浙江省蔬菜生产系统重金属污染生态健康风险

为了解浙江省蔬菜生产系统重金属污染情况和居民的膳食健康风险,选择浙江省典型的蔬菜生产基地作为研究区域,采集了102对蔬菜和土壤样本,分析了浙江省蔬菜生产系统重金属Cd、 Cu、 Pb、 Cr、 As、 Ni和Hg的分布和富集特征,并采用内梅罗综合污染指数、潜在生态风险指数和膳食暴露评估模型系统评价了蔬菜生产系统生态健康风险.结果表明,研究区土壤Cd超标严重,超标率为97.2%,土壤以中轻度污染风险为主,Cd污染风险最大,其次是Pb、 Cu和As.蔬菜中仅有少量豆类和瓜果类蔬菜Cd超标,超标率分别为12.5%和8.7%.不同种类蔬菜的重金属富集能力差异明显,总体表现为：叶菜>豆类>瓜果类>根茎类.浙江省居民食用本地蔬菜的非致癌风险和致癌风险都在可接受范围,儿童比成人更容易面临风险(P<0.01),Cd和Pb对健康风险的贡献率最高.浙江省蔬菜生产系统生产的蔬菜整体处于安全水平,但需要加强对Cd和Pb的污染源管控.     

河北省某大型焦化厂地下水中多环芳烃的污染特点、源解析及生态风险评价

为研究焦化厂地下水中美国EPA优先控制的16种多环芳烃(PAHs)的分布特点和污染来源,本研究联合使用统计技术、正定矩阵因子分析(PMF)模型和风险商值法,深入分析了焦化厂地下水中PAHs的分布规律,定量解析了PAHs的污染来源,并且对其生态风险进行了科学评价.结果表明,焦化厂地下水中16种PAHs的总检出率较高,达到46.7%.地下水中∑₁₆PAHs的浓度范围是n.d.～444.9μg·L^-1,均值为1.88μg·L^-1.不同生产车间地下水中PAHs的浓度存在明显差异,其中污染最重的车间位于焦油精制区,地下水中∑₁₆PAHs的浓度为444.92μg·L^-1.应用PMF源解析模型,识别出该焦化厂地下水中PAHs有二类污染源：一是石油的燃烧源,二是煤和生物质燃烧以及石油类的泄漏,二种污染源对焦化厂地下水中PAHs的贡献率分别为38.6%和61.4%.焦化厂地下水中∑₁₆PAHs处在高生态风险等级,且有53.4%的地下水采样点单体PAHs的生态风险处在高风险等...     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

白银矿区土壤重金属及可培养细菌重金属耐受性研究

对白银矿区土壤重金属进行分析研究,分离、筛选和鉴定了受污染土壤中的可培养细菌,并考察其对土壤中重金属胁迫的耐受性。结果表明：白银矿区王岘镇尾渣堆和东大沟河沟土壤重金属污染Pb＞Zn＞Cu;从污染最严重的土壤中分离、筛选出砖红色微杆菌（Microbacterium testaceum）、特基拉芽孢杆菌（Bacillus tequilensis）和解单端孢菌素微杆菌（Microbacterium trichothecenolyticum）3种细菌,其中菌株M1（Microbacterium testaceum）对Pb、Zn、Cu、Cd的胁迫具有明显的耐受性。     

Floristic contamination of suburban forests near the city of Ivanovo

In the course of floristic studies in suburban forests near the city of Ivanovo, 206 species of vascular plants have been recorded, including 39 invasive species (18.9%). Information on specific features of distribution and activity of introduced species growing feral and accidental weeds in different forest types is presented. The most aggressive species are distinguished. Factors responsible for the invasion of alien species to forest cenoses and main tendencies in their dynamics are discussed.     

人类活动对土壤环境的影响及对策

人类不合理的开发行为给土壤环境带来了沉重的负面影响，主要表现为土壤沙化、土壤侵蚀、土壤盐渍化、土壤污染等。本文以此为出发点展开讨论，分析各种现象的成因，并提出相应对策。     

基于 GJB 150.26 的军用装备流体污染试验实施方法

从适用性、参数选取、试验程序等角度深度梳理GJB 150.26试验标准,并研究流体污染试验程序的实施方法。通过对流体污染试验的研究背景和应用需求、相关标准应用现状和试验适用范围进行说明介绍,围绕基于GJB 150.26的流体污染试验参数选取原则、试验程序制定方法,探究符合标准架构的试验实施流程体系。按照所归纳研究整理的试验实施流程体系,结合工程实例对某型机载产品基于GJB 150.26的试验标准,针对其实际使用情况和相关技术协议文件制定了流体污染试验程序,进行了试验实施验证。流体污染试验实施目前在我国环境适应性工程领域缺乏理论基础指导,具备较大的开拓空间,通过对其实施方法和程序的研究能够有效地提升军用装备开展特种环境试验的技术能力。     

平果岩溶型铝土矿区苔藓多样性及对金属污染的监测指示

以广西平果岩溶型铝土矿区苔藓为研究对象,采集苔藓样品140份。采用经典形态分类法,发现苔藓属于13科25属44种,其中藓类10科22属41种,苔类3科3属3种,其中丛藓科(Pottiaceae)石灰藓属(Hydrogonium)疣叶石灰藓(Hydrogonium gangeticum)为主要优势种。丰富度指数从大到小的样地依次为原矿区、矿区农田、矿石堆、开采区、香蕉地、浓缩池、废矿区,Shannon-Wiener指数从大到小的样地依次为原矿区、矿区农田、矿石堆、浓缩池、香蕉地、开采区、废矿区。利用ICP-OES测定了该矿区土壤和苔藓的Al、Fe、Mn、Mg和Cr质量比,其中土壤Al质量比从大到小的样地依次为废矿区、浓缩池、矿石堆、原矿区、开采区、矿区农田、香蕉地,苔藓Al质量比从大到小的样地依次为开采区、废矿区、浓缩池、矿石堆、原矿区、矿区农田、香蕉地。冗余分析(RDA)表明,细叶石灰藓(Hydrogonium gracilentum)、疣叶石灰藓(Hydrogonium gangeticum)和卷叶毛口藓(Trichostomum involutum)对Al有一定耐受作用;相关性分析表明,丰富度指数与ShannonWiener指数呈极显著正相关(p0.01),丰富度指数与苔藓Al质量比呈极显著负相关(p0.01),土壤Al与苔藓Al质量比、苔藓Al与苔藓Fe质量比呈显著正相关(p0.05)。研究表明,苔藓可作为该矿区金属污染的监测指示植物。     

废旧塑料处置地沉积物中邻苯二甲酸酯污染特征及其生态风险

为了解废旧塑料处置活动对区域水体的影响,采用气相质谱联用仪(GC-MS),对河北省某废旧塑料处置地沉积物中16种PAEs(phthalate esters,邻苯二甲酸酯)的污染特征和生态风险进行了研究. 结果表明:研究样地的w(∑₁₆PAEs)为0.527~102 μg/g, 平均值为18.9 μg/g,其中,DEHP〔邻苯二甲酸(2-乙基己基)酯〕是PAEs最主要的污染单体,平均占w(∑PAEs)的66.6%. 对该处置地的污染物源分析表明,沉积物中PAEs主要来源于废旧塑料回收利用过程中的环境排放. 沉积物中w(DEHP)(14.2 μg/g)和w(DBP)(1.41 μg/g)(DBP为邻苯二甲酸二正丁酯)均超过各自环境风险限值(ERLs),w(DIBP)(DIBP为邻苯二甲酸二异丁酯)超过了美国华盛顿州颁布的沉积物质量警戒限值(0.610 μg/g). 研究显示,沉积物中DBP对鱼类的生态风险及DEHP对藻类和鱼类的生态风险水平不可接受,应引起足够重视.