集中式饮用水源地邻苯二甲酸酯类物质分布特征与健康风险评估

以某地区7个集中式饮用水源地为研究对象,采用固相萃取气相色谱-质谱法(SPE-GC-MS)对水体中16种邻苯二甲酸酯的分布特征和溯源进行了研究,并利用健康风险评估模型对水体PAEs进行了健康风险评价。结果表明:邻苯二甲酸二正丁酯、邻苯二甲酸二(2-乙基己基)酯、邻苯二甲酸二异丁酯和邻苯二甲酸二正辛酯在所有PAEs同系物中含量丰富,而所有样品均无邻苯二甲酸二(2-甲氧基)乙酯和邻苯二甲酸二戊酯的检出;二水厂和亨达水务断面Σ_(16)PAEs浓度最高,四水厂和五水厂断面Σ_(16)PAEs浓度最低;水体12种PAEs共提出3个主成分,揭示了91%的影响因子;层次聚类分析表明:7个采样断面聚为2类,分别代表了内河和长江水体。水体中PAEs的致癌风险值和非致癌风险值均远低于参考值,说明研究水体PAEs不会对居民构成致癌风险或其他明显的健康风险,但需加强该地区PAEs使用的规范与监管,强化末端处理,以规避风险。     

江苏某县地下水邻苯二甲酸酯类的检测与风险评价

在江苏某癌症高发区对地下水进行布点,采用固相萃取与气相色谱-质谱联用方法测定深层地下水和浅层地下水中邻苯二甲酸酯类(PAEs)的浓度。检测结果表明,地下水中PAEs污染程度较严重,邻苯二甲酸二丁酯(DBP)和邻苯二甲酸二(2-乙基己基)酯(DEHP)均有超标现象,其中,丰水期深层地下水和枯水期浅层地下水中DBP超标率达到100%,最大超标10.7倍。PAEs总质量浓度均值为10 034.56~14 872.91 ng/L,丰水期总浓度均值大于枯水期,浅层地下水的总浓度均值大于深层地下水。采用优化的USEPA风险评价模型,对PAEs进行人体健康风险评价,评价结果表明,该地区52.5% 地下水的PAEs总致癌风险超过10^-6的水质监控值,总非致癌风险在可接受范围内。     

重庆典型岩溶区地下河水体PAEs分布特征研究

分别采集了重庆典型岩溶区5条地下河水体10个样品,用气相色谱法对样品中的邻苯二甲酸酯类(PAEs)含量进行测定,分析其在重庆典型岩溶区地下河水体中的分布特征。结果表明,常见的19种邻苯二甲酸酯类(PAEs)在重庆典型岩溶区地下河水体中均有检出,其浓度范围为103.29~2268.78μg/L;5种被美国环保局列为优先控制的PAEs类污染物中,邻苯二甲酸二丁酯(DBP)和邻苯二甲酸双(2-乙基己基)酯是主要的污染物;与国内外其他地区水体进行比较,重庆典型岩溶区地下河水体PAEs含量处于较高污染水平。     

特定环境下大气颗粒物中酞酸酯的分析与研究

以南京城区大气环境为对照,通过检测特定环境(新配置塑胶跑道的体育场、新轿车内、塑料蔬菜大棚内)中大气悬浮颗粒物上酞酸酯(PAEs)的组成和分布,文中还就特定环境与城区大气环境PAEs污染的差异,评价了PAEs在特定环境中的环境污染行为,这些结果可为自然条件下PAEs迁移过程的研究提供重要依据.     

珠海市农业土壤和农产品PAEs污染健康风险评估

在珠海市3种不同类型农业区域内采集69个表层土壤和26个农产品样品，使用GC FID方法检测6种优先控制的邻苯二甲酸酯(PAEs)化合物，分析区域内PAEs的污染特征和对人体的健康风险。结果表明，农业土壤样品中Σ6PAEs值范围为未检出～1.21 mg/kg，平均值为0.40 mg/kg，空间分布上表现出中西部高、东部低的特征，土壤中PAEs以DnBP和DEP为主。农产品中∑6PAEs值范围为0.08 mg/kg～3.80 mg/kg，平均值为1.54 mg/kg，水稻、水果和蔬菜对PAEs的富集系数分别为6.23、2.50和2.80～7.06。成人和儿童PAEs的致癌风险分别为2.86×10-5和5.02×10-5，均高于人体致癌风险10-6水平。饮食摄入PAEs是致癌和非致癌风险的最大暴露途径，DEHP对人体两种风险的贡献最大。     

北京公园水体中邻苯二甲酸酯类物质的测定及其分布特征

为了正确评估北京市公园水体受PAEs污染的程度,采集了北京11个公园湖水的水样,采用固相萃取-气相色谱联用技术检测了其中六种邻苯二甲酸酯类物质(PAEs)的含量,该方法加标回收率在73%～89.3%,RSD为5.9%～18.1%,检出限在0.40～4.58mg/L。实验结果为北京公园水体中总PAEs浓度在6.4～138.1μg/L,平均值为27.9μg/L,证明北京公园水体受到不同程度的PAEs污染,主要的污染物为邻苯二甲酸二丁酯(DBP)和邻苯二甲酸双(2-乙基己基)酯(DEHP),其中东南部以及西北部的公园污染较严重。分析了PAEs在公园湖水底泥中和水体中的分布特征,结果显示,PAEs在湖水底泥中的含量明显大于在水体中的含量。     

高效液相色谱-三重四级杆/复合线性离子阱质谱法测定饮用水中22种邻苯二甲酸酯

建立了一种同时测定饮用水中22种邻苯二甲酸酯(PAEs)的高效液相色谱-三重四级杆/复合线性离子阱质谱方法:饮用水样品经针头过滤器过滤,选用Biphenyl液相色谱柱进行分离,以含0.1%甲酸的水溶液和含0.1%甲酸的甲醇溶液为流动相,电离模式为电喷雾正离子,选用多反应监测触发增强子离子扫描模式进行检测。结果表明,22种PAEs的灵敏度良好,定量限为0.001～0.1 μg/L。配制浓度为0.1～100.0 μg/L的混标溶液进行进样分析,分析结果显示,22种PAEs在该范围内的线性关系良好,相关系数均大于0.995,方法的平均回收率为82.9%～108.9%,相对标准偏差为0.9%～11.2%。同时,使用增强子离子扫描谱图进行搜库匹配,定性准确性高。该方法适用于饮用水中PAEs的检测。     

长江流域饮用水水源地及国控断面邻苯二甲酸酯赋存状况调查及生态风险评估

于2022年9月，对长江流域饮用水水源地及重要国控断面共37个点位的16种邻苯二甲酸酯（PAEs）残留开展了监测调查及生态风险评估。结果显示，长江流域饮用水水源地和国控断面中检出的PAEs质量浓度为0.499~6.018 μg/L，其中，邻苯二甲酸二异丁酯（DIBP）、邻苯二甲酸二正丁酯（DBP）和邻苯二甲酸二（2-乙基己基）酯（DEHP）为主要检出物质。不同地区的PAEs赋存水平呈现差异，其中湖南省饮用水水源地的PAEs检出浓度最高。此外，PAEs质量浓度与监测点位所在地区的人口数量和国内生产总值（GDP）存在一定的正相关性。生态风险评估结果表明，DBP处于低风险等级，DIBP对鱼类具有中、高风险影响；而DEHP生态风险水平最高，对藻类、甲壳类和鱼类均表现出中、高风险水平。已有文献研究比对分析发现，2009—2022年长江流域大部分地区的PAEs残留浓度随时间呈下降趋势，且饮用水水源地的PAEs质量浓度比地表水环境更低。研究结果可为长江流域饮用水水源地保护和地表水环境改善提供决策依据。     

湛江湾滩涂沉积物中邻苯二甲酸酯的污染监测和生态风险评估

邻苯二甲酸酯(PAEs)是一类环境激素,主要用作塑料增塑剂,是当前海洋环境最为关注的有机污染物之一。我国是世界上主要的塑料制品生产国和消费国,广泛开展了海洋PAEs污染研究,但湛江湾的研究较少。为了解湛江湾PAEs的污染状况和潜在生态风险,文章研究在湛江湾滩涂的15个典型区域采集沉积物样品,采用索氏提取-GC/MS测试了21种PAEs化合物的含量,以此获得PAEs的组成特征、空间分布,以及生态风险信息。结果表明,湛江湾滩涂沉积物中ΣPAEs的含量为(138.7～4056.4)ngμg~(-1),平均值为651.1 ngμg~(-1),DBP、DIBP、DEHP的检出率达100%,占总PAEs的95.5%,是主要的PAEs污染物;3种主要PAEs化合物的空间分布体现出人类活动影响特征,在中心城区、人群聚集的站位,PAEs的含量也较高,反之则低;通过环境风险限值的方法进行风险评估,结果显示除了S9站位外,其它站位均未达到生态风险限值。研究表明,虽然湛江湾近岸PAEs的含量较低,但部分区域仍然值得关注,相关部门应加强管理。     

土壤中邻苯二甲酸酯类检测空白研究

通过气质联机法检测六种主要的邻苯二甲酸酯类(PAEs),对土壤中PAEs检测过程中各个环节进行了空白影响的测定.实验表明,邻苯二甲酸二乙酯(DEP)、邻苯二甲酸二丁酯(DBP)、邻苯二甲酸二(2-乙基己基)酯(DEHP)三种物质对空白造成的影响较大.试剂污染是土壤中PAEs检测空白偏高的主要原因.而在不同的前处理方法中,水浴浓缩对空白的污染最小.     

Distribution of phthalate esters in topsoil: a case study in the Yellow River Delta, China

The Yellow River Delta (YRD) is a typical agricultural and petrochemical industrial area of China. To assess the current status of phthalate esters (PAEs) of soil residues, soil samples (0～20 cm) (n?=?82) were collected in Bincheng District, at the geographic center of the YRD. PAEs were detected in all topsoil samples analyzed, which indicated that PAEs are ubiquitous environmental contaminants. Concentrations of 11 PAEs are in the range of 0.794～19.504 μg g^?1, with an average value of 2.975 μg g^?1. It was presented that PAEs pollution in this area was weak and monotonously increasing along the rural–urban gradient. Higher concentrations were observed from roadsides (and/or gutters), densely anthropogenic activities areas (such as the urbanization and industrialization), and agriculture influence district, which mainly originated from construction waste, municipal sewage, agricultural waste and pesticide, discarded plastic effusion and atmospheric depositions. Concentrations of PAEs were weakly positivity correlated with soil organic carbon content and pH, which suggested both of them can affect the distribution of PAEs. The concentration of di (2-ethylhexyl) phthalate and di-n-butyl phthalate dominated in the 11 PAEs, with the average values of 0.735 and 1.915 μg g^?1, respectively, and accounted for 92.1 % of the whole PAEs’ concentrations. No significant differences of PAE congeneric profiles were observed between our work and others previously reported, which is consistent with the use of similar commercial PAEs around the world.     

Validation of two air quality models for Indian mining conditions

All major mining activity particularly opencast mining contributes to the problem of suspended particulate matter (SPM)directly or indirectly. Therefore, assessment and prediction are required to prevent and minimize the deterioration of SPM due tovarious opencast mining operations. Determination of emission rate of SPM for these activities and validation of air quality models are the first and foremost concern. In view of the above, the study was taken up for determination of emission rate for SPMto calculate emission rate of various opencast mining activitiesand validation of commonly used two air quality models for Indianmining conditions. To achieve the objectives, eight coal and three iron ore mining sites were selected to generate site specific emission data by considering type of mining, method of working, geographical location, accessibility and above all resource availability. The study covers various mining activitiesand locations including drilling, overburden loading and unloading, coal/mineral loading and unloading, coal handling orscreening plant, exposed overburden dump, stock yard, workshop, exposed pit surface, transport road and haul road. Validation of the study was carried out through Fugitive Dust Model (FDM) and Point, Area and Line sources model (PAL2) by assigning the measured emission rate for each mining activity, meteorologicaldata and other details of the respective mine as an input to the models. Both the models were run separately for the same set ofinput data for each mine to get the predicted SPM concentrationat three receptor locations for each mine. The receptor locationswere selected such a way that at the same places the actual filedmeasurement were carried out for SPM concentration. Statisticalanalysis was carried out to assess the performance of the modelsbased on a set measured and predicted SPM concentration data. The value of coefficient of correlation for PAL2 and FDM was calculated to be 0.990-0.994 and 0.966-0.997, respectively, which shows a fairly good agreement between measured and predicted values of SPM concentration. The average index of agreement values for PAL2 and FDM was found to be 0.665 and0.752, respectively, which represents that the prediction by PAL2 and FDM models are accurate by 66.5 and 75.2%, respectively. These indicate that FDM model is more suited for Indian mining conditions.     

Distribution of PAEs in the middle and lower reaches of the Yellow River, China

Samples of water, sediment and suspended particulates were collected from 13 sites in the middle and lower reaches of the Yellow River in China. Phthalic acid esters (PAEs) concentrations in different phases of each sample were determined by Gas Chromatogram GC-FID. The results are shown as follows: (1) In the Xiao Langdi–Dongming Bridge section, PAEs concentrations in water phase from the main river ranged from 3.99 × 10⁻³ to 45.45 × 10⁻³ mg/L, which were similar to those from other rivers in the world. The PAEs levels in the tributaries of the Yellow River were much higher than those of the main river. (2) In the studied branches, the concentration of PAEs in sediment for Luoyang Petrochemical Channel (331.70 mg/Kg) was the highest. The concentrations of PAEs in sediment phase of the main river were 30.52 to 85.16 mg/Kg, which were much higher than those from other rivers in the world. In the main river, the concentration level of PAEs on suspended solid phases reached 94.22 mg/Kg, and it reached 691.23 mg/Kg in the Yiluo River – one tributary of the Yellow River. (3) Whether in the sediment or on the suspended solid phases, there was no significant correlation between the contents of PAEs and TOC or particle size of the solid phase; and the calculated Koc of Di (2-Ethylhexyl) Phthalate (DEHP) in the river were much less than the theoretical value, which inferred that PAEs were not on the equilibrium between water and suspended solid phases/sediment. (4) Among the measured PAEs compounds, the proportions of DEHP and di-n-butyl phthalate (DBP) were much higher than the others. The concentrations of DEHP exceeded the Quality Standard in all the main river and tributary stations except those in the Mengjin and Jiaogong Bridge of the main river. This indicates that more attention should be paid to pollution control and further assessment in understanding risks associated with human health.     

江苏省空气质量预报准确度及改进方法分析

介绍了江苏省重污染天气监测预报预警系统以及大气重污染预警会商流程,将2015年13个地级市的模式预报、人工预报结果分别与实际观测值进行比较。结果表明:人工预报更准确,PM_(2.5)日均值、臭氧日最大8 h平均值、AQI 3个指标人工预报和实况的相关性分别比模式预报高出12.8%、0.3%、11.4%,平均标准误差(MNE)分别低20.7%、3.1%、23.1%。依据国家空气质量预报技术指南评分办法,对各市2015年全年空气质量级别为"良"时进行评分。通过开展07∶00预报更新,使2015年上半年空气质量预报级别得分平均提高了0.9分,全年级别得分平均提高了2.6分;通过改进模式预报参数,使PM_(2.5)日均预报值、臭氧日最大8 h平均预报值、AQI预报值和实际观测值的相关性比上年同期分别提高26.0%、5.0%、33.9%,MNE分别降低3.6%、31.3%、7.6%。     

基于集合经验模态分解和支持向量机的溶解氧预测

应用集合经验模态分解(EEMD)和支持向量机(SVM)相结合的方法,建立一种天然水体溶解氧浓度预测模型。首先,利用EEMD方法将溶解氧时序分解成不同频段的分量,以降低序列的非平稳性;然后,根据各序列分量的自身特征建立合适的SVM预测模型,此过程通过相关分析确定各分量输入量;最后,将各子分量预测值合成得到最终的预测结果。使用该模型对嘉陵江北温泉段的溶解氧浓度进行预测,结果表明,与传统单一的SVM和BP神经网络模型相比,该模型能有效提高预测精密度,具有良好的应用前景。     

基于BP神经网络的贵阳市空气质量指数预报模型

采用贵阳市2013年1月1日—2015年12月31日的空气质量指数(AQI)日均值,常规的地面和高空观测资料,基于不同季节,调整BP神经网络的隐藏层个数和隐藏层节点数,建立不同的BP神经网络预报模型,进行参数检验,最终选取预报效果最好的模型带入实况进行检验。结果表明,夏季的预报效果最好,采用的模型TS评分为81.6%,平均绝对误差为9.1,正确率为97.4%,用该模型检验预报效果,实况和预报的相关系数为0.71,平均误差为9;而冬季的预报效果明显低于其他季节,采用的模型TS评分为65.7%,平均绝对误差为19.5,正确率为72.9%,用该模型检验预报效果,实况和预报的相关系数为0.79,平均误差为19。而且BP神经网络模型的预报效果同隐藏层个数与隐藏层节点数没有显著关系。     

几种空气质量预报方法对冬季预报效果的评估与对比

基于2016年冬季泰州市环境空气质量自动监测数据,定量评估NAQPMS模式、CMAQ模式和人工订正对污染物质量浓度和空气质量等级的预报效果。结果表明,模式预报和人工订正对各污染物预报的相关系数由高到低排列为PM_(2.5)、PM_(10)、NO_2、SO_2、O_3-8h,颗粒物预报效果最好。除O_3-8h外,NAQPMS对各项污染物预报的相关系数R为0.47～0.82,CMAQ为0.75～0.81,人工订正为0.43～0.78,3种预报方式均能准确反映污染物浓度的变化趋势;模式预报、人工订正对O_3-8h预报相关系数均0.4。在发生颗粒物污染过程时,人工订正结果相对更为准确。NAQPMS、CMAQ和人工订正对空气质量等级24 h预报准确率分别为38.9%、41.1%和35.6%,NAQPMS对优类别的预判准确率较高,CMAQ、人工订正对良类别的预判准确率较高。对比不同时效的预报效果,24 h预报时效的准确率高于48和72 h。提出,城市空气质量预报可采用集合预报方式,综合1～2种运行较稳定的主流预报模式预报结果,预报员对模式模拟结果进行人工修订,提高预报准确率。