广州地区孕妇羊水中PCBs暴露水平初步研究

随机采集广州地区非职业PCBs(polychlorinated biphenyls,多氯联苯)暴露单胎孕妇分娩时羊水,分析羊水中PCBs浓度水平;结合孕妇临床资料(年龄、孕周、身体质量指数(body mass index,BMI))以及孕妇居住环境信息等,阐明了广州地区非职业暴露孕妇羊水PCBs蓄积的影响因素。结果表明,∑33PCBs浓度为15.91-324.38 ng·g-1,平均值为114.34±83.52 ng·g-1,中值为84.8 5ng·g-1;其中十氯含量最低,平均浓度为2.66±3.46 ng·g-1,中值为1.47 ng·g-1;六氯PCBs含量最高,平均浓度为28.79±33.84 ng·g-1,中值为17.48 ng·g-1。样本中有31种PCBs同分异构体在孕妇羊水中检出,分别为PCB 8、28、30、49、52、70、74、77、82、87、99、101、105、114、118、126、128、138、153、156、158、166、169、170、179、180、183、187、189、198和209;羊水主要的PCBs暴露以五氯PCBs、六氯PCBs和七氯PCBs为主,主要的同分异构体为PCB 138(4.73%)、179(4.15%)、153(3.92%)、183(3.89%)、166(3.77%)、187(3.74%)以及180(3.67%);暴露水平与美国、西班牙、加拿大等地区相比属于严重水平。统计结果显示,年龄、身体质量指数和孕周与PCBs蓄积有显著性正相关关系;工业区居住的孕妇相与非工业区居住孕妇相比,上述统计结果更为显著。     

有机化学品不同温度下(过冷)液体蒸气压预测模型的建立与评价

(过冷)液体蒸气压(PL)是评价化学品在环境中分配、迁移和归趋行为的重要参数。PL具有较强的温度依附性。发展一种能够精确预测不同环境温度下化学品PL的方法,有助于填补化学品生态风险评估的大量数据缺失。本研究收集整理了661种有机化合物在不同温度下(200~830 K)共计10 478个log PL值。在此基础上,采用偏最小二乘(PLS)回归和支持向量机(SVM)方法,构建了PL的线性和非线性预测模型。结果表明:2种模型均具有良好的拟合度、稳健性及预测能力,SVM模型的预测性能略高于PLS模型(PLS:R2adj.tra=0.912,RMSEtra=0.477,Q2ext=0.910;SVM:R2adj.tra=0.997,RMSEtra=0.092,Q2ext=0.967)。机理分析表明,温度是影响PL的主要因素,温度越高,蒸气压越大;其次,X1sol也影响PL大小,X1sol用来描述分子间的色散作用,分子间色散力越小,蒸气压越大;此外,化合物的氢键个数、极性和分子构型等因素也影响PL大小。采用Wiliams plot方法表征了PLS模型应用域。所建立的模型可用来预测烷烃、烯烃、醇、酮、羧酸、苯、酚、联苯、卤代芳香烃、含N化合物及含S化合物在不同温度下的PL数据。     

Multi-objective transportation route optimization for hazardous materials based on GIS

Safety and security are of paramount importance, it is important to optimize and improve the routes of trucks that carry hazardous materials. In this study, we not only ensure the risk in the network, but also consider the transportation cost and the factors such as buildings and emergency facilities around the routes. The Geographic Information System (GIS) is used to quantify the factors on each section in the network. We present an epsilon constrained multi-objective mixed-integer linear programming optimization model to find the robust and stable transportation optimization solutions. At the end, we complete a case analysis of the proposed methodology to determine the motorway segments in Jiangsu province, China and test the above algorithm on the network, which has 144 nodes and 388 sections. The results we get show that the factors of buildings play a very important role in the model, and the multi-objective mixed-integer linear optimization model is reasonable and performs good quality.     

Experimental and theoretical research on the inhibition performance of ethanol gasoline/air explosion by C6F12O

The safety issue of ethanol gasoline and the methods to control or weaken its explosion have attracted attention. To clarify the effect of C₆F₁₂O (perfluoro(2-methyl-3-pentanone)) on the explosion of ethanol gasoline-air mixtures and intrinsic mechanism, the explosion overpressure and flame propagation behavior under different equivalence ratios (φ = 0.6–0.8) and C₆F₁₂O concentrations (χ_inh = 0–4.0%) were experimentally obtained. The detailed inhibitor reaction process was also obtained by CHEMKIN based on a new assembly kinetic mechanism. The results show that the effects of C₆F₁₂O on the explosion characteristics of ethanol gasoline varied with χ_inh and φ. For rich flames, C₆F₁₂O is more effective than and heptafluoropropane (C₃HF₇) and nitrogen (N₂) in suppressing explosions; for lean and equivalence ratio flames, the addition of C₆F₁₂O may result in more severe explosions. The decrease in chemical reactivity is mainly because the mole fractions of OH and H radicals and the proportion of paths H radicals involved decrease after adding C₆F₁₂O, and R1500: CF₃COF + H = CF₃CO + HF, R965: CF₂:O + H = CF:O + HF, R863: CF₃ + H = CF₂ + HF are main suppressing reactions.     

基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析*

为解决电力行业事故报告文本较长、语义复杂,难以进行有效文本识别问题,提出1种以BERT作为底层的预训练模型,并设计1种双重注意力机制编码器,结合BILSTM-CRF深度挖掘事故文本语义特征,从而实现文本智能分析。首先构建电力词典,通过对BERT预训练,进行BIO标注,然后引入BILSTM-CRF模型实现对文本标签智能分类,最后将该模型与现行其他4种深度学习模型进行对比。研究结果表明:该模型智能识别精确率、召回率及F1值(查准率)均达到约97%,较其他4种模型中效果最好的模型分别提高0.02,0.03,0.02。研究结果可为电力行业事故报告文本分析提供1种新思路。     

基于缓冲区重采样的LSTM林火预测模型*

为提高林火风险预测精度,挖掘地图上隐含的空间信息、时间序列上隐含的长期趋势和循环波动,提出1种基于缓冲区重采样的长短期记忆（LSTM）林火预测模型,选取15个与林火相关的影响因素,以方差膨胀因子为评价指标对其进行多重共线性检验,方差膨胀因子大于10的因素具有共线性,并采用信息增益率验证筛选结果的合理性。考虑到火灾的空间聚集特性,采用缓冲区分析与过采样相结合方法减少样本不均衡现象的影响,最终得到176 732条样本。对12个影响因素和研究时间段的火点建立LSTM预测模型,对森林火灾发生风险进行预测。研究结果表明:基于缓冲区重采样的LSTM林火预测模型有效考虑时空上隐含的信息,预测模型准确率为87.06%,特异性为97.99%,敏感度为76.12%,阳性预测率为97.43%,阴性预测率为80.41%,ROC曲线与AUC值均优于随机森林（RF）和支持向量机（SVM）这2种基准算法。维尔克松秩和检验发现,本文提出的模型与基准算法结果具有显著性差异。研究结果可为提高林火风险预测精度提供参考。     

SPE-UPLC-MS/MS法测定地下水中多种农药残留

采用固相萃取超高效液相色谱串联质谱法(SPE-UPLC-MS/MS)同时测定地下水中涕灭威、克百威、2,4滴和五氯酚等4种农药残留,通过优化试验条件,使方法在10.0μg/L～200μg/L范围内线性良好,检出限为0.003μg/L～0.006μg/L.标准溶液低、中2个质量浓度水平的加标回收率为84.1％ ～98.8...     

Some properties of a sequencing batch reactor for treatment of wastewater containing thiocyanate compounds

Thiocyanate (SCN) compounds in photo-processing wastewater (PPWW) could be treated by an SBR system without any release of thiocyanate to the atmosphere during the aeration step. An SCN loading greater than 84 g m(-3)d(-1) showed negative effects on the growth of bio-sludge and removal efficiencies of the system. The acclimatization period of the system was increased with an increase in SCN concentration or loading. The COD, BOD(5), TKN, and SCN removal efficiencies were 96.0 +/- 1.6%, 72 +/- 2%, 49 +/- 5%, and 82 +/- 3%, respectively, under an SCN loading of up to 84 g m(-3)d(-1). The removal efficiency of the system was repressed by SCN due to the repressed growth rate of nitrification bacteria. However, the removal efficiency could be increased with an increase in HRT or a decrease in SCN loading. Also, increases in HRT or decreases in SCN loading led to increased sludge age or solid retention time (SRT) and decreased the sludge volume index (SVI) value. The SRT and SVI of the system with synthetic wastewater containing 840 mg l(-1) SCN under an HRT of 3 days (SCN loading of 280 g m(-3)d(-1)) were 3.9 +/- 0.7 days and 65 +/- 4 ml g(-1), respectively, while they were 11.2+/-0.8 days and 55 +/- 6 ml g(-1), respectively under an HRT of 10 days (SCN loading of 84 g m(-3)d(-1)).     

Response of Fish and Macroinvertebrate Bioassessment Indices to Water Chemistry in a Mined Appalachian Watershed

Multimetric indices based on fish and benthic macroinvertebrate assemblages are commonly used to assess the biological integrity of aquatic ecosystems. However, their response to specific stressors is rarely known. We quantified the response of a fish-based index (Mid-Atlantic Highlands Index of Biotic Integrity, MAH-IBI) and a benthic invertebrate-based index (West Virginia Stream Condition Index, WV-SCI) to acid mine drainage (AMD)-related stressors in 46 stream sites within the Cheat River watershed, West Virginia. We also identified specific stressor concentrations at which biological impairment was always or never observed. Water chemistry was extremely variable among tributaries of the Cheat River, and the WV-SCI was highly responsive across a range of AMD stressor levels. Furthermore, impairment to macroinvertebrate communities was observed at relatively low stressor concentrations, especially when compared to state water quality standards. In contrast to the WV-SCI, we found that the MAH-IBI was significantly less responsive to local water quality conditions. Low fish diversity was observed in several streams that possessed relatively good water quality. This pattern was especially pronounced in highly degraded subwatersheds, suggesting that regional conditions may have a strong influence on fish assemblages in this system. Our results indicate that biomonitoring programs in mined watersheds should include both benthic invertebrates, which are consistent indicators of local conditions, and fishes, which may be indicators of regional conditions. In addition, remediation programs must address the full suite of chemical constituents in AMD and focus on improving linkages among streams within drainage networks to ensure recovery of invertebrate and fish assemblages. Future research should identify the precise chemical conditions necessary to maintain biological integrity in mined Appalachian watersheds.     

Assessing the economic impact of invasive species: the case of yellow starthistle (Centaurea solsitialis L.) in the rangelands of Idaho, USA

Yellow starthistle (Centaurea solsitialis L.) is an invasive weed that creates problems for the management of Idaho's rangelands. A bioeconomic approach combined with an input-output economic model is used to estimate direct and secondary economic costs of the weed in relation to its interference with agricultural and non-agricultural benefits that rangelands provide. Direct economic costs of the infestations were estimated to be of 8.2 million '05 dollars per year, and secondary costs of 4.5 million '05 dollars per year, for a total of 12.7 million '05 dollars; agricultural related economic impacts accounted for 79% of this total cost, and non-agricultural for 21%.