布不均衡程度加剧,成为全国人口密度差异进一步扩大的主要原因。③ 中国城市群发展水平差异较大,处在不同发展水平的城市群表现出不同的人口集聚和扩散效应;处于发展水平较高的城市群主要位于东部沿海人口稠密地区,对人口有较强的吸引力,人口呈现总体集聚的特征,且逐步形成一定的等级结构。人口较稀疏的中西部地区的城市群大多仍处于发展水平较低阶段,对人口的吸引力较弱,人口呈现核心集聚边缘扩散的特征,城市体系结构尚未稳定。 相似文献
17.
利用位序-规模分析法、ESDA-GIS、空间变异函数、重心迁移与趋势面分析等方法,对2004和2014年全国343个地级及以上城市房价收入比的空间分异格局、总体趋势、空间异质性和自相关性进行研究。结果表明：中国地级单元房价收入比空间分异显著,2004年呈现东部>西部>中部、北方>中部>南方的趋势,2014年则呈现自北向南、自西向东依次递增的态势。10 a间,房价收入比的重心向正南方向迁移;地级单元的房价收入比整体呈扩大趋势;从一线城市到五线城市,房价收入比整体表现出下降的态势。房价收入比的自相关性在不断增加,冷点区大幅扩张而热点区大幅收缩。城市化率和人均可支配收入对城市房价收入比具有较大的正面影响。 相似文献
18.
This research analyzes the relationship between tropical cyclones and fine particulate matter (PM 2.5) for landfalling Atlantic tropical cyclones from 2000 to 2015. Daily mean PM 2. 5 concentrations were collected from the United States Environmental Protection Agency. Tropical cyclone data were acquired from Tropical Prediction Center Best Track Reanalysis in Unisys weather. GRIdded Binary (GRIB formatted) data were downloaded from the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR). Tracks of tropical cyclones were overlaid with the interpolated daily mean PM 2. 5 concentration value. Results suggest that, in general, tracks are distant from areas with the largest PM 2. 5 concentrations. To examine the cause-effect nature of this relationship, simulation using the Weather Research and Forecasting (WRF) model suggests that the intensity of Hurricane Lili was weakened only after passing the most PM 2. 5-polluted area in Louisiana. This result suggests that aerosol loading may weaken the intensity of tropical cyclones, at least in some cases. 相似文献
19.
依托中国15个重要旅游城市,基于雾霾主导因素PM2.5观测数据、遥感气溶胶数据,运用土地利用回归方法模拟绘制PM2.5时空分布图,分析中国重要旅游城市PM2.5质量浓度的时空分异特征。结果表明：1）2013―2015年中国重要旅游城市PM2.5年均质量浓度总体呈逐年下降趋势,且明显呈现夏季低、冬季高、春秋季居中的季节变化特征;2）重要旅游城市PM2.5质量浓度在不同等级城市中存在明显差异,其PM2.5质量浓度整体规律为副省级市＞直辖市＞地级市;3）月均尺度上各重要旅游城市的宜游时间主要集中在4―9月,且2015年春冬季月份宜游城市明显增多;宜游时间较长的城市主要分布在空气质量优良的东南部沿海和森林覆盖率较高地区的地级市和部分副省级市,中西部地区和长三角地区的城市宜游时间则相对较短。 相似文献
20.
基于2000~2012年中国31个省（市、自治区）面板数据,运用探索性空间数据分析方法对中国工业废气排放的空间分布特征进行研究,结果显示中国各省域（不含港澳台）工业废气排放存在显著的空间自相关和空间集聚效应;总体呈现东部、西部地区集聚的空间分布特征,其中东部多为高 -高集聚区、西部则多为低 -低集聚区,并且高值集聚现象的显著性逐渐增强,显著区域呈持续扩张趋势。在此基础上,以STIRPAT模型为基础构建空间计量模型,分析经济发展、人口规模、产业结构、技术水平和国家政策等因素对工业废气排放量的影响。研究结果表明,中国各省域工业废气排放存在空间依赖作用和正的空间溢出效应;经济发展、产业结构与工业废气排放之间呈现显著的正相关关系;技术进步和国家政策对工业废气排放具有抑制作用,而人口增长对工业废气排放的影响并不显著。 相似文献
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