Statistics: an essential technology in environmental research and management

Statistical methods as developed and used in decision making and scientific research are of recent origin. The logical foundations of statistics are still under discussion and some care is needed in applying the existing methodology and interpreting results. Some pitfalls in statistical data analysis are discussed and the importance of cross examination of data (or exploratory data analysis) before using specific statistical techniques are emphasized. Comments are made on the treatment of outliers, choice of stochastic models, use of multivariate techniques and the choice of software (expert systems) in statistical analysis. The need for developing new methodology with particular relevance to environmental research and policy is stressed.Dr Rao is Eberly Professor of Statistics and Director of the Penn State Center for Multivariate Analysis. He has received PhD and ScD degrees from Cambridge University, and has been awarded numerous honorary doctorates from universities around the world. He is a Fellow of Royal Society, UK; Fellow of Indian National Science Academy; Foreign Honorary Member of American Academy of Arts and Science; Life Fellow of King's College, Cambridge; and Founder Fellow of the Third World Academy of Sciences. He is Honorary Fellow and President of International Statistical Institute, Biometric Society and elected Fellow of the Institute of Mathematical Statistics. He has made outstanding contributions to virtually all important topics of theoretical and applied statistics, and many results bear his name. He has been Editor of Sankhya and theJournal of Multivariate Analysis, and serves on international advisory boards of several professional journals, includingEnvironmetrics and theJournal of Environmental Statistics. This paper is based on the keynote address to the Seventh Annual Conference on Statistics of the United States Environmental Protection Agency.     

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。     

上流式厌氧污泥床反应器处理饮料废水

通过监测西安可口可乐饮料有限公司污水处理站的上流式厌氧污泥床(UASB)反应器,探讨饮料废水厌氧处理过程中COD去除率、沼气组分、氢分压、出水挥发性脂肪酸、产甲烷活性以及污泥形态。结果表明,UASB反应器对饮料废水中的COD去除率达到84.55%,产生的沼气中甲烷组分含量高达74.5%。反应器内的氢分压仅2.5 Pa,为水解发酵和产氢产乙酸过程创造了良好的条件。UASB反应器中污泥具有较高的产甲烷活性,以乙酸、丙酸和丁酸为基质时,最大比产甲烷活性分别为407.14、331.74和241.27 mL CH4/(g VSS·d)。UASB反应器中的污泥形态主要为絮状,甲烷菌中的优势菌为甲烷丝菌,同时存在少量甲烷杆菌。针对该厂UASB反应器中污泥的现状,提出了影响颗粒化的因素和控制措施,为后期的运行和管理提供了参考依据。     

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.     

基于最小二乘法的高抽巷CO浓度变化规律分析

我国许多煤矿的煤层同时具有高瓦斯和严重自燃倾向,在抽放瓦斯的同时,容易造成采空区遗煤自燃。因此,监测采空区指标气体的浓度变化对预防采空区自燃非常重要。本文采用最小二乘法对山西阳煤五矿8403工作面高抽巷CO浓度进行分析,拟合得出了高抽巷CO浓度变化曲线,并对拟合方法、拟合效果进行了分析。结果表明,用最小二乘法进行拟合是可行、有效的。结合现场实际条件,分析了高抽巷CO浓度偏高的原因和之所以选取分析高抽巷CO浓度来预测采空区自燃的原因。结果显示,分析高抽巷CO浓度变化可以较早地发现采空区自燃。最后,分析了CO浓度与CO2、O2浓度的相关性,验证了用CO浓度预测采空区自燃的准确性。     

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.     

不同温度下应用比值控制实现连续流好氧颗粒污泥短程硝化

在连续流反应器中接种成熟好氧颗粒污泥（AGS）处理低氨氮污水,通过控制溶解氧（DO）和出水氨氮（NH₄⁺-N）的浓度,研究了控制DO/NH₄⁺-N（R值）实现连续流好氧颗粒污泥系统短程硝化的可行性和不同温度（30、20、10℃）条件下实现短程硝化系统对R值的需求.结果表明,通过比值控制,连续流好氧颗粒污泥系统可以快速实现短程硝化;在30、20、10℃条件下,系统实现短程硝化所需要的R值分别为0.50（±0.05）、0.35（±0.03）和0.20（±0.02）.因此可知,温度越低,系统实现短程硝化所需要的氧抑制越强.采用荧光原位杂交（fluorescence in situ hybridization,FISH）实验表明,通过比值控制,氨氧化菌（AOB）得到一定的富集,而亚硝酸盐氧化菌（NOB）的相对数量逐渐减少.基于比值控制和污水水质的特点,选择短程硝化的方式有所不同,低氨氮废水选择半量亚硝化,而高氨氮污水则选择全量亚硝化.     

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.     

部分亚硝化-厌氧氨氧化处理磁混凝生活污水

以磁混凝预处理后的生活污水为处理对象,构建了部分亚硝化-厌氧氨氧化分体式反应器,通过曝气调控与生物强化促进部分亚硝化反应的稳定进行,并耦合厌氧氨氧化反应进行深度脱氮.近100d的运行结果表明,在生物强化和间歇曝气的控制条件下,亚硝酸盐积累率达到了89.93%;提高亚硝化反应器中曝气阶段溶解氧浓度（从0.6~0.8mg/L升高至1.0~1.2mg/L）有利于氨氮与总氮去除.该系统最高能够去除95.45%的氨氮和86.28%的总氮,实现了稳定、高效脱氮;磁混凝预处理后的生活污水在亚硝化反应器中,间歇曝气条件促进了残留的溶解性有机物为反硝化提供碳源,COD总去除率达到64.65%~74.42%,并且亚硝化反应器出水与系统最终出水的有机物组分相似,主要为难降解有机物.