地热水的开发利用对环境和健康的影响

在对我国地热水的分布,理化性状和利用现状调查的基础上,就地热水对环境和人群产生的影响进行了卫生学研究,结果表明地热点周围环境已受到了不同程度的污染,人群健康的远期影响也不容忽视。并提出了今后地热水开发中应重视的问题。     

某市岩溶地下水四氯化碳污染特征研究

赋存于寒武系和奥陶系灰岩中的岩溶水是某市的主要供水水源,每天供水量约为35×104t.2000年11月地下水监测资料表明,南郊水源地的岩溶水已受到了四氯化碳污染.污染源为一农药厂,该厂位于七里沟水源地补给区的山坡上,生产农药时用四氯化碳做溶剂.该厂生产废水中四氯化碳的浓度从281 0μg·L-1到2584 3μg·L-1.到2001年5月,水源地中53口水井中发现了四氯化碳,污染面积达17 5km2,井水中四氯化碳浓度最高达3909 2μg·L-1.根据四氯化碳浓度,污染区可以被划分为3个亚区:污染源附近亚区(初始最大浓度为3909 2μg·L-1,2003年4月最大浓度1891 5μg·L-1),集中抽水排泄区污染亚区(初始最高浓度195 5μg·L-1,2003年4月最大浓度504 5μg·L-1)和中间污染相对较轻的过渡区(一般20～200μg·L-1).过去3a间64口水井水质资料监测表明,岩溶含水层中四氯化碳含量受降雨、承压水位和含水层深度等多种因素的综合控制.由于四氯化碳为一种微溶于水的比水重的非水相有机物(DNAPL),它主要沿含水层底部运移,因此比较浅的水井(深度<150m)中四氯化碳含量比较低,而在深度较大的水井(深度>150m)中则浓度较高.     

外来污染物对土壤磷酸酶影响的研究进展

磷酸酶在磷循环中占有重要地位 ,对植物有着非常重要的意义。根据几十年的国内外资料 ,本文概述了重金属、农药、酸沉降以及其他各种外来污染物对土壤磷酸酶的影响 ,并探讨了磷酸酶作为表征土壤污染程度的指标的可行性。土壤磷酸酶与各种污染物的相互作用关系在环境中的应用方面还需进一步研究。     

矿山环境重金属污染的事故树分析

有色金属矿产资源的开采与利用过程不可避免地伴随着矿物化学组成和物理状态变化、重金属污染向生态环境释放和迁移的过程。笔者从采矿过程的分析入手,应用事故树分析方法研究了矿山环境重金属污染物来源,结果表明矿山环境重金属污染物的主要来源是尾矿库,因此矿山环境重金属释放迁移规律和重金属的污染治理与控制研究重点应针对尾矿库。     

化工污染场地氟污染土壤的稳定化技术研究

通过以某电解铝厂内的氟污染土壤为研究对象,添加钙、镁、铝剂及碱性材料共8种不同材料作为稳定剂开展稳定化修复技术研究,分析不同稳定剂对土壤pH值和氟化物浸出毒性的影响,探讨稳定剂对氟化物的稳定效果.结果表明:钙剂、镁剂、铝剂均降低了土壤pH值和氟化物的浸出浓度,其中氯化铝的投加量为3％时稳定效果最佳,氟化物浸出浓度下降了...     

气候变化视角下的污染场地绿色可持续修复新方向

全球气候变化背景下,绿色可持续修复已成为修复领域的必然趋势。极端天气频发和水文条件改变等气候变化会影响污染场地修复工程实施和修复技术适用性等,绿色可持续修复亟待进一步发展和完善。弹性修复是在绿色可持续修复的基础上,进一步应对和适应气候变化的新兴修复方向。英美等发达国家已经初步发展了弹性修复的理论和实践,本文通过探究污染场地修复与气候变化的交互作用,借鉴发达国家的相关经验和实践案例,为我国污染场地绿色可持续修复适应气候变化提出发展建议。     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

Arsenic and Other Metal Contamination of Groundwaters in the Industrial Area of Thessaloniki, Northern Greece

In groundwater, used for drinking water supply in the greater industrial area of Thessaloniki, in Northern Greece, concentrations of total arsenic exceeded the WHO provisional guideline value and the EU maximum contaminant level (MCL) of 10 μg/L. The concentration of total arsenic was in the range between 4–130 μg/L, whereas the median value was 36 μg/L and the average concentration 46 μg/L. Nine out of the eleven wells contained total arsenic at concentration higher than 10 μg/L and it should be stressed that 6 of them contain arsenic at concentrations between 10 (new MCL) and 50 μg/L (previous MCL). The examined groundwaters were found to contain elevated concentrations of manganese and phosphate. Arsenic had a positive correlation with the pH, indicating the possible effect of pH on arsenic mobilisation. These findings emerge the problem of contamination from arsenic, since, according to the EU directive 98/83, all drinking water sources within the European Union should have achieved compliance with the new limits by 12/2003, implying that the situation requires urgent remedial action.     

Leachate Characterization and Assessment of Groundwater Pollution Near Municipal Solid Waste Landfill Site

Leachate and groundwater samples were collected from Gazipur landfill-site and its adjacent area to study the possible impact of leachate percolation on groundwater quality. Concentration of various physico-chemical parameters including heavy metal (Cd, Cr, Cu, Fe, Ni, Pb and Zn) and microbiological parameters (total coliform (TC) and faecal coliform (FC)) were determined in groundwater and leachate samples. The moderately high concentrations of Cl⁻, NO⁻ ₃, SO²⁻ ₄, NH⁺ ₄, Phenol, Fe, Zn and COD in groundwater, likely indicate that groundwater quality is being significantly affected by leachate percolation. Further they proved to be as tracers for groundwater contamination. The effect of depth and distance of the well from the pollution source was also investigated. The presence of TC and FC in groundwater warns for the groundwater quality and thus renders the associated aquifer unreliable for domestic water supply and other uses. Although some remedial measures are suggested to reduce further groundwater contamination via leachate percolation, the present study demand for the proper management of waste in Delhi.     

基于响应曲面模型的多元复合体系中沉积物吸附阿特拉津的规律

文章建立了阿特拉津（AT）、铜（Cu）、镉（Cd）和马拉硫磷（Ma）四元复合体系中沉积物吸附阿特拉津的BP神经网络模型,模型训练集、验证集、预测集的均方差分别为0.070 3、0.336 0、和0.195 0,模型输入数据与其模拟值相关系数r2=0.841 6。利用所建立的BP神经网络模型构建多元复合体系中沉积物吸附AT的中心复合实验设计并绘制响应曲面模型,结果显示,响应曲面模型可以直观反映多元复合体系中各因素间影响AT吸附的交互作用,AT×Ma的交互作用最大,而除Cu×Cd外,其他两两因素之间的交互作用均呈显著效应,其中,AT×Ma、AT×Cd和Cu×Ma对阿特拉津的吸附产生协同作用,而AT×Cu和Cd×Ma则产生拮抗作用,交互作用占AT吸附量的60.28%,对阿特拉津在沉积物上的吸附影响不可忽略。