基于机器视觉的鱼类行为特征提取与分析

近年来,水污染问题备受关注。生物式水质监测成为目前国家环境保护工作的重要任务之一。为准确监测水质污染情况,本文以青鳉鱼(Oryzias latipes)为研究对象,采用非接触式的机器视觉监测技术,提取青鳉鱼的生理特征(呼吸频率)和运动特征(胸鳍和尾鳍的摆动频率),并分析这些特征与水质之间的关系。本文采用支持向量机(Support Vector Machine,SVM)准确提取鱼鳃,并根据鱼鳃呼吸面积大小变化计算出鱼的呼吸频率。基于形态学细化算法提取青鳉鱼骨架,求出胸鳍和尾鳍的摆动频率。结果显示:不同浓度铜离子暴露实验测得的青鳉鱼生理特征和运动特征与实际情况一致;通过对不同铜离子浓度下的毒性实验数据对比,发现了青鳉鱼的生理特征和运动特征会随不同的铜离子浓度发生相应变化,可以作为水质监测的评价标准。     

应用底栖动物为指示生物的松花江干流水生态预警研究

指示生物监测及水生态预警是利用水环境中指示物种的数量、群落结构指标和个体生理指标等描述水生态系统的健康状态,其相比于常规理化监测和预警更直接地反映水体的生态质量。本研究在松花江干流2012—2015年大型底栖无脊椎动物监测结果的基础上,结合各监测点生态质量管理目标,通过分析物种的种类、出现的频次、物种污染敏感性(耐污值),尝试提出了松花江干流监控断面以底栖动物为指示生物的水生态预警模式,研究思路和结果对流域水环境风险管理指标的拓展有积极作用。     

Antibiotic resistant bacteria/genes dissemination in lacustrine sediments highly increased following cultural eutrophication of Lake Geneva (Switzerland)

This study investigates faecal indicator bacteria (FIB), multiple antibiotic resistant (MAR), and antibiotic resistance genes (ARGs), of sediment profiles from different parts of Lake Geneva (Switzerland) over the last decades. MARs consist to expose culturable Escherichia coli (EC) and Enterococcus (ENT) to mixed five antibiotics including Ampicillin, Tetracycline, Amoxicillin, Chloramphenicol and Erythromycin. Culture-independent is performed to assess the distribution of ARGs responsible for, β-lactams (blaTEM; Amoxicillin/Ampicillin), Streptomycin/Spectinomycin (aadA), Tetracycline (tet) Chloramphenicol (cmlA) and Vancomycin (van). Bacterial cultures reveal that in the sediments deposited following eutrophication of Lake Geneva in the 1970s, the percentage of MARs to five antibiotics varied from 0.12% to 4.6% and 0.016% to 11.6% of total culturable EC and ENT, respectively. In these organic-rich bacteria-contaminated sediments, the blaTEM resistant of FIB varied from 22% to 48% and 16% to 37% for EC and ENT respectively, whereas the positive PCR assays responsible for tested ARGs were observed for EC, ENT, and total DNA from all samples. The aadA resistance gene was amplified for all the sediment samples, including those not influenced by WWTP effluent water. Our results demonstrate that bacteria MARs and ARGs highly increased in the sediments contaminated with WWTP effluent following the cultural eutrophication of Lake Geneva. Hence, the human-induced changing limnological conditions highly enhanced the sediment microbial activity, and therein the spreading of antibiotic resistant bacteria and genes in this aquatic environment used to supply drinking water in a highly populated area. Furthermore, the presence of the antibiotic resistance gene aadA in all the studied samples points out a regional dissemination of this emerging contaminant in freshwater sediments since at least the late nineteenth century.     

微生物生理群在猪粪秸秆高温堆肥碳氮转化中的作用

在自制的强制通风静态堆肥反应箱中,猪粪与秸秆以鲜重7∶1的比例进行了堆肥化实验,在堆制的23 d里根据堆温变化分阶段采集堆肥样品,利用MPN法测定了堆料中纤维素分解菌和氮素微生物生理群的数量变化,同时测定了相应的碳、氮含量。结果表明,纤维素分解菌在稳定腐熟阶段较多,对于后期有机碳的降解和腐殖质含量的增大起了很大的作用,在堆制的23 d里,腐殖质增加了2.4%。整个堆制过程中,氨化细菌的数量最大且与氨气释放浓度和铵态氮含量呈显著正相关,都在高温期增加,降温期后减少,氨化细菌的数量在高温期的增加率远高于降温期后的减少率,而铵态氮在高温期的增加率远低于在降温后期的减少率,铵态氮总体上减少了74.1%;亚硝化细菌数量与硝态氮呈正相关;反硝化细菌数量在降温期上升幅度较大,堆制结束时为堆制初期的13倍,且与堆肥中硝态氮含量呈正相关;硝态氮含量增加了87.5%;堆肥后期硝态氮的增加可能与堆肥中存在能进行硝化作用的反硝化细菌有关。固氮菌数量在堆制结束时达堆制初期的2.61倍,主要在降温期增加较多,对堆肥中有机氮的形成起很大作用。     

对普光气田油菜的二氧化硫熏气实验

为探讨普光气田废气对周围农作物的影响和生物监测方法,选择气田周边油菜作为受试样本,对其进行二氧化硫现场熏气实验。结果表明,油菜在受到1.43 mg/m3浓度、3小时二氧化硫熏气后,植株耐受,不产生明显受害症状;叶片含硫量、MDA含量、可溶性蛋白含量等指标的测定结果与二氧化硫熏气浓度、熏气时间存在显著的相关关系。     

Development and field validation of an indicator to assess the relative mobility and risk of pesticides in the Lourens River catchment,South Africa

A GIS based pesticide risk indicator that integrates exposure variables (i.e. pesticide application, geographic, physicochemical and crop data) and toxicity endpoints (using species sensitivity distributions) was developed to estimate the Predicted Relative Exposure (PREX) and Predicted Relative Risk (PRRI) of applied pesticides to aquatic ecosystem health in the Lourens River catchment, Western Cape, South Africa. Samples were collected weekly at five sites from the beginning of the spraying season (October) till the beginning of the rainy season (April) and were semi quantitatively analysed for relevant pesticides applied according to the local farmers spraying programme. Monitoring data indicate that physicochemical data obtained from international databases are reliable indicators of pesticide behaviour in the Western Cape of South Africa. Sensitivity analysis identified K_OC as the most important parameter influencing predictions of pesticide loading derived from runoff. A comparison to monitoring data showed that the PREX successfully identified hotspot sites, gave a reasonable estimation of the relative contamination potential of different pesticides at a site and identified important routes of exposure (i.e. runoff or spray drift) of different pesticides at different sites. All pesticides detected during a monitored runoff event, were indicated as being more associated with runoff than spray drift by the PREX. The PRRI identified azinphos-methyl and chlorpyrifos as high risk pesticides towards the aquatic ecosystem. These results contribute to providing increased confidence in the use of risk indicator applications and, in particular, could lead to improved utilisation of limited resources for monitoring and management in resource constrained countries.     

Use of Scale Invariance in Evaluating Judgement Indicators

Indicators are used to draw conclusions about ecological endpoints when these endpoints cannot be measured directly. In many cases, inferences about an endpoint are only possible because assumptions have been made about the relationship between indicator and endpoint; we refer to such indicators as judgement indicators. The validity of inferences made using a judgement indicator can be gauged by examining the known or assumed form of the general relationship between indicator and endpoint. The rules for this kind of inference are a consequence of scale invariance, which originates from measurement theory. For simple indicators comprised of a single indicator measurement, the inferences allowed – equivalence, rank, equality of intervals, and equality of ratios – depend on whether the data are nominal, ordinal, interval, or ratio scaled. For composite indicators containing two or more simple indicators, inferences are also affected by the mathematical form of combination; e.g., whether the terms are summed or multiplied. Standardizing simple or composite indicators can allow inferences about the relative importance of observations, based on the natural range of occurrence. Scale invariance is a particularly important consideration in landscape assessments, since these often make use of judgement indicators.     

不同生境苔类和角苔类植物对生态环境的指示作用

利用植物对生态环境进行监测能综合地判断生态环境变化情况。通过对黔北丹霞地貌区、香纸沟喀斯特地貌区及烂泥沟卡林型金矿区苔类、角苔类植物的比较研究,统计分析种群的分布情况。结果表明,角苔类植物主要生长于生态环境保护较好的区域,扁萼苔科、角苔科为生态环境保护较好区域监测指示科植物,蛇苔属为生态环境保护较好区域监测指示属植物,小蛇苔、蛇苔为生态环境保护较好区域监测指示种植物。     

全二维气相色谱-飞行时间质谱法分离复杂体系中7种指示剂 多氯联苯

采用全二维气相色谱-飞行时间质谱法(GC×GC TOF-MS)分析了混标样品Aroclor 1260、Aroclor 1254、Aroclor 1242中的多氯联苯(PCBs)单体,考察了在复杂体系下对7种PCBs指示剂的分离能力。结果表明,与优化柱系统的分离效果相比,136种单体的混标样品中共分离出121种单体,7种指示剂经一维和二维的保留时间及质谱定性,在121种单体的2D斑点图中清晰可辨。全二维对于复杂体系的PCBs和指示剂的分离表明其强大的分离能力和对于检测复杂体系中指示剂的分辨能力,对该类物质的定性和定量检测具有重要意义。     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.