The effect of five forage species on transport and transformation of atrazine and isoxaflutole (balance) in lysimeter leachate

A field lysimeter study with bare ground and five different ground covers was established to evaluate the effect of forage grasses on the fate and transport of two herbicides in leachate. The herbicides were atrazine (ATR; 2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine) and isoxaflutole [IXF; 5-cyclopropyl-4-(2-methylsulfonyl-4-trifluormethyl-benzoyl)isoxazole], which has the commercial name Balance (Aventis Crop Science, Strasbourg, France). The ground covers included orchardgrass (Dactylis glomerata L.), smooth bromegrass (Bromus inermis Leyss.), tall fescue (Festuca arundinacea Schreb.), timothy (Phleum pratense L.), and switchgrass (Panicum virgatum L.). The results suggested that the total IXF (parent + metabolites) showed higher mobility than ATR and its metabolites. Differences in the timing of transport reflected the rapid degradation of IXF to the more soluble, stable, and biologically active diketonitrile (DKN) metabolite in the system. Although grass treatments did not promote the hydrolysis of DKN, they significantly reduced its transport in the leachate through enhanced evapotranspiration. Grass treatments significantly enhanced ATR degradation in the leachates and soils, especially through N dealkylation, but they did not reduce total ATR transported in the leachate. Leachate from the orchardgrass lysimeters contained the highest proportion of ATR metabolites (64.2%). Timothy and smooth bromegrass treatments also displayed a significant increase in ATR metabolites in leachate. Grass-treated lysimeters showed higher microbial biomass carbon than bare ground. For ATR treatments, the proportion of metabolites in the leachate strongly correlated with the elevated soil microbial biomass carbon in forage treatments. In contrast, DKN degradation was poorly correlated with soil microbial biomass carbon, suggesting that DKN degradation is an abiotic process.     

Retention equations of nonionic organic chemicals in soil column chromatography with methanol-water eluents

Research efforts dealing with chemical transportation in soils are needed to prevent damage to ground water. Methanol-containing solvents can increase the translocation of nonionic organic chemicals (NOCs). In this study, a general log-linear retention equation, log k' = log k'w - Sphi (Eq. [1]), was developed to describe the mobilities of NOCs in soil column chromatography (SCC). The term phi denotes the volume fraction of methanol in eluent, k' is the capacity factor of a solute at a certain phi value, and log k'w and -S are the intercept and slope of the log k' vs. phi plot. Two reference soils (GSE 17204 and GSE 17205) were used as packing materials, and were eluted by isocratic methanol-water mixtures. A model of linear solvation energy relationships (LSER) was applied to analyze the k' from molecular interactions. The most important factor determining the transportation was found to be the solute hydrophobic partition in soils, and the second-most important factor was the solute hydrogen-bond basicity (hydrogen-bond accepting ability), while the less important factor was the solute dipolarity-polarizability. The solute hydrogen-bond acidity (hydrogen-bond donating ability) was statistically unimportant and deletable. From the LSER model, one could also obtain Eq. [1]. The experimental k' data of 121 NOCs can be accurately explained by Eq. [1]. The equation is promising to estimate the solute mobility in pure water by extrapolating from lower-capacity factors obtained in methanol-water mixed eluents.     

人工神经网络方法在资源与环境预测方面的应用

用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验。结果表明：人工神经网络方法在模拟和预测方面 优于传统的统计回归模型,在资源与环境方面的应用是可行的。具有较强的模拟预测能力。与传统的回归模型相比,人工神经网络方法不要求监测数据具有很强的规律性,就可用后的网络模型对其进行预报,燕且预测相对误差均比回归模型预测相对误差要小,具有一定的实用性。两个实例的应用     

基于环境保护大数据的监测与智能诊断研究

为了提高环境保护的智能监测能力,采用大数据分析方法进行环境监测和诊断分析,提出一种基于环保大数据关联特征挖掘的环境保护智能监测与诊断技术。构建环境监测的约束参量模型,以土壤监测数据、空间污染排放量以及水资源监测数据为原始统计数据,采用关联规则特征提取方法进行环保大数据的有用信息挖掘,结合模糊C均值聚类算法实现对环保大数据的分类处理,根据分类结果进行环保监测和智能诊断分析。仿真结果表明,采用该方法进行环保大数据挖掘的准确性较好,对各种污染信息的分类精度较高,提高了环保监测和诊断的有效性与实时性。     

快速高效去除微囊藻的GO/QPEI复合纳米材料

本研究合成了一种新型高效的去除铜绿微囊藻(Microcystis aeruginosa)的氧化石墨烯/季铵盐聚乙烯亚胺(GO/QPEI)纳米复合材料.GO/QPEI在pH为4~10的条件下都具有高效去除M.aeruginosa的能力,其去除能力在2 min内可达96%以上.GO/QPEI对微囊藻的吸附更符合Freundlich方程,最大吸附量为5.58×1011cells·mg-1.吸附动力学表明GO/QPEI的假二级吸附反应.GO纳米片和QPEI的协同效应是其高效去除微囊藻的主要机制.     

Conversion of waste plastics into value-added carbon materials

Plastic pollution is a major environmental issue worldwide, calling for advanced methods to recycle waste plastics in the context of the circular economy. Here we review methods and strategies to convert waste plastics into value-added carbon materials, with focus on sources, properties, pretreatment of waste plastics, and on preparation of carbon materials. Pretreatment techniques include mechanical crushing, plastic stabilization and electrospinning. Carbon materials such as carbon nanotubes, graphene, carbon nanosheets, carbon spheres and porous carbon are prepared by oxygen-limited carbonization, catalytic carbonization, the template-based method, and pressure carbonization. We emphasize the conversion of polyethene terephthalate, polyethylene, polypropylene, polystyrene, halogenated plastics, polyurethane and mixed plastics.

     

可靠性强化试验技术在某型伺服作动器研制中的适用性探索

目的探索可靠性强化试验技术在典型机电液一体化产品伺服作动器研制过程中的适用性。方法以某型伺服作动器为研究对象,从故障激发的角度对可靠性强化试验技术的应用进行可行性分析,在响应调查和应力分析的基础上,结合产品的工作特点设计适用于该类伺服作动器的可靠性强化试验方案,包含低温步进应力试验、高温步进应力试验、快速温度循环试验10个循环、振动步进应力试验(包含气锤式三轴向六自由度超高斯随机振动方式及电磁振动台随机振动方式)及综合环境应力试验5个循环,并依此进行试验。结果在快速温度循环试验及综合环境应力试验过程中,均有效地激发出了产品的漏油故障,与相似产品外场暴露的漏油故障模式相吻合。结论可靠性强化试验技术可有效地应用于典型机电液一体化产品伺服作动器的研制过程,设计的可靠性强化试验方案可有效地激发外场出现频率较高的故障,可作为该类产品研制的试验手段之一。     

船舶大气污染排放的研究进展

船舶是大气污染的重要来源,其排放的NOx占化石燃料源的15%左右,SO2占人为源的4%~9%。国内外从3个方向开展对船舶大气污染的研究:(1)对大气气候影响;(2)源清单计算;(3)排放因子测量。船舶排放的多种污染物可改变空气质量;对大气辐射收支产生直接和间接影响;对海洋大气的成云效应产生影响。船舶源清单主要有2种计算方法:基于燃料消耗的方法,适用于长时间大尺度计算;基于船舶活动情况的方法,适用于高空间分辨率计算。排放因子测量有台架试验和空中烟羽测量2种方法:前者测量结果准确,条件设定自由;后者可行性高,并能提供颗粒物增长转化信息。国内研究尚处于起步阶段,需要建立完整的船舶排放清单,开展排放因子的测试和修正工作。     

基于小波变换的山西省PM2.5污染特征及影响因素

基于山西省11城市2015～2019年PM_2.5日均浓度、社会影响因素数据和气象数据,利用小波变换确定PM_2.5浓度周期,通过Spearman相关性和小波相干谱分别探究PM_2.5与社会影响因素和气象因素的关联,确定PM_2.5长短周期管控的主要影响因子.结果表明,2015～2017年山西省PM_2.5浓度年均值呈上升趋势,年均上升率为4.3%, 2018～2019年呈下降趋势,年均下降率为4.2%;ρ(PM_2.5)月均值呈“U”型分布,1月最高(95μg·m^-3), 8月最低(34μg·m^-3),冬季均值约为夏季的2倍;临汾等南部城市ρ(PM_2.5)均值为62μg·m^-3,大同等北部城市均值为45μg·m^-3,空间上呈南高北低.11城市PM_2.5浓度存在显著周期性变化,主要周期包括293 d左右的长周期和27 d左右的短周期.其中...     

RBCA、CLEA及CalTOX模型在苯并[a]芘污染场地健康风险评估中的应用比较

为探讨不同模型对污染场地健康风险评估结果的影响,以苯并[a]芘为例,采用RBCA、CLEA和CalTOX模型对某工业污染场地表层土壤进行健康风险评估,分析了评估结果的差异和原因,同时对模型的主要暴露参数进行了敏感性分析,并推导出基于风险概率分布的土壤修复限值。结果表明,RBCA、CLEA和CalTOX模型计算的苯并[a]芘致癌总风险分别为2.40×10-4、6.32×10-4和7.04×10-6,且经口摄入和皮肤接触2个途径对人体健康造成的危害最大。降解作用是影响CalTOX模型风险评估结果不同于RBCA和CLEA模型的重要因素,3个模型间参数取值及方法学的差异也会导致风险评估结果不同。各模型暴露参数的敏感性排序也有差异。采用基于风险概率分布的方法推导土壤修复限值,RBCA、CLEA和CalTOX模型所得结果分别为0.18、0.08、0.13(不考虑降解作用CalTOX模型)和10.74(考虑降解作用CalTOX模型)mg·kg-1,为各模型直接推导值的1.5～2.6倍。基于风险概率分布的方法可有效降低风险评估过程中参数不确定性的影响,为工业污染场地土壤修复值的制定提供参考。