光电协同催化氧化苯甲酸的试验研究

在自制的光电协同催化氧化反应器中,对苯甲酸溶液进行了光电协同催化氧化处理研究,考察了阳极偏电压、反应氛围和制备光阳极所用基材对苯甲酸降解效率的影响,并比较了光电协同催化氧化与光解、光催化氧化和电解处理对苯甲酸去除率的差异.试验结果表明,经过1h的处理,光电协同催化氧化对苯甲酸的去除率可达85%以上,远远高于光解、光催化氧化和电解处理对苯甲酸的去除率(依次为48%、30%和约0%),具有明显的光电协同作用;工作电极与对电极采用内外同心圆柱形的布置方式;在通入高纯氮的条件下,TiO₂/Ti板光阳极对苯甲酸的去除率在1h时可达98%,优于TiO₂/Al板光阳极在仆时对苯甲酸85%的去除率;在通入纯氧的条件下,TiO₂/Ti板光阳极和TiO₂/Al板光阳极经过1h对苯甲酸的去除率均接近100%.     

水中五氯酚钠对发光细菌毒性测定的影响因素

应用发光细菌检测法,考察了不同的影响因素对水体中典型的有机污染物五氯酚钠的生物毒性测定结果的影响.结果表明,五氯酚钠对发光细菌的EC₅₀值随着pH值和硬度的增加而增加;五氯酚钠的15 min EC₅₀和20 min EC₅₀基本相符;与水体中相同浓度的常规有机污染物相比,五氯酚钠表现出更强的毒性,实验中常规有机污染物的存在对五氯酚钠毒性测定的结果影响较小.原水毒性检测实验显示一定毒性(相对发光率降低14%),原水水质对测定结果影响程度与去离子水相似.     

脉冲变频电磁场对水中藻类的抑制及去除效能

研究了脉冲变频电磁场对水华鱼腥藻的抑制及去除效果.结果显示,磁场对循环水样表现出很好的杀藻效果,水样在磁场中累积停留时间为6min时,杀藻率可以达到94%,累积停留时间为10min时,杀藻率可以达到96%.处理后的水样,藻细胞密度在120h内没有增加,生长受到抑制,经磁场处理的循环水样"磁记忆"时间可以达到120h.对于静置于磁场中的水样,处理1h后,藻的生长开始受到抑制.不同的处理方式会产生不同的效果,对于脉冲变频磁场,水以运动的形式通过磁场更有利于杀藻.     

哑铃湾网箱养殖对水环境的影响

2002-04～2003-01对哑铃湾不同网箱养殖区水质和底泥的实地监测调查和分析表明,哑铃湾网箱养殖对环境的影响主要是:①使得养殖水环境中营养盐、BOD、COD、有机质和TSS增加,尤其是PO₄^3--P和NH₃-N增加更为明显;②使得底泥沉积物中N、P、硫化物、有机质等大量富集,其中富集现象最明显的是P、硫化物和NH₄-N,其次是TN和有机质.     

电力市场管理与负荷分析预测系统的开发应用

针对传统市场管理和分析预测中存在的数据源不完整、预测方法单一、人工汇总分析导致的准确率不高的问题,通过分析对比,采用 B/S 应用模式,J2EE 技术架构和 JAVA 解决方案,开发了电力市场管理与负荷分析预测系统,实现了海量数据的有序整合优化和负荷需求的科学预测。应用结果表明:该系统信息分类及管理完善、负荷预测模型先进、有效提高了预测准确率和工作效率,达到了为电力生产、经营提供辅助决策的预期目标。     

壬基酚及其短链聚氧乙烯醚在污泥和土壤中的存在和降解

壬基酚聚氧乙烯醚(NPnEO)是广泛使用的表面活性剂．在污水处理厂中会降解为具有内分泌干扰效应的短链产物和最终产物壬基酚(NP)。剩余污泥是其进入环境的重要途径。介绍了NP和短链NPnEO在污泥中的存在情况、污泥处理过程对其存在的影响、以及这些物质随污泥处置过程进入土壤后的降解。     

污水回用中的循环浓缩水质模型与污水回用率

污水回用中因循环浓缩,存在着水质变差问题．探索其变化规律,定量确定运行参数,做到既节水又保证回用水质合格、稳定,具有重要理论和实用意义．通过数学归纳法推导出一个循环浓缩水质数学模型和计算杂用水质和污水回用率的公式．应用该模型,以厕所污水回用为例,就污水水质、污染物去除率、处理水质和杂用水质标准对杂用水质、污水回用率的影响进行讨论．结果表明,在符合城市杂用水水质标准的前提条件下,分别按生化需氧量、浊度、氨氮及色度等水质指标计算得出的污水回用率是65．3％,82．9％,57．5％和37％．在厕所污水回用中,色度为水回用率的限制指标．由于色度指标的限制,经计算水循环厕所的污水回用率一般不宜超过40％。     

锡林郭勒盟植被物候枯黄期对干湿变化的时间多尺度响应

基于全球监测与建模组(GIMMS)1982～2015年第三代归一化植被指数(NDVI)GIMMS NDVI 3g数据集和气象观测数据,采用累计NDVI的Logistic曲线曲率极值法提取锡林郭勒草原植被枯黄期,并结合不同时间尺度标准化降水蒸散指数(SPEI)分析植被枯黄期对干湿变化的响应特征.结果表明:1982～201...     

Understanding and addressing the environmental risk of microplastics

Over the past decades, the plastic production has been dramatically increased. Indeed, a category of small plastic particles mainly with the shapes of fragments, fibers, or spheres, called microplastics (particles smaller than 5 mm) and nanoplastics (particles smaller than 1 μm) have attracted particular attention. Because of its wide distribution in the environment and potential adverse effects to animal and human, microplastic pollution has been reported as a serious environment problem receiving increased attention in recent years. As one of the commonly detected emerging contaminants in the environment, recent evidence indicates that the concentration of microplastics show an increasing trend, for the reason that up to 12.7 million metric tons of plastic litter is released into aquatic environment from land-based sources each year. Furthermore, microplastic exposure levels of model organisms in laboratory studies are usually several orders of magnitude higher than those found in environment, and the microplastics exposure conditions are also different with those observed in the environment. Additionally, the detection of microplastics in feces indicates that they can be excreted out of the bodies of animal and human. Hence, great uncertainties might exist in microplastics exposure and health risk assessment based on current studies, which might be exaggerated. Policies reduce microplastic emission sources and hence minimize their environmental risks are determined. To promote the above policies, we must first overcome the technical obstacles of detecting microplastics in various samples.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.