China-PEARL和PRZM-GW模型潍坊市场景农药地下水风险评估研究

农药地下水暴露模型China-PEARL已开始在我国农药地下水风险评估中应用。本文利用China-PEARL潍坊市场景数据,为PRZM-GW构建了潍坊市场景。在潍坊市场景下,利用2个模型计算了56种农药在5种作物上共计145种施用方式下的预测环境浓度（PEC）值,利用商值法（RQ）进行风险评估。结果显示有8种农药共13种施用方式在潍坊市场景下存在不可接受的地下水风险。其中,2个模型均显示有不可接受风险的农药是多菌灵和氟磺胺草醚。2个模型PEC值比较结果显示,PRZM-GW的农药风险评估趋势与China-PEARL一致性高,从而验证了China-PEARL的可信性。模型PEC值影响因素分析显示,土壤有机碳分配系数（Koc）对2个模型输出影响最大,可将Koc〉400L·kg^-1作为判断某种农药预测浓度〈0.1μg·L^-1经验性指标。2个模型的PEC值和土壤好氧半衰期的对数呈线性关系,当土壤好氧半衰期〉10d时,模型的PEC值随土壤好氧半衰期的增大而迅速增高。水解半衰期为PRZM-GW模型输入项,决定了PRZM-GW模型模拟的农药浓度随年变化趋势。水中溶解度是China-PEARL的输入项,但对模型PEC值影响很小。     

污染土壤的生态风险评估标准、方法和模型

本文介绍了生态风险评估的定义,国外在污染土壤生态风险评估领域的相关土壤标准、标准设置情况及其内容,生态风险评估的方法和步骤,土壤-有机污染物变化及迁移暴露模型、农药根区模型、土壤模型、土壤迁移及变化数据库和模型管理系统、农药径流对地表水的污染模型、多介质污染物变化、迁移和暴露模型等计算机模型及其理论基础。尤其是美国环保署在这一领域的系列工作及其思路,为国内在这方面的工作和研究提供一些参考。     

某化工企业搬迁原场地土壤和地下水环境调查研究

基于对某化工企业搬迁原场地是否可变更为居住用地的关注,对其进行土壤和地下水环境调查。通过初步识别确定场地潜在污染物为有机污染物和重金属。在场地中布设土壤采样点9个,采集土壤样品54个,地下水采样点6个,采集地下水样品6个。检测结果显示,地下水中检测出的有机物和重金属离子浓度均远小于标准;土壤中镍的最大检出浓度为55mg/kg,超出健康风险评价筛选标准,需启动健康风险评估工作。     

浙江台州典型电子废弃物无序拆解区土壤多环芳烃污染风险评估

电子废弃物的无序拆解会导致其中多环芳烃(polycyclic aromatic hydrocarbons, PAHs)的无规律释放,进而对周边生态环境和人体健康构成威胁。为了量化电子废弃物无序拆解区土壤中PAHs的含量及其对人体健康的风险,本研究从浙江省台州市路桥区某典型电子废弃物无序拆解区采集了3个作坊式电子废弃物拆解点、3个作坊式塑料或金属加工点和1个电子废弃物拆解尾渣倾倒点的土壤样品,以气相色谱-质谱联用法和美国环保署规定的暴露量化及风险表征方法为分析手段开展PAHs含量分析及其风险评估。结果发现:上述7个土壤样品PAHs的总量范围为32.62～1053.71μg/kg,平均为414.05μg/kg,其中一个作坊式电子废弃物拆解点的PAHs含量属于重污染级别,土壤存在强烈的环境风险,单体菲(Phe)、蒽(Ant)、荧蒽(Fla)和芘(Pyr)对环境风险的贡献率最大。此外,PAHs通过不同途径对人体造成的健康风险程度为呼吸途径﹥皮肤接触﹥经口途径。结果表明,电子废弃物无序拆解区土壤PAHs污染急需建立相关环境管理措施及污染控制标准,逐步规范电子废弃物无序拆解活动。     

EHC原位生物化学修复地下水技术

<正>由北京宜为凯姆环境技术有限公司开发的EHC原位生物化学修复地下水技术,适用于饱和层地下水和土壤的原位修复,可处理的有机污染物包括含氯溶剂、氯苯、有机炸药、大多数农药、卤代烷和硝酸盐等。主要技术内容一、基本原理EHC药剂由缓释碳源、强还原性矿物质和营养物质组成,协同化学还原和厌氧生物修复机理降解地下水中     

土壤中污染物迁移模型在油田环境影响评价中的应用

在石油的生产、运输、贮存以及炼制等过程中都存在泄油、漏油风险,各生产过程中的废弃物,如油类、重金属等各种化学物质也会危害水土环境,进而危及当地地下水源。文章分析了油田企业污染物对土壤及地下水的主要污染途径,提出了将污染物在土壤中的迁移模型用于预测污染物浓度的方法。建立了污染物由土壤迁至室内空气、地下水、农作物及由地下水转移到地表水的迁移数学模型,该模型可以运用于油田环境影响评价。     

土壤气中挥发性有机物被动采样技术研究进展

土壤气样品的采集是污染场地调查中挥发性有机物（VOCs）检测和场地风险评估的关键，常用的土壤气采集分为主动式、被动式采样。文章主要介绍了被动式采样的基本原理和被动采样器吸附速率计算公式、采样器结构和常用的吸附剂类型，总结了土壤气中VOCs被动采样技术在国内外的应用研究进展及现场应用中存在的问题，对被动采样技术未来研究方向进行了展望。     

有机农药对土壤的污染及生物修复技术研究

农药的大量使用污染了大气、水体及生态系统。有机农药以直接施用、拌种、喷撒、随降水落入等方式进入土壤。农药在土壤中会以吸附、扩散稀释和降解等几种方式发生转化，并改变土壤结构、对土壤中生物的生存及酶的活性产生影响。生物修复技术可以通过动植物、微生物及根际环境对农药污染的降解来治理土壤中的农药，是治理农药污染的一种推荐方法。     

长输油气管道环境敏感区风险评估指标体系构建

在肯特法指标体系的基础上，充分结合长输油气管道企业的实际情况，修正肯特法各评分项，调整各项的分值与权重，整合管道风险及敏感区属性，构建了长输油气管道环境敏感区风险评估指标体系，建立 了符合长输油气管道特点的风险评估方法，从安全距离、人口数量、地下水敏感程度、土壤类型4个方面综合分 析评估。利用该指标体系对企业所属敏感区进行分级、分类，依据管段得分划分出相应的风险等级，并以输油 管道穿越人口密集区为例详细进行打分说明。该指标体系构建有助于企业对敏感区实施有效管理和重点监 控，减少管道环境违规事件发生。     

专家带您看展会 绿色可持续环境修复将大有可为

正正通过十年的发展,污染土壤和地下水修复产业已快速成长,涉及城市污染场地修复、矿山土壤污染修复及农田土壤污染修复和管控等领域,形成了土壤污染调查、分析检测、修复、管控、工程监理和效果评估等咨询、检测、工程及药剂和装备生产的产业链,出现了一批龙头企业。为打赢"净土保卫战",污染土壤和地下水修复这个新兴环保行业,已成为我国环保产业的重要组成部分。近年来生态环境部正抓紧建立土壤和地下水环境修复的技术标准,2019年1月1日起开始实施的《中华人民共和国土壤污染防治法》,为土壤和地下水环境修复的规范发展提供了法律和技术保障,可以预计土壤和地下水修复产业发展势头良好,是环保产业新的增长点,促进了土地     

Comparison of models of simazine transport and fate in the subsurface environment in a citrus farm

Contamination of groundwater by agrochemicals is now widely recognized as an extremely important environmental problem. Modern agricultural practices involve the combined use of irrigation with the application of large amounts of agrochemicals to maximize crop yield. Due to flood irrigation and natural runoff, agricultural activities might generate soil, surface water and groundwater contamination problems and leaching of pesticides. Modeling of the transport and fate of pesticides, such as simazine, may help understand the long-term potential risk to the subsurface environment. This paper illustrates a comparative study via the use of three different pesticide transport simulation models and the applicability of those models in determining the groundwater vulnerability to pesticides contamination in a citrus orchard located at the Lower Rio Grande Valley (LRGV). The three models used in the study are the pesticide root zone model-3 (PRZM-3), the pesticide analytical model (PESTAN) and integrated pesticide transport modeling (IPTM). The concentration values obtained from all three models are in agreement, and they show a decreasing trend from the surface through the vadose zone. The problem is how to use this information and, specifically, how to combine the testimony of a number of experts into a single useful judgment. With the aid of the fuzzy multiattribute decision making method, PRZM-3 is deemed as the most promising one for such precision farming applications.     

Developing risk models of Cryptosporidium transport in soils from vegetated, tilted soilbox experiments

Transport of Cryptosporidium parvum through macroporous soils is poorly understood yet critical for assessing the risk of groundwater contamination. We developed a conceptual model of the physics of flow and transport in packed, tilted, and vegetated soilboxes during and immediately after a simulated rainfall event and applied it to 54 experiments implemented with different soils, slopes, and rainfall rates. Using a parsimonious inverse modeling procedure, we show that a significant amount of subsurface outflow from the soilboxes is due to macropore flow. The effective hydraulic properties of the macropore space were obtained by calibration of a simple two-domain flow and transport model that accounts for coupled flow in the matrix and in the macropores of the soils. Using linear mixed-effects analysis, macropore hydraulic properties and oocyst attenuation were shown to be associated with soil bulk density and rainfall rate. Macropore flow was shown to be responsible for bromide and C. parvum transport through the soil into the underlying pore space observed during the 4-h experiments. We confirmed this finding by conducting a pair of saturated soil column studies under homogeneously repacked conditions with no macropores in which no C. parvum transport was observed in the effluent. The linear mixed-effects and logistic regression models developed from the soilbox experiments provide a basis for estimating macropore hydraulic properties and the risk of C. parvum transport through shallow soils from bulk density, precipitation, and total shallow subsurface flow rate. The risk assessment is consistent with the reported occurrence of oocysts in springs or groundwater from fractured or karstic rocks protected only by shallow overlying soils.     

Propagation of uncertainties in soil and pesticide properties to pesticide leaching

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.     

Turfgrass thatch effects on pesticide leaching: a laboratory and modeling study

Process-based models are frequently used to assess the water quality impacts of turfgrass management emanating from proposed or existing golf courses. Thatch complicates the prediction of pesticide transport because surface-applied pesticides must pass through an organic-rich layer before entering the soil. This study was conducted to (i) compare the use of a linear equilibrium model (LEM) and two-site nonequilibrium (2SNE) model to predict pesticide transport through soil and thatch + soil columns, and (ii) evaluate thatch effects on pesticide transport through soil columns with a volume-averaging approach. Pesticide breakthrough curves were obtained for soil and thatch + soil columns from a 1 cm h(-1) flux applied one day after applying triclopyr (3,5,6-trichloro-2-pyridinyloxyacetic acid) and carbaryl (1-napthyl-methyl carbamate). Pesticide and bromide transport parameters indicated that nonequilibrium processes were affecting pesticide transport. Columns containing zoysiagrass (Zoysia japonica Steud.) thatch had lower triclopyr and carbaryl leaching losses than did soil-only columns, although total reductions attributable to thatch did not exceed 15% of the applied pesticide. When laboratory-based retardation factors were used, the 2SNE model explained 88 to 93% of the variability for triclopyr and 70 to 94% of the variability for carbaryl. Laboratory-based retardation factors performed well in a 2SNE model to predict the peak concentration and tailing behavior of triclopyr and carbaryl with a volume-averaging approach. These results suggest that separate representation of the thatch layer in process-based models is not a prerequisite to obtain reasonable estimates of pesticide transport under steady state flow conditions.     

Sensitivity and first-step uncertainty analyses for the preferential flow model MACRO

Sensitivity analyses for the preferential flow model MACRO were carried out using one-at-a-time and Monte Carlo sampling approaches. Four different scenarios were generated by simulating leaching to depth of two hypothetical pesticides in a sandy loam and a more structured clay loam soil. Sensitivity of the model was assessed using the predictions for accumulated water percolated at a 1-m depth and accumulated pesticide losses in percolation. Results for simulated percolation were similar for the two soils. Predictions of water volumes percolated were found to be only marginally affected by changes in input parameters and the most influential parameter was the water content defining the boundary between micropores and macropores in this dual-porosity model. In contrast, predictions of pesticide losses were found to be dependent on the scenarios considered and to be significantly affected by variations in input parameters. In most scenarios, predictions for pesticide losses by MACRO were most influenced by parameters related to sorption and degradation. Under specific circumstances, pesticide losses can be largely affected by changes in hydrological properties of the soil. Since parameters were varied within ranges that approximated their uncertainty, a first-step assessment of uncertainty for the predictions of pesticide losses was possible. Large uncertainties in the predictions were reported, although these are likely to have been overestimated by considering a large number of input parameters in the exercise. It appears desirable that a probabilistic framework accounting for uncertainty is integrated into the estimation of pesticide exposure for regulatory purposes.     

Combination of multispectral remote sensing, variable rate technology and environmental modeling for citrus pest management

The Lower Rio Grande Valley (LRGV) of south Texas is an agriculturally rich area supporting intensive production of vegetables, fruits, grain sorghum, and cotton. Modern agricultural practices involve the combined use of irrigation with the application of large amounts of agrochemicals to maximize crop yields. Intensive agricultural activities in past decades might have caused potential contamination of soil, surface water, and groundwater due to leaching of pesticides in the vadose zone. In an effort to promote precision farming in citrus production, this paper aims at developing an airborne multispectral technique for identifying tree health problems in a citrus grove that can be combined with variable rate technology (VRT) for required pesticide application and environmental modeling for assessment of pollution prevention. An unsupervised linear unmixing method was applied to classify the image for the grove and quantify the symptom severity for appropriate infection control. The PRZM-3 model was used to estimate environmental impacts that contribute to nonpoint source pollution with and without the use of multispectral remote sensing and VRT. Research findings using site-specific environmental assessment clearly indicate that combination of remote sensing and VRT may result in benefit to the environment by reducing the nonpoint source pollution by 92.15%. Overall, this study demonstrates the potential of precision farming for citrus production in the nexus of industrial ecology and agricultural sustainability.     

Effects of soil variability and weather conditions on pesticide leaching--a farm-level evaluation

In line with European regulations, Dutch law imposes an environmental threshold of 0.1 microg L(-1) on pesticide concentrations in ground water. During registration, the risk of exceeding this threshold is assessed through simulations for one or a few standard scenarios that do not reflect spatial variability under field conditions. The introduction of precision agriculture, where soil variability is actively managed, can increase control over pesticide leaching. This study presents a step-wise evaluation of the effects of soil variability and weather conditions on pesticide leaching. The evaluation was conducted on a 100-ha arable farm and aimed at identifying opportunities for precision management. As a first step, a relative risk assessment identified pesticides presenting a relatively high risk to the environment. Second, the effect of weather conditions was analyzed through 20 years of simulations for three distinct soil profiles. Results were summarized in cumulative probability plots to provide a probabilistic characterization of historical weather data. The year matching 90% probability (1981) served as a reference to simulate pesticide leaching from 612 soil profiles. After interpolation, areas where concentrations exceeded the environmental threshold were identified. Out of a total of 19 pesticides, isoproturon [N-dimethyl-N'-(4-(1-methylethyl)phenyl)urea], metribuzin [4-amino-6-tert-butyl-3-(methylthio)-as-triazin-5(4H)-one], and bentazon [2,1,3-benzothiadiazin-4(3H)-one, 3-isopropyl-, 2,2-dioxide] showed the highest risk for leaching. Leaching was strongly affected by soil variability at the within-field, field, and farm levels. Opportunities for precision management were apparent, but depended on the scale level at which environmental thresholds were implemented. When legislation is formulated in this issue, the presented step-wise evaluation can serve as a basis for identification and precision management of high-risk pesticides.     

A GIS-BASED MODELING APPROACH FOR EVALUATING GROUNDWATER VULNERABILITY TO PESTICIDES1

ABSTRACT: The Attenuation Factor (AF), a screening model, was used to evaluate the relative degree of vulnerability of groundwater to pesticide contamination in Louisa County, Virginia. For evaluating the contamination potential of pesticides, three scenarios of pesticide leaching represented by high, moderate, and low cases of degradation and sorption in the soil were considered. Data layers were overlaid within a Geographic Information System (GIS) for spatial computation of AF for the actual and 2m groundwater depths. This spatial database was divided into five contamination potential categories namely high, medium, low, very low, and unlikely, based on the numerical values of the AF for each cell (119 ha). The results for the three most mobile pesticides are presented in this paper. The performance of the AF model was evaluated by comparing its predicted results with the field data from an experimental watershed. The AF model was able to identify most of the frequently detected pesticides in the watershed. A sensitivity analysis was also performed. The results of this study provide information about the potential groundwater threat by pesticides to the citizens ahd decision-makers in the County and can be used for formulating an appropriate land use management plan to protect the groundwater quality.