山东省农田土壤中拟除虫菊酯类农药污染特征与风险评价

在山东省范围内采集农田表层土壤样品91个,采用气相色谱法分析了7种拟除虫菊酯类农药(SPs)的含量和组成,分析了其分布特征,比较了大田土壤和大棚土壤中该类农药含量的差异,并评价了其生态风险.结果表明,7种拟除虫菊酯类农药总含量(∑_7SPs)范围在0.01—88.51μg·kg~(-1)之间,均值为5.65μg·kg~(-1).与国内其他地区农田土壤相比,山东省农田土壤中拟除虫菊酯含量处于中等偏低水平.山东省各地区的拟除虫菊酯含量存在差异,但组成相近,均是高效氯氟氰菊酯、联苯菊酯和氯氰菊酯的比例较高.部分地区大棚土壤中的含量高于大田土壤.风险评价结果表明,山东省农田土壤个别采样点中甲氰菊酯和高效氯氟氰菊酯具有生态风险,三分之一的采样点存在潜在生态风险,其余采样点不存在生态风险.     

哈尔滨市区区域环境噪声分析

随着人们环境保护意识的增强和对环境质量要求的提高,声环境质量已成为影响城市人民生活的一个重要污染指标,城市的不断发展,使噪声污染越来越成为人们日益关注的环境问题之一。采用定点测量方法和噪声分析仪进行监测,对哈尔滨市区区域环境噪声的监测结果进行了分析,监测结果表明:生活在低分贝值区域的人口逐渐增多,哈尔滨市环境状况有所好转。     

城市高污染水体生物生态处理技术研究

针对城市高污染水体的特点,建成了粉煤灰基质滤料生物过滤与复合水生植物滤床组合处理系统。生物过滤在有机物和氨氮的去除方面起主要作用,磷则主要是通过人工湿地去除。运行结果表明,组合工艺出水COD、TP、NH3-N、TN平均浓度分别为16.35 mg/L、0.08 mg/L、2.68 mg/L、15.63 mg/L,均满足《城市污水再生利用——城市杂用水水质》(GB/T18920-2002)标准要求。     

高密度沉淀池技术概述

高密度沉淀池(Densadeg)以其结构紧凑、处理效率高、出水水质稳定等特点,越来越多地被应用于城市污水一级强化处理和深度处理。本文主要介绍了高密度沉淀池的三种基本类型,重点阐述了RL型高密度沉淀池的工作原理及组成,以及污泥外部循环系统、高效斜板设置的特点;同时介绍了Densadeg的另一种运行方式:超高速沉淀池(ACTIFLO工艺),展望了高密度沉淀池的应用前景。     

高桩码头的振动测量与分析

测量了高平潮、低平潮及两潮位之间三个时段某高桩码头工作平台的振动情况,比较了不同潮位下振动的最大位移值.对测量数据进行了分析,发现海浪对结构振动的位移大小影响明显,得到了工作平台的前三阶自振频率.利用两点差法,根据码头平动记录求取了码头沿垂直轴向的码头扭转分量,经计算码头转角在10-7rad量级,码头扭转的转动中心按照...     

含油污泥无害化处置优化研究及生命周期评价

提出含油污泥的优化处置方案,并通过生命周期评价方法(LCA)对优化方案进行环境影响评价。结果表明,与原处置工艺相比优化后环境负荷降低了99.62%。     

铁屑对低浓度含铬地下水的处理研究

以某铬渣污染场地上抽提上来的低浓度含铬地下水作为实验研究对象。选用铁屑对含铬废水进行处理。通过静态试验考察了影响六价铬去除率的因素:pH、铁屑用量、反应时间,得到了较好的反应工艺参数。并在静态试验的基础上,用铁屑填充自制的反应柱研究了动态试验的效果。结果表明:六价铬的出水浓度低于0.05 mg/L,达到GB/T 14848-93《地下水质量标准》Ⅲ类排放标准。     

北京市建筑废弃物资源化利用研究与项目示范

针对北京市建筑废弃物产量大、资源化利用率低的问题，分析了北京市建筑废弃物的来源、产量、组分及处理现状，阐述了建筑废弃物资源化利用途径和资源化利用工艺，介绍了朝阳区建筑废弃物资源化示范项目建设情况，给出了北京市建筑废弃物资源化利用设施建设合理建议。     

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.     

Discrete-choice experiments valuing local environmental impacts of renewables: two approaches to a case study in Portugal

Despite the often mentioned environmental benefits associated with transition from fossil fuels to renewable energy sources, their use for electricity production has non-negligible negative environmental impacts. The most commonly mentioned in surveys concern different types of landscape impacts, impacts on the fauna and flora, and noise. These impacts differ by size and location of plants, and by source of energy, rendering the policy decision complex. In addition, there are other welfare issues to take into consideration, as positive and negative environmental impacts are not evenly distributed among population groups. This paper proposes to compare the welfare impacts of renewable energy sources controlling for the type of renewable as well as the specific environmental impact by source. To this end, two discrete-choice experiments are designed and applied to a national sample of the Portuguese population. In one case, only individual negative impacts of renewables are used, and in another case, the negative impacts interact with a specific source. Results show the robustness of discrete-choice experiments as a method to estimate the welfare change induced by the impacts of renewable energy sources. Overall, respondents are willing to pay to reduce the environmental impacts, thus making compensation for local impacts feasible. Moreover, the estimations reveal that respondents are significantly sensitive to the detrimental environmental effects of specific renewable energy sources, being willing to pay more to use these sources of energy relative to others.