污染场地修复决策支持系统的几个关键问题探讨

场地土壤和地下水环境污染问题已成为近年来我国环境保护领域关注的焦点和热点.污染场地修复决策支持系统是在场地勘察与风险评估基础上,筛选最佳的修复技术,以制定经济高效的场地污染治理方案.本文探讨与分析修复决策系统的框架、模型、方法等关键环节,评估与总结了国际上现有40多种修复决策软件的模块及功能.针对我国场地污染问题,综合国际前沿的思想与理念,提出适合我国现阶段经济社会发展水平的决策支持系统开发思路.     

典型再生水人工湿地净化系统水质时空变异研究——以北京市奥林匹克森林公园人工湿地为例

选取奥林匹克森林公园人工湿地作为典型再生水补水人工湿地净化系统进行水质数据检测,应用模糊综合评价法综合评估各净化单元的水体污染状况,采用综合判别分析和Spearman相关分析阐释影响各净化单元时空变异的水环境要素,并通过因子分析识别不同时间段污染源的种类和组成.结果表明,奥林匹克森林公园人工湿地净化系统水质状况较好,符合人体非直接接触的景观用水国家标准.人工湿地各净化单元时空区间污染程度不同:季节尺度上,秋季污染更为严重;空间尺度上,主湖区、混合氧化塘区污染最为严重.CODMn、NO-3-N、ORP、TN 4项指标即可完全表征水质的季节差异(91.8%),应适当加大监测力度;Chl-a、CODMn、DO、pH 4项指标用于表征空间差异(55.1%),较低的判别正确率暗示了各功能区间类似的污染状况.不同时空区间污染源种类和组成存在差异性:水体的内源杂质是各季度影响水质的主要因子,春季有机污染最为严重,夏季则转为氮、磷等营养物质污染,秋季水体则更易受富营养化的威胁;再生水区水体污染的主要影响因子为氮、磷等营养盐类,其余功能区水体主要影响因子仍为内源杂质.增强水体流通力,缩短水力停留时间,能够有效减弱富营养盐类和有机污染物的影响.     

龙口市污水灌溉区农田重金属来源、空间分布及污染评价

采集龙口市污水灌溉区农田土壤,分析测定土壤pH和重金属含量,以采集的70个土壤样点为研究对象,根据多元统计中的相关分析和主成分分析探究研究区重金属的来源;采用地统计理论与GIS空间插值相结合的方法研究重金属元素的空间结构和分布特征;最后利用内梅罗指数法和改进的模糊综合评判法做重金属污染评价.结果表明,研究区内9种重金属元素在土壤中均有一定程度的富集,其中重金属Cd的均值是当地背景值的3.06倍,富集情况最为明显;指数法进行的污染评价显示Cu、Cd和Pb的综合污染指数分别为7.06、6.10和5.54,三者均属重度污染;相关分析和主成分分析结果显示,Cu、Zn和Pb、Cd主要受人为因素影响,污水灌溉是它们的共同污染因素,前两者为农业生产中化肥农药的过量使用与长时间的累积作用,而北部煤矿开采和煤矸石的堆积产生的污染以及电镀、机械制造等工业污染是后两者的污染来源;Co、Cr、Mn、Ni和As主要是受成土母质等自然因素的影响;据模糊综合评判的结果,研究区70个样点,有13个是中污染程度,23个属于轻污染,28个是警戒程度,6个样点处在安全范围内;从空间分布上看,重金属含量的高值区主要集中在黄水河下游的诸由观镇和徐福镇,说明污水灌溉给当地土壤造成了一定程度的重金属污染.     

基于距离相关系数和支持向量机回归的PM2.5浓度滚动统计预报方案

针对目前空气质量统计预报方法存在的主要缺陷,本文提出了距离相关系数和支持向量机回归相结合的统计预报方案DC-SVR.利用淮安市2013年1—12月PM_(2.5)观测资料和常规气象观测资料,首先在选入预报当日气象要素的基础上,增加选取前期污染物和气象要素作为预报因子,再采用距离相关系数分季节从预报因子中筛选出重要预报因子,最后采用支持向量机回归对PM_(2.5)浓度值进行逐日滚动统计预报.研究发现,淮安地区气温和气压对PM_(2.5)的距离相关性要高于其他气象要素,夏秋季PM_(2.5)与气象要素的距离相关性较春冬季好.基于距离相关系数和支持向量机回归建立DC-SVR模型,PM_(2.5)的试预报值和实测值的全年相关系数高达0.76,平均偏差仅为1.13μg·m~(-3),平均绝对误差为23.47μg·m~(-3).通过与支持向量机回归、人工神经网络的统计预报效果对比,DC-SVR模型有效降低预报因子维数且能自适应选取最佳参数,预报精度显著优于其他3种统计预报方案,可为业务化预报提供参考.     

Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli

Convenient to apply and available on the Internet software CORAL (http://www.insilico.eu/CORAL) has been used to build up quantitative structure-activity relationships (QSAR) for prediction of cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of concentration for 50% effect pEC50). In this study six random splits of the data into the training and test set were examined. It has been shown that the CORAL provides a reliable tool that could be used to build up a QSAR of the pEC50.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

PHAST软件在丁腈反应釜定量后果分析中的应用

丁腈反应釜运行过程中若发生泄漏事故,不仅厂区内部,而且附近区域的人员生命与财产都将遭受巨大的损失和危害.过去对一些物质的泄漏后果,人们提出了一些可靠的数学模型,但模型计算量大,容易出错.而采用后果软件PHAST具有很强的操作性.以某化工厂1台丁腈反应釜为例,用DNV公司的PHAST软件预测反应釜中丙烯腈和1,3-丁二烯的混合物发生泄漏后,对周围环境造成的影响.分别讨论了火灾、爆炸和毒性的影响,预测事故危害程度和范围.可为识别主要危险因素,采取相应的安全对策措施,发生事故后的现场控制等提供科学的参考依据.     

Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds1

Abstract: Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient‐reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight‐digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.     

喻家湖水质时空分布特征和影响因子分析

通过设计合理的水质监测网,采用多元统计分析,并结合地理信息技术对武汉市喻家湖在2011年-2012年期间12个监测点、13个水质参数监测值进行水质时空分布特征研究.结果表明,喻家湖13个水质指标概括为4个主成分:第一主成分代表喻家湖的重金属污染,第二主成分代表其富营养化水平,第三主成分代表有机污染程度,第四主成分间接指示富营养化程度;在时间和空间变化上都可分为二组,显著性指标的时空差异较明显,水质污染程度从南至北逐渐减弱,湖溪河是喻家湖的最主要污染源.并对水质参数,监测点位和频次进行了优化.