废旧计算机CRT监视器的管理和资源化技术

分析了废旧计算机CRT监视器的材料组成 ,并且阐明了它对环境和人体健康的潜在危害 ,同时比较了国内外相关管理现状 ,总结了目前已有的资源化技术 ,主要是拆解技术 ,并且介绍了目前国内外的资源化实践 ,并提出有关建议。     

生态工业园区及其建设

生态工业园区是实现生态工业和工业生态学的重要途径 ,代表了未来工业生态系统发展方向。本文从生态工业园区的概念出发 ,分析了生态工业园区的基本特征与功能 ,阐述了生态工业园区的类型及其建设的重点内容 ,介绍了生态工业园区建设实践 ,提出了生态工业园区建设的对策与措施     

氧化锌法处理低浓度SO_2烟气的模拟实验研究

文中介绍了氧化锌法脱硫的基本原理,总结了国内外研究及应用现状,同时详细叙述了模拟实验采用的流程、主要仪器,着重分析了不同SO2浓度下的吸收率以及亚硫酸锌的氧化率与氧化时间、浆液温度的关系,提出了实验过程中发现的问题及可采取的措施,为后期中试以及工业化应用提供了设计依据。     

三索式格栅除污机电控系统的国产化设计

介绍了三索式格栅除污机的工艺及控制原理 ,提出了一些以低价的元器件代替高价元器件的方案 ,设计了国产化的电控系统 ,使整机完全实现国产化 ,降低了系统的成本 ,同时便于调试和维修。     

旋风除尘器环形空间气流运动的数值研究

运用计算流体力学软件FLUENT,对旋风除尘器内部流场进行了数值模拟,利用模拟结果对旋风除尘器入口不同位置气流在除尘器顶部的环形空间的轨迹进行了分析研究,探明了气流在环形空间的运动规律和基本特征,阐明了旋风除尘器顶部"尘环"形成的根本原因,指出了粉尘发生短路的原因和多发区域并在此基础上就除尘器的增效问题给出了改进方案.     

降低火电厂项目地下水环境影响的措施研究

正分析了火电厂项目主要的地下水污染源、污染途径,提出了优化选址、重点区域防渗等措施,模拟分析了防渗效果,提出了地下水的预警、监控及修复方案,为最大限度降低火电厂地下水环境影响提供了系统的设计方案。     

陕西神木大柳塔洗煤厂煤泥水快速沉降剂试验研究

为了解决陕西神木大柳塔洗煤厂煤泥水的快速沉降和洗水循环再利用 ,在深入研究原煤泥水处理工艺技术效能的基础上 ,研究和发展了一种新型高效、经济的煤泥水处理工艺技术 ,合成了一种快速沉降剂。经过反复试验研究 ,使得原煤泥水系统有了极大改善 ,实现了洗水闭路循环和煤泥厂内回收 ,降低了药耗 ,缩短了反应时间 ,达到了快速沉降的目的。     

堆肥过程中腐殖酸的生成演化及应用研究进展

腐殖酸是堆肥过程中生成的最具代表性的次生产物,对堆肥的稳定性、腐熟度等性质有重要影响。介绍了堆肥过程中腐殖酸的生成及动态变化,分析了堆肥腐殖酸的结构特性,归纳了堆肥过程中腐殖酸生成机制,并在此基础上探讨了影响堆肥腐殖酸生成演化的主要影响因素,总结了堆肥腐殖酸作为堆肥腐熟度指标、表面活性物质、吸附剂等的应用情况,最后提出了当前堆肥腐殖酸研究中存在的主要问题,并对未来的研究方向进行了展望。     

旋转式滗水器设计的动力学计算

在对旋转式滗水器进行了水力学和运动学计算的基础上,对旋转式滗水器进行了动力学计算,给出了其动力学计算公式,与水力学和运动学计算公式一起,共同给出了旋转式滗水器完整的设计计算公式.     

中小型工业窑炉NO_x废气的净化技术与应用

介绍了中小型工业窑炉生产过程中排放NOx的现状,并针对NOx的治理问题,从工程应用角度,简述了湿法和干法净化NOx的原理和化学反应过程。讨论了NOx排放特性和NOx废气中NO2/NOx比例的问题。提出了影响NOx治理的主要因素,包括吸收剂、吸附剂、催化剂和设备的部分应用参数,并结合工程实例指出工程需要注意的问题。介绍了多项采用湿法和干法净化NOx的工程应用项目,丰富了中小工业窑炉NOx废气的治理的实践经验,为中小工业窑炉排放NOx治理奠定了基础。     

Development of risk-based air quality management strategies under impacts of climate change

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies. Implications: Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.     

Selected International Receptor-Based Air Quality Standards

Abstract

The ambient air quality standards (AAQS) of twenty-one nations for eight commonly regulated substances are presented. Many countries are adding a receptor-based component to their air quality management, which traditionally have been emission oriented. Automation of air quality monitoring stations has meant that local air quality evaluation can now be more easily achieved. However, a majority of countries have no active air quality standards (emission or receptor-based) or ambient air quality monitoring. One possible monitoring procedure is outlined and the variation in international standards is discussed.     

New indicator approaches for effective urban air quality management

Measurements of urban air quality at monitoring stations in developed countries have frequently involved the criteria gaseous pollutants, particulates, hazardous air pollutants, perceived air quality and relevant meteorological conditions. Large numbers of indicators have therefore been established to quantify emissions, concentrations and environmental and human health impacts of each of these groups of substances. To simplify the data for management, several indicators have been grouped together to form urban air quality indices but the weightings of individual variables is contentious. In industrialising and developing countries, data may be limited and traditional air pollutant indicators cannot often be constructed. The emphasis therefore has to be placed on the development of policy-relevant indicators, such as Response Indicators that reflect different policy principles for regulating air pollutant emissions. Indices that quantify the air quality management capabilities and capacities at the city level provide further useful decision-relevant tools. Four sets of indices, namely, 1. air quality measurement capacity, 2. data assessment and availability, 3. emissions estimates, and 4. management enabling capabilities, and a composite index to evaluate air quality management capability, were constructed and applied to 80 cities. The indices revealed that management capability varied widely between the cities. In some of the cities, existing national knowledge on urban air quality could have been more effectively used for management. It was concluded that for effective urban air quality management, a greater emphasis should be given, not just to monitoring and data capture programmes, but to the development of indicators and indices that empower decision-makers to initiate management response strategies. Over-reliance on restricted, predetermined sets of traditional air quality indicators should be avoided.     

Conjunctive use of models to design cost-effective ozone control strategies

The management of tropospheric ozone (O3) is particularly difficult. The formulation of emission control strategies requires considerable information including: (1) emission inventories, (2) available control technologies, (3) meteorological data for critical design episodes, and (4) computer models that simulate atmospheric transport and chemistry. The simultaneous consideration of this information during control strategy design can be exceedingly difficult for a decision-maker. Traditional management approaches do not explicitly address cost minimization. This study presents a new approach for designing air quality management strategies; a simple air quality model is used conjunctively with a complex air quality model to obtain low-cost management strategies. A simple air quality model is used to identify potentially good solutions, and two heuristic methods are used to identify cost-effective control strategies using only a small number of simple air quality model simulations. Subsequently, the resulting strategies are verified and refined using a complex air quality model. The use of this approach may greatly reduce the number of complex air quality model runs that are required. An important component of this heuristic design framework is the use of the simple air quality model as a screening and exploratory tool. To achieve similar results with the simple and complex air     

An Index and Model of Human Response to Air Quality

This study undertook an empirical Investigation of human response to air quality. Home interviews of 475 respondents living in 22 neighborhoods of Los Angeles County had elicited information on respondent socioeconomic characteristics, behavioral patterns, and measures of human response to air quality. This data base was then augmented with nine measures of actual air quality for six time frames for each neighborhood.

An observer-based air quality index (OBAQI) was constructed based upon which combination of air quality variables correlated best with the percentage of neighborhood respondents who perceived “smoggy air.” The best combination (OBAQI 3) consisted of prevailing visibility, O₃, and SO₂, each measured as the annual number of days that a selected standard had been equalled or exceeded. Subsequently, multiple regression models were constructed using this index as a predictor of aggregated perception of air quality. In addition to a general model for all neighborhoods, separate models were constructed for clusters of neighborhoods of similar micrometeorology as approximated by uniform elevations and/or coastal distances. Zones of homogeneous air quality and micrometeorology were then defined. Within these zones variation in four measures of human response to air quality was associated with respondent socioeconomic characteristics and behavioral patterns. Both the index and model could prove useful in gauging public response to proposed actions of air quality management districts.     

A comparison of air quality indices for a port and harbour region in India

The present paper discusses and compares four different air quality indices, viz. arithmetic, multiplicative, unweighted arithmetic, and unweighted multiplicative, which were considered for characterising the coastal air quality at the Jawaharlal Nehru Port, Mumbai, India. Suspended particulate matter, oxides of nitrogen, and sulphur dioxide were used as the parameters of air quality indices. Value function graphs for above-mentioned variables were developed using Indian ambient air quality standards for different areas. Product moment correlation coefficients for various air quality indices were determined using the SPSS software package to evaluate correlation among various indices.     

Spatial interpretation of ambient air quality for the territory of the Czech Republic

A method for spatial interpretation and visualisation of measured air quality data is presented that may serve as a tool for ambient air quality characterisation using the least possible number of factors. This method enables comparison of different areas in relative terms and has practical consequences in decision making and public information. The 1996 data for the Czech Republic was used as a database. According to my results, not a single universal indicator can fully describe the ambient air quality albeit the three factors identified as "ambient air pollution", "ground-level ozone" and "wet atmospheric deposition" which are recommended. These selected factors represent three different aspects of ambient air quality and its impact on receptors. For the above factors black and white charts are presented classifying the Czech Republic territory into five categories as to relative ambient air quality. The air quality picture differs for the respective factors considerably.     

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.     

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City,India

Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.

Implications: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.     

Interactions of Physical, Chemical, and Biological Weather Calling for an Integrated Approach to Assessment, Forecasting, and Communication of Air Quality

This article reviews interactions and health impacts of physical, chemical, and biological weather. Interactions and synergistic effects between the three types of weather call for integrated assessment, forecasting, and communication of air quality. Today’s air quality legislation falls short of addressing air quality degradation by biological weather, despite increasing evidence for the feasibility of both mitigation and adaptation policy options. In comparison with the existing capabilities for physical and chemical weather, the monitoring of biological weather is lacking stable operational agreements and resources. Furthermore, integrated effects of physical, chemical, and biological weather suggest a critical review of air quality management practices. Additional research is required to improve the coupled modeling of physical, chemical, and biological weather as well as the assessment and communication of integrated air quality. Findings from several recent COST Actions underline the importance of an increased dialog between scientists from the fields of meteorology, air quality, aerobiology, health, and policy makers.