华西雨屏区典型人工林对降雨过程中NH_4~+-N和NO_3~--N的过滤作用

氮沉降已成为河流、湖泊及城市等生态系统的主要污染威胁因素之一,森林生态系统能从林冠、地被和土壤自上而下截留过滤大气降雨中的NH+4-N和NO-3-N,是区域大气环境污染生态防治的重要途径.选取华西雨屏区几种典型人工林森林生态系统为研究对象,采用定位监测与室内分析相结合的研究方法,于2011年4—12月对大气降水、穿透雨、树干茎流、地表径流和地下渗滤NH+4-N和NO-3-N的含量与分配进行了研究.结果表明:观测期内40次降雨总雨量为492.72 mm,NH+4-N和NO-3-N的总沉降量分别为13.248 kg·hm-2和16.320 kg·hm-2;3种人工林林冠层对NH+4-N和NO-3-N的过滤能力表现为香樟林混交林柳杉林,而地表和土壤层均表现为混交林香樟林柳杉林;无林地、香樟林、柳杉林和混交林生态系统无机氮截留过滤净输入量分别为19.557、44.079、42.331、64.896 kg·hm-2,对无机氮的综合过滤作用表现为混交林香樟林柳杉林无林地.这些结果说明华西雨屏区合理配置混交林能更加有效地降低大气氮沉降对生态系统的影响.     

PBDEs的来源特征、环境分布及污染控制

多溴联苯醚(Polybrominated diphenyl ethers,PBDEs)生产应用、环境分布与生态风险之间的关系将如何影响我国的未来,具体涉及到工业、环境、健康、人类繁衍等的社会问题.由于PBDEs阻燃效率高,热稳定性好,价格便宜,作为添加剂被广泛应用在电子、电器、化工、交通、建材、纺织、石油等领域的阻燃产品中.PBDEs进入环境后迁移扩散并富集于沉积物和生物体内,进入人体后引起肝脏毒性、内分泌干扰、神经毒性和生育能力下降等而危害人类的健康.基于此,从生产应用、品种生产量、分子的物理化学特性以及毒性等的文献数据分析了PBDEs的来源特征与环境危害,分析这些性质对环境分布与污染控制的基础支持关系;从沉积物、水体、大气、水生生物、人体5个方面考察了PBDEs的环境本底浓度变化、分配关系、迁移规律、时间效应,分析了生产、应用与扩散的多因素影响关系,发现全球性PBDEs污染物蔓延的现象,沉积物是主要的归趋场所,室内空气中的浓度远高于室外,水中浓度较低,水生生物和人体均能富集PBDEs,通过饮食、母乳和呼吸摄入,可实现代际传播,污染的分布呈现介质与区域的不同特征;污染控制需要考虑点源技术与面源修复的联合,结合PBDEs的物理性质、分子结构及其化学特性,统计分析了微生物法、光化学降解法和零价铁还原法的原理及其有效性,根据PBDEs在产品中存在与分布的特点,提出收集-分离-富集-超临界催化还原和氧化毁毒的工艺,针对实际环境(以电子垃圾塑料和流域水体及沉积物为对象)中的PBDEs进行回收或处理,结合材料如催化剂的应用,从动力学和热力学方面提高PBDEs无害化的效率.最后,从生产管理、环境监测、风险评价、技术集成等方面提出了未来的努力方向.     

苏州地区水稻土重金属污染源解析及端元影响量化研究

利用国家土壤质量标准和当地背景值评价了苏州地区表层水稻土重金属(Pb、Cd、As、Hg、Cu、Zn、Ni、Cr)的污染(富集)状况.结果发现,苏州地区土壤Cd、Hg、As、Pb、Zn、Cu、Cr和Ni平均含量分别是0.23、0.23、9.14、35.36、103.36、35.75、77.12和33.18μg·g-1;除As外,其余7种重金属平均含量皆明显高于当地土壤背景值.通过与中国土壤环境质量标准(GB15618—1995)相比,发现苏州地区土壤中Cd和Hg是8种重金属中风险最高的.地质累积指数分析表明,苏州水稻土重金属Cd、Hg、Pb、Cu和Zn出现了污染,其中以Cd和Hg的污染程度最突出,约80%的土壤样品出现了Pb轻度污染.因子得分-多元回归法(PCS-MLR)估算表明,70%的土壤Cd来自与有色金属或磷有关的人为活动;土壤中约50%的Pb和72%的Hg源于大气沉降的贡献;土壤的Zn和Cu污染主要来自与有色金属或磷有关的人为活动,该端元对土壤Zn和Cu的影响率分别为53%和45%.     

北京市MODIS气溶胶光学厚度与PM_(10)质量浓度的相关性分析

利用北京地区2012年1—12月NASA MODIS气溶胶光学厚度(AOD)和通过空气污染指数(API)转换得到的PM10质量浓度进行了相关性分析.结果发现,二者的直接相关程度较低,在引入季节变化的气溶胶标高且考虑了气溶胶的垂直分布后,进行标高订正,二者的相关系数有所提高;在考虑了湿度影响因子后,进行湿度订正,二者的相关系数显著提高;引入平均风速、平均气温和平均气压等气象因素,进行多元回归分析,相关系数进一步提高.证实了卫星遥感气溶胶光学厚度在经过垂直和湿度订正并考虑气象因素的情况下,可以作为监测北京地区颗粒物污染物地面分布的一个有效手段.     

太湖西部河湖氮污染物来源及转化途径分析

通过2014年枯水、丰水两期监测,综合分析了太湖西部入湖河流与湖区水体及其沉积物的无机氮形态与同位素特征,并利用δ~(15)N识别了太湖西部上游区氮污染来源及转化途径的生物化学作用机制.结果表明:NO_3~--N与NH_4~+-N为研究区域入湖河流无机氮的主要形态,而NO_3~--N为西部湖区水体无机氮的主要形态;δ~(15)N-NO_3~-的数值范围揭示了西部入湖河流在枯水季NO_3~--N主要来源于农用化肥,有少量矿化土壤有机氮,而丰水季则以生活污水为主,有少量矿化土壤有机氮及农用化肥;δ~(15)N-NH_4~+的数值范围说明了生活污水是河流水体NH_4~+-N的主要来源;通过水体及沉积物样品NO_3~--N、NH_4~+-N、δ~(15)N-NO_3~-、δ~(15)N-NH_4~+的协同分析可知,湖区氮的赋存形态主要受湖区水体硝化作用及沉积物内反硝化作用的影响.     

雄黄矿区不同砷污染土壤中微生物群落结构及碳源利用特征

土壤砷污染是我国乃至世界范围内比较严重的环境问题.有关砷污染的生态毒理效应有很多研究,但对砷污染土壤中微生物群落的关注相对较少.本文以我国湖南省石门县雄黄矿地区长期砷污染土壤为例,采用PLFA及BIOLOG微平板技术考察了7个砷(As)污染程度不同的样点以及一个对照样点土壤微生物群落结构及碳源利用特征.结果发现,雄黄矿区存在多种重金属复合污染,除As含量较高以外,镉(Cd)和镍(Ni)的含量也超过了国家土壤环境质量三级标准.雄黄矿区土壤微生物的群落结构受到土壤有机碳(SOC)、有效磷(AP)、p H以及镁(Mg)、Cd、铁(Fe)、铜(Cu)含量的显著影响.各样点土壤微生物群落均以细菌为主,占微生物总量的71.54%~80.66%,真菌次之,放线菌最少.雄黄矿区土壤中的有效砷对微生物造成了较严重的胁迫.严重砷污染降低了微生物对于碳源利用的多样性以及均匀度.各样点微生物对于碳源的利用也表现出明显的差异.31种碳源中微生物利用较多的只有7类,分别属于糖类、胺类、羧酸类与多聚物,并且以糖类为主.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Assessment of lead pollution in topsoils of a southern Italy area: Analysis of urban and peri-urban environment

Exposure to lead (Pb) may affect adversely human health. Mapping soil Pb contents is essential to obtain a quantitative estimate of potential risk of Pb contamination. The main aim of this paper was to determine the soil Pb concentrations in the urban and peri-urban area of Cosenza–Rende to map their spatial distribution and assess the probability that soil Pb concentration exceeds a critical threshold that might cause concern for human health. Samples were collected at 149 locations from residual and non-residual topsoil in gardens, parks, flower-beds, and agricultural fields. Fine earth fraction of soil samples was analyzed by X-ray Fluorescence spectrometry. Stochastic images generated by the sequential Gaussian simulation were jointly combined to calculate the probability of exceeding the critical threshold that could be used to delineate the potentially risky areas. Results showed areas in which Pb concentration values were higher to the Italian regulatory values. These polluted areas were quite large and likely, they could create a significant health risk for human beings and vegetation in the near future. The results demonstrated that the proposed approach can be used to study soil contamination to produce geochemical maps, and identify hot-spot areas for soil Pb concentration.     

Predicted no-effect concentrations for mercury species and ecological risk assessment for mercury pollution in aquatic environment

Mercury(Hg) exists in different chemical forms presenting varied toxic potentials. It is necessary to explore an ecological risk assessment method for different mercury species in aquatic environment. The predicted no-effect concentrations(PNECs) for Hg(Ⅱ) and methyl mercury(Me Hg) in the aqueous phase, calculated using the species sensitivity distribution method and the assessment factor method, were 0.39 and 6.5 × 10-3μg/L, respectively. The partition theory of Hg between sediment and aqueous phases was considered, along with PNECs for the aqueous phase to conduct an ecological risk assessment for Hg in the sediment phase. Two case studies, one in China and one in the Western Black Sea, were conducted using these PNECs. The toxicity of mercury is heavily dependent on their forms,and their potential ecological risk should be respectively evaluated on the basis of mercury species.     

New insights into disruption of iron homeostasis by environmental pollutants

Among the numerous health conditions environmental pollutants can cause, chronic exposure to pollutants including persistent organic pollutants (POPs) and heavy metals has been shown to disturb a specific biological homeostatic process, the iron metabolism in human body. Disorders of iron metabolism are among the common diseases of humans and encompass a broad spectrum of diseases with different clinical manifestations, ranging from anemia to iron overload, and possibly to neurodegenerative diseases and cancer. Hepcidin–ferroportin (FPN) signaling is one of the key mechanisms responsible for iron supply, utilization, recycling, and storage, and recent studies demonstrated that exposure to environmental pollutants including POPs and heavy metals could lead to disruption of the hepcidin–FPN axis along with disordered systemic iron homeostasis and diseases. This article introduces and highlights the accompanying review article by Drs. Xu and Liu in this journal, which elaborates in detail the adverse effects of environmental pollutants on iron metabolism, and the mechanisms responsible for these toxicological outcomes. It also points out the knowledge gaps still existing in this subject matter. Research that will fill these gaps will improve our understanding of the issue and provide useful information to prevent or treat diseases induced by environmental pollutants.