Watershed Regressions for Pesticides (warp) Models for Predicting Atrazine Concentrations in Corn Belt Streams1

Stone, Wesley W. and Robert J. Gilliom, 2012. Watershed Regressions for Pesticides (WARP) Models for Predicting Atrazine Concentrations in Corn Belt Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 970‐986. DOI: 10.1111/j.1752‐1688.2012.00661.x Abstract: Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region‐specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP‐CB) were developed for annual maximum moving‐average (14‐, 21‐, 30‐, 60‐, and 90‐day durations) and annual 95th‐percentile atrazine concentrations in streams of the Corn Belt region. The WARP‐CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model‐development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model‐development sites. The WARP‐CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine‐use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP‐CB models. The WARP‐CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine‐use intensities of 17 kg/km² of watershed area or greater.     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

代森锰锌在柑橘橘肉、橘皮及土壤中的残留分析

确立了柑橘橘肉、橘皮及土壤中代森锰锌残留量的气相色谱分析方法,采用反应后恒温处理以及代森锰锌工作曲线法进行测定并计算,消除了由于CS2-代森锰锌的质量转换系数不确定所带来的困难.结果表明,柑橘样品中代森锰锌平均回收率为86.46%～95.82%,变异系数为1.04%～7.69%,最小检出量为1.8×1010g,最低检测浓度为0.025mg/kg.     

基于固定化AChE的流动注射型酶传感器研究

以固定化鲅鱼脑组织乙酰胆碱酯酶[acetylcholinesterase,AChE(EC 3.1.1.7)]为识别元件,以pH电极为换能器,构建流动注射型AChE酶传感器.该传感器在以磷酸盐缓冲液为载液的条件下具有良好的重现性(RSD=1.427%,n=10)和敏感性,可实现对有机磷化合物的在线监测;对甲基对硫磷具有线性响应的浓度范围为4.29×10-10^-4.29×10^-8mol·L^-1,最低检测限为1.3×10^-     

北方低温条件下沼渣资源化培育佛甲草的应用研究

为克服厌氧发酵沼渣中碳氮磷比严重失调、可生化性差、总氮和总磷浓度高等难以直接资源化利用的难题,同时为北方秋冬季节低温条件下海绵城市建设过程中推广绿色屋顶提供技术支撑,通过调节基质中本地砂质壤土与厌氧发酵沼渣的比例,在北方秋冬季节室外培育佛甲草并研究其生长状态。结果表明:沼渣显著降低了基质的容重,减轻了屋顶的承受负荷;增大了基质的孔隙度,更适宜佛甲草根系下扎生长。基质中添加沼渣改善了低温条件下佛甲草的生长状况,增加了佛甲草的株高和覆盖度,提高了其分蘖能力及叶绿素总量;品质评价得分均高于6分,表明佛甲草的生长基本正常;低温条件下,沼渣和砂质壤土按4:5混配后的基质培育出的佛甲草综合品质最优。     

臭氧-生物活性炭对微污染原水中典型持久性有机物的去除效果

以长三角地区J市典型有机微污染水源P水厂为研究对象,考察了臭氧-生物活性炭深度处理工艺对微污染水源水中有机物的去除效果.结果表明,臭氧-生物活性炭深度处理使高锰酸盐指数、总有机碳(TOC)和UV254的平均去除率分别提升19. 2%、10. 4%和23. 0%.水厂原水中检出8种多环芳烃(polycyclic aromatic hydrocarbon,PAHs)、16种有机氯农药(organochlorine pesticides,OCPs)、5种卤乙酸(haloacetic acids,HAAs),其总浓度分别为53. 9~100. 0、6. 5~41. 8、2. 5×103~1. 1×104ng·L~(-1),臭氧-生物活性炭深度处理对多环芳烃和有机氯农药的平均去除率分别为32. 5%和25. 9%,比常规处理工艺出水的水质有显著提升.卤乙酸的去除率为33. 8%~87. 0%,主要通过常规处理工艺去除;臭氧-生物活性炭深度处理对氯代乙酸略有去除,溴代乙酸有少量生成.     

含铬废渣的综合利用

着重介绍了重庆东风化工厂铬渣综合治理的成效。其中包括该厂已成功应用于工业化生产的铬渣作玻璃着色剂，钙镁磷肥助熔剂，炼铁烧结熔剂等利用途径及其还原解毒机理，以及在水泥等建材方面的应用尝试。     

深圳市冬、夏两季大气中有机氯农药的研究

利用大流量主动采样器,选择深圳市13个有代表性采样点,于2009年12月~2010年1月和2010年6月,分2个采样时期对深圳市大气中有机氯农药(OCPs)进行监测.结果显示,深圳市冬、夏大气中,13个点位总有机氯农药浓度变化范围分别为742~3522 pg/m3(平均值1769pg/m3)和507~2197pg/m3(平均值1163pg/m3).冬季大气中有机氯农药主要是DDTs、林丹、七氯、氯丹、六氯苯,占有机氯农药总量的87%;夏季大气中有机氯农药主要为DDTs、氯丹、林丹,三者总量占有机氯农药的89%.研究表明,深圳市大气中存在“新”DDT的输入,认为工业DDT是其主要来源,而γ-HCH以及氯丹的高检出与林丹和用于杀灭白蚁的氯丹的继续使用有关.     

脱硫灰与钾矿石复合生产钾钙硅镁硫肥料研究

对脱硫灰与钾矿石复合生产钾钙硅镁硫肥的研究意义、反应原理、生产流程、环境安全性能以及施肥方法等进行了介绍和评估,并对其应用前景进行了展望。研究结果表明：利用脱硫灰与钾矿石复合生产钾钙硅镁硫肥在理论上可行;生产出的产品中硫酸钾的含量达10.34%～12.0%,枸溶氧化钙19.06%～32.28%,枸溶二氧化硅10.98%～14.46%,枸溶氧化镁1.46%～1.82%。产品的pH值从原脱硫灰的10.65下降到9.60。重金属含量低于农用粉煤灰国家标准,生产过程中不会产生SO2等有害气体污染。肥料的生产成本低于350元/t,该肥料不但可以增加土壤中钾、钙、硅、镁和硫等中微量元素的含量,而且可以提高或改善农作物的产量和品质。达到变废为宝,促进循环经济发展之目的。     

Residues of organochlorine pesticides in surface soils from college school yards in Beijing, China

Organochlorine pesticides (OCPs) have been a major environmental issue, attracting much scientific concern because of their nature of toxicity, persistence, and endocrine disrupting effects. Soil samples were collected from ten college school yards in Beijing in 2006 and analyzed to determine fifteen OCPs. Dichlorodiphenyltrichloroethanes (DDTs) were found to be the main pollutants, accounting for 93.70% of total OCPs, followed by hexachlorohexanes (HCHs) (2.25%) and hexachlorobenzene (HCB) (1.82%). Content...