Understanding and addressing the environmental risk of microplastics

Over the past decades, the plastic production has been dramatically increased. Indeed, a category of small plastic particles mainly with the shapes of fragments, fibers, or spheres, called microplastics (particles smaller than 5 mm) and nanoplastics (particles smaller than 1 μm) have attracted particular attention. Because of its wide distribution in the environment and potential adverse effects to animal and human, microplastic pollution has been reported as a serious environment problem receiving increased attention in recent years. As one of the commonly detected emerging contaminants in the environment, recent evidence indicates that the concentration of microplastics show an increasing trend, for the reason that up to 12.7 million metric tons of plastic litter is released into aquatic environment from land-based sources each year. Furthermore, microplastic exposure levels of model organisms in laboratory studies are usually several orders of magnitude higher than those found in environment, and the microplastics exposure conditions are also different with those observed in the environment. Additionally, the detection of microplastics in feces indicates that they can be excreted out of the bodies of animal and human. Hence, great uncertainties might exist in microplastics exposure and health risk assessment based on current studies, which might be exaggerated. Policies reduce microplastic emission sources and hence minimize their environmental risks are determined. To promote the above policies, we must first overcome the technical obstacles of detecting microplastics in various samples.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

Screening of indicator pharmaceuticals and personal care products in landfill leachates: a case study in Shanghai,China

● A systematic framework was developed to identify i-PPCPs for landfill leachate. ● The wide-scope target analysis offered a basis for comprehensive i-PPCP screening. ● Source-specificity and representativeness analysis helped to refine i-PPCPs. ● Erythromycin, gemfibrozil and albendazole were identified as i-PPCPs for leachate. Identifying potential sources of pharmaceuticals and personal care products (PPCPs) in the environment is critical for the effective control of PPCP contamination. Landfill leachate is an important source of PPCPs in water; however, it has barely been involved in source apportionment due to the lack of indicator-PPCPs (i-PPCPs) in landfill leachates. This study provides the first systematic framework for identifying i-PPCPs for landfill leachates based on the wide-scope target monitoring of PPCPs. The number of target PPCPs increased from < 20 in previous studies to 68 in the present study. Fifty-nine PPCPs were detected, with median concentrations in leachate samples ranging from below the method quantification limit (MQL) to 41 μg/L, and 19 of them were rarely reported previously. A total of 29 target compounds were determined to be PPCPs of high concern by principal component analysis according to multiple criteria, including occurrence, exposure potential, and ecological effect. Coupled with source-specificity and representativeness analysis, erythromycin, gemfibrozil, and albendazole showed a significant difference in their occurrence in leachate compared to other potential sources (untreated and treated municipal wastewater and livestock wastewater) and correlated with total PPCP concentrations; these were recommended as i-PPCPs for leachates. Indicator screening procedure can be used to develop a sophisticated source apportionment method to identify sources of PPCPs from adjacent landfills.     

A method to extract algae toxin of microcystin-LR

A simple and low-cost method to obtain cyanobacterial toxin microcystin-LR(MC-LR) was developed. A new strain of Microcystis aeruginosa,named DC-1, producing microcystin-LR but not microcystin-RR, was separated from the field blooming algae samples of Dianchi Lake.in southwest of China. Following three times‘freeze and thaw treatment, the cultivated DC-cells were extracted with 40% methanol in water.The extract was centrifuged and the supernatant applied to a Hydrophilic-Lipophilic Balance(HLB) SPE cartridge.Eluted impuries with a certain gradient from 30% to 50% methanol in water, MC-LR was finally eluted from the HLB cartridge with 60%.may be used in adsorption and biodegradation experiments instead of using expensive standard reagents.     

Photocatalytic remediation of γ-HCH contaminated soil induced by α-Fe2O3 and TiO2

Heterogeneous photocatalytic degradation of γ-HCH on soil surfaces was carried out to evaluate the photocatalytic effectiveness of α-Fe2O3 and TiO2 toward degrading γ-HCH on soil surfaces. After being spiked with γ-HCH, soil samples were loaded with α-Fe2O3 or TiO2 and exposed to UV-light irradiation. Different catalyst loads(0%, 2%, 5%, 7%, and 10% (wt). α-Fe2O3; 0%, 0.5%, 1%, 2% wt. TiO2) were tested for up to 7 d irradiation. The effects of soil thickness, acidity, and humic substances were also investigated. The obtained results indicated that the γ-HCH photodegradation follows the pseudo-first-order kinetics. The addition of α-Fe2O3 or TiO2 accelerates the photodegradation of γ-HCH, while the photodegradation rate decreases when the content of α-Fe2O3 exceeds 7%(wt.). The degradation rate increases with the soil pH value. Humic substances inhibit the photocatalytic degradation of γ-HCH. Pentachlorocyclohexene, tetrachlorocyclohexene, and trichlorobenzene are detected as photodegradation intermediates, which are gradually degraded with the photodegradation evolution.     

孔隙液体相态对饱和砂土力学特性影响试验∗

天然气水合物开采过程中,水合物分解会引起沉积物孔隙液体相态变化,降低海床的安全稳定性。基于低温、高压三轴试验仪,以粉细砂土为骨架,制备含冰/不含冰甲烷水合物沉积物试样以及饱和砂土试样,开展三轴压缩试验,分析饱和砂土中孔隙液体处于不同相态条件下试样的应力?应变和强度特性,比较在剪切过程中孔隙水压力以及切线模量等参数变化。试验结果表明：含冰/不含冰甲烷水合物沉积物以及饱和砂土的强度比值约为 2.6∶ 1.7∶1,且均表现为应变硬化;含冰水合物沉积物的结构性较其余两者相对更强,即初始切线模量值相对更高;但是在遭受剪切后结构均破坏,切线模量迅速降低。     

洪水冲刷和地震联合作用下砌体结构的失效风险评估∗

基于风险理论,提出一种用概率方法来考虑洪水和地震作用下砌体结构的失效风险评估方法。根据村镇建筑结构在灾害作用下的性能要求,量化结构的性能指标,分析村镇建筑结构在洪水冲刷和地震联合作用下的易损性,综合考虑洪水冲刷、地震的灾害危险性和结构易损性,通过易损性函数与联合灾害概率密度函数在灾害域上积分,获得结构在不同破坏状态下的超越概率,引入损失比和结构累计失效概率,进而获得全性能状态下的可靠度指标,并与目标可靠度指标对比,进行结构性能评估。算例展示了本文方法的建立过程,结果表明：冲刷对结构轻微破坏和中度破坏的易损性影响较小,对严重破坏和倒塌状态的易损性影响较大;洪水冲刷对结构抗震性能具有较大的影响,相比于仅考虑地震作用下,同时考虑洪水冲刷和地震联合作用时,结构各种破坏状态下的失效概率均有所增加,且随着破坏程度的加剧,冲刷对结构失效概率的影响越来越大。     

蛋白组学技术的发展及在水生态毒理学中的应用

作为后基因组时代重要的研究工具,蛋白组学技术的发展对水生态毒理学研究产生了巨大的促进作用。对近年来应用于水生态毒理学研究中的蛋白组学技术的发展历程和应用现状进行了全面的阐述。从蛋白提取、蛋白分离和鉴定、蛋白定量等方面对蛋白组学研究技术的发展进行了系统的介绍,重点介绍和比较了蛋白分离和鉴定技术中的基于胶的技术和非胶技术。在简介蛋白组学技术发展的基础上,以蛋白的识别和定量,特定功能蛋白的研究,蛋白相互作用研究这3个蛋白组学的研究方向为切入点,详细阐述了各类技术在水生态毒理学研究中的应用,如蛋白组学在阐明各种污染物对水生生物的毒性作用机制方面的应用,以及在水体污染状况的监测和评价方面的应用等。最后,指出了目前蛋白组学研究的不足,并有针对性地提出了水生态毒理学研究中蛋白组学的发展方向。     

证据权重法在天津市洪涝灾害风险区划中的应用研究

为了客观定量地分析天津市行政区域中的洪涝灾害风险,对天津市的洪涝灾害进行了风险区划研究.区划中考虑天津市的地理环境和灾害特征,选择降水量、地形高程、人均GDP、人口密度和耕地密度作为一级区划指标.在一级指标的基础上,又划分了15个二级指标,然后制作了GIS图层.风险区划过程首先根据已知洪涝灾害发生区在不同证据层中的分布,确定相应证据层的权重值,最后利用不同指标权重值的叠加来确定某一单元洪涝灾害风险的后验概率,并据此在GIS系统上进行洪涝灾害风险区划的可视化表达.天津市洪涝灾害风险区划结果表明,天津市区遭受洪涝灾害的风险最大,其次为武清区和蓟县.实践证明,基于GIS的证据权重方法是洪涝灾害风险区划的简捷、直观的制作方法.