Geospatial Mapping of Soil Nitrate-Nitrogen Distribution Under a Mixed-Land Use System

Mapping the spatial distribution of soil nitrate-nitrogen （NO3=N） is important to guide nitrogen application as well as to assess environmental risk of NO3-N leaching into the groundwater. We employed univariate and hybrid geostatistical methods to map the spatial distribution of soil NO3-N across a landscape in northeast Florida. Soil samples were collected from four depth increments （0-30, 30-60, 60-120 and 120-180 cm） from 147 sampling locations identified using a stratified random and nested sampling design based on soil, land use and elevation strata. Soil NO3-N distributions in the top two layers were spatially autocorrelated and mapped using lognormal kriging. Environmental correlation models for NO3-N prediction were derived using linear and non-linear regression methods, and employed to develop NO3-N trend maps. Land use and its related variables derived from satellite imagery were identified as important variables to predict NO3-N using environmental correlation models. While lognormal kriging produced smoothly varying maps, trend maps derived from environmental correlation models generated spatially heterogeneous maps. Trend maps were combined with ordinary kriging predictions of trend model residuals to develop regression kriging prediction maps, which gave the best NO3-N predictions. As land use and remotely sensed data are readily available and have much finer spatial resolution compared to field sampled soils, our findings suggested the efficacy of environmental correlation models based on land use and remotely sensed data for landscape scale mapping of soil NO3-N. The methodologies implemented are transferable for mapping of soil NO3-N in other landscapes.

An Agent-Based Assessment of Land Use and Ecosystem Changes in Traditional Agricultural Landscape of Portugal

This paper presents an assessment of land use changes and their impacts on the ecosystem in the Montado, a traditional agricultural landscape of Portugal in response to global environmental change. The assessment uses an agent-based model (ABM) of the adaptive decisions of farmers to simulate the influence on future land use patterns of socio-economic attributes such as social relationships and farmer reliance on subsidies and biophysical constraints. The application and development of the ABM are supported empirically using three categories of input data: 1) farmer types based on a cluster analysis of socio-economic attributes;2) agricultural suitability based on regression analysis of historical land use maps and biophysical attributes;and 3) future trends in the economic and climatic environments based on the A1fi scenario of the Intergovernmental Panel on Climate Change. Model sensitivity and uncertainty analyses are carried out prior to the scenario analysis in order to verify the absence of systematic errors in the model structure. The results of the scenario analysis show that the area of Montado declines significantly by 2050, but it remains the dominant land use in the case study area, indicating some resilience to change. An important policy challenge arising from this assessment is how to encourage next generation of innovative farmers to conserve this traditional landscape for social and ecological values.

Assessing land use/land cover change impacts on the hydrology of Nyong River Basin, Cameroon

Uncontrolled land use land cover change(LULCC) is impacting watershed hydrology,particularly in tropical watersheds in developing countries. We assessed the extent of LULCC in the southern portion of the Nyong River basin through analysis of three land use maps in 1987, 2000 and2014. LULCC impact on hydrological variables of the Mbalmayo, Olama, Pont So’o, Messam, and Nsimi sub-watersheds of the southern portion of the Nyong River basin were evaluated by using the linear regression modeling and the Mann-Kendall test. This study reveals that dense forest cover decreased by16%, young secondary forest increased by 18%,agricultural/cropland increased by 10%, and built-up area/bare soil increased by 3% from 1987 to 2014.The decrease in dense forest cover at 0.6% per year on average was driven by indiscriminate expansion of subsistence agricultural/cropland through shifting and fallow cultivation farming systems. Nonsignificant trends in total discharge, high flows, and low flows were observed in the large sub-watersheds of Mbalmayo and Olama from 1998 to 2013 with LULCC within the watershed. In contrast, significant decreasing trends in stream discharge(up to-5.1%and-5.9%), and significant increasing trends in high flows(up to 2.1% and 6.3%), respectively, were observed in the small sub-watersheds of Pont So’o and Messam from 1998 to 2013, particularly with increase in agricultural/cropland cover and decrease in dense forest cover. However, we found nonsignificant trends in mean annual discharge and low flows for all and whole watershed with LULCC. The results reveal spatially varying trends of stream discharge, low flows and high flows among the subwatersheds with LULCC within the study watershed.The results suggest that the impacts of LULCC on watershed hydrology are easily detected in small subwatersheds than in large sub-watersheds. Therefore,the magnitude of dense forest cover loss must be significantly greater than 16% to cause significant changes and common trends in the hydrology of the sub-watersheds of the southern portion of the Nyong River basin. The Mann-Kendall and Regression approaches show appreciable potential for modelling the impacts of LULCC on the hydrology of the southern portion of the Nyong River basin and for informing forest management.

Exploration of spatial and temporal characteristics of PM2.5 concentration in Guangzhou, China using wavelet analysis and modified land use regression model

This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geographic information system software and a modified land use regression model.In this modified model,an important variable(land use data)is substituted for impervious surface area,which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method.Impervious surface has higher precision than land use data because of its sub-pixel level.Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood.Results include:(1)the highest concentration of PM2.5 occurs in October and the lowest in July,respectively;(2)average concentration of PM2.5 in winter is higher than in other seasons;and(3)there are two high concentration zones in winter and one zone in spring.

基于LUR模型的北京市PM_(2.5)浓度的空间分布模拟

通过北京市34个国控监测站点,建立0.5、1、1.5、2、3、4、5km的缓冲区,应用土地利用回归模型(Land Use Regression,LUR)对北京市采暖季与非采暖季PM_(2.5)浓度进行空间分布模拟,并采用留一交叉互验法验证模型精度。结果表明:采暖季LUR模型调整R^(2)为0.799,模拟精度为0.7992,均方根误差(Root Mean Square Error,RMSE)为6.66μg·m^(-3);非采暖季LUR模型调整R^(2)为0.807,模拟精度为0.8198,均方根误差为5.91μg·m^(-3),模型表现良好。从模拟结果来看,北京市PM_(2.5)主要分布在东南部人口、交通密集的平原区域,整体呈现南高北低的状态。

基于LUR和GIS的西安市PM2．5的空间分布模拟及影响因素

从地学角度出发,基于土地利用、人口状况、道路交通和地形等自然人文因素构建土地利用回归模型(LUR),模拟西安市主城区2014年和2015年冬季采暖季PM_(2.5)平均浓度的空间分布,并分析其空间分布成因和年际变化原因。结果显示:LUR模型构建中,2014年采暖季选择的变量主要有500m缓冲区内植被面积、1 000m缓冲区内植被和居民地面积以及人口密度,2015年采暖季选择的变量主要有1 500m、2 000m、2 500m和3 000m缓冲区内道路总长度。2014年和2015年采暖季LUR模型的R2分别为0.933和0.832,拟合效果很好。2014年采暖季各城区PM_(2.5)平均浓度均较高,碑林区绝大部分区域空气质量为严重污染,新城区次之,其他区空气质量基本为重度污染。2015年各城区PM_(2.5)平均浓度均有所下降,大部分区域为轻度污染。土地利用、污染源、道路交通、人口密度、国家环保相关政策、风向和DEM是西安市2014年和2015年采暖季PM_(2.5)浓度空间分布规律、成因、污染来源和年际变化的影响因素。

基于SVR-LUR模型的城市道路PM10空间浓度分布模拟

针对传统土地利用回归模型(Land use regression,LUR)未考虑影响因子与大气污染物之间非线性复杂关系和易出现多重共线性的问题.以PM10为例,采用支持向量回归机(Support vector machine regression,SVR)改进土地利用回归模型的建模方法构建SVR-LUR模型,对上海市南浦大桥周边区域PM10空间分布进行模拟.研究结果表明:1、研究区域PM10浓度与100 m缓冲区内的空地面积,150 m缓冲区内的建筑工地面积、空地面积、河流面积,200 m缓冲区内的绿地面积和河流面积,以及湿度、交通流量和背景浓度相关性较高.2、 SVR-LUR模型可较好地对研究区PM10浓度进行空间分布预测.SVR-LUR模型与LUR模型相比,SVR-LUR模型预测精度较高,其测试集比LUR模型测试集的平均绝对误差(Mean absolute error,MAE)及均方根误差(Root mean squares error,RMSE)分别减小了22.92%、33.51%,拟合指数(Index of agreement,IA)值增加了13.20%.相较于普通克里金插值模型所得到的单一梯度空间分布预测结果,SVR-LUR模型能够更有效揭示小范围内的空间差异.3、研究区PM10浓度空间分布呈现出西高东低的总格局,在建筑物和路网密集的地方浓度较高,而在靠近江面和空地的区域浓度相对较低.模拟结果与实际情况相符.

Application of land use regression for estimating concentrations of major outdoor air pollutants in Jinan, China

SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these three air pollutants. In this study, we developed a land use regression (LUR) model using regulatory monitoring data to predict the spatial distribution of air pollutant concentrations in Jinan, China. Traffic, land use and census data, and meteorological and physical conditions were included as candidate independent variables, and were tabulated for buffers of varying radii. SO2, NO2, and PM10 concentrations were most highly correlated with the area of industrial land within a buffer of 0.5 km (R2=0.34), the distance from an expressway (R2=0.45), and the area of residential land within a buffer of 1.5 km (R2=0.25), respectively. Three multiple linear regression (MLR) equations were established based on the most significant variables (p<0.05) for SO2, NO2, and PM10, and R2 values obtained were 0.617, 0.640, and 0.600, respectively. An LUR model can be applied to an area with complex terrain. The buffer radii of independent variables for SO2, NO2, and PM10 were chosen to be 0.5, 2, and 1.5 km, respectively based on univariate models. Intercepts of MLR equations can reflect the background concentrations in a certain area, but in this study the intercept values seemed to be higher than background concentrations. Most of the cities in China have a network of regulatory monitoring sites. However, the number of sites has been limited by the level of financial support available. The results of this study could be helpful in promoting the application of LUR models for monitoring pollutants in Chinese cities.

基于LUR构建武汉市NO_2暴露浓度预测模型

构建武汉市NO_2浓度的土地利用回归(Land use regression,LUR)模型,可用于个体NO_2长期暴露水平的估计。收集了武汉市10个空气质量监测站2015年的日均NO_2监测数据作为因变量,以武汉市土地利用、海拔高度、人口密度和道路总长度数据作为预测变量,采用逐步回归方法构建LUR模型,并采用留一交叉验证法对模型的精度进行评价。结果显示,武汉市NO_2浓度主要与所在地半径2千米缓冲区内的植被地面积和半径5千米缓冲区内的农用地面积相关。LUR模型的调整R^2大小为0.85,表明模型能解释大部分的变异;LOOCV检验的调整R^2大小为0.63,表明模型具有较好的拟合精度。

土地利用回归模型在大气污染物中的应用进展

空气污染及其引发的人群健康风险正日益受到社会的广泛关注。因此,精准模拟和预测空气污染物的浓度及其时-空分布的重要性不言而喻。土地利用回归(LUR)模型可准确定量小尺度下污染物的时-空趋势及其环境健康风险,已在国外广泛应用,并日趋完善。而LUR模型在国内仅有少量针对常规污染物的实例研究。该文总结了近期国内外LUR模型在大气污染物中的应用研究,对模型构建方法的改进、卫星遥感和地面监测数据联合使用、时间分辨率的改进以及模型目标污染物的拓展应用等方面进行了总结,并探讨了未来LUR模型的改进和发展方向,为其进行环境健康风险评价和空气污染流行病学研究提供了方法学参考。

Identifying the most important spatially distributed variables for explaining land use patterns in a rural lowland catchment in Germany

Land use patterns arise from interactive processes between the physical environment and anthropogenic activities. While land use patterns and the associated explanatory variables have often been analyzed on the large scale, this study aims to determine the most important variables for explaining land use patterns in the 50 km2 catchment of the Kielstau, Germany, which is dominated by agricultural land use. A set of spatially distributed variables including topography, soil properties, socioeconomic variables, and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes. Spatial validation indicates a reasonable performance as the relative operating characteristic (ROC) ranges between 0.73 and 0.97 for all land use classes except for corn (ROC = 0.68). The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level (maximum deviation 0.1). Non-agricultural land use is generally better explained than agricultural land use. The most important variables are the share of drained area, distance to protected areas, population density, and patch fractal dimension. These variables can either be linked to agriculture or the river course of the Kielstau.

哈尔滨市PM2.5浓度空间分布模拟

基于稀疏监测点的监测数据无法直接获取城市内部空气污染物的高分辨率空间分布.以哈尔滨市为例,研究了基于土地利用回归模型(Land Use Regression,LUG)的大气PM2. 5浓度高分辨率空间分布模拟.采用双变量相关分析识别出与PM2. 5浓度相关性最高的800 m缓冲区耕地、2000 m缓冲区水体、500 m缓冲区污染源、5000m缓冲区人口、3000 m缓冲区居民区与PM2. 5相关性最强5个影响因子.将这5个因子与月平均气温、平均风速、降水量进行多元逐步线性回归,相关系数R2达到0. 9849,F检验统计量为82. 76 (p=0. 01),均方误差为0. 48μg/m3.在研究区建立均匀的格网点(5km×5km),利用得到的土地利用回归模型计算每个格网点的PM2. 5的浓度值,利用空间差值的方法,得到研究区高分辨率的PM2. 5空间浓度模拟图.

基于改进LUR模型的区域土壤重金属空间分布预测

以江苏省常州市金坛区为例,借鉴传统LUR模型思路,考虑土壤重金属的源汇关系,加入土壤属性因子,构建LUR-S模型模拟预测了研究区土壤重金属含量空间分布,并与传统LUR模型及普通克里格插值模型结果进行对比,结果表明:(1)研究区土壤重金属含量受到以土地利用为主的源因子及反映重金属在土壤中赋存环境的汇因子的共同影响.就源影响因子而言,土壤Cu、Zn含量分别与2 000 m缓冲区内交通用地面积、2 000 m缓冲区内城市建设用地面积极显著相关(P<0.01);就汇影响因子而言,土壤Cr、Cu、Zn含量与OM、Corg、TC、TN极显著相关(P<0.01).(2)研究区土壤重金属Pb、Cr、Cu、Zn空间分布预测的LUR-S模型方程R2较传统LUR模型分别提高了0.041、0.406、0.102、0.501,精度检验R2较普通克里格插值模型分别提高了0.147 7、0.011 6、0.231 0、0.081,RMSE较普通克里格插值分别减少了2.413、0.631、1.112、2.138,表明考虑了源汇关系的LUR-S模型预测精度高于传统LUR模型和普通克里格插值模型;(3)LUR-S模型对污染较低、变异较小重金属空间分布预测的适用性较好,而对污染较高、变异较大重金属则较差.

基于LUR模型的2019年北京地区PM2.5与PM10浓度空间分异模拟

在城市区域内,空气污染物的浓度在小范围内存在显著差异,而离散的地面监测点分布不均匀,且监测范围有限,无法满足污染物暴露评估等研究的需求.本研究基于GIS空间分析和多元逐步回归的模型构建的方法,建立了土地利用回归(LUR)模型,并模拟了北京市2019年PM2.5和PM10浓度的空间分布特征.选择土地覆盖数据、气象数据(风速、降水、温度)和植被覆盖度数据等预测变量,以研究区34个监测站点为中心建立0.1~5 km共7个系列缓冲区,表征不同尺度下各变量对PM2.5和PM10浓度的影响.研究结果表明:①进入PM2.5回归模型中的变量有:年均风速、温度、降水量和周围中等植被覆盖、耕地和不透水面的面积;进入PM10回归模型中的变量有:年均风速和周围中等植被覆盖的面积.两个模型的调整R2分别为0.829和0.677,模型精度较高.②抑制污染物浓度的变量,影响力随着空间范围扩大而增强;使污染物浓度增加的变量,影响力随着空间范围缩小而增强.③浓度模拟结果显示,PM2.5和PM10在西北部山区浓度较低,南偏东的城区浓度较高,并且向南有逐渐增加趋势.④植被覆盖度这一变量不仅进入了上述两个方程,且影响力都强于其他土地利用类型,故以后的模型改进应该考虑植被覆盖度这一因素.

基于LUR模型的PM2.5浓度空间分布监测及分析

为有效解决传统监测技术无法获取城市内部高分辨率PM2.5浓度空间分布情况的问题,基于土地利用回归(land use regression,LUR)模型,以关中平原城市群为例模拟其PM2.5空间分布状况,通过获取研究范围内54个监测站点的PM2.5浓度数据,结合土地利用类型、气象、地形、植被指数、人口密度、交通和污染源等因素,分别建立春、夏、秋、冬及年均5个LUR模型。结果表明:LUR模型调整后各季节及年平均值的R2分别达到0.831(春)、0.817(夏)、0.874(秋)、0.857(冬)、0.900(全年平均),5种模型拟合度均较好;采取交叉互验的方法进行了精度检验,显示5种模型的平均精度均达到80.4%,说明LUR模型在模拟关中平原城市群PM2.5浓度空间分布时适用性良好。模拟结果显示,研究区各季节的PM2.5浓度在空间分布上大致相同,呈现出东部高、西部低的明显特征,且空间分布状况受地形因素的影响较大。但在浓度均值的季节变化上则具有夏季低、冬季高的明显差异。本研究结果可为关中平原城市群PM2.5污染防治提供科学依据,亦可为城市内部PM2.5浓度空间分布数据的获取提供新思路。

Spatiotemporal Patterns and Drivers of Forest Change from 1985–2000 in the Beijing-Tianjin-Hebei Region of China

Forests are fundamental to maintaining ecological security and achieving regional sustainable development in China. Forest land change can result in many ecological problems including soil erosion, water shortages drought and biodiversity loss. Based on landscape ecology and logistic regression we explored the spatiotemporal patterns and factors affecting forest land changes from 1985 to 2000 in the Beijing-Tianjin-Hebei Region of China. The results show decreased local fragmentation of woodland landscapes and that the shapes of forest patches have become more regular. For forest land cover change, soil organic matter content, slope type I （〈5°）, distance to the nearest village and per capita GDP were the most important independent variables from 1985 to 2000. This study indicates that spatial heterogeneity can affect the predictability of logistic regression models for forest land change.

土地利用回归模型在大气污染时空分异研究中的应用

随着城市化、工业化、机动化进程的加快,大气污染已成为我国最严重的环境问题之一,对公众健康和生态环境造成了诸多负面影响.土地利用回归,即Land-use Regression(LUR)模型是目前模拟城市尺度大气污染时空分异的常用方法之一,其在欧洲和北美得到广泛的应用,而在国内,相关研究却不多.本文在整理国内外文献的基础上,从构建LUR模型的主要步骤入手,包括监测数据获取、模型自变量生成、模型构建、模型检验和回归映射等方面,对LUR模型在大气污染时空分异研究中的进展进行归纳总结,进而展望了未来的研究重点与方向,即突出时空分异、扩展模型变量类别和改进模型构建方法.本文旨在普及LUR模型在我国的应用,为人口暴露、流行病学研究和健康风险评价等提供方法论基础.

南昌市中心城区主要大气污染物分布模拟及土地利用对其影响

随着我国城市化、工业化的快速推进,城市大气污染问题日益突出,研究城市大气污染物的分布情况及其土地利用影响对解决城市大气污染问题具有重要意义.本研究以南昌市中心城区为研究区,基于土地利用回归模型(LUR)模拟了PM_(2.5)、PM_(10)、SO_2、NO_2、CO、O_3等6种主要大气污染物浓度,并分析其时空分布特征;基于主导土地利用类型,选择南昌市中心城区内居住、商业、教育和工业用地各15个样本区,为了减少气象因子的影响,分四季统计各样本区6类大气污染物浓度,运用双因素方差分析和多重比较,定量分析土地利用(样本区)对6类大气污染物的影响.结果表明:采用LUR模型模拟研究区PM_(2.5)、PM_(10)、SO_2、NO_2、CO、O_3浓度的平均绝对误差率分别为11.9%、13.4%、12.5%、12.0%、12.7%和13.5%,模型误差较小,方法可行.研究区6类污染物浓度具有明显的时空分布特征,PM_(2.5)、PM_(10)、SO_2、NO_2和CO浓度在冬季最高,春季和秋季次之,夏季最低;O_3浓度则为夏季高,春季和秋季次之,冬季低.PM_(2.5)、PM_(10)、SO_2、NO_2、CO浓度整体呈现从城区中心到郊区递减的趋势,而O_3浓度则反之.不同季节与不同土地利用样本区间6种大气污染物浓度差异显著,表明在中心城区尺度上,气象条件和土地利用都对大气污染物有显著影响.不同土地利用对主要大气污染物浓度分布有不同程度的影响,其中,对PM_(2.5)、NO_2和O_3的影响较大,对CO的影响较小.

城市公园绿地对周边环境空气PM_(10)和PM_(2.5)的影响及效应场特征——以武汉市中山公园为例

选取武汉市中山公园及其周边建成环境作为研究对象,采用LUR模型、小尺度指标测试与空间插值相结合的技术方法,分析春、夏、秋、冬公园绿地周边建成环境空气PM_(10)、PM_(2.5)浓度时空效应场特征,旨在为公园绿地周边建成环境的规划与建设提供科学依据。结果表明:城市公园绿地周边建成环境500 m缓冲区范围对空气PM_(10)、PM_(2.5)浓度关键影响因子(交通道路、水体面积、非硬质下垫面面积、距城市中心距离、距公园绿地边界距离地理变量和气温、相对湿度、风速气象变量)构建的LUR模型具有一定的可行性,调整后的R2值分别为0.203~0.873(PM_(10))、0.306~0.837(PM_(2.5)),经留一交叉验证调整后R2分别为0.302~0.999(PM_(10))、0.344~0.991(PM_(2.5))(P<0.05);构建的不同季节LUR模型中交通道路、空气温度、距城市中心距离变量对空气PM_(10)、PM_(2.5)浓度空间分布相关性显著;冬、春季节公园绿地显著降低空气PM_(10)、PM_(2.5),对周边250 m(冬季)、400 m(春季)范围建成环境影响更为显著;空气PM_(10)日均消减量(公园内部与周边500 m缓冲区建成环境空气PM_(10)平均浓度差值)最大值为47.2μg·m^(-3),PM_(2.5)日均消减量最大值为15.5μg·m^(-3);空气PM_(10)、PM_(2.5)浓度在春、夏、秋三季的8:00—9:00时段最高,在冬季的15:00—16:00时段最高。