Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box–Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.     

安全投入模型的研究

从安全投入产出分析的角度为安全投入决策提出一个新视角,相比于直接用生产函数等数学方法得到安全投入与安全产出的关系,通过软件分析得出各个要素与安全产出的关系,再进行拟合,最后得到安全产出回归方程,这样得到的分析结果更准确和可信。通过实例可以证明如果要确定安全投入要素之间的分配比例可以采用生产函数分析方法,如要预测安全产出可以采用根据散点图做出的回归方程。另外,在分析安全投入要素相对重要度时,因为各个投入要素的数量级和单位的不同,回归方程系数应选择标准化回归系数;要预测安全投入产生的安全产出则应采用非标准化回归系数。     

利用多次回归拟合水的饱和蒸气压与温度的关系表达式

在使用于湿球测定锅炉烟气含湿量的过程中,需要查找水的饱和蒸气压,非常不方便.利用D-196和<化工原理>上册中取得部分数据,用多次线性回归的方法,拟合出水在0～200℃范围内的饱和蒸气压与温度的关系表达式;并对该表达式的误差进行了分析,误差均值为0.2‰,最大值也只有0.8‰,完全可以满足环境监测中数据处理的精度要求.     

典型异味源的感官特性及特征污染物筛选

为研究不同异味源的感官特性以及不同行业特征污染物的排放差异,以卷烟厂、烘培坊、烤漆厂为研究对象,采用愉悦度9级度量法,测定源样品不同稀释倍数（5~6个浓度梯度）下的愉悦度等级,绘制3种典型异味源愉悦度-臭气浓度指数特征曲线,并进行回归分析,同时筛选出3个典型异味源的特征污染物（烤漆厂的特征污染物为乙酸乙酯、对二乙苯、乙苯、间二甲苯、乙酸丁酯,卷烟厂为乙醇、萘、2-丁酮、柠檬烯、丙烷,烘培坊为乙醇、柠檬烯、α-蒎烯、甲苯、丙酮）,并建立了异味评价干扰潜力的数学模型.结果表明:3种典型异味源的愉悦度与臭气浓度指数之间的关系均符合二次多项式模型,卷烟厂和烤漆厂异味源的愉悦度均为负值,并且厌恶程度随臭气浓度的增大而增强;而烘焙坊异味源的愉悦度为正值.当愉悦度等级为-2时,卷烟厂和烤漆厂对应的臭气浓度分别为30和26.干扰潜力的强弱是由臭气浓度和愉悦度2个因素直接决定的,烤漆厂的干扰潜力为-14.48,卷烟厂为-14.17,烘培坊为11.77.研究显示,愉悦度与臭气浓度指数间量化关系模型以及异味源干扰潜力评估模型的建立,可为异味源的分类分级以及异味源环境影响标准的制订提供科学依据.      

近20年来中国典型区域PM2.5时空演变过程

近20年来PM_(2.5)污染严重制约了中国可持续发展.长时间序列历史监测数据的缺失阻碍了相关研究.为此,本文以四大典型区域2013~2016年的PM_(2.5)浓度监测值和2000~2016年MODIS AOD数据、边界层高度、温度等气象数据作为基础数据,将反向人工神经网络和支持向量回归机两种算法相结合,构建组合模拟模型,并利用地理空间分析技术实现近20年来PM_(2.5)浓度历史变化过程的情景再现.研究结果表明,组合模型具有较低的误差和更高的泛化能力;时空分析结果表明,2000~2010年京津冀和东三省PM_(2.5)浓度持续增长,珠三角PM_(2.5)浓度缓慢下降,3个研究区PM_(2.5)污染范围呈扩大趋势,长三角PM_(2.5)浓度值及污染范围基本保持稳定.2012年4个研究区PM_(2.5)浓度值降低且污染范围缩小,但2013~2016年PM_(2.5)浓度略微上升后又下降,高污染范围缩小,这与国家采取PM_(2.5)区域联防等治理措施有关.     

中国“四化”建设对水足迹强度的影响分析

采用2000—2016年中国30省市（不包括香港、澳门、台湾、西藏四省区）的数据,构建四化指标体系,计算四化发展指数,进行地区四化水平评价,通过OLS和GMM回归模型考察四化发展对水足迹强度的影响,同时设置四化之间的交互项来识别四化指标之间的相互作用。主要结论如下：首先,从四化水平的地域发展差异上来看,中国四化水平总体上呈现出东部优于中部、中部优于西部的格局,水足迹强度的分布与之类似,说明四化水平发展与水足迹强度之间可能存在相关关系;其次,计量模型显示四化水平的提升对于水足迹强度的改善有积极影响,但每一化对于水足迹强度改善的影响大小不一;第三,以交互项衡量的四化相互作用对于水足迹强度的影响作用为正,表明四化发展对水足迹强度的改善效果逐渐趋于收敛。因此,优先提升落后地区的四化水平,以四化水平的提升推动水足迹强度的降低,能够有效降低我国总体水足迹强度水平。     

中国碳排放及影响因素的市域尺度分析

评估区域碳排放及其与社会经济状况的关系对于制定碳减排措施至关重要.以中国339个地级及以上城市(不含新疆部分城市和港澳台地区)为研究对象,探究了非化石能源占比、土地开发度、常住人口城镇化率、第二产业占比、人均GDP和人均建设用地面积对人均CO₂排放量的影响.通过构建模拟人均CO₂排放量的贝叶斯信念网络,识别各因素对人均CO₂排放量的全局影响;采用多尺度地理加权回归模型,分析各因素对人均CO₂排放量的局部影响.结果表明：(1)2020年,中国地级及以上城市人均CO₂排放量呈现出由南向北递增,东部沿海向内陆递减的格局.(2)从全局来看,人均CO₂排放量对各因素的敏感性从高到低依次为：人均建设用地面积>人均GDP>常住人口城镇化率>土地开发度>第二产业占比>非化石能源占比.(3)从局部来看,各因素与人均CO₂排放量的空间关系方向与全局关系一致,关系强度上存在空间异质性.(4)清洁能源、脱碳技术、土地节约集约利用...