获取日均值的大气监测时段选择

利用数理统计方法，以污染物瞬时监测数据为基础数据，选用控制在一定置信度下最大允许误差的统计公式确定出样本容量，并采用模糊集合运算—优化组合选择法，确定出获取日均值的最佳监测时段．     

Methylmercury, total mercury and total selenium in four common freshwater fish species from Ya-Er Lake, China

Snakehead fish (Ophiocephalus argus cantor), silver carp (Hypophthalmichthys molitrtix), crucian carp (Carassius carassius), and common carp (Cyprinus carpio) are four common freshwater fish species in China. In this study, the level of methylmercury (MeHg), total mercury (T-Hg), and total selenium (T-Se) in muscle samples of these four fish species from Ya-Er Lake, China, were analyzed using atomic fluorescence spectrometry coupled with high-performance liquid chromatography, and inductively coupled plasma mass spectrometry. The concentrations of MeHg in all the fish species were significantly correlated with those of T-Hg. Higher T-Hg and MeHg concentrations had accumulated in the snakehead fish, which is a strongly predatory fish, than in the other three species. The concentration ratios of MeHg and T-Hg in the muscles of these four fish species were almost equal. Conversely, there was negative correlation between the concentrations of T-Hg and T-Se, which implies that there is a competition between these two elements with respect to bioaccumulation. It is noteworthy that of all the muscle samples tested, the level of T-Hg exceeded the maximum allowable limit in fish [0.4 mg kg⁻¹ (w/w) recommended by the World Health Organization] in 38.46% of those of the silver carp, 18.18% of those of the crucian carp, and 100% of those of snakehead fish. These results show that the consumption of contaminated fish is a potential threat to human health and that necessary preventive measures to safeguard public health should be emphasized.     

Inherently safer reactors: Improved efficiency of 3-picoline N-oxidation in the temperature range 110–125 °C

Alkylpyridine N-oxides are important intermediates in the pharmaceutical and agrochemicals industries. The N-oxides are produced via the homogeneously catalyzed oxidation of the respective alkylpyridines using a 50% excess of hydrogen peroxide. The competitive hydrogen peroxide decomposition produces oxygen in the flammable environment of alkylpyridines and thus forms a key hazard for this reaction. In this work, the N-oxidation was performed under pressure in the temperature range of 110–125 °C with different catalyst concentrations. It was shown that temperature had an undisputable positive effect on the N-oxidation efficiency. The accurate measurement of the pressure rise due to decomposition was difficult. However, only 5% of the added H₂O₂ decomposed when stoichiometric quantities were employed, even in the temperature of 110 °C. The N-oxidation was very efficient, even when the lowest concentration of catalyst employed in this study was used.     

阿尔山市TSP(总悬浮微粒)浓度变化与气象因素关系探讨

通过阿尔山市2005年大气中总悬浮微粒（TSP）的全年三个监测点的监测结果，结合采样时有关气象参数，着重对TSP与气象条件的关系做一下分析、研讨。     

Characterization particulate matter from several Chinese cooking dishes and implications in health effects

Cooking fume produced by oil and food at a high temperature releases large amount of fine particulate matter(PM) which have a potential hazard to human health. This chamber study investigated particle emission characteristics originated from using four types of oil(soybean oil, olive oil, peanut oil and lard) and different kinds of food materials(meat and vegetable). The corresponding emission factors(EFs) of number, mass, surface area and volume for particles were discussed. Temporal variation of size-fractionated particle concentration showed that olive oil produced the highest number PM concentration for the entire cooking process. Multiple path particle dosimetry(MPPD) model was performed to predict deposition in the human respiratory tract. Results showed that the pulmonary airway deposition fraction was the largest. It was also found that particles produced from olive oil led to the highest deposition. We strongly recommend minimizing the moisture content of ingredients before cooking and giving priority to the use of peanut oil instead of olive oil to reduce human exposure to PM.     

炼油行业泄漏检测与修复技术实践研究

炼油行业排放的大气污染物主要是挥发性有机物,是形成二次污染物的重要前体物,也是引发大气环境问题的重要污染物.以天津某炼油企业泄漏检测与修复技术的工作实践为例,结合炼油行业生产装置的工艺特点,介绍在该行业开展泄漏检测与修复技术时的工作流程,包括设备和管线组件密封点位的建档、检测、泄漏点位的修复、复测以及泄漏量核算方法,对炼油行业开展泄漏检测与修复工作减少挥发性有机物无组织的排放起到借鉴作用.     

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...     

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.