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。     

钢渣掺量对泡沫混凝土砌块性能的影响

泡沫混凝土是一种环保、节能的保温隔热材料,以其优良的保温性能,受到越来越多研究者的关注。对钢渣的特性进行了分析测试,对钢渣不同掺量对泡沫混凝土砌块特性的影响进行了研究,结果表明,采用添加5％～35％钢渣所制备的钢渣泡沫混凝土砌块,其密度等级为JC／T1062—2007中的B10级,掺人5％、15％钢渣的泡沫混凝土抗压强度达到A3．5等级,掺人25％、35％钢渣的泡沫混凝土抗压强度达到A2．5等级。随着钢渣掺量的增大,抗压强度、抗折强度降低,吸水率增大。在同一钢渣掺量的情况下,泡沫混凝土砌块的抗压强度变化率增加显著,而且随着钢渣的加入量升高而增大。钢渣的加入有利于提高泡沫混凝土砌块的后期抗压强度。     

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。     

工业废水中多金属离子的吸附净化

以含有Si、Al、Ca、C元素的矿物材料作为基质,经特定条件处理后造粒成型加工成轻质多孔Si-Al-Ca-C结构的吸附材料。研究了该吸附材料对工业废水中多种金属离子的吸附性能,探讨了影响吸附性能的因素。研究结果表明,在一定条件下,Si-Al-Ca-C吸附材料对工业废水中Al3+、Ca2+、Mn2+、Cu2+、Fe3+、Mg2+、As3+和Zn2+的净化率分别高达99.34%、99.82%、98.26%、98.16%、97.76%、97.01%、100%和89.09%。金属离子残留浓度分别为0.152、0.07、0.012、0.02、0.119、0.311、0和0.259 mg/L,均低于国家污水综合排放标准(GB8978-1996)一级标准。     

高碱度水库水混凝过程中残留铝控制

针对高碱度水库水源的某水厂残留铝超标问题,选取碱化度(B)与Alb含量不同的3种铝盐絮凝剂,研究不同投量与pH值下混凝效果与残留铝浓度水平。结果表明,碱化度和Alb含量显著影响混凝效果。DOC和浊度的去除率随着3种絮凝剂AlCl3(B=0)、PACl-1(B=1.2)、PACl-2(B=2.2)投量增大而升高。3种絮凝剂投量在1.5～2.0 mg/L(以铝计)范围内,总铝和溶解铝含量最低。对于该水厂自制的絮凝剂PACl-2,可通过降低絮凝剂碱化度,或将水的pH值降低至7～7.5之间,以此可以提高PACl-2混凝效果,而且可以降低出厂水残留铝浓度。考虑工程应用可行性,可优先考虑调整絮凝剂生产工艺。     

青萍(Lemna minor L.)对氮磷的吸收特征

以西部地区优势浮萍品种青萍为实验对象,研究了青萍对不同浓度硝态氮和磷酸盐的吸收特征。结果表明,硝态氮浓度在1～10 mg/L范围内,青萍对硝态氮有较好的吸收效果,M-M方程可以较好地描述硝态氮浓度与青萍对硝态氮的吸收速率之间的关系,通过M-M方程拟合得到青萍对硝态氮的最大吸收速率为0.1167 mg/(g FW.h),亲和力常数为6.9274。磷酸盐浓度在0.1～1.0 mg/L范围内,青萍对磷酸盐也有较好的吸收效果,二次多项式回归可以较好地描述青萍吸收速率与磷酸盐浓度的关系,回归方程得到青萍对磷酸盐的最大吸收速率为0.0193 mg/(g FW.h),对应磷酸盐浓度为0.6 mg/L。     

An occupational exposure assessment of polychlorinated dibenzo-p-dioxin and dibenzofurans in firefighters

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are unintentional byproducts of combustion and industrial processes. Firefighters face the risk of occupational exposure to PCDD/Fs. Congener-specific analyses of 17 PCDD/Fs were performed on 20 serum samples collected from firefighters and fire scene investigators, and four soot samples that had deposited on the surfaces of the fire helmets and were collected after the firefighters had fought fires. The PCDD/F concentrations on the helmets that were contaminated by being worn at the fire scenes were 63-285 times higher than those on a clean helmet. The median serum PCDD/F concentration of the 16 firefighters (12 pg WHO₂₀₀₅-TEQ g⁻¹ lipid) was not different from those of the males from the general Taiwanese population (9.4 pg WHO₂₀₀₅-TEQ g⁻¹ lipid). However, the median PCDD/F level in the four fire scene investigators (15 pg WHO₂₀₀₅-TEQ g⁻¹ lipid) was higher than those in the male from the general Taiwanese population (Mann-Whitney U test, p < 0.01). Furthermore, the serum samples from the firefighters and fire scene investigators, and the soot samples from the fire scenes presented similarly distinctive PCDD/F profiles that had elevated proportions for 10 PCDF congeners. Limited data indicated that the fire scene investigators were occupationally exposed to PCDD/Fs at the fire scenes. We suggested that the firefighters were not occupationally exposed to PCDD/Fs at the fire scenes due to appropriate protection. However, the fire scene investigators may have had more occupational exposure to PCDD/Fs due to poor protection, and further research must be performed to confirm this.     

Annual variations of odor concentrations and emissions from swine gestation, farrowing, and nursery buildings

To obtain annual odor emission profiles from intensive swine operations, odor concentrations and emission rates were measured monthly from swine nursery, farrowing, and gestation rooms for a year. Large annual variations in odor concentrations and emissions were found in all the rooms and the impact of the seasonal factor (month) was significant (P < 0.05). Odor concentration was low in summer when ventilation rate was high but high in winter when ventilation rate was low, ranging from 362 (farrowing room in July) to 8934 (nursery room in December) olfactory unit (OU) m(-3). This indicates that the air quality regarding odor was significantly better in summer than that in winter. Odor emission rate did not show obvious seasonal pattern as odor concentration did, ranging from 2 (gestation room in November) to 90 (nursery room in April) OU m(-2) sec(-1); this explains why the odor complaints for swine barns have occurred all year round. The annual geometric mean odor concentration and emission rate of the nursery room was significantly higher than the other rooms (P < 0.05). In order to obtain the representative annual emission rate, measurements have to be taken at least monthly, and then the geometric mean of the monthly values will represent the annual emission rate. Incorporating odor control technologies in the nursery area will be the most efficient in reducing odor emission from the farm considering its emission rate was 2 to 3 times of the other areas. The swine grower-finisher area was the major odor source contributing 53% of odor emission of the farm and should also be targeted for odor control. Relatively positive correlations between odor concentration and both H2S and CO2 concentrations (R(2) = 0.58) means that high level of these two gases might likely indicate high odor concentration in swine barns.     

Development of a livestock odor dispersion model: part I. Model theory and development

A livestock odor dispersion model (LODM) was developed to predict mean odor concentration, odor frequency, instantaneous odor concentration, and peak odor concentration from livestock operations. This model is based on the Gaussian fluctuating plume model and has the ability to consider the instantaneous concentration fluctuations and the differences between odor and traditional air pollutants. It can predict odor frequency from the routine hourly meteorological data input and deal with different types of sources and multiple sources. Also, the relationship between odor intensity and odor concentration was incorporated into the model.