基于Г型分布的空气质量评价普适指数公式

在设定各项空气污染物的浓度“参照值”基础上,提出了一个适用于多项空气污染物的Г型分布空气质量评价普适指数公式。采用基于实数编码的遗传算法对公式中的参数优化,得出优化后适用于多项空气污染物的空气质量评价普适指数公式。公式被应用于多个实例分析计算,并与其他若干评价方法评价结果相比较,结果表明,该公式不受空气污染物种类和数目多少的限制。公式形式简单,计算快速,具有可比性、通用性和实用性。     

免疫粒子群算法优化的环境空气质量评价方法

为了提高免疫算法的收敛速度,将粒子群优化思想引入到免疫算法中,设计了一种免疫粒子群优化算法。采用该算法对大气污染损害公式的参数进行寻优,得到了适用于臭氧、PM2．5等6种大气污染物的环境空气质量评价的污染损害指数公式及环境空气质量评价模型。为了使评价结果更准确,采用了国家环保部最新发布的空气质量标准中给出的大气污染物种类、数目及各级浓度限值。将该评价方法应用于大气质量评价领域,实验结果表明,该方法评价结果准确,具有较好的灵活性、实用性和应用前景。     

萧山区环境空气质量变化及其影响因素分析

利用杭州市萧山区环境空气的监测资料,采用空气综合污染指数、空气污染指数(API)、Daniel趋势检验等评价和分析方法,研究了萧山区"十一五"期间(2005—2010年)环境空气质量的变化趋势及其影响因素。结果显示:(1)PM10是萧山区环境空气中的首要污染物。SO2、PM10浓度以及空气综合污染指数先升高后降低,总体呈现不显著下降趋势,整体环境空气质量在2007年后逐年改善。(2)空气中NO2污染负荷系数逐年上升,NO2/SO2和污染物相关性分析结果表明,萧山区空气污染特征正处于由煤烟型向汽车尾气型转变的初期阶段。(3)萧山区月均API呈现出7-9月4-6月1-3月10-12月的变化特征。(4)在持续快速增长、能源消耗逐年增加的情况下,萧山区环境空气质量的改善说明节能减排、产业升级、污染源综合治理、能源清洁使用等措施对改善环境空气质量起到了关键作用。     

江苏省污染减排与空气质量变化相关性分析

利用2000—2007年大气污染物排放量数据和同期环境空气质量监测数据,分析了江苏省主要大气污染物减排与环境空气质量变化的相关性。结果表明,近年来江苏省SO2排放量与环境空气中SO2浓度存在正相关,而烟尘和粉尘排放总量与空气中可吸入颗粒物的浓度呈现出弱的负相关关系。对江苏省经济发展和环境关系的进一步分析揭示,江苏省环境库兹涅茨曲线呈现出倒U型关系,表明江苏省已经进入经济环境双赢区间,但近年来政策对经济环境关系的影响突出。该研究对中国十二五环境管理政策的制定有着重要的参考意义。     

焚烧秸秆对城市空气质量的影响及对策

通过夏收季节农民焚烧秸秆的烟尘，引起对城市环境空气质量变化的分析，结果表明：焚烧秸秆对城市环境空气质量有很大影响，使环境空气质量明显下降，而主要污染物为可吸入颗粒物PM10。有关部门应采取有效措施，禁止焚烧秸秆，同时应积极为农民寻找合理利用秸秆的途径，减少对城市空气的烟尘污染。     

兼顾异味污染物的环境空气质量综合评价案例研究

异味污染是人们比较敏感的一种大气环境污染,是影响公众对空气质量满意程度的一个重要方面,也是导致公众的直接感官与官方发布的空气质量指数(AQI)存在落差的原因之一。以江苏省某大学城地区为对象,建立了一种兼顾常规污染物和异味污染物的环境空气质量综合评价方法,并引入异味指数分级方案,将公众的异味投诉强度纳入到综合评价指标体系中。结果表明,2013年该地区的综合评价结果相比只基于常规污染物的评价结果,空气质量下降明显。此评价方法为异味污染评价提供了一次尝试,有利于完善现有的空气质量评价体系,具有一定的应用价值。     

南京市“十五”期间环境空气质量状况解析

综述并分析了南京市“十五”期间环境空气质量状况,指出空气中首要污染物仍为可吸入颗粒物.南京市虽然主要工业污染物排放总量继续得到有效控制,部分污染物明显缩减,空气质量有所改善,但酸雨污染依然严重,控制和治理二氧化硫污染已刻不容缓,要采取合理科学规划,加大工业污染治理力度,同时要增强市民自我环境保护意识.     

水污染指数的综合问题

一、污染物相对污染值迭加型指数的特点我国自1974年开始用水污染指数综合评价水质污染以来,对水污染指数的研究发展很快。如目前各地所使用的计算公式就有10余种,有人还对这个问题进行了专题研究或探讨。这对我国的水污染研究和水污染综合评价工作,都有很大的促进作用。总结一下我国各家所提出的水污染指数,可以看出,基本上全属污染物相对污染值迭加型指数     

环境空气质量自动监测站的管理与维护

随着工业化、城镇化的深入推进,二氧化硫、氮氧化物、烟粉尘和挥发性有机物等各类污染物排放到环境中,致使中国大气受到严重污染,给人体的健康、动植物的生长、发育和繁殖等带来负面的影响。为实时监测环境空气质量,建立环境空气质量自动监测站逐渐成为大气污染防治的主要手段。文中以环境空气质量自动监测站为研究对象,提出环境空气质量自动监测站管理与维护面临的问题,探讨相应的解决措施,以期为环境空气质量自动监测站的管理与维护提供参考依据。     

徐州市环境空气质量20年变化趋势及对策

徐州市地处苏鲁豫皖四省交界处,近年来随着人们生活水平的提高,环境空气的污染问题日益引起人们的关注。以徐州市市区20年的环境空气质量监测数据为依据,综合分析了该区域的环境空气质量状况、主要污染物、污染原因及变化趋势,并提出了进一步改善该区域环境空气质量的对策。     

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.     

Monitoring of air pollution in Indian metropolitan cities: modelling and quality indexing

Air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. Delhi, as well as many other cities in India, is facing problems concerning air pollution. The increase in industrialisation and the vehicle fleet, poor control on emissions and little use of catalytic converters, produce a great amount of particulate and toxic gases. Data on air pollutants and meteorological variables were collected in the metropolitan cities Delhi, Kolkata, Mumbai and Chennai for the period July–August, 2001. Data were treated with the bivariate regression model to explore the influence of the meteorological variables on air pollutant concentrations, and were also used to compute an Air Quality Index, using the weighted arithmetic mean method. The proposed index seems to be applicable in the assessment of overall air quality with respect to air pollutants.     

Derivation of exemption formulas for air quality regulatory applications

The regulatory agencies and the industries have the responsibility for assessing the environmental impact from the release of air pollutants, and for protecting environment and public health. The simple exemption formula is often used as a criterion for the purpose of screening air pollutants. That is, the exemption formula is used for air quality review and to determine whether a facility applying for and described in a new, modified, or revised air quality plan is exempted from further air quality review. The Bureau of Ocean Energy Management’s (BOEM) air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. If a facility is not exempt after completing the air quality review, a refined air quality modeling will be required to regulate the air pollutants. However, at present, the scientific basis for BOEM’s exemption formula is not available to the author. Therefore, the purpose of this paper is to provide the theoretical framework and justification for the use of BOEM’s exemption formula. In this paper, several exemption formulas have been derived from the Gaussian and non-Gaussian dispersion models; the Gaussian dispersion model is a special case of non-Gaussian dispersion model. The dispersion parameters obtained from the tracer experiments in the Gulf of Mexico are used in the dispersion models. In this paper, the dispersion parameters used in the dispersion models are also derived from the Monin-Obukhov similarity theory. In particular, it has been shown that the total amount of emissions from the facility for each air pollutant calculated using BOEM’s exemption formula is conservative.

Implications:?The operation of offshore oil and gas facilities under BOEM’s jurisdiction is required to comply with the BOEM’s regulations. BOEM’s air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. The exemption formulas have been used by BOEM and other regulatory agencies as a screening tool to regulate air emissions emitted from the oil and gas and other industries. Because of the BOEM’s regulatory responsibility, it is important to establish the scientific basis and provide the justification for the exemption formulas. The methodology developed here could also be adopted and used by other regulatory agencies.     

Air pollution in cities

Air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. Air pollution in cities is a serious environmental problem – especially in the developing countries. The air pollution path of the urban atmosphere consists of emission and transmission of air pollutants resulting in the ambient air pollution. Each part of the path is influenced by different factors. Emissions from motor traffic are a very important source group throughout the world. During transmission, air pollutants are dispersed, diluted and subjected to photochemical reactions. Ambient air pollution shows temporal and spatial variability. As an example of the temporal variability of urban air pollutants caused by motor traffic, typical average annual, weekly and diurnal cycles of NO, NO₂, O₃ and O_x are presented for an official urban air-quality station in Stuttgart, southern Germany. They are supplemented by weekly and diurnal cycles of selected percentile values of NO, NO₂, and O₃. Time series of these air pollutants give information on their trends. Results are discussed with regard to air pollution conditions in other cities. Possibilities for the assessment of air pollution in cities are shown. In addition, a qualitative overview of the air quality of the world's megacities is given.     

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.     

A new multipollutant, no-threshold air quality health index based on short-term associations observed in daily time-series analyses

Air quality indices currently in use have been criticized because they do not capture additive effects of multiple pollutants, or reflect the apparent no-threshold concentration-response relationship between air pollution and health. We propose a new air quality health index (AQHI), constructed as the sum of excess mortality risk associated with individual pollutants from a time-series analysis of air pollution and mortality in Canadian cities, adjusted to a 0-10 scale, and calculated hourly on the basis of trailing 3-hr average pollutant concentrations. Extensive sensitivity analyses were conducted using alternative combinations of pollutants from single and multipollutant models. All formulations considered produced frequency distributions of the daily maximum AQHI that were right-skewed, with modal values of 3 or 4, and less than 10% of values at 7 or above on the 10-point scale. In the absence of a gold standard and given the uncertainty in how to best reflect the mix of pollutants, we recommend a formulation based on associations of nitrogen dioxide, ozone, and particulate matter of median aerodynamic diameter less than 2.5 microm with mortality from single-pollutant models. Further sensitivity analyses revealed good agreement of this formulation with others based on alternative sources of coefficients drawn from published studies of mortality and morbidity. These analyses provide evidence that the AQHI represents a valid approach to formulating an index with the objective of allowing people to judge the relative probability of experiencing adverse health effects from day to day. Together with health messages and a graphic display, the AQHI scale appears promising as an air quality risk communication tool.     

Analysis of air toxics emission inventory: inhalation toxicity-based ranking

Air toxics emission inventories play an important role in air quality regulatory activities. Recently, Minnesota Pollution Control Agency (MPCA) staff compiled a comprehensive air toxics emission inventory for 1996. While acquiring data on the mass of emissions is a necessary first step, equally important is developing information on the potential toxicity of the emitted pollutants. To account for the toxicity of the pollutants in the emission inventory, inhalation health benchmarks for acute effects, chronic effects, and cancer were used to weight the mass of emissions. The 1996 Minnesota emissions inventory results were ranked by mass of emissions and by an index comprised of emissions divided by health benchmarks. The results show that six of eight pollutants ranked highest by toxicity were also the pollutants of concern indicated in environmental monitoring data and modeling data. Monitoring data and modeling results did not show high impacts of the other two pollutants that were identified by the toxicity-based emission ranking method. The biggest limitation in this method is the lack of health benchmark values for many pollutants. Despite uncertainties and limited information, this analysis provides useful information for further targeting pollutants and source categories for control.     

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

ABSTRACT

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO₂ and NO_x concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes.