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.     

基于区域环境空气质量和碳排放数据的减污降碳协同效果评估

化石燃料燃烧产生的温室气体与大气污染物具有同根同源性,但具体治理中减污降碳的协同效果尚不明确。以浙江省11个设区市为研究样本,对环境空气质量和二氧化碳(CO₂)排放数据进行分析研究,结果显示：2016—2020年浙江省环境空气质量持续改善,但CO₂排放总量仍处于增长阶段。11个设区城市PM_2.5年均浓度降幅在26%～41%之间,二氧化氮(NO₂)年均浓度下降趋势不明显,大部分城市呈现碳排放增加、NO₂浓度下降的特征,只有杭州和温州两市呈现碳排放总量和NO₂、PM_2.5浓度协同下降的趋势。因子相关性分析结果表明,各设区市呈现NO₂浓度与碳排放相关性较大、协同性强,PM_2.5浓度与碳排放相关性较小的特点。进一步通过减污降碳协同定量评价分析表明,浙江地区在环境空气质量改善和温室气体减排已表现出一定成效,但各设区市因产业结构、环境基础条件、协同程度等不同导致减污降碳综合绩效有明显差异。从源头减排实现...     

长三角地区二次电池生产场地土壤重金属污染评价

采用X射线荧光光谱法测定土壤重金属Cd,Cu,Ni,Mn,Co的含量,并选用地积累指数法和潜在生态危害指数法对其进行环境质量评价。结果表明,Cd是各场地中产生污染最为严重的一种金属,几乎各个车间都存在不同程度的Cd污染,外围农田土壤严重的Cd含量超标也说明了这个问题。     

碳基层状双金属氢氧化物对含氮污染物的去除研究进展

水中含氮污染物种类繁杂,危害巨大,是亟待解决的环境问题之一.层状双金属氢氧化物(LDH)与多孔碳的复合材料(LDH/C)具备记忆效应、结构可调性、阴离子可交换性等优势,在去除水体中含氮污染物方面极具潜力.根据2015年以来LDH/C研究成果,系统综述了LDH/C的合成方法;详细介绍了LDH/C去除含氮污染物的吸附性能;...     

不规则波情况下破波带内污染物水平二维输移扩散实验研究

研究破波带内污染物输移扩散特性可为近岸水环境保护和污染物的排放设计提供依据。由于实际情况中海岸入射波都是不规则波,因此本文基于物理模型实验、图像处理和形心点追踪等方法对5组不规则入射波况下的污染物水平二维输移扩散特性进行了研究,着重对不同波况下污染物垂直岸线方向与沿岸方向的输移速度及垂直岸线方向的扩散系数进行了计算分析。结果表明,不规则入射波情况下污染物垂直岸线方向的输移速度V_x约为沿岸方向输移速度V_y的4.4%～64.7%;沿岸方向的污染物输移速度约为时均沿岸流速最大值的35%;在不规则波入射情况下,垂直岸线方向的扩散系数为2.3×10?4 ～2.2×10?3 m2/s,并且扩散系数随不规则入射波周期的增大而减小,而入射波波高对其影响并不显著。     

底栖穴居动物对潮滩N迁移转化的影响

以软体动物河蚬(Corbiculafluminea)为例,运用实验模拟的方法,通过对上覆水、孔隙水和沉积物的对比分析,研究了底栖动物对潮滩N迁移转化的影响。实验结果发现,河蚬主要通过排泄和生物扰动影响N在沉积物-水界面间的迁移转化。在富氧的环境下,底栖动物的活动结果使NO3 N在上覆水中有明显的持续累积效应,NH4 N在较深层孔隙水中积累,而NO2 N易在沉积物中累积。     

地铁沿线老旧房屋结构安全预警模型

为研究城市地铁沿线老旧房屋普遍存在结构安全问题,基于机器学习模型,选取房屋年份、楼层、面积等11个属性构建预警指标体系,采用SMOTE过采样、独热编码等方法解决样本离散化、不均衡的问题;利用KNN、Bayes、Logistic、SVM 4种机器学习模型对房屋结构安全数据学习并测试,综合应用Accuracy、F1、AP、...