中国PM2.5污染空间分布的社会经济影响因素分析

中国的细颗粒物(PM_(2.5))污染具有危害性强、覆盖范围大、空间分布不均匀的特点.本研究以2015年中国PM_(2.5)监测站点数据为基础,尝试结合空间分析的方法,对PM_(2.5)污染空间分布的社会经济影响因素进行分析.首先以省级行政区划为基本单元,选取Moran's I指数和局部自相关指数(LISA)分析PM_(2.5)在国家尺度上的分布特征.然后利用普通最小二乘回归模型(OLS)和地理加权回归模型(GWR)分析PM_(2.5)浓度的空间分布和各项社会经济指标的相关性.结果表明,GWR模型比OLS模型更好地揭示出PM_(2.5)浓度分布和各项因素之间的关系.PM_(2.5)浓度在空间分布上存在以京津冀为中心的高浓度聚集区向四周逐渐递减,在广西、四川等南部省份形成低浓度聚集区的空间分布结构.另外,森林覆盖率和人均电力消费量与PM_(2.5)浓度显著负相关,人均私家车保有量和PM_(2.5)浓度显著正相关,其中人均私家车保有量是对PM_(2.5)浓度影响最大的因素.     

县级尺度的重庆市碳排放时空格局动态

碳排放具有明显的时间和空间分布特征,研究区域碳排放时空格局动态特征可为制定合理的碳减排政策和措施提供重要的依据.本文以重庆为例,基于其38个区县的碳排放数据,利用空间统计、空间自相关和位序-规模法则探讨了其县级尺度碳排放的区域差异和空间格局演变特征.结果表明,重庆市各区县都经历了快速的碳排放增长过程,但碳排放的二元空间分布结构并没有改变;重庆市县级尺度碳排放全局Moran's I指数呈现出波浪式的降低趋势,主城区区县在中心相互辐射,形成一个碳排放HH中心;位序-规模法则分析结果则表明重庆市县级尺度碳排放基本属于首位型分布,1997~2012年区县碳排放规模分布趋于分散的力量均大于趋于集中的力量;1997和2012年,第二产业比例和城市化率成为影响重庆市碳排放的最重要的因素,人口与碳排放的相关关系却并不显著.     

2016年长三角城市群O3浓度的时空变化规律

基于2016年长三角城市群40个城市的监测数据,利用空间内插、空间自相关分析、热点分析等地统计分析方法,研究了2016年长三角城市群O₃浓度的时空变化规律.结果表明：2016年长三角城市群O₃平均超标天数比例为8.8%,O₃已成为造成长三角城市群空气污染的仅次于PM_2.5的重要污染物;夏、春、秋、冬季O₃浓度依次递减,由于梅雨的影响,O₃月均浓度变化曲线呈M型分布,2个峰值出现在5月和8月,谷值出现在6月;O₃超标主要发生在4~9月,超标天数占全年的98.1%,月均超标天数比例为17.3%;O₃浓度具有明显的空间分异规律,大体呈东北高西南低的态势,过杭州和马鞍山的直线可将长三角城市群O₃浓度划分为高值区和低值区,杭州-马鞍山线以东是O₃高污染城市聚集区,尤以环太湖经济圈最为严重.O₃浓度的空间分布格局与长三角城市群经济发展格局大体一致;O₃浓度具有空间集聚规律,4~7月O₃热点集中分布在环太湖经济圈至上海区域,受东南季风加强的影响,8~9月热点西移至以南京为中心的区域.     

基于DOAS地空观测的典型热带地区臭氧敏感性研究

为实现中国节能减排与产业经济协调发展的目标，本研究选取中国碳排放重要来源之一的能源密集型产业，采用空间全局自相关，标准差椭圆，空间局部自相关以及空间杜宾模型等方法对2007—2016年中国30个省域能源密集型产业格局、碳排放强度时空演变特征进行了分析，在空间溢出视角下对能源密集型产业结构演变的碳排放效应进行了探讨.结果表明：①空间自相关Moran''s I检验表明碳排放强度与能源密集型产业集聚均存在显著的空间正相关.②标准差椭圆表明，能源密集型产业有重心迁移的现象，但迁移效果不明显.③空间杜宾结果显示，能源密集型产业集聚与碳排放强度呈现"N"型曲线关系，能源密集型产业的集聚对碳排放强度的影响存在明显的空间溢出效应，人口数量和外商投资会显著抑制碳排放强度.     

CHARACTERISTICS OF EXTREME PRECIPITATION1

ABSTRACT: Records of extreme precipitation were investigated using the Discrete Autoregressive Moving Average (DABMA) process, which can explain long persistences of wet and dry spells that exist in daily precipitation data. The results show that the daily precipitation with strong autocorrelation is inclined to be better fit by a Discrete Autoregressive (DAB) model. On the other hand, those data with weak autocorrelations tend to be best fit by a Discrete Moving Average (DMA) model. It can also be concluded that based on the records from extremely wet and dry regions there is no geographic preference regarding the selection of the best model.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)

Spatial autocorrelation (SAC) is frequently encountered in most spatial data in ecology. Cellular automata (CA) models have been widely used to simulate complex spatial phenomena. However, little has been done to examine the impact of incorporating SAC into CA models. Using image-derived maps of Chinese tamarisk (Tamarix chinensis Lour.), CA models based on ordinary logistic regression (OLCA model) and autologistic regression (ALCA model) were developed to simulate landscape dynamics of T. chinensis. In this study, significant positive SAC was detected in residuals of ordinary logistic models, whereas non-significant SAC was found in autologistic models. All autologistic models obtained lower Akaike's information criterion corrected for small sample size (AICc) values than the best ordinary logistic models. Although the performance of ALCA models only satisfied the minimum requirement, ALCA models showed considerable improvement upon OLCA models. Our results suggested that the incorporation of the autocovariate term not only accounted for SAC in model residuals but also provided more accurate estimates of regression coefficients. The study also found that the neglect of SAC might affect the statistical inference on underlying mechanisms driving landscape changes and obtain false ecological conclusions and management recommendations. The ALCA model is statistically sound when coping with spatially structured data, and the adoption of the ALCA model in future landscape transition simulations may provide more precise probability maps on landscape transition, better model performance and more reasonable mechanisms that are responsible for landscape changes.     

The effect of species response form on species distribution model prediction and inference

Ecological theory and current evidence support the validity of various species response curves according to a variety of environmental gradients. Various methods have been developed for building species distribution models but it is not well known how these methods perform under various assumptions about the form of the underlying species response. It is also not well known how spatial correlation in species occurrence affects model performance. These effects were investigated by applying an environmental envelope method (BIOCLIM) and three regression-based methods: logistic regression (LR), generalized additive modelling (GAM), and classification and regression tree (CART) to simulated species occurrence data. Each simulated species was constructed as a sum of responses with varying weights. Three basic species response curves were assumed: Gaussian (bell-shaped), Beta (skew) and linear. The two non-linear responses conform to standard ecological niche theory. All three responses were applied in turn to three simulated environmental variables, each with varying degrees of spatial autocorrelation. GAM produced the most consistent model performance over all forms of simulated species response. BIOCLIM and CART were inclined to underrate the performance of variables with a linear response. BIOCLIM was less sensitive to data density. LR was susceptible to model misspecification. The use of a linear function in LR underestimated the performance of variables with non-linear species response and contributed to increased spatial autocorrelation in model residuals. Omission of important environmental variables with non-linear species response also contributed to increased spatial autocorrelation in model residuals. Adding a spatial autocovariate term to the LR model (autologistic model) reduced the spatial autocorrelation and improved model performance, but did not correct the misidentification of the dominant environmental determinant. This is to be expected since the autologistic approach was designed primarily for prediction and not for inference. Given that various forms of species response to environmental determinants arise commonly in nature: (1) higher order functions should always be tested when applying LR in modelling species distribution; (2) spatial autocorrelation in species distribution model residuals can indicate that environmental determinants with non-linear response are missing from the model; and (3) deficiencies in LR model performance due to model misspecification can be addressed by adding a spatial autocovariate to the model, but care should be taken when interpreting the coefficients of the model parameters.     

我国环境污染与经济发展空间格局分析

以2006年我国31个省(市、自治区)的环境和经济统计数据为基础,利用三次曲线拟合了各地区人均污染物指标与人均GDP的相关性,同时利用系统聚类分析方法,将不同省(市、自治区)按人均污染物指标和人均GDP的相关性分为5类,并通过计算各类别相关指标的空间自相关系数,揭示了不同类别中各省(市、自治区)环境污染和经济发展的空间相关性.结果表明:在空间尺度上,我国没有出现环境库兹涅茨曲线特征,经济越发达地区的环境污染越严重;在人均污染物指标和人均GDP的空间关系上,东南沿海经济较发达地区为正相关,广大中西部地区为随机分布,少数经济落后地区为负相关.      

基于空间自相关模型的农村居民点时空演变格局与特征研究

农村居民点作为农村人口重要空间聚集区,其空间布局、演变特征受历史、自然、社会、经济、传统文化等多重因素的影响。科学识别农村居民点的时空分布形态,并揭示其内在的变化规律和驱动因素,对促进农村居民点科学规划、提高农村土地资源空间布局优化均具有重要意义。利用都江堰市2005和2012年两期遥感影像提取农村居民点、城镇、道路、河流等矢量数据,借助RS、GIS空间分析技术,定量研究都江堰市农村居民点时空变化过程、格局和特征,并引入空间自回归模型深入分析不同环境因素对农村居民点空间布局的影响程度。研究结果表明:(1)都江堰市农村居民点的空间分布密度存在显著的空间正相关性,即密度值较高或较低的地区在空间上呈现显著的聚集状态,但局部的空间异质性在增强;(2)密度的高值集群主要集中分布在都江堰市东南部沙西线沿线以及南部成青快速通道一线,并且有进一步沿道路延线纵深扩张的趋势,而密度的低值集群由于受地形的影响,在空间分布上变化不大,主要位于龙门山沿线的乡镇;(3)2005~2012年,地形位指数每增加1%,农村居民点的空间密度减少0.505%,而距城镇、河流和道路的距离每增加1%,农村居民点的空间密度分别增加0.124%、0.144%、0.006%;(4)不同环境因素对农村居民点空间分布的影响程度大小为:地形影响城镇辐射影响河流影响道路影响,并且随着时间的推移,各环境因素的影响程度都在不断地增强。该研究以期为今后同类研究提供一定的方法借鉴,为农村居民点动态变化监测、农村土地节约集约利用、新农村规划等提供理论方法和技术应用支撑。