徐州市电磁辐射环境的污染现状调查及评价

调查了徐州市电磁辐射污染源的状况,分析了其电磁辐射环境质量的现状,并对全市电磁辐射环境质量进行了评价,指出了分析工作中存在的问题,提出了防治对策。     

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。     

宝应县畜禽养殖业的现状调研及污染防治对策

调查了宝应县畜禽养殖业的发展现状及分布情况,阐述了该县畜禽养殖业的污染防治状况,分析了污染防治中存在的主要问题,并提出了当地畜禽养殖业污染防治的基本思路和治理措施,对贯彻落实防治措施提出了建议。     

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。     

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。     

中国环境影响评价中的公众参与问题浅析

简述了环境影响评价中,公众参与的概念,分析了公众参与的必然性及其发展历程,同时也指出了目前环境影响评价中,公众参与存在的问题,通过分析,提出了若干建议。     

1，4-二氯苯在土壤中吸附特性的研究

在实验室条件下,以平衡振荡法研究了土壤吸附1,4-二氯苯的影响因素,用Freudlich和Langmuir经验公式,对实验数据进行了拟合,研究了土壤对1,4-二氯苯的吸附模型,并对其吸附动力学进行了初步探讨,并测定了吸附速率常数。     

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.     

Physical and chemical indicators of urban visual air quality judgments

Key issues in the selection of physical/chemical measures of atmospheric properties as indicators of judgments of visual air quality are addressed. The relations between measures and judgments made over a variety of times, observation locations and atmospheric conditions are examined experimentally. Light scattering measured throughout the day at a site characterized by high aerosol concentrations was the single measure most strongly related to judgments of visual air quality. A combination of measures taken at a single site and/or other sites are somewhat better indicators of visual air quality than light scattering alone. Light extinction measured by a telephotometer is strongly related to midday visual air quality. The 4-h averaged fine particle sulfur and 12-h averaged fine particle S, sulfate, nitrate and ammonium are all strongly related to the corresponding mean visual air quality. Although these midday hourly and 4-h and 12-h average measures may be used to indicate trends in visual air quality, hourly measures taken throughout the day are required for monitoring diurnal patterns or worst case visual air quality. The application of the method and findings to understanding visual air quality in other urban areas is discussed.     

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.     

Association between air quality and quality of life

Air quality—or its converse, air pollution—is a significant risk factor for human health. Recent studies have reported association between air pollution and human health. There are numerous diseases that may be caused by air pollution such as respiratory infection, lung cancer, cardiovascular disease, chronic obstructive pulmonary disease, and asthma. In this study, the relationship between air quality and quality of life was examined by using canonical correlation analysis. Data of this study was collected from 27 countries. WHO statistics were used as the main source of quality of life data set (Y variables set). European Environment Agency statistics and (for outdoor air-PM10) WHO statistics were used as the main source of air quality data set (X variables set). It is found that there are significant positive correlation between air quality and quality of life.