基于地理加权回归的NO2排放预测模型

为了探究影响NO2排放的交通相关因素,以交通分析小区为基本单位,提取了美国洛杉矶城市的人口特征、道路网络特征、交通状况等数据,采用地理加权回归模型(GWR)分析各交通相关因素对NO2排放的影响,从而建立交通小区NO2排放预测模型。结果表明,交通小区路网密度、交通小区机动车吸引量、通勤时间在30~60 min的工作人数与NO2的排放呈正相关,表明3种影响因素的增加会造成NO2排放的增加;而交通小区在家工作的人数、慢行交通(步行、自行车等绿色出行方式)吸引量与NO2的排放呈负相关,表明适当鼓励在家工作的新型办公方式、鼓励居民出行选择慢行交通,能有效减少交通小区NO2的排放。     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.     

NATIONWIDE REGRESSION MODELS FOR PREDICTING URBAN RUNOFF WATER QUALITY AT UNMONITORED SITES1

ABSTRACT: Regression models are presented that can be used to estimate mean loads for chemical oxygen demand, suspended solids, dissolved solids, total nitrogen, total ammonia plus nitrogen, total phosphorous, dissolved phosphorous, total copper, total lead, and total zinc at unmonitored sites in urban areas. Explanatory variables include drainage area, imperviousness of drainage basin to infiltration, mean annual rainfall, a land-use indicator variable, and mean minimum January temperature. Model parameters are estimated by a generalized-least-squares regression method that accounts for cross correlation and differences in reliability of sample estimates between sites. The regression models account for 20 to 65 percent of the total variation in observed loads.     

Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing,China

A method was proposed to identify the main influence factors of soil heavy metals. The influence degree of different environmental factors was ranked. Parent material, soil type, land use and industrial activity were main factors. Interactions between some factors obviously affected soil heavy metal distribution. Identifying the factors that influence the heavy metal contents of soil could reveal the sources of soil heavy metal pollution. In this study, a categorical regression was used to identify the factors that influence soil heavy metals. First, environmental factors were associated with soil heavy metal data, and then, the degree of influence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regression. The results showed that the soil parent material, soil type, land use type, and industrial activity were the main influencing factors, which suggested that these four factors were important sources of soil heavy metals in Beijing. In addition, population density had a certain influence on the soil Pb and Zn contents. The distribution of soil As, Cd, Pb, and Zn was markedly influenced by interactions, such as traffic activity and land use type, industrial activity and population density. The spatial distribution of soil heavy metal hotspots corresponded well with the influencing factors, such as industrial activity, population density, and soil parent material. In this study, the main factors affecting soil heavy metals were identified, and the degree of their influence was ranked. A categorical regression represents a suitable method for identifying the factors that influence soil heavy metal contents and could be used to study the genetic process of regional soil heavy metal pollution.     

Impact of pregnancy and other factors including smoking on the urinary levels of triclosan

Data from National Health and Nutrition Examination Survey were used to evaluate the impact of pregnancy on the levels of triclosan (TCS) in urine. Regression models were fitted to evaluate this association with adjustment for other factors that may affect the levels of TCS. Pregnant females had higher levels of TCS than non-pregnant females but the differences were not statistically significant. Levels of TCS were statistically significantly lower during the second trimester than during the third trimester. Smoking was associated with statistically significantly lower levels of TCS. The reasons for the association between smoking and the levels of TCS are not known. Further research is needed in this area.     

GRNN模型在煤与瓦斯突出及瓦斯含量预测中的应用

煤与瓦斯突出的作用机理非常复杂,是诸多因素如地应力、煤层瓦斯、煤体物理力学性质等共同作用的结果。在分析广义回归神经网络(GRNN)的基本原理和算法的基础上,建立煤与瓦斯突出等级以及基于构造复杂程度定量评价的瓦斯含量GRNN模型。然后用收集到的工程实例样本训练和检验该模型。结果表明,GRNN模型具有很好的预测能力和泛化能力,能较好揭示瓦斯含量和诸影响因素间的关系,可用于煤与瓦斯突出判别以及瓦斯含量预测。同时可以看出,光滑因子的合理选取对于提高GRNN模型的预测精度非常重要,因此,在以后的实际应用中需要不断尝试,找出最合理的光滑因子。     

城市湖泊流域面源污染的源-汇效应研究--以武汉市东湖为例

随着城市点源污染控制不断完善,城市面源污染已成为危害中国城市水体的重要污染源.由城市土地利用变化引起的地表径流污染是导致湖泊水质恶化和富营养化的主要因素,加强面源污染的源-汇效应研究是控制、管理流域污染的重要途径之一.研究选取中国最大的城市湖泊武汉市东湖为研究对象,运用遥感和GIS技术,进行流域划分和地类解译,建立土地利用基础数据库,后对水质统计数据进行定量化处理,通过建立了地类——湖泊水质关系模型,科学地分析了用地与水质的关系,并对城市湖泊水质的季节动态、影响因素进行了初步探索.研究结果表明土地利用和气候变化对流域内湖泊水质的影响显著.其中,农田和建设用地是城市湖泊面源污染的主要来源,绿地和坑塘水面能够有效的截流、吸收污染物,起到保护湖泊水质的作用;受降水、温度等气候因子的影响,湖泊水质状态呈现季节性波动.研究选取东湖为研究对象,研究面源污染的源-汇效应,具有代表性,对于更好保护城市湖泊,合理利用湖泊资源提供了理论依据.     

基于气象因子的PM_(2.5)回归预测模型研究

采用Pearson相关系数分析了2013—2016年3大典型城市北京、南京和广州的ρ(PM_(2.5))与各气象因子的关系。结果表明,3个城市ρ(PM_(2.5))与各风速因子最大的相关系数依次为-0.44,-0.29和-0.37,与各气温因子最大的相关系数依次为-0.44,-0.33和-0.37,气压与南京和广州的ρ(PM_(2.5))正相关,气压因子最大的相关系数分别为0.25和0.34,湿度与北京ρ(PM_(2.5))正相关,与广州ρ(PM_(2.5))负相关,湿度因子最大的相关系数分别为0.49和-0.36,日照时数与北京ρ(PM_(2.5))相关系数为-0.46,降水量与南京和广州ρ(PM_(2.5))相关系数分别为-0.20和-0.24;采用逐步线性回归方法建立城市次日ρ(PM_(2.5))与气象因子的预测模型,复合相关系数分别为0.722 8,0.770 6和0.809 9。模型预测3个城市2016年PM_(2.5)年均值分别偏高4,5和3μg/m3,日均值平均相对误差为±45.6%,±32.9%和±26.0%,模型对高ρ(PM_(2.5))普遍低估。     

环境空气 PM2．5、PM10相关性及污染水平研究

PM2．5能够透过人体组织,对健康产生重要影响,研究环境空气中PM2．5的污染特征具有实际意义。以遂宁市大气连续采样监测数据为基础,利用SPSS软件对可吸入颗粒物PM10和细颗粒物PM2．5进行相关性分析,建立回归方程并预测2012年PM2．5日均浓度值。结果表明,PM2．5和PM10呈显著相关,回归模型 R2为0．618,标准估计误差为0．023630。PM2．5预测值为60μg／m3,超过国家一级标准限值,且PM2．5污染水平高于PM10,细颗粒物是城市大气污染控制的重要污染物。     

Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey

Introduction

The aim of the study is to describe the inter-province differences in traffic accidents and mortality on roads of Turkey.

Method

Two different risk indicators were used to evaluate the road safety performance of the provinces in Turkey. These indicators are the ratios between the number of persons killed in road traffic accidents (1) and the number of accidents (2) (nominators) and their exposure to traffic risk (denominator). Population and the number of registered motor vehicles in the provinces were used as denominators individually. Spatial analyses were performed to the mean annual rate of deaths and to the number of fatal accidents that were calculated for the period of 2001-2006. Empirical Bayes smoothing was used to remove background noise from the raw death and accident rates because of the sparsely populated provinces and small number of accident and death rates of provinces. Global and local spatial autocorrelation analyses were performed to show whether the provinces with high rates of deaths-accidents show clustering or are located closer by chance. The spatial distribution of provinces with high rates of deaths and accidents was nonrandom and detected as clustered with significance of P < 0.05 with spatial autocorrelation analyses.

Results

Regions with high concentration of fatal accidents and deaths were located in the provinces that contain the roads connecting the Istanbul, Ankara, and Antalya provinces. Accident and death rates were also modeled with some independent variables such as number of motor vehicles, length of roads, and so forth using geographically weighted regression analysis with forward step-wise elimination. The level of statistical significance was taken as P < 0.05. Large differences were found between the rates of deaths and accidents according to denominators in the provinces. The geographically weighted regression analyses did significantly better predictions for both accident rates and death rates than did ordinary least regressions, as indicated by adjusted R² values. Geographically weighted regression provided values of 0.89-0.99 adjusted R² for death and accident rates, compared with 0.88-0.95, respectively, by ordinary least regressions.

Impact on industry

Geographically weighted regression has the potential to reveal local patterns in the spatial distribution of rates, which would be ignored by the ordinary least regression approach. The application of spatial analysis and modeling of accident statistics and death rates at provincial level in Turkey will help to identification of provinces with outstandingly high accident and death rates. This could help more efficient road safety management in Turkey.