SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

基于改进遗传算法的河流水污染源反演方法

针对河流水污染应急响应过程中污染源排放历史迟知、未知的问题,结合多种群遗传算法和自适应遗传算法,利用一维河流水质模型和水质监测数据,研究建立了基于改进遗传算法的河流水污染定量源反演方法,实现了对河流污染源排放历史的识别与重构.将该方法应用于美国特拉基河流的3个不同流量下的示踪剂实验中,对示踪剂排放历史进行定量源反演分析.结果表明：IGA算法对高、中、低不同流量下的3次示踪剂实验均可以很好的重构和识别示踪剂排放历史,对于实际河流水污染源反演分析的误差均在可接受范围内.IGA算法在河流水污染源反演分析中具有一定的可靠性和稳定性,可为河流水污染精准溯源与治理提供科学的技术支撑.     

沱江沉积物中有机氯农药分布特征及其与藻类有机质的关系和生态风险评估

使用气相色谱-质谱联用仪分析了长江支流沱江流域48个表层沉积物中有机氯农药（organochlorine pesticides,OCPs）的残留水平,探讨了其分布和组成特征及其与总有机碳（total organic carbon,TOC）、藻类有机质之间的关系,以及评估其生态风险.结果表明沱江流域表层沉积物中OCPs的总含量为3.17~127 ng·g^-1,其中六六六（hexachlorocyclohexane,HCHs）类农药的含量为2.83~86.0 ng·g^-1,滴滴涕（dichlorodiphenyltrichloroethane,DDTs）类农药的含量为0.340~40.9 ng·g^-1.OCPs空间分布特点为：上游 < 中游 < 下游 < 支流.HCHs和DDTs的组成成分分析表明,沱江流域存在林丹输入的现象,主要来自于历史残留,这与绝大多数流域的DDTs的输入状况相似.OCPs含量与TOC、藻类有机质含量之间存在着极显著的正相关性关系,表明藻类有机质在TOC对沉积物中的OCPs分配中起更为重要的作用.生态风险评估表明,沱江流域表层沉积物的有机氯农药存在较大的生态风险,可能对河流的底栖生物及生态环境造成明显的影响.     

金属表面涂装行业VOCs排放特征及排放系数

对比了浙江省2014和2018年金属表面涂装企业的有机废气排放及治理情况,分析了该行业涂料及稀释剂的使用、主要污染因子,测算了溶剂型、水性涂料的挥发性有机物（VOCs）产生系数和排放系数.结果表明：2018年VOCs治理水平明显高于2014年,水性涂料使用企业比例由18%上升至36%,纯溶剂型企业由82%下降至64%;金属表面涂装行业的主要排放污染物为二甲苯、丁醇、乙酸乙酯、乙酸丁酯、甲苯、丙二醇、乙苯、苯乙烯等8种有机物.溶剂型和水性涂料的VOCs产生系数分别为0.72和0.31kg/kg;溶剂型和水性涂料2014年VOCs排放系数为0.64和0.29kg/kg,2018年为0.48和0.21kg/kg.     

特殊区域地表水环境背景值表征技术方法研究与应用

选取黑龙江省源头水保护区作为研究对象,根据DEM数据对保护区内随机布设的水质监测点控制单元进行划分并提取单元内的相关指标建立监测点背景特性量化模型,基于量化结果提出地表水环境背景值监测方案,建立背景值数据库.选取研究区自然属性指标与空间属性指标,以空间叠置技术与聚类分析方法对研究区地表水进行背景值分区,最终将黑龙江省分为六大地表水水质背景值地理分区,并计算了各分区地表水环境背景值表征范围.表征范围显示：依据现行标准进行水质评价,保护区内水质背景值已超出Ⅱ类水质标准限值.因此,基于水质背景值研究成果提出了考虑背景值影响下的水环境质量评价方法,并将方法应用于研究区,结果表明方法切实可行且优于单因子评价法.研究成果可为区域制定背景值影响下的水环境评价方法提供科学的数据支撑与理论依据.     

水源水库贫营养好氧反硝化菌群脱氮特性研究

为削减微污染水库中氮素的浓度,通过对西安市李家河水库沉积物进行定向富集驯化,筛选出以Pseudomonas菌为主、具有高效好氧反硝化特性的混合菌群-A1.摇床实验表明,贫营养好氧反硝化菌群A1在15h时硝酸盐氮去除率可达93.39%,硝酸盐氮平均去除速率为0.2073mg/（L·h）;总氮去除率为52.11%,总氮平均去除速率为0.1153mg/（L·h）,无亚硝酸盐积累.氮平衡分析表明,约45%的初始氮被去除转化为气体产物.响应面法（RSM）结果表明,C/N比9.96,温度22.67℃,pH8.01,转速91r/min,溶解氧8.55mg/L是去除总氮（TN）的最优条件.     

油脂对红壤结构性质与水流运动特征的影响

本文通过室内灌水入渗试验,研究了灌溉水油脂浓度（0,1.0,3.0和10.0g/L）、灌水频率（1d1次,2d1次,4d1次）和灌溉模式（纯清水灌溉,纯含油脂灌溉水灌溉,含油脂灌溉水-清水交替灌溉）对受灌土壤结构性质与水流运动特征的影响.结果显示,随灌水进入到受灌土壤中的油脂主要集中在优先流通道中快速运动,优先流湿润锋处的土壤油脂浓度较高且不受灌溉水中油脂浓度的影响,表明即便是采用较低油脂浓度的灌溉水灌溉,依然对地下水系统存在较高的污染风险;高频率、低定额的含油脂灌溉水灌溉有利于进入到受灌土壤中的油脂向深层土壤运动,使得斥水性土层厚度增大,从而导致入渗水流运动的不确定性及地下水系统的污染风险均增大;油脂对受灌土壤团聚体的再团聚作用只有在其浓度较高时才能形成,含油脂灌溉水-清水交替灌溉因降低了受灌土壤的油脂浓度反而导致土壤团聚体分散和破碎.研究成果对再生水和原污水农田灌溉制度设计具有参考价值.     

纤维增强气凝胶复合材料高温结构转变及热稳定性研究

目的研究高温受热条件下纳米复合隔热材料的结构转变特征及热稳定性。方法采用扫描电镜、X射线衍射仪、红外光谱仪及热重仪等检测方法。结果纤维增强气凝胶材料从室温到650℃存在连续的质量损失,从室温到放热前,质量损失为1.66%;365℃开始出现放热,温度升至398℃时达到峰值,整个放热过程对应质量损失约为1.3%;从435℃放热结束开始到650℃的质量损失为1.46%。经过400℃热处理后,试样比表面积从268m~2/g增加到437m~2/g;当试样热处理温度达到600℃时,试样的比表面积明显随之降低至198m~2/g。结论 SiO_2气凝胶复合材料以无定形结构为主,存在少量的二氧化钛晶体。在400℃左右,SiO_2气凝胶结构中硅甲基Si—CH_3发生氧化,产生明显的放热峰,之后硅羟基Si—OH之间发生缩聚反应,使600℃热处理后气凝胶中Si—O—Si网络骨架强度有所提高。未处理的纤维增强气凝胶材料试样上气凝胶纳米颗粒构成的块体较为良好地包裹在玻璃纤维表面,而经过600℃的高温热处理1 h后,块体气凝胶脱离了光滑的纤维表面,气凝胶纳米粒子发生收缩,致使材料比表面积下降。     

基于SPIS的月球表面充电模拟研究

目的研究月球表面不同等离子体环境下航天器表面充放电效应情况。方法利用欧空局开发的SPIS软件建模,并仿真模拟,通过分析表面电流种类、大小,得出不同环境下的一般性的充放电规律。结果月球表面探测器存在表面充电风险,磁鞘层、磁尾瓣、等离子体片及太阳风4种不同典型等离子体环境下表面充电电位差异较大,连接处充电电位存在分布不均匀的渐变现象。表面充电电位区间约为-1784~142 V。结论光照条件能显著影响探测器表面电位,特殊区域充电电位会受到临近区域充放电效应形成的新的小范围等离子体环境的影响。