江苏油田生活污水生物处理技术述评

简介了生物处理生活污水的技术，对几种主要生物处理法进行了综合评价，并结合江苏油田生活污水生物处理的实践，综合分析了活性污泥法、生物转盘法、接触氧化法等三种主要生物处理技术，初步提出了中小规模生活污水处理场的设计标准，可供油田生活基地中小规模生活污水处理场设计时参考。     

用二氧化氯降解废水中的苯胺类化合物（2）──降解机理的探讨

本文在上文（１）的基础上，进一步研究了经二氧化氯处理前后ＢＯＤ＿５／ＣＯＤ的变化规律，其比值大于或接近０．３．其降解产物有醌及醌的氧化产物和ＮＨＩ．对苯胺及二氧化氯来说其．反应级数为１，整个反应为二级反应．     

时延神经网络对宝兴树蛙异质种群鸣叫的聚类分析

对瓦屋山宝兴树蛙3个不同海拔分布(A:山下;B:山腰;C:山上)的异质种群鸣叫的时频参数进行分析,发现三地鸣叫各参数互相重叠,无明显界线.常规统计分析不能使三地的鸣叫聚类为各自的组.使用时延神经网络对三地宝兴树蛙鸣叫的时频参数进行有指导聚类,发现三种群鸣叫有地区性差异,并表现出由个体交流引起的山腰种群群体特征的不显著.推测差异可能是因为长期地理隔离所导致.基于神经网络的分析结果,对各种群间的个体交流情况进行了初步估计.图6表1参20     

南通市夏季VOCs污染特征与来源研究

运用大气挥发性有机物(VOCs)快速在线连续自动监测系统,于2018年7月对南通市区环境空气中VOCs进行观测,分析VOCs的浓度状况、组成特征、对臭氧生成潜势的贡献及主要来源。结果表明:观测期间共检出100种VOCs,总挥发性有机物(TVOCs)的平均体积分数为(38. 18±23. 63)×10^-9,各物种体积分数从大到小顺序依次为烷烃>含氧有机物>芳香烃>卤代烃>烯、炔烃;芳烃和烯烃是最主要的活性物种,间/对二甲苯、甲苯、邻二甲苯等是VOCs的关键活性组分;利用PMF模型解析得到VOCs的主要污染来源是工业排放与溶剂使用、机动车尾气排放、燃料挥发排放和生物源排放。     

Calcined Oil Shale Semi-coke for Significantly Improved Performance Alginate-Based Film by Crosslinking with Ca2+

Journal of Polymers and the Environment - Oil shale semi-coke (OSSC) is the residual solid waste after refining of oil shale, which principally contains organic matter and minerals. The common...     

Purification of spent cathode carbon from aluminum electrolysis cells by alkali fusion-acid leaching process

Journal of Material Cycles and Waste Management - Spent cathode carbon (SCC), with high recycling potential of carbon, is an unavoidable solid waste in the aluminum electrolysis industry. In this...     

Improving protected area effectiveness through consideration of different human-pressure baselines

Previous assessments of the effectiveness of protected areas (PAs) focused primarily on changes in human pressure over time and did not consider the different human-pressure baselines of PAs, thereby potentially over- or underestimating PA effectiveness. We developed a framework that considers both human-pressure baseline and change in human pressure over time and assessed the effectiveness of 338 PAs in China from 2010 to 2020. The initial state of human pressure on PAs was taken as the baseline, and changes in human pressure index (HPI) were further analyzed under different baselines. We used the random forest models to identify the management measures that most improved effectiveness in resisting human pressure for the PAs with different baselines. Finally, the relationships between the changes in the HPI and the changes in natural ecosystems in PAs were analyzed with different baselines. Of PAs with low HPI baselines, medium HPI baselines, and high HPI baselines, 76.92% (n=150), 11.11% (n=12), and 22.86% (n=8) , respectively, showed positive effects in resisting human pressure. Overall, ignoring human-pressure baselines somewhat underestimated the positive effects of PAs, especially for those with low initial human pressure. For PAs with different initial human pressures, different management measures should be taken to improve effectiveness and reduce threats to natural ecosystems. We believe our framework is useful for assessing the effectiveness of PAs globally, and we recommend it be included in the Convention on Biological Diversity Post-2020 Strategy.     

Photoelectrochemical technology for solar fuel generation,from single photoelectrodes to unassisted cells: a review

Environmental Chemistry Letters - Photoelectrochemical solar fuel generation requires a highly integrated technology for converting solar energy into chemical fuels. Dihydrogen (H2) and...     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.