AERMOD在国内环境影响评价中的实例验证与应用

AERMOD是美国环保局推出的新一代空气质量模式系统,它由AERMET(气象数据预处理器)、AERMAP(地形数据预处理器)和AERMOD(大气扩散模型)3部分组成.结合宁波市北仑区域大气环境影响评价,对该模式系统进行模式验证,并应用于实际预测评价.验证结果表明,在采用适当的模型参数时,该系统预测值与实际监测值具有很好的一致性,SO2、NO2日均最高浓度预测准确率分别达到64.3%和85.7%.最后结合实际预测评价工作,提出AERMOD模式系统在国内环境影响评价工作中的优势及不足.     

《燃煤二氧化硫排放污染防治技术政策》绩效定量评估

基于全国46个城市(其中38个城市位于"两控区")1996~2005年的面板数据,运用difference-in-differ-ences(DID)模型对《燃煤二氧化硫排放污染防治技术政策》的绩效进行了定量评估。结果表明,该政策的实施对于降低工业二氧化硫排放量起到积极作用,使"两控区"城市年工业二氧化硫的排放量平均降低了19.3%。     

电子元器件的腐蚀

电子元器件在大气环境下,因潮湿及其它条件下会发生大气腐蚀,从而导致其材料产生腐蚀,电子结构系统发生故障如短路、点接触不良等.本文简单论述了因空气污染、塑料和有机物分解气体等导致的电子元器件的大气腐蚀原理及原因.     

Roles of semivolatile and intermediate-volatility organic compounds in secondary organic aerosol formation and its implication: A review

Secondary organic aerosol(SOA) is a very important component of fine particulate matter(PM_2.5) in the atmosphere. However, the simulations of SOA, which could help to elucidate the detailed mechanism of SOA formation and quantify the roles of various precursors, remains unsatisfactory, as SOA levels are frequently underestimated. It has been found that the performance of SOA formation models can be significantly improved by incorporating the emission and evolution of semivolatile and ...     

关于室内装修有害气体烘焙排风稀释技术数值研究

依据实测数据,应用数值计算手法,对烘焙排风稀释技术不同工况下总挥发性有机化合物的排除量、烘焙排风稀释阶段室内空气中总挥发性有机化合物的浓度变化进行计算并讨论烘焙温度、换气次数对去除效果的影响。     

数据挖掘在预测组织事故防控效果中的应用*

为进一步探索数据挖据技术在组织事故预防工作中的融入性与适用性,基于24Model构建事故预控基础模型,通过预测准确率数值及接受者操作特性曲线（ROC曲线）对比分析随机森林（RF）、支持向量机(SVM)、决策树（DT）与神经网络（NN）4种方法对组织事故防控效果的预测性能。结果表明:针对事故率控制（Y1）、职业危害预防（Y2）、财产损失3类预测目标（Y3）,RF方法均能达到较高的准确率及稳定性,具有较优的预测性能。根据特征重要度（FI）排序,明确对组织事故水平影响最显著的因素为安全实践活动认知（SC5）及安全管理程序文件（SMS3）,FI值均大于0.150 0。研究结果可为有效预测组织事故防控效果提供方法依据,同时为企业安全工作的规划设计提供思路。     

喀斯特峡谷区工程性缺水原因及解决途径——以贵州省花江峡谷示范区为例

喀斯特峡谷区河深坡陡,地下水深埋,水资源利用难度大,加之经济和水利工程薄弱,是贵州省工程性缺水的典型区域。通过对花江峡谷示范区工程性缺水的原因剖析,提出一系列解决工程性缺水问题的措施,如雨水收集、表层喀斯特水和基岩裂隙水的开发利用、节水、加强对水利工程设施的管理和维护、加大水利投入以及提高植被覆盖率等,这对解决喀斯特峡谷及类似区域的工程性缺水问题有一定的指导和参考意义。     

Modelling the fate of hydrophobic organic contaminants in a boreal forest catchment: A cross disciplinary approach to assessing diffuse pollution to surface waters

The fate of hydrophobic organic compounds (HOCs) in soils and waters in a northern boreal catchment was explored through the development of a chemical fate model in a well-characterised catchment system dominated by two land types: forest and mire. Input was based solely on atmospheric deposition, dominated by accumulation in the winter snowpack. Release from soils was governed by the HOC concentration in soil, the soil organic carbon fraction and soil-water DOC content. The modelled export of selected HOCs in surface waters ranged between 11 and 250 ng day⁻¹ during the snow covered period, compared to 200 and 9600 ng/d during snow-melt; highlighting the importance of the snow pack as a source of these chemicals. The predicted levels of HOCs in surface water were in reasonable agreement to a limited set of measured values, although the model tended to over predict concentrations of HOCs for the forested sub-catchment, by over an order of magnitude in the case of hexachlorobenzene and PCB 180. This possibly reflects both the heterogeneity of the forest soils and the complicated and changing hydrology experienced between the different seasons.     

Evaluating and reducing a model of radiocaesium soil-plant uptake

An existing model of radiocaesium transfer to grasses was extended to include wheat and barley and parameterised using data from a wide range of soils and contact times. The model structure was revised and evaluated using a subset of the available data which was not used for model parameterisation. The resulting model was then used as a basis for systematic model reduction to test the utility of the model components. This analysis suggested that the use of 4 model variables (relating to radiocaesium adsorption on organic matter and the pH sensitivity of soil solution potassium concentration) and 1 model input (pH) are not required. The results of this analysis were used to develop a reduced model which was further evaluated in terms of comparisons to observations. The reduced model had an improved empirical performance and fewer adjustable parameters and soil characteristic inputs.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.