结构滞回阻尼比计算方法的改进研究∗

目前计算结构滞回阻尼比常用的方法是能量比法,该方法在给定滞回模型的基础上采用滞回耗能与结构总弹性应变能的比值求得结构滞回阻尼比,但该方法是建立在简谐激励下结构稳态响应的基础之上,对于真实地震下结构滞回阻尼比计算并不完全适用。考虑到这一不足,本文提出一种新的迭代计算结构滞回阻尼比的方法,在Carlos提出的结构滞回阻尼比计算方法的基础上,考虑地震激励频率对阻尼比的影响,通过改进Carlos迭代计算方法,得到了考虑地震激励频率影响的滞回阻尼比计算公式,通过该公式分析得出了不同延性系数和周期比对滞回阻尼比的影响规律,结果表明不同延性系数的结构滞回阻尼比会随着周期比的增大先增大再减小。本研究可为结构进入非线性状态后减震结构总体阻尼比的计算提供理论依据。     

Association of exposure to multiple metals with papillary thyroid cancer risk in China

Papillary thyroid cancer (PTC) has inflicted huge threats to the health of mankind. Metal pollution could be a potential risk factor of PTC occurrence, but existing relevant epidemiological researches are limited. The current case-control study was designed to evaluate the relationships between exposure to multiple metals and the risk of PTC. A total of 262 histologically confirmed PTC cases were recruited. Age- and gender-matched controls were enrolled at the same time. Urine samples were used as biomarkers to reflect the levels of environmental exposure to 13 metals. Conditional logistic regression models were adopted to assess the potential association. Single-metal and multi-metal models were separately conducted to evaluate the impacts of single and co-exposure to 13 metals. The increased concentration of urinary Cd, Cu, Fe, and Pb quartiles was found significant correlated with PTC risk. We also found the decreased trends of urinary Se, Zn, and Mn quartiles with the ORs for PTC. These dose-response associations between Pb and PTC were observed in the single-metal model and remained significant in the multi-metal model (OR_25-50th=1.39, OR_50-75th=3.32, OR_>75th=7.62, p for trend <0.001). Our study suggested that PTC was positively associated with urinary levels of Cd, Cu, Fe, Pb, and inversely associated with Se, Zn, and Mn. Targeted public health policies should be made to improve the environment and the recognition of potential risk factors. These findings need additional studies to confirm in other population.

     

应急处理苯胺污染水源水的粉末活性炭吸附工艺的研究

以浑河水为原水,模拟突发苯胺污染,通过投加粉末活性炭(PAC)进行应急处理的试验研究.试验结果表明:PAC对苯胺的吸附在30 min内能达到80%～90%的吸附容量;PAC对苯胺的吸附等温线符合弗兰德里希(Freundlich)吸附模式,在苯胺的平衡质量浓度为0.030 mg/L时,PAC对其吸附容量约为5 mg/g;比表面积较大的木屑炭对苯胺的吸附效果比煤质炭好,但粒度以300目左右为宜;炭浆浓度越小对苯胺的吸附效果越好;溶液pH以不小于5为最好;絮凝剂最佳的投加顺序是先投加炭浆然后投加絮凝荆;对突发的浑河水苯胺污染,在取水口处投加PAC是十分有效的应急处理措施.     

洗相废水中银回收技术研究

采用电解法和离子交换法回收洗相废水中的银.结果表明,电解法对高浓度含银洗相废水的银回收处理是一种行之有效的方法.对于中低浓度洗相废水中的银的回收,采用4%硫酸再生IRA-68离子交换树脂,树脂的交换能力可以增加4倍,再生时由于已交换的银被直接固定在树脂上,结果使得再生剂处置和银回收过程更加简单方便.IRA-68离子交换树脂回收中低浓度洗相废水中银的技术具有银回收效率更高、离子交换运行时间更长和操作更为简便等优点,使得传统的离子交换技术得到极大的改进和提高.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

城市天然气管道风险特征与肯特法的改进

根据城市高压天然气管道特点,将适合于城市天然气管道风险分析的肯特评分法加以改进,包括调整评分项目和事故因素权重,使其成为适于城市天然气管道的风险分析法。将改进后的肯特评分法应用于常州21km高压天然气管道的风险评价,得出8个相对高风险管段,其分析结果可作为安全管理人员合理分配维护资源的理论依据。工程应用实例证明:改进后的肯特法具有较强的可行性,是城市高压天然气管道风险评估的有效工具。     

可变模糊评价法在洪涝灾情评价中的应用

灾情评价实质上是一个模式识别问题.以往的灾情评价模型,虽解决了各单项指标灾情等级评估结果的不相容问题,但往往不能检验所给结果的准确性.基于可变模糊集理论,采用可变模糊评价模型可以科学、合理地确定样本指标对各级指标标准区间的相对隶属度和相对隶属函数,并且能够通过变换参数(α与P)变化模型进行评价.通过对多个评价结果进行比较分析,可以合理地确定出样本的评价等级.同时,由于该模型确定的灾级是连续的实数值,因而提高了灾情等级评价的灾级分辨率.最后将该方法应用于河南省洪涝灾情的综合评价中.应用实例表明,该模型计算简便,评价结果可信度高,可推广应用到其它灾情等级评估中.     

中美环境监测体制之比较

对中美环境监测体制进行对比性评述,指出了中国环境监测行业存在的分析标准滞后、体制不完善、人员匮乏等问题,提出借鉴美国经验,完善法律体系,引入市场机制,逐步培育竞争有序、监管到位的环境监测行业的建议。     

Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale

Both the net primary productivity (NPP) and the normalized difference vegetation index (NDVI) are commonly used as indicators to characterize vegetation vigor, and NDVI has been used as a surrogate estimator of NPP in some cases. To evaluate the reliability of such surrogation, here we examined the quantitative difference between NPP and NDVI in their outcomes of vegetation vigor assessment at a landscape scale. Using Landsat ETM+ data and a process model, the Boreal Ecosystem Productivity Simulator, NPP distribution was mapped at a resolution of 90 m, and total NDVI during the growing season was calculated in Heihe River Basin, Northwest China in 2002. The results from a comparison between the NPP and NDVI classification maps show that there existed a substantial difference in terms of both area and spatial distribution between the assessment outcomes of these two indicators, despite that they are strongly correlated. The degree of difference can be influenced by assessment schemes, as well as the type of vegetation and ecozone. Overall, NDVI is not a good surrogate of NPP as the indicators of vegetation vigor assessment in the study area. Nonetheless, NDVI could serve as a fairish surrogate indicator under the condition that the target region has low vegetation cover and the assessment has relatively coarse classification schemes (i.e., the class number is small). It is suggested that the use of NPP and NDVI should be carefully selected in landscape assessment. Their differences need to be further evaluated across geographic areas and biomes.     

Design,Modelling and Assessment of Emission Scenarios Resulting from a Network of Wood Biomass Boilers

The use of wood biomass as a fuel for domestic and industrial heating systems allows for a reduction of CO₂ emissions at a global scale, but it may also result in worse local air quality conditions, due to their emissions of particulate matter. The aim of this study is to assess the actual trend of atmospheric pollution in a study area, assuming that all heating systems are replaced by small size biomass boilers linked to the buildings through district heating network. Ground level concentrations of particulate matter, emitted by different heating systems, are therefore evaluated through numerical simulations performed by means of an atmospheric dispersion model (Sirane). As a first step, we have compared the environmental impact of a woodchip boilers network with that given by the use of traditional heating systems, i.e. wood stoves and natural gas boilers. As a second step, we have analysed the impact of such a network taking into account different emission scenarios, related to different boilers operating conditions. Results show that the environmental performances of a woodchip boilers network can be optimized by combining it with other renewable sources of energy devoted to the supply of hot water. The adopted analysis methodology can be applied to other real or hypothetic punctual sources on the territory.