基于指标规范值的环境质量的指数幂乘积指数公式

传统的环境质量指数幂乘积指数的分级标准值随指标不同和指标数目不同而不同,因而综合指数的分级标准值不能规范和统一.在适当设定指标参照值和指标值的规范变换式基础上,使同类环境不同指标的同级标准的规范值差异尽可能小,而不同标准的指标规范值差异尽可能大,从而可以认为,用规范值表示的同类环境的各指标皆等效于某个规范指标,因而同类环境的任意m项指标的指数幂乘积综合指数可以有共同的分级标准值,使基于指标规范值的环境质量指数幂乘积指数公式具有普适性、可比性和通用性.     

基于灰色模型和模糊神经网络的综合水质预测模型研究

水质状态变化趋势预测研究对水资源管理和维护具有重要的现实意义。提出了一种将灰色模型和模糊神经网络相结合的水质预测模型。首先基于改进的灰色模型预测出水体中各理化因子在未来一段时间内的指标变化,然后采用T-S模糊神经网络对各单因子的预测值进行数据融合,构建水质变化综合趋势预测模型,预测出下一时间段的水质整体状态指标。实验表明,这种方式用来预测湖泊水质变化趋势具有可行性;与BP网络模型相比,基于T-S模糊神经网络系统的模型具有预测精度高、模型系统稳定等优越性。     

关于室内环境污染检测依据的探讨

中国现有两个关于控制室内空气污染方面的标准，即GB50325--2001《民用建筑工程室内环境污染控制规范》和GB／T18883--2002《室内空气质量标准》，但在实际工作中，依据那个标准对室内空气污染进行检测，存在着理解和认识上的不统一，往往带来很多争议和麻烦，因此通过对两个标准的对比分析，阐述了个人的观点和看法。     

工业企业集聚区周边土壤环境质量综合评价指标体系与分区管理对策研究

土壤环境质量综合评价指标体系是识别土壤环境污染、指导土壤环境管理及开发利用的有效工具。在土壤环境质量评价方法和国内外标准分析的基础上,提出了建立土壤环境质量综合评价指标体系的基本框架;结合"压力-状态-响应"模型(PSR模型),考虑不同工业区特征污染物,建立了包含通用指标和备选指标的土壤环境质量综合评价指标体系。以此评价指标体系为导向,为指导不同类型工业集聚区科学发展提出了工业区周边土壤环境质量分区管理的对策建议。     

基于随机-模糊耦合的填埋场地下水污染风险评价方法

为分析参数不确定性对填埋场渗漏风险评估结果的影响,构建了填埋场地下水污染风险评价的物理过程模型,在此基础上,分别采用模糊理论和概率理论刻画模糊不确定性参数和随机不确定性参数,同时采用基于随机理论的Monte Carlo方法模拟模糊不确定参数,最终构建了基于模糊随机耦合的填埋场地下水污染风险评价方法。采用该模型对东北某一般工业固废填埋场进行案例研究,结果表明,实测浓度在模型模拟的的浓度区间(10%~90%分位值浓度)之内。说明本模型构建的模糊-随机耦合的地下水污染风险评价模型能较准确地预测地下水中污染物实际浓度,可以用于填埋场地下水污染风险评价.风险评估结果表明,该填埋场地下水的潜在污染物为As和Mn,其中As为主要健康风险物质,其非致癌风险值超过风险可接受水平的概率为22%,致癌风险超过10-4的概率为33%,超过10-5的概率为86%,应该采取措施控制含As填埋废物中As的溶出,降低其环境风险;Mn的非致癌风险值小于风险可接受水平的概率为100%,无风险。     

海岸带生态安全评估技术研究进展

自然灾害、城镇扩展及人类活动干扰在很大程度上影响着海岸带生态安全。归纳了国内外海岸带生态安全的研究热点,国内主要集中在生态安全管理、评估、格局规划3个方面,国外学者侧重于研究海岸带综合管理、环境演化及灾害预测、生态系统健康评价、风险评价等方面。生态安全评估是实施生态环境管理与保护行动的基础工作,但国内外现有研究中对海岸带生态安全的评估还没有统一的标准。针对海岸带生态安全的特点,对比相关概念模型、评价方法的优缺点,采用指标重要值的方法确定了海岸带生态安全评估指标的重要性程度。最后,基于"驱动力—压力—状态—响应"概念框架模型,提出海岸带生态安全评估的指标体系框架,以期为海岸带生态安全评估提供参考。     

基于层次分析法的企业污水治理评价指标体系权重确定

确定评价指标的权重是进行工业园区企业污水治理绩效评价的重要前提。以太湖流域工业园区企业为对象,从环境保护效益、资源利用效益和循环利用效益等3个方面构建了12个指标的园区企业污水治理绩效评估指标体系,进而采用层次分析法确定了该评价体系各指标权重值。计算结果表明,建立的4个判断矩阵一致性比例(CR)均小于0.1,具有较好的一致性,层次单排序结果均满足一致性检验的要求;各指标的层次总排序顺序与工业园区企业污水的实际情况较为相符。因此,用层次分析法计算出的污水治理绩效评价指标的权重值比较准确、可信,可为下一步进行企业污水治理绩效评价提供技术支持。     

基于正态随机模拟的武烈河环境健康风险评价

为了解武烈河的水环境健康状况,基于US EPA健康风险评价方法,将正态模糊数和随机模拟方法进行耦合,建立基于正态随机模拟的环境健康风险综合评价模型,对武烈河干支流主要断面进行环境健康风险评价。评价结果表明:所有监测断面的成人健康风险值和儿童健康风险值均小于最大风险可接受水平(5.0×10~(-5)a~(-1)),处于可接受范围内,说明武烈河流域水环境健康风险状况良好;在已有研究工作的基础上,将三角模糊数改进为正态模糊数,考虑小概率事件发生的情况、选择99%概率所对应的风险值进行评价,使监测值模拟概率分布更加合理、预测结果概率分布更符合实际情况。     

基于BP网络的水质综合评价模型及其应用

讨论了BP网络模型存在的不足及建模条件,提出了建立合理的BP网络模型的基本原则和步骤.针对水质评价问题,通过在各类水质污染指标浓度区间内生成随机分布样本的方法,组成足够多用于BP网络训练、检验和测试用的样本,建立了辽河水质综合评价的BP网络模型;给出了区分不同类别水质的模型分界值样本和模型输出分界值.     

基于BP网络的水质综合评价模型及其应用

讨论了BP网络模型存在的不足及建模条件，提出了建立合理的BP网络模型的基本原则和步骤。针对水质评价问题，通过在各类水质污染指标浓度区间内生成随机分布样本的方法，组成足够多用于BP网络训练、检验和测试用的样本，建立了辽河水质综合评价的BP网络模型；给出了区分不同类别水质的模型分界值样本和模型输出分界值。     

Forecasting Daily Source Air Quality Using Multivariate Statistical Analysis and Radial Basis Function Networks

Abstract

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.     

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO2, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well. Implications: Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO2 pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.     

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover, the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO₂, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.

Implications: Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO₂ pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.     

Critical evaluation of approaches in setting indoor air quality guidelines and reference values

The importance of good indoor air quality for the health of the individual was recognized as long as 150 years ago and that period also saw recommendations, which essentially related to questions of ventilation and carbon dioxide. The first evaluation standards for organic and inorganic substances were laid down in the 1970s, often on an empirical basis. It was in the mid-1980s of the 20th century that a shift occurred towards systematically evaluating the results of indoor air measurements, carrying out representative environmental surveys and deriving guideline values and reference values on the basis of toxicological, epidemiological and statistical criteria. Generally speaking the indoor environment is an area which can only be assessed with difficulty since its occupants are in most cases exposed to mixtures of substances and there can be great local and temporal variations in the substance spectrum. Data are available today for a large number of substances and this makes it possible, with the aid of statistically derived reference values and toxicologically based guideline values, to make useful recommendations regarding good indoor air quality. Nevertheless, it is still difficult to evaluate reactive compounds and reaction products. What is disadvantageous, however, is the fact that different guideline values may be published for one and the same substance, whose justification and area of application are often not transparent. A guideline or reference value can only be regarded as rational when necessary and when a strategy for its verification is available.     

Carbon monoxide concentration forecasting in Santiago, Chile

In the city of Santiago, Chile, air quality is defined in terms of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) concentrations. An air quality forecasting model based on past concentrations of PM10 and meteorological conditions currently is used by the metropolitan agency for the environment, which allows restrictions to emissions to be imposed in advance. This model, however, fails to forecast between 40 and 50% of the days considered to be harmful for the inhabitants every year. Given that a high correlation between particulate matter and carbon monoxide (CO) concentrations is observed at monitoring stations in the city, a model for CO concentration forecasting would be a useful tool to complement information about expected air quality in the city. Here, the results of a neural network-based model aimed to forecast maximum values of the 8-hr moving average of CO concentrations for the next day are presented. Forecasts from the neural network model are compared with those produced with linear regressions. The neural network model seems to leave more room to adjust free parameters with 1-yr data to predict the following year's values. We have worked with 3 yr of data measured at the monitoring station located in the zone with the worst air quality in the city of Santiago, Chile.     

地下水水质评价的逻辑斯谛曲线模型

为检验已订的地下水水质评价标准的合理性，解决各单项水质指标的评估结果的不相容性问题，提高水质等级模型的分辨率，提出了一种新的水质评价模型－逻辑斯谛曲线模型，它的水质等级是连续的实数值。根据LOG模型的参数值可以分析各水质指标值对水质等级的影响程度，据此检验原订水质等级标准的合理性。并给出了基于实码遗传算法的LOG建模的实施方案。实例研究的结果说明，该方案是可行、有效且通用的，在环境质量综合评价中具有广泛的应用价值。     

基于因子分析法优选河流水体中氮浓度预测方法的研究

定量的河流水体中氮浓度预测方法有很多种,如何优选出预测精度较高的方法一直是学术界多年来致力于研究的重点。本研究采用因子分析法对预测方法的精度评价指标进行分析,并建立了预测方法精度的评价模型,对回归分析法、神经网络法、灰色系统法和增长率统计法4种水体氮浓度预测方法进行综合评估,优选出精度较高的河流水体氮浓度预测模型——BP神经网络预测模型。结果表明,此评估模型对类似研究具有一定的参考价值,能为选择出合适的河流水体氮浓度预测方法提供依据。     

Forecasting human exposure to atmospheric pollutants in Portugal – A modelling approach

Air pollution has become one main environmental concern because of its known impact on human health. Aiming to inform the population about the air they are breathing, several air quality modelling systems have been developed and tested allowing the assessment and forecast of air pollution ambient levels in many countries. However, every day, an individual is exposed to different concentrations of atmospheric pollutants as he/she moves from and to different outdoor and indoor places (the so-called microenvironments). Therefore, a more efficient way to prevent the population from the health risks caused by air pollution should be based on exposure rather than air concentrations estimations. The objective of the present study is to develop a methodology to forecast the human exposure of the Portuguese population based on the air quality forecasting system available and validated for Portugal since 2005. Besides that, a long-term evaluation of human exposure estimates aims to be obtained using one-year of this forecasting system application. Additionally, a hypothetical 50% emission reduction scenario has been designed and studied as a contribution to study emission reduction strategies impact on human exposure.To estimate the population exposure the forecasting results of the air quality modelling system MM5-CHIMERE have been combined with the population spatial distribution over Portugal and their time-activity patterns, i.e. the fraction of the day time spent in specific indoor and outdoor places. The population characterization concerning age, work, type of occupation and related time spent was obtained from national census and available enquiries performed by the National Institute of Statistics. A daily exposure estimation module has been developed gathering all these data and considering empirical indoor/outdoor relations from literature to calculate the indoor concentrations in each one of the microenvironments considered, namely home, office/school, and other indoors (leisure activities like shopping areas, gym, theatre/cinema and restaurants). The results show how this developed modelling system can be useful to anticipate air pollution episodes and to estimate their effects on human health on a long-term basis. The two metropolitan areas of Porto and Lisbon are identified as the most critical ones in terms of air pollution effects on human health over Portugal in a long-term as well as in a short-term perspective. The coexistence of high concentration values and high population density is the key factor for these stressed areas. Regarding the 50% emission reduction scenario, the model results are significantly different for both pollutants: there is a small overall reduction in the individual exposure values of PM₁₀ (<10 μg m^?3 h), but for O₃, in contrast, there is an extended area where exposure values increase with emission reduction. This detailed knowledge is a prerequisite for the development of effective policies to reduce the foreseen adverse impact of air pollution on human health and to act on time.     

不同功能区空气负离子的监测分析

监测了西宁地区不同功能区空气负离子状况，以公园游览区空气负离子浓度最高，平均３５８个／ｃｍ＾３；工业区最低，平均浓度２５９个／ｃｍ＾３。各功能区空气负离子浓度明显不同，Ｐ〈０．０１。用空气负离子浓度、空气离子单级系数和安培空气质量评价指数评价各功能区空气质量，从好到差依次为：公园游览区、生活居住区、商业交通繁忙区、工业区。３种评价指标结果相同，符合一般规律。