铅锌矿区玉米中重金属污染特征及健康风险评价

以西南某铅锌矿区为研究区域,测定了92份玉米样品中Pb、Zn、Cd、Cr和Ni的含量,开展了矿区玉米籽粒中重金属污染特征及健康风险研究.应用单因子污染指数和内梅罗综合污染指数分析了重金属在玉米中的污染状况,采用健康风险评价模型评价了重金属对人体健康造成的风险,同时利用主成分分析法解析了重金属的主要来源.结果表明,玉米籽粒中Pb、Zn、Cd、Cr和Ni的平均含量分别为0.30、23.75、0.21、1.33和1.15 mg ·kg^-1;除重金属Zn外,其他4种重金属均超过我国食品卫生标准限值,研究区玉米籽粒中Pb、Cd、Cr和Ni的综合污染指数范围介于4.32~9.07之间,均属于重度污染,Zn的综合污染指数小于1;重金属通过玉米籽粒摄入引起的复合重金属污染对成人和儿童均存在健康风险,且对儿童造成的健康风险高于成人.玉米籽粒中5种重金属可以由2个主成分来反映,第一主成分主要支配Pb、Cd、Cr和Ni的来源,第二主成分支配Zn的来源.玉米籽粒重金属含量与土壤重金属含量之间没有明显相关性.     

基于绝对主成分-多元线性回归的滇池污染源解析

定量解析污染源是湖泊流域水环境管理的重要基础.基于滇池草海和外海多年水质监测数据,采用主成分分析（PCA）方法识别了主要水质指标的污染源类型,利用绝对主成分-多元线性回归模型（APCS-MLR）得到不同污染源对水质的贡献程度.结果表明,草海主要的污染源有农业面源、城市面源和内源3类,外海的主要污染源是农业面源、城镇生活污染源、城市面源和内源4类.与河流水污染源解析结果不同,底泥内源与气象因子对滇池主要水质指标的影响较大.     

离子液体吸收废印刷线路板热拆解过程中挥发性有机物——基于COSMO-RS模型

模拟废印刷线路板（WPCB）的热拆解过程,分析热拆解过程中的挥发性有机物（VOCs）组分;利用真实溶剂似导体屏蔽（COSMO-RS）模型对浓度较高的污染物进行量子力学模拟,研究离子液体（ILs）组成单元对目标污染物溶解度的影响差异,分析溶解过程中主导分子间作用力类型,确定优选吸收剂;测定不同溶剂进行溶解性,验证模型适用性.结果表明：①乙酸乙酯和环戊酮是浓度较高的VOCs组分,在240和250℃时浓度分别为43.1,153mg/m³和105,252mg/m³,质量百分比总和分别为76.3%和67.3%.②高表面屏蔽电荷密度分布峰、长烷基链阴阳离子和亲电基团的存在可提高乙酸乙酯和环戊酮在ILs中的溶解度.双三氟甲磺酰基亚胺盐（NTf₂^-）类ILs是一类优良吸收剂.静电力和范德华力对溶解过程起主导作用.③COSMO-RS模型可定性和半定量用于预测乙酸乙酯和环戊酮的溶解度.     

Investigation of chlorinated phenols sorption mechanisms on different layers of the Danube alluvial sediment

The characteristics of the Danube river alluvial sediment are of great importance in assessing the risk for transport of pollutants to drinking water sources. Characterization of the sediment column layers has shown that the alluvial sediment, sampled near the city of Novi Sad, is a mesoporous sandy material with certain differences in the properties of individual layers. In order to investigate the sorption mechanisms of four chlorinated phenols (CPs) on the alluvial deposit, static sorption experiments were performed at pH 4, 7 and 10. The results of sorption experiments, confirmed by principal components analysis sugest different mechanisms govern the sorption process at different pH conditions. This can be attributed to the molecular characteristics of CPs, geosorbent properties and to variations in the surface charge of the sorbent at different pH conditions.     

京津冀西北典型区域地下水位时空演变及驱动因素

京津冀西北地区是北京、天津重要的水源涵养区和生态屏障,也是“京津冀协同发展”战略重要的支撑区域。为揭示京津冀西北地区地下水位时空演变规律及其驱动因素,以张家口地区为研究区域,选取1981—2015年56个地下水位观测井连续观测数据、水文地质相关资料、气象数据、地下水资源开采数据和社会经济数据,对研究区内坝上高原、柴宣盆地、涿怀盆地和蔚阳盆地四个地貌单元地下水位时空演变规律和驱动因素进行分析。结果表明：张家口地区四个地貌单元及下辖各区县地下水位呈整体下降趋势,且下降速率逐步加快。35年间坝上高原水位降幅3.59 m<蔚阳盆地水位降幅3.6 m<涿怀盆地水位降幅7.17 m<柴宣盆地水位降幅20.41 m。张家口地区四个地貌单元地下水位动态变化与降雨量、蒸发量、气温等自然因素相关性较差,但与第一产业产值、常用耕地面积、有效灌溉面积、粮食总产量、蔬菜总产量、猪牛羊肉总产量、第二产业产值、第三产业产值和年末总人口等社会经济因素存在显著的相关性。在主成分分析中四个地貌单元前两个主成分的累计方差贡献率均>80%,其中第一个主成分主要为蔬菜总产量、猪牛羊肉总产量、年末总人口和第一、第二、第三产业产值等社会经济因素,不同地貌单元均可解释66%以上,表明社会经济因素是造成张家口地区地下水位持续下降的主要驱动因素。研究成果可为该地区未来社会经济的可持续发展和区域水资源的合理利用、调配以及京津冀西北地区水源涵养和生态功能保持提供理论依据。     

郑州市氧化铝工业炉窑污染物及源谱特征

采用稀释通道采样法对郑州市一家拜耳法氧化铝企业及一家α-氧化铝企业的PM_2.5、PM₁₀样品进行采集,利用烟气分析仪检测SO₂和NO_x气体的质量浓度.对颗粒物样品中的38种化学组分进行分析,构建相应的源成分谱,运用分歧系数（CD）和组分差异权重分布函数R/U对其特征进行对比研究.结果表明：α-氧化铝源谱中以Cl^-为主（质量分数18.29%~25.21%）,拜耳法氧化铝源谱中以OC和NO₃^-为主（质量分数9.01%~41.87%）.源谱间的分歧系数（CD）显示,同一种生产工艺两种粒径间的源谱比较相似,相同粒径的两种生产工艺之间的源谱差异较大.R/U值对比结果显示,EC、Ca、F^-和Cl^- 4种元素可作为区分两种氧化铝生产工艺的标识组分.     

深圳市综合水质指数研究及其在水资源资产评估上的应用

自然资源资产评估技术是开展自然资源资产负债表实践和实行环境责任离任审计的重要技术支撑。水资源作为一项重要自然资源,如何有效评估水资源资产是不可回避的问题。论文以深圳市为研究区域,一是采用综合污染指数法和主成分分析法,以总氮、氨氮、总磷、化学需氧量、生化需氧量、固体悬浮物为指标参数构建了深圳市综合水质指数以评价水资源综合水质。二是以该综合水质评估模型为基础,通过采用治污成本法分析综合水质与治污成本间的关系,从而构建深圳市景观水和饮用水资源的价值量评估模型。论文还以深圳市大鹏新区为试点开展了水资源价值量评估,评估结果显示,2015年大鹏新区景观水和饮用水资源的价值量分别为3.60亿、0.35亿元。     

Relationship between physicochemical properties and dewaterability of hydrothermal sludge derived from different source

Sewage sludge (SS) and deinking sludge (DS) were used to comparatively study the hydrothermal dewatering of sludge with different components. For a better overview, an insight into the relationship between physicochemical properties and dewaterability of hydrothermal sludge was provided. Results found that not all kinds of sludge were suitably conditioned by hydrothermal treatment (HT) in term of the elevation of dewaterability. Higher hydrothermal temperature tended to enhance the dewaterability of SS rather than DS, which was supported by the variation of their physicochemical properties (including water distribution, bonding energy, extracellular polymeric substance (EPS), particles size, acid functional groups and zeta potential in this study). In addition, the changes in surface morphology suggested that the reverse effect of HT on sludge dewaterability was mainly due to their dewatering behavior. For SS, the destruction of EPS structure leaded to the release of bound water, thereby strengthening sludge dewatering. Conversely, “Bridging effect” generated by lignocellulose in DS was beneficial for sludge dewatering; however, the increasing hydrothermal temperature degraded part of lignocellulose and weakened “bridging effect”, finally resulting in worse dewaterability of DS.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

ASM1中废水和污泥组分浓度的测定方法研究

对呼吸计量法应用于校准ASM1作了简明的阐述。在呼吸速率与模型组分关系的基础上,介绍了ASM1中废水与污泥各组分浓度的测定方法,并指出模型组分细化的意义。