响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．DNS32的产量,分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分（玉米粉、豆饼粉、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L,豆饼粉25．638g／L和K。HPO。3．265g／L时,预测发酵活菌最大生物量为7．079×10^8CFU／mL,实测量为7．194×10^8CFU／mL;人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39．650g／L,豆饼粉为25．500g／L,K2HPO4为2．624g／L时,预测最大值为7．199×10^8CFU／mL,实测量为7．244×10。CFU／mL;最终确定培养基配方：玉米粉为39．650g／L,豆饼粉为25．500g／L,K2HPO4为2．624g／L,CaCO3为3．000g／L,MgSO4·7H2O和NaCl均为0．200g／L;优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36．6％。结果表明,在阿特拉津降解菌DNS32发酵培养基组分优化方面,响应面法和基于人工神经网络的遗传算法都是可行的,基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。     

基于AERMOD模式的固定源对不同楼层大气污染预测研究

环境影响评价中,大气污染预测通常考虑地面的浓度影响,对高层住宅楼不同楼层影响的考虑较少.通过案例设计,模拟4种不同的污染排放情景,采用预测推荐模式AERMOD分别计算各情景下5个代表高度共20种组合情况的预测受体结果,并基于三维可视仪软件VOXLER直观显示各情景污染物浓度的空间连续分布情况,对小时最大浓度的分布规律利用分析工具CANOCO进行了分析.结果表明:(1)污染源高为15～80 m时,不同楼层间小时浓度分布差异性较大;污染源高为120～240 m时,楼层间差异性较小.(2)污染源高为15m时,小时最大浓度以50 m为分界线随楼层高度的增加先增后降;污染源高为80～240 m时,小时最大浓度随楼层高度的增加而增加.     

深型地下土壤渗滤系统中氮的去除途径

为了系统研究氮在深型地下土壤渗滤系统中的去除途径,本次实验采用直径30cm,高200cm的有机玻璃柱模拟地下土壤渗滤系统;柱内分层装填取自北京顺义的土壤。在水力负荷为8cm／d的条件下,取得了较好的脱氮效果;氨氮去除率为99．80％;TN去除率为83．68％。通过观察氮沿土柱深度的变化规律发现,在1．30m以上的区域随着氨氮浓度的降低硝氮浓度逐渐增大,同时总氮浓度也在不断降低,约有30．55％在此区域被去除;通过氮元素质量平衡证明这部分氮是通过厌氧氨氧化反应去除的。在1．30m以下反硝化反应是脱氮的主要途径,在此过程中难降解有机物被利用。     

响应面法和神经网络优化Acinetobacter sp. DNS32发酵基质

为了提高阿特拉津降解菌Acinetobacter sp.DNS32的产量,分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分(玉米粉、豆饼粉、K2HPO4)进行优化研究。响应曲面法确定3种成分的含量为玉米粉39.494 g/L,豆饼粉25.638 g/L和K2HPO43.265 g/L时,预测发酵活菌最大生物量为7.079×108CFU/mL,实测量为7.194×108CFU/mL;人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L时,预测最大值为7.199×108CFU/mL,实测量为7.244×108CFU/mL;最终确定培养基配方:玉米粉为39.650 g/L,豆饼粉为25.500 g/L,K2HPO4为2.624 g/L,CaCO3为3.000 g/L,MgSO4.7H2O和NaCl均为0.200 g/L;优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36.6%。结果表明,在阿特拉津降解菌DNS32发酵培养基组分优化方面,响应面法和基于人工神经网络的遗传算法都是可行的,基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。     

响应面分析法优化稀土废水MAP沉淀法脱氮

经过预处理后的稀土生产废水,其氨氮浓度大幅降低,但并未达到中华人民共和国《稀土工业污染物排放标准》（GB26451-2011）中氨氮浓度限值。实验通过响应面分析法中的Box-Behnken实验设计（BBD）,取pH、n（Mg）：n（N）、n（P）：n（N）3因素,采用Design-Expert 8.0.6,建立合适的剩余氨氮浓度及剩余总磷浓度模型,得到回归方程,并分析模型各项指标,各因素及其相互作用对剩余氨氮浓度及剩余总磷浓度的影响。利用预测模型预测最佳实验条件,在最佳实验条件下验证预测结果,并对沉淀物进行X射线衍射（XRD）分析。结果显示,二次响应模型适用于剩余氨氮浓度及剩余总磷浓度,2个模型均拥有较好的拟合程度、可信度及精密度,最优反应条件为：pH=9.88、n（Mg）：n（N）=1.50：1、n（P）：n（N）=1.38：1时,剩余氨氮浓度为46.58 mg/L,剩余总磷浓度为7.85 mg/L。在最优条件下所得到的沉淀物并非纯净的MgNH₄PO₄·6H₂O,还有Mg₃（PO₄）₂·22H₂O生成。     

Characterization of ACC deaminase-producing endophytic bacteria isolated from copper-tolerant plants and their potential in promoting the growth and copper accumulation of Brassica napus

One hundred Cu-resistant-endophytic bacteria were isolated from Cu-tolerant plants grown on Cu mine wasteland, of which, eight Cu-resistant and 1-aminocyclopropane-1-carboxylate (ACC) deaminase-producing endophytic bacteria were obtained based on the ACC deaminase activity of the bacteria and characterized with respect to metal resistance, production of ACC deaminase, indole-3-acetic acid (IAA) as well as siderophores and mineral phosphate solubilization. Ralstonia sp. J1-22-2, Pantoea agglomerans Jp3-3, and Pseudomonas thivervalensis Y1-3-9 with higher ACC deaminase activity (ranging from 213 to 370 μM α-ketobutyrate mg(-1)h(-1)) were evaluated for promoting plant growth and Cu uptake of rape grown in quartz sand containing 0, 2.5, and 5 mg kg(-1) of Cu in pot experiments. The eight bacteria were found to exhibit different multiple heavy metal resistance characteristics, to show different levels of ACC deaminase activity and to produce indole acetic acid. Seven bacteria produced siderophores and solubilized inorganic phosphate. Pot experiments showed that inoculation with the strains (J1-22-2, Jp3-3, and Y1-3-9) was found to increase the biomass of rape. Increases in above-ground tissue Cu contents of rape cultivated in 2.5 and 5 mg kg(-1) of Cu-contaminated substrates varied from 9% to 31% and from 3 to 4-fold respectively in inoculated-rape plants compared to the uninoculated control. The maximum Cu uptake of rape was observed after inoculation with P. agglomerans Jp3-3. The results show that metal-resistant and plant growth promoting endophytic bacteria play an important role in plant growth and Cu uptake which may provide a new endophytic bacterial-assisted phytoremediation of Cu-contaminated environment.     

Correction methods for organic carbon artifacts when using quartz-fiber filters in large particulate matter monitoring networks: the regression method and other options

Sampling and handling artifacts can bias filter-based measurements of particulate organic carbon (OC). Several measurement-based methods for OC artifact reduction and/or estimation are currently used in research-grade field studies. OC frequently is not artifact-corrected in large routine sampling networks (e.g., U.S. Environmental Protection Agency (EPA)'s Chemical Speciation Network). In some cases, the OC artifact has been corrected using a regression method (RM) for artifact estimation. In this method, the gamma-intercept of the regression of the OC concentration on the fine particle (PM2.5) mass concentration is taken to be an estimate of the average OC sampling artifact (net of positive and negative artifacts). This paper discusses options for artifact correction in large routine sampling networks. Specifically, the goals are to (1) articulate the assumptions and limitations inherent to the RM, (2) describe other artifact correction approaches, and (3) suggest a cost-effective method for artifact correction in large monitoring networks. The RM assumes a linear relationship between measured OC and PM mass: a constant slope (OC mass fraction) and a constant intercept (RM artifact estimate). These assumptions are not always valid. Additionally, outliers and other individual data points can have a large influence on the RM artifact estimates. The RM yields results within the range of measurement-based methods for some datasets and not for others. Given that the adsorption of organic gases increases with atmospheric concentrations of organics, subtraction of an average artifact from all samples (e.g., across multiple sites) will underestimate OC for lower-concentration samples (e.g., clean sites) and overestimate OC for higher-concentration samples (e.g., polluted sites). For relatively accurate, simple, and cost-effective artifact OC estimation in large networks, the authors suggest backup filter sampling on at least 10% of sampling days at all sites with artifact correction on a sample-by-sample basis as described herein.     

在线喷水加湿控制道路扬尘排放的研究

利用实验室模拟装置,模拟道路扬尘在自由下落过程中的飞扬溢出情况,并采用气水两相流雾化喷头,通过在线喷水加湿的方法以控制道路扬尘的排放量,探讨了喷头的空气压力和加水流量对道路扬尘排放的影响,并考察了最佳加湿条件(最佳的空气压力和加水流量)下道路扬尘中可吸入颗粒物的排放控制效果.结果表明,当气水两相流雾化喷头的空气压力为0...     

负压蒸发技术处理废弃钻井液

采用负压蒸发技术处理3种不同组分的废弃钻井液。实验结果表明:最佳蒸发温度为79℃;当蒸发温度低于95℃时,相15~#原液的出水氨氮质量浓度、悬浮物质量浓度、色度分别低于3.40 mg/L、0.093 mg/L、11倍,达到GB 8978—1996《污水综合排放标准》中的一级排放标准;当蒸发温度低于79℃时,钻井液1和钻井液2的出水氨氮质量浓度均低于37.00 mg/L,悬浮物质量浓度均低于0.080 mg/L,色度低于13倍,COD低于114.0 mg/L,达到GB 8978—1996《污水综合排放标准》中的二级排放标准。通过后续过滤除油工艺可降低钻井液中的石油类质量浓度,使最终出水达标排放。     

基于控制单元的水环境容量核算研究——以锦江流域为例

水环境容量核算是流域水环境容量总量分配的重要依据,关系到流域水质目标的实现。控制单元作为流域水环境管理的一个基本实施单位,以其为基础开展水环境容量核算,对于科学制定控制单元容量总量分配具有重要意义。以江西省锦江流域为例,根据水环境容量核算的基本原理,结合锦江流域的污染状况、水质现状和水环境功能区划,对流域各控制单元COD和氨氮的水环境容量进行分析,结果表明,控制单元的水环境容量与其内排污口的分布及功能区水质目标密切相关,COD的水环境容量以高安控制单元的最大,为 21 811 t/a;其次为上宜控制单元,为 21 168 t/a;再次为新丰控制单元,为 14 493 t/a;万载控制单元的最小,为 7 607 t/a。氨氮的水环境容量在各控制单元的分布特征与COD的略有不同,以上宜控制单元的最大,为790 t/a;其次为高安控制单元,为664 t/a;再次为新丰控制单元,为462 t/a;万载控制单元的最小,为303 t/a