Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.     

重点流域水系沉积物中多环芳烃标准样品制备

环境标准样品是监测过程中重要的质量控制手段,中国受天然基体标准样品制备技术的制约,尚未拥有自己的沉积物中多环芳烃标准样品。为制备符合中国重点流域沉积物类型及多环芳烃浓度水平的沉积物标准样品,对中国6个重点流域的沉积物进行了采样,获得了制备多环芳烃标准样品的原料。分析常温及冷冻2种干燥方式对多环芳烃的影响,为制备大批量多环芳烃标准样品提供数据支持。对11个点位样品采用气相色谱质谱法测定16种优控多环芳烃化合物浓度,结果表明,样品均匀性良好,样品中多环芳烃检出率在99%以上,江河沉积物样品中多环芳烃浓度区间为664~2.91×103μg/kg,湖泊样品最高值达到1.25×105μg/kg,其主要污染源是化学燃料的燃烧。为中国制备水系沉积物中多环芳烃标准样品积累了技术基础。     

气相色谱-质谱法测定大气中98种挥发性有机物

通过色谱-质谱联用法(简称GC-MS),利用低温冷阱技术对大气中的VOCs进行浓缩富集,然后经过加热解吸分别至毛细管色谱柱和FID检测器及MS检测器,对大气中98种挥发性有机化合物(VOCs)进行分离、定性、定量测定。方法检出限为0.008×10-9~0.100×10-9(V/V);线性相关系数的平方值为0.992 7~1,相对标准偏差为4.0%~20.2%,总体标准偏差为0.154 2~0.952 1。     

天津市基于新标准的空气质量预报模型效果评估

利用2015年环境空气质量监测数据,对天津市OPAQ空气质量统计预报模型预测效果进行验证评估。结果表明,模型对天津市AQI和PM_(2.5)、PM_(10)、O_3、NO——2的预测结果与实测结果具有较好的趋势一致性,且预测时间越临近,拟合度越好,24 h预报的相关系数r全部达到0.8以上。对PM_(2.5)的预报性能明显优于PM_(10)、O_3和NO_2,PM_(2.5)平均值预测略呈正偏差,但重污染预测值偏低约15%;O_3和NO_2预测值呈明显负偏差,O_3峰值预测不足,NO_2预测值整体偏低,均以24 h预报趋势性最好,但负偏差最为突出。     

废水污染物排放总量控制监测技术路线及要求

从中国污染物宏观控制目标出发,阐述了中国实施废水污染物排放总量控制监测的技术路线、监测项目以及相关技术要求。对污染物排放总量控制监测分析方法的应用、采样及测流等有关技术问题,提出了见解。以１９９８年污染源调查统计资料为例,提出了污染物排放总量控制监测方案及有关监测质量的保证措施。     

环境空气中丙烯酸乙酯的气相色谱法测定

建立了环境空气中丙烯酸乙酯的TenaxGC吸附 -热解吸气相色谱测定方法。方法回收率为 87 3%～1 0 7 1 % ,变异系数为 5 3%～ 7 2 %。当采样体积为 2L时 ,检测限为 0 0 2mg/m3,具有采样时间短 ,不用任何溶剂等特点。     

大气中酮类化合物污染事故应急监测

研究了大气中低分子量酮类化合物的气相色谱测定方法 ,进行了线性范围、精密度和准确度实验。结果表明 ,该方法具有简便快捷、准确灵敏的特点 ,适合于大气污染事故应急监测     

Effect of heavy metals (Cu, Pb, and As) on the ultrastructure of Sargassum pallidum in Daya Bay, China

Concentrations of Cu, Pb, and As were determined in seawater, surface sediment, Sargassum pallidum collected from the Daya Bay, China. The influence of metal contamination on the marine alga was investigated at chemical and ultrastructural level. Mean concentrations of Cu (19.44 mg kg^?1) and Pb (33.99 mg kg^?1) were found to be high in sediment, whereas concentration of As (122.29 mg kg^?1) in S. pallidum was higher than that in water and sediment. The ultrastructure of S. pallidum cells was anomalous and aberrant. Energy-dispersive x-ray spectroscopic analysis revealed that the nanometal particles in the form of comparatively high-electron density substance diffused in the cell structures constituted by Cu, Pb, As, etc. There is a remarkable similarity or correspondence in the anomalous elements between the geochemistry and the botanic cell, and the heavy metals have potential hazardous effect on the ocean ecology system in Daya Bay.