profiling areas of ground water contamination by pesticides in california: phase ii – evaluation and modification of a statistical model

A well sampling study was conducted to evaluate anempirical approach to classifying areasof land in California as vulnerable to ground watercontamination by pesticides (Troiano et al., 1994). Wells were sampled from sections of land that had noprevious detections of pesticideresidues. The sections had been classified into vulnerablesoil clusters or into a not-classified groupusing a procedure based on Principal Components Analysis(PCA). Grape, citrus, and olive growingareas of Fresno and Tulare Counties were targeted, areas wherepre-emergence herbicide residues hadbeen detected in well water. Overall, herbicide residues weredetected in 75 of 176 sampled wells, ahigh frequency of detection in relation to results fromprevious targeted well sampling studies. Sinceresidues were also detected in the not-classified group, theclassification procedure was modified usingan approach based on Canonical Variates Analysis (CVA). Moresections were classified intovulnerable soil clusters with the CVA approach than with thePCA method. Data from two otherexplanatory variables, depth to ground water and amount ofpesticide used per section, were includedto illustrate how additional information can be incorporatedinto this approach of identifying vulnerable areas.     

IMPACT OF COAL SURFACE MINING ON THREE OHIO WATERSHEDS - SURFACE-WATER HYDROLOGY1

ABSTRACT: A study was conducted to determine the effects of mining and reclaiming originally undisturbed watersheds on surface-water hydrology in three small experimental watersheds in Ohio. Approximately six years of data were collected at each site, with differing lengths of premining (Phase 1), mining and reclamation (Phase 2), and post-reclamation (Phase 3) periods. Mining and reclamation activities showed no consistent pattern iii base-flow, and caused slightly more frequent higher daily flow volumes. Phase 2 activities can cause reductions in seasonal variation in double mass curves compared with Phase 1. Restoration of seasonal variations was noticeably apparent at one site during Phase 3. The responses of the watersheds to rainfall intensities causing larger peak flow rates generally decreased due to mining and reclamation, but tended to exceed responses observed in Phase 1 during Phase 3. Natural Resources Conservation Service (NRCS) curve numbers increased due to mining and reclamation (Phase 2), ranging from 83 to 91. During Phase 3, curve numbers remained approximately constant from Phase 2, ranging from 87 to 91.     

渭河径流特性研究

由于工农业的快速发展,渭河径流逐年减少,污染加重,河道淤积不断发展,洪涝灾害时有发生,对中下游人民生产生活造成严重影响.为了减少洪害,减轻污染,必须保证渭河常年径流稳定.     

不同降雨-径流过程中农业非点源污染研究

以大宁河流域为研究区域,应用SWAT模型进行了流域农业非点源污染负荷的模拟计算。利用巫溪水文站2000～2004年的实测日径流和泥沙数据进行模型的调参计算,验证结果表明模型适用于大宁河流域。利用验证后的模型分析了不同降雨-径流条件下非点源污染的产输出特性。结果表明:降雨量对径流污染负荷有较大的影响,年内丰水段,非点源污染物浓度峰值和径流峰值同步出现;年内平水段,泥沙浓度、有机氮浓度和径流峰值同步出现,硝酸盐浓度峰值滞后于径流峰值出现时间;年内枯水段,非点源污染物浓度滞后于径流峰值度出现时间;降雨—径流与污染物浓度之间存在着密切的线性相关关系。     

镇江城市径流颗粒粒径分布及其与污染物的关系

为了解城市中不同粒径颗粒物对于径流中污染物的影响,2006年3月在镇江城市不同功能区地表采集了沉积物样品和径流样品,分析了颗粒物的粒径分布和污染物浓度.结果表明,晴天条件下道路沉积物主要由粒径＜250μm的颗粒组成;降雨初期主要为＜5μm的颗粒物随径流迁移,随降雨历时的延长较大颗粒开始随径流迁移,降雨期间随地表径流迁移主要为小于150μm的颗粒物,特别是5～40μm粒径段的颗粒要特别予以关注;同时污染物浓度也由降雨初期的高浓度逐渐下降并趋于稳定.明确了径流中污染物的主要输出形态,并通过分析不同降雨历时污染物与固体悬浮物和颗粒粒径的相关性探明了径流污染物形态输出的原因,从而为城市非点源污染的管理以及控制方法的选择提供科学依据.     

镇江城市降雨径流营养盐污染特征研究

城市降雨径流污染是城市水体水质恶化的主要原因之一,为了分析降雨径流的污染特征,从2006-05开始,选择镇江具有代表性的土地使用功能对其降雨径流水质进行监测,分析了径流中固体悬浮物和营养盐的变化特征.结果表明,镇江不同功能区中5～40 μm粒径的固体悬浮物体积分数最大;商业区径流污染物浓度很高,其中SS浓度高达978 mg/L,是城市面源污染的主要来源;降雨初期的30 min径流污染物浓度很高,随降雨历时的延长污染物浓度逐渐下降并趋于稳定;溶解态氮、颗粒态磷和固体悬浮物是径流中污染物输出的主要形态;通过分析固体悬浮物与其他污染物的相关性,发现固体悬浮物是径流中营养盐吸附的载体,去除固体悬浮物是治理镇江降雨径流污染的有效途径.     

SWMM 模型在城市不透水区地表径流模拟中的参数识别与验证

为了研究城市不透水下垫面的降雨径流过程和污染负荷,以屋面为例,选择径流管理模型SWMM,采用独立场次实测数据,应用基于不确定性分析的HSY算法和Monte Carlo采样方法对模型中的水文水力和水质参数进行识别和验证.结果表明,地表不透水区径流模型中主要包含6个关键参数,分别为不透水区初损填洼深度(S-imperv)、不透水区曼宁系数(N-imperv),指数累积方程中的最大可能累积值(max buildup)、累积常数(rate constant),指数冲刷方程中的冲刷系数(coefficient)和冲刷指数 (exponent).水文水力参数的识别可以最小二乘法偏差作为目标函数,水质参数的识别可以场次污染负荷和污染物峰值浓度作为目标函数.参数识别结果为N-imperv0.012～0.025, S-imperv 0～0.7, max buildup 15～30, rate constant0.2～0.8, coefficient0.01～ 0.05, exponent1.0～1.2.参数的区域灵敏度由大到小排序为 coefficient、S-imperv、N-imperv、max buildup、exponent、rate constant.识别后的参数可以通过模型验证,但是在模拟一些雨型特殊的降雨径流污染物浓度曲线时,仍然存在一定的困难.     

合流制排水系统降雨径流污染物的特性及来源

在昆明市典型合流制排水小区对降雨径流进出水水量、水质进行了研究,旨在揭示城市区域合流制排水系统中降雨径漉不同来源的污染物特性及各个污染源的比倒.分别监测了合流制排水系统日常污水以及4场降雨期间小区出口断面、街道、屋顶、庭院降雨径流的水量、水质.结果表明,人为干扰是影响城市径流污染物输出强度的主要因素,城市下垫面降雨径流污染物输出浓度顺序为:道路>庭院>屋顶,道路是城市面源污染的关键源区;道路次降雨径流量约25%,却产出了40%～80%的污染物,而屋顶次降雨径流量约50%,却仅有4%～30%的污染物负荷.合流制排水系统中管道沉积物在降雨期间的迁移是重要的污染源,4场降雨中管道沉积物的TN、TP、SS、COD和BOD5的污染贡献率在30%以上.降雨强度是影响管道沉积物输出的重要因素,在高强度降雨下,管道沉积物污染贡献率高50%以上.在不同的降雨特性条件下,合流制排水系统主导污染源有所不同.     

Validation of an agricultural non-point source （AGNPS） pollution model for a catchment in the Jiulong River watershed, China

This study tested and evaluated the agricultural non-point source(AGNPS)model for the Wuchuan catchment, a typical agricultural area in the Jiulong River watershed, Fujian Province. China. The AGNPS model was calibrated and validated for the study area with observed data onten storms. Thedata on eight stormsin 2002 were used for calibration while data on two stormswere used for validation of the model. Considering the lack of water quality data over a long-term series, a novel method, comparing an internal nested catchment with its surrounding catchment, was used to supplement the less long-term series data. Dual calibration and validation of the AGNPS model was obtained by this comparison. The results indicate that the correlation coefficients were 0. 99 and 0. 98 for runoff, 0. 94 and 0. 95 forthe peak runoff rate of the large catchment and the small catchment, respectively, and 0. 76 forthe sediment of the small catchment only. Each pair of correlation coefficients is homogeneous for the same event for the two catchments. With the exception of the sediment yield and particulate phosphorus, the peak nmofr rate and other nutrients were well predicted. Sensitivity analysis showed that the Soil Conservation Service curve number and rainfall quantity were the most sensitive parameters, which resulted in high output variations. Erosivitv and other parameters had little influence on the hydrological and quality outputs.