全二维气相色谱-电子捕获检测器测定复杂基质土壤中24种有机氯和拟除虫菊酯类农药

目前针对环境样品中有机氯和拟除虫菊酯类农药的检测方法主要有气相色谱法和气相色谱-质谱法（GC-MS），由于气相色谱法采用电子捕获检测器从而具有较高的灵敏度，在污染物的环境行为研究中得到广泛应用，GC-MS法的定性效果好，但检测灵敏度相对偏低。本文研究了2018年土壤详查江苏地区样品中有机氯农药和拟除虫菊酯的残留状况，结果显示，即使经过多次净化仍有20%的样品存在假阳性或基质干扰现象。为了提高定性准确度，同时保证检测灵敏度，实验建立了复杂基质土壤中20种有机氯农药和4种拟除虫菊酯的全二维气相色谱-电子捕获检测器检测方法，最终选择中等极性的TG-35MS为第一色谱柱，非极性的（DB-1）为第二色谱柱，将鲜样与无水硫酸钠混合均匀后，以正己烷-丙酮（1：1，V/V）为提取剂进行索氏提取，采用全二维气相色谱-电子捕获检测器进行检测，外标法定量。各物质的质量浓度均在1.0~500μg/L内与其峰面积呈线性关系，相关系数均大于0.995，检出限为0.02~0.17μg/kg。用标准加入法进行回收实验，测得回收率为80.7%~103.5%，测定值的相对标准偏差（n=6）为1.84%~10.12%。本方法将高灵敏度检测器与全二维色谱相结合，在保证检测灵敏度的同时增加了峰容量，有效去除了基质干扰。相比2018年土壤详查参考方法HJ 835—2017的检出限0.02~0.09mg/kg（全扫模式），本方法检出限显著降低，且操作简单，可为复杂基质样品中的痕量超痕量检测提供参考。     

白洋淀表层沉积物中有机氯农药和全多氯联苯的分布特征及风险评估

为了解白洋淀表层沉积物中有机氯农药(OCPs)和多氯联苯(PCBs)的污染情况,采用改进的GC-μECD方法对白洋淀11处沉积物进行了20种OCPs和全部209种PCB单体的定量检测和分析.结果显示:白洋淀11个沉积物样品共检出10种OCPs和24种PCBs,∑OCPs和∑PCBs的含量范围分别为1.22～52.45 ng/g(DW)和nd～37.61 ng/g,在国内处于中等水平; OCPs组成中以HCHs和Dieldrin(狄氏剂)为主,分别占到∑OCPs的39.9%和31.5%,其中7个采样点的HCHs以林丹输入为主,4个采样点以工业六六六污染为主.DDTs检出率较低,来源主要为历史残留;检出的PCB单体以低氯联苯为主,其中一氯、二氯和三氯联苯占∑PCBs的64.73%;采用沉积物质量标准法进行生态风险评估,结果表明白洋淀地区沉积物中p,p'-DDD和∑PCBs生态风险较低,Dieldrin生态风险尚需关注,γ-HCH生态风险较高,不容忽视.     

环境水样中农药污染分析技术研究进展

随着农业集约化和城市化的推进,世界上大量水环境中农药残留量已超过规定的限值,水环境中农药污染问题受到社会各界的广泛关注。作为世界上最大的农药生产国和使用国,中国水环境中农药残留量远高于其他发达国家,已有研究表明在我国七个典型流域(长江、太湖、黄河、松花江、黑龙江、大运河和东江)中检测到19种农药,平均浓度范围为0.02～332.75ng/L。农药及其转化产物对生态环境和人体健康具有潜在威胁,水环境中农药残留的研究是水质评估中必不可少的组成部分,而靶向筛查难以检测未知农药及其转化产物。因此,环境中农药残留及其转化产物的非靶向筛查亟需完善。本文依据农药组分非靶向筛查的分析流程,对近五年水质样品中农药残留靶向及非靶向筛查方法进行综述,梳理了近年来国内外食品与水环境中农药残留限量的相关法律法规,对水环境中农药残留分析方法的研究进展进行概述;总结了液液萃取(LLE)、固相萃取(SPE)、固相微萃取(SPME)等样品前处理方法的特点,在这些方法中,固相萃取是农药非靶向筛查的主要前处理方法,具有良好应用前景。本文还探讨了分析仪器从色谱检测到色谱-质谱联用的发展趋势,多种高分辨率质谱的产生为农药非靶向筛查提供了多层次的分析需求;同时通过总结近年来农药筛查确证相关的指导标准、质谱数据库与多种鉴定方法,指出水环境中农药污染分析技术的发展趋势。     

Numerical simulation of agricultural sediment and pesticide runoff: RZWQM and PRZM comparison

Agricultural sediment and pesticide runoff is a widespread ecological and human health concern. Numerical simulation models, such as Root Zone Water Quality Model (RZWQM) and Pesticide Root Zone Model (PRZM), have been increasingly used to quantify off‐site agricultural pollutant movement. However, RZWQM has been criticized for its inability to simulate sedimentation processes. The recent incorporation of the sedimentation module of Groundwater Loading Effects of Agricultural Management Systems has enabled RZWQM to simulate sediment and sediment‐associated pesticides. This study compares the sediment and pesticide transport simulation performance of the newly released RZWQM and PRZM using runoff data from 2 alfalfa fields in Davis, California. A composite metric (based on coefficient of determination, Nash–Sutcliffe efficiency, index of agreement, and percent bias) was developed and employed to ensure robust, comprehensive assessment of model performance. Results showed that surface water runoff was predicted reasonably well (absolute percent bias <31%) by RZWQM and PRZM after adjusting important hydrologic parameters. Even after calibration, underestimation bias (?89% ≤ PBIAS  ≤ ?36%) for sediment yield was observed in both models. This might be attributed to PRZM's incorrect distribution of input water and uncertainty in RZWQM's runoff erosivity coefficient. Moreover, the underestimation of sediment might be less if the origin of measured sediment was considered. Chlorpyrifos losses were simulated with reasonable accuracy especially for Field A (absolute PBIAS  ≤ 22%), whereas diuron losses were underestimated to a great extent (?98% ≤ PBIAS  ≤ ?65%) in both models. This could be attributed to the underprediction of herbicide concentration in the top soil due to the limitations of the instantaneous equilibrium sorption model as well as the high runoff potential of herbicide formulated as water‐dispersible granules. RZWQM and PRZM partitioned pesticides into the water and sediment phases similarly. According to model predictions, the majority of pesticide loads were carried via the water phase. On the basis of this study, both RZWQM and PRZM performed well in predicting runoff that carried highly adsorptive pesticides on an event basis, although the more physically based RZWQM is recommended when field‐measured soil hydraulic properties are available.     

长江入海口浅层沉积物中典型有机氯农药分布特征

对长江入海口包括江苏启东,上海崇明岛、长兴岛、横沙岛等地区的浅层沉积物中典型有机氯农药(OCPs)的分布情况进行了研究。采用气相色谱-电子捕获检测器进行检测,方法检出限为0.10 ng/g,回收率为62.4%～116.7%,精密度(RSD)为2.7%～8.3%。调查分析结果显示,长江入海口沿岸均存在轻度和中度的OCPs污染,主要检出物总滴滴涕(DDTs)浓度范围为1.22～626.43 ng/g。深度(0～80 cm)采样检测结果表明,研究区表层及深度样品中DDTs均有不同程度检出,浅层沉积物中DDTs在0～20 cm区域占检出总量的58.1%;其次是20～40 cm区域,DDTs占总量的30.8%;40～60 cm区域DDTs占总量的8.2%;60～80 cm区域DDTs占总量的2.9%,即浅层沉积物中DDTs主要集中在0～40 cm区域,部分点位40～80 cm能够检出少量DDTs。本研究提供的数据为该地区OCPs垂直分布提供参考。     

Analyses of the Compositional Characteristics and Sources of the Organochlorine Pesticides in the Surface Sediments of Guanghai Bay,Taishan City,Guangdong Province

Organochlorine pesticides (OCPs) are a class of toxic and harmful persistent organic pollutants widely found in environment. Hexachlorocyclohexane (HCHs) and DDTs are two of the most widely used OCPs (Qiu et al., 2004), so it is important to find out their compositional characteristics and sources in surface sediments. Guanghai Bay is located in the south of Guanghai Bay Industrial Park in Taishan City, Guangdong Province. It borders Huangmaohai to the east and Zhenhai Bay to the west, covering an area of about 236 square kilometers. In this study, surface sediments were collected at 16 sites in Guanghai Bay (Fig. 1). After the surface sediments were pretreated, GC‐MS was used to analyze OCPs. A total of 14 OCPs were detected and their content was shown in Table 1. The concentration of ΣOCPs ranged between 0.507～0.860ng·g‐1, with an average of 0.680 ng?g‐1. In general, the content of ΣOCPs was the highest at No. A12 site. The concentrations of these 14 detected OCPs ordered as dieldrin > epoxy heptachlor > P, P'‐DDD > γ‐HCH >endrin >aldrin > P, P'‐DDE >mirex >hexachlorobenzene >P, P'‐DDT > α‐HCH > δ‐HCH >β‐HCH > heptachlor.     

太湖、洞庭湖野生青虾肌肉中有机氯农药的气相色谱-质谱法测定

使用气相色谱质谱技术测定了青虾肌肉组织中的8种有机氯农药.包括六氯苯、氯丹、滴滴涕(ΣDDTs)、艾氏剂、狄氏剂、异狄氏剂、七氯和灭蚁灵,对太湖和洞庭湖15个点位29组野士青虾样品的测定结果显示,青虾肌肉组织内残留的有机氯农药主要是六氯苯和滴滴涕,六氯苯含量为nd-13.2μg/kg(湿重),滴滴涕含量为0.790-5.82μg/kg(湿重),洞庭湖青虾肌肉中有机氯农药的含量略高于太湖青虾,两湖青虾肌肉残留有机氯农药在枯水期时的含量多数大于丰水期时的含量.     

北京通州某改造区土壤中DDTs和HCHs的地球化学特征及风险评价

选取北京通州环球影城附近某改造区为研究区域,共采集15件表层土壤样品,主要分析了该地区土壤中DDTs和HCHs的空间分布地球化学特征;采集垂向样品,并分析了DDTs和HCHs在土壤垂向剖面中的分布情况。表层土壤样品中大部分的DDTs和HCHs有检出,ΣDDTs的残留量为494~19615 μg·kg-1,ΣHCHs为082~1021 μg·kg-1。土壤垂向剖面分析结果表明,DDTs部分有检出,两剖面中残留量整体随深度变化不明显,但局部出现突变现象;HCHs全部检出,两剖面中残留量整体随着深度增加不断减少。参照国家相关标准对该区土壤环境整体进行评价,初步分析通州区环球影城地区土壤中残留DDTs除个别点符合二级标准外,其余都处于一级标准,残留风险较低;土壤中残留HCHs基本符合一级标准。结合不同异构体之间含量特征及分布,认为该地区局部近期可能存在HCHs输入现象。最后,对该改造区进行了健康风险评价,结果表明该区DDTs和HCHs在致癌风险和部分非致癌风险方面影响较小。     

广州地区蔬菜生产基地有机污染物含量状况及分布

为了解广州地区蔬菜基地有机污染物的分布情况,选取了6个代表性基地进行调查采样,测试分析了表层土壤、蔬菜及灌溉水中15种邻苯二甲酸酯(PAEs)化合物、16种多环芳烃(PAHs)化合物和19种有机氯农药(OCPs)化合物的质量分数及质量浓度.结果表明:在15种PAEs化合物的总质量分数(w(ΣPAEs))中,土壤样品为158.0~4 321.0 ng/g,蔬菜样品为1 134.0~48 576.0 ng/g;水样中15种PAEs化合物的总质量浓度(ρ(ΣPAEs))为632.0~14 271.0 ng/L;就单个化合物而言,以DiBP、DBP、DEHP三种污染物为主,其三项之和占所有PAEs质量分数的97.5%~99.1%;在16种PAHs化合物的总质量分数(w(ΣPAHs))中,土壤样品为28.48~1 121.96 ng/g,蔬菜样品为238.09~1 000.61 ng/g;水样中16种PAHs化合物的总质量浓度(ρ(ΣPAHs))为338.21~1 239.78 ng/L;在19种OCPs化合物的总质量分数(w(ΣOCPs))中,土壤为0.04~71.28 ng/g,蔬菜为1.08~9.18 ng/g.总体上看,各基地土壤中DBP质量分数均超标,多环芳烃类在灵山、花东、黄埔菜地土壤中存在轻微污染,有机氯农药均未出现污染.     

表层岩溶带土壤中有机氯农药的分布及来源特征研究——以重庆南川区为例

为了研究有机氯农药(OCPs)在表层岩溶带土壤中的分布趋势、组成特征和来源,采用气相色谱-微池电子捕获检测器(GC-μECD)分析了重庆市南川区水房泉、后沟泉、柏树湾泉、兰花沟泉等典型表层带岩溶泉上覆土层中有机氯农药的浓度。结果显示,总体上表层岩溶带土壤中的OCPs的浓度范围是7.13~323.37ng/g,其中后沟泉、柏树湾泉、兰花沟泉表层土壤中的17种OCPs检出率为100%,水房泉土壤中除p,p'-DDD外其余全部检出,但不同种类有机氯含量差异较大。其中HCHs、DDTs、CHLs、灭蚁灵是主要检出物。研究区内土壤样品中的HCHs来源于工业品HCHs和林丹使用的残留,且由于环境影响,土壤中HCH的同系物组成发生了明显变化。水房泉和柏树湾泉土壤中的DDTs来自于工业DDTs和三氯杀螨醇的混合源,而后沟泉和兰花沟泉土壤中的DDTs可能来自于工业DDTs的使用,而非三氯杀螨醇类型的DDT。对比中国和荷兰的土壤质量标准,柏树湾泉土壤中DDTs浓度接近于荷兰无污染土壤的参考值,兰花沟泉土壤中的DDTs应属于轻度污染,后沟泉土壤中的DDTs和氯丹类化合物污染程度较重,而水房泉土壤为无污染土壤。