电絮凝法去除水中微量叔丁醇的研究

采用电絮凝法对水中微量的叔丁醇(TBA)进行去除,分别考察了电极材料、极板间距、电流密度、溶液初始pH值对叔丁醇去除效果的影响.结果表明,以铝为阳极、不锈钢为阴极,极板间距为1.4cm,电流密度为20mA/cm2,溶液初始pH值为8时,电解120min后叔丁醇的去除率为80.4％,此时铝阳极实际损耗量(1.93kg/m...     

Characteristics of the colony structure of A2O processes under different ultraviolet conditions in plateau areas

Environmental Science and Pollution Research - In this text, a laboratory-scale A2O was performed in Linzhi City at a 3000-m altitude. During the test operation, the UV irradiation was carried out...     

基于商值法的镧水生态风险评价方法研究及应用

随着我国稀土资源开发和利用，稀土元素对水生生态环境的影响及其生态风险越来越受关注。本文以稀土镧的通用敏感生物急性半致死/效应浓度(half lethal/effect concentration, L(E)C_(50))和慢性无观察效应浓度(no observed effect concentration, NOEC)数据，分别采用评价因子法和物种敏感度分布法(species sensitivity distribution, SSD)推导计算了镧的预测无效应浓度(predicted no effect concentration, PNEC),比较不同数据类型和计算方法的结果差异及不确定性，并以四川安宁河为例进行了水生态风险表征。采用评价因子法推导的急性和慢性PNEC分别为1.180μg·L^(-1)和4.000μg·L(-1)和4.000μg·L^(-1)。由急性数据拟合SSD曲线推导的PNEC为42.770μg·L(-1)。由急性数据拟合SSD曲线推导的PNEC为42.770μg·L^(-1),通过急慢性比(acute to chronic ratio, ACR)转换的PNEC为2.032μg·L(-1),通过急慢性比(acute to chronic ratio, ACR)转换的PNEC为2.032μg·L^(-1)。在慢性数据缺乏的情况下，ACR-SSD是相对可行的方法。安宁河镧的水生态风险评价结果表明，所有调查断面均处于中等以上生态风险，其稀土污染问题应引起重视。     

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.