"三河三湖"流域重污染行业经济和污染贡献率剖析

应用"污染贡献率"这一指标,分析了"三河三湖"流域COD和氨氮排放的重污染行业以及地区分布,指出了各流域COD和氨氮排放的控制重点.同时分析了各流域重污染行业的"经济贡献率",最后结合行业的污染贡献率和经济贡献率以及行业在地区所占的比例,提出了几点重要的结论.     

甘肃干旱、半干旱地区尘类污染特征分析

本文用近１０年来ＴＳＰ、降尘监测数据对甘肃省干旱、半干旱地区尘类污染进行了较系统、全面地分析。总结出省内不同区域内尘污染时空分布特征,进一步阐明尘类污染是影响我省城市大气环境的重要指标。     

利用世界银行贷款灌溉农业二期项目江苏省项目区水环境评价

对利用世界银行贷款灌溉农业二期项目(1998年2002年)江苏省项目区的水环境进行了监测与评价。项目实施期间，对地表灌溉用水河段监测结果表明，各监测河段参评指标的单项污染指数及综合污染指数均小于1，地表灌溉水不构成单项指标污染和综合污染，其水质符合农业灌溉用水水质要求；对地表灌溉退水河段监测结果表明，退水河段中仅有部分监测指标比灌溉用水中的相应指标有所升高，但不构成综合污染，退水水质仍能满足其他地区农业灌溉用水水质要求；对地下灌溉水测井的监测结果表明，有个别地下灌溉井单项指标超标，但所测井的综合污染指数均远远小于l，对地下水环境未造成不利影响。     

庞庄煤矿煤矸石淋溶液对周边水体影响分析

为研究庞庄煤矿张小楼煤矸石堆场淋溶液对其场地和周边地表水、地下水水质影响情况，围绕煤矸石堆场及周边采集13组水样做分析比对。结果表明，煤矸石堆场淋溶液对周边地表水体形成普遍污染，且污染物浓度随着污染距离增加逐步降低，一般化学污染指标主要包括溶解性总固体、硫酸盐、铁、锰、氨氮等，毒理学污染指标包括亚硝酸盐、氟化物、砷，超标倍率约为0.02~5.36。受距离及成井深度等因素影响，地下水体未遭受淋溶液明显的污染和影响。     

骆马湖水体氮污染特征分析

通过对骆马湖2005年水质监测，并结合近几年的监测资料，对结果进行了分析。结果表明，氮是骆马湖水体的主要污染指标之一，主要是总氮、氨氮和硝酸盐氮污染，其主要污染特征是入湖河流、降水、围网养殖和点源带来的污染。针对实际提出了骆马湖整治的建议。     

2001-2015年松花江流域水污染变化特征研究

利用2001-2015年松花江流域国家监测网30个可比断面监测数据，从流域和断面水质变化、各项指标超标情况和浓度变化、污染负荷等方面分析了松花江流域水质变化情况。结果表明，松花江流域水质总体呈好转趋势，特别是2007年以来有比较明显的改善。该流域长期以来主要污染指标为高锰酸盐指数、石油类、五日生化需氧量和氨氮。高锰酸盐指数、石油类和五日生化需氧量污染均有明显改善，但氨氮污染改善不明显。在继续加强COD排放总量控制的基础上，有效削减氨氮排放是促进松花江水质改善的重点。     

象山港海域水质状况分析与污染防治对策

根据2003年-2007年5a对象山港水质监测资料，以COD、DIN、PO^3-4P、DO作为化学指标参数，分析了象山港的有机污染和营养类型。综合分析结果表明，象山港水质明显呈高N低P状态，水质基本上属于重度污染，且整个象山港水质处于富营养状态。根据污染特征，分析了影响水质的因素，并在此基础上提出相应的污染防治对策。     

米泉市大气污染与气象因子变化特征分析

通过对米泉市大气污染状况进行分析，揭示大气污染与其所处的地理特征、气象特征的关系，并对大气污染物TSP、降尘污染与气象因子变化特征进行分析，进一步阐述尘污染是影响米泉市大气环境质量的重要指标。     

苏南运河底泥污染现状评价

在对苏南运河自镇江谏壁闸至吴江鸭子坝全程３１个监测断面（点）底泥的１２个污染指标监测调查基础上，参照国内外有关标准，建立了底泥污染评价标准，评价结果表明苏南运河底污染严重，总体污染分布是中段严重，两端稍好，个别项目（如总锌，总镉）在全河段均重污染级，底泥污染程度在区域分布上与河水污染有较好相关性。     

实验室"三废"的处理方法

论述了含氰等7类实验室常见废弃物的处理方法，介绍了一种有机溶剂和3种贵重金属的回收与提纯，经防止实验室污染事故的发生。     

“十三五”时期哈尔滨空气质量变化特征及影响因素分析

对"十三五"期间哈尔滨市空气质量状况、变化特征及影响因素进行分析,为哈尔滨市打赢蓝天保卫战提供科学参考。以哈尔滨市为研究区域,基于哈尔滨市12个国控监测点位监测结果,对"十三五"期间哈尔滨市环境空气质量优良天数及6项污染物进行分析,总结了环境空气质量变化特征及影响因素。结果表明:"十三五"期间,哈尔滨市环境空气质量呈现波动向好趋势,较"十二五"期间大幅改善,除细颗粒物外,其他指标已稳定达二级标准,臭氧成为仅次于细颗粒物的首要污染物,冬季燃煤污染是环境空气最主要的污染来源,秋季秸秆焚烧及春季清除秸秆根茬也会产生一定影响。     

Environmental Modeling and Methods for Estimation of the Global Health Impacts of Air Pollution

Air pollution is increasingly recognized as a significant contributor to global health outcomes. A methodological framework for evaluating the global health-related outcomes of outdoor and indoor (household) air pollution is presented and validated for the year 2005. Ambient concentrations of PM2.5 are estimated with a combination of energy and atmospheric models, with detailed representation of urban and rural spatial exposures. Populations dependent on solid fuels are established with household survey data. Health impacts for outdoor and household air pollution are independently calculated using the fractions of disease that can be attributed to ambient air pollution exposure and solid fuel use. Estimated ambient pollution concentrations indicate that more than 80% of the population exceeds the WHO Air Quality Guidelines in 2005. In addition, 3.26?billion people were found to use solid fuel for cooking in three regions of Sub Saharan Africa, South Asia and Pacific Asia in 2005. Outdoor air pollution results in 2.7?million deaths or 23?million disability adjusted life years (DALYs) while household air pollution from solid fuel use and related indoor smoke results in 2.1?million deaths or 41.6?million DALYs. The higher morbidity from household air pollution can be attributed to children below the age of 5 in Sub Saharan Africa and South Asia. The burden of disease from air pollution is found to be significant, thus indicating the importance of policy interventions.     

中国城市PM2.5空气质量改善分阶段目标研究

中国城市细颗粒物(PM_(2.5))空气质量达标率低,且城市间的污染程度差异较大。为了整体改善PM_(2.5)空气质量,需要针对不同污染程度的城市,制定分阶段改善目标加以考核和管理,研究探讨了城市PM_(2.5)空气质量改善目标体系及不同污染程度城市各阶段目标值。首先运用文献综述法、国内外对比分析法梳理评述了WHO、欧美等发达国家PM_(2.5)的空气质量标准和达标要求,提出中国城市PM_(2.5)空气质量改善的考核目标体系,包括PM_(2.5)浓度目标值或下降率、严重污染天数上限、达标天数下限等指标。通过历史数据分析法研究了2000—2013年美国、日本一些城市和2013—2016年中国74个环保城市PM_(2.5)年均浓度的变化趋势,推论出中国城市PM_(2.5)年均浓度年均下降5%~8%是可能实现的;结合环境保护部及各省市PM_(2.5)污染防治规划,提出PM_(2.5)空气质量改善目标的设定原则和达标天数的回归计算方法;以2014年114个城市PM_(2.5)年均浓度为基数,计算得出不同污染程度城市2020、2025、2030年PM_(2.5)年均浓度年下降率和达标天数的目标值。     

Higher plants as indicators and accumulators of gaseous air pollution

Some higher plant species or varieties are very sensitive to certain gaseous air pollutants, and the resulting effects show sometimes more or less specific, well-visible and measurable symptoms.On this basis several species and varieties of natural and cultivated plants have been selected to serve as biological indicators for the possible presence of certain air polluting substances. But these indicator plants may also be used for the quantitative determination of the effect intensities of the air pollutants involved.Besides, some plant species or varieties may accumulate certain components of air pollution, without changing these substances, in such a way that after accumulation in the plants these substances may be analyzed physicochemically (qualitatively and quantitatively).Definitions are proposed and examples are given of both indicator and accumulator plants. Also information is displayed on the methods for the use of plants as indicators and accumulators of air pollutants (standardized system of plant cultivation and exposure). Some applications of biomonitoring the effects of air pollution with plants are discussed and illustrated with data from The Netherlands.Paper presented at a Symposium held on 14 and 15 October 1982, in Utrecht, The Netherlands.     

Latitudinal and longitudinal variations in the impact of air pollution on well-being in China

Air pollution has resulted in high costs of living due to additional expenditures to combat the negative health effects, and the relationship between air pollution and well-being is of burgeoning interest. In this paper, the development and life index including 42 indicators is introduced to measure much broader dimensions of well-being in China at the macro level, which helps to compare the well-being of different regions. This paper uses provincial-level panel data from 2004 to 2013 in China, to empirically analyze and compare the impact of air pollution on well-being in latitudinal and longitudinal settings, based on regression methods, such as 2SLS, LIML, GMM and IGMM. The results show that although increased air pollution negatively affects well-being in general, the magnitude of the impact differs by region. Specifically, in China's northern regions, air pollution had a significantly negative impact on well-being, while the negative impact was weaker in southern China; moreover, a strong negative impact was found in western China, while a weak negative impact was found in eastern and central China. The time effect was also found to influence air pollution. Finally, this paper found that the natural birth rate and income level served as functional mechanisms of the effect of air pollution on regional well-being. These findings are useful in guiding government policy for determining the optimal balance between air quality control for economic growth and regional well-being.     

Examining the Factors Affecting Air Pollution Emission Growth in China

In this study, a structural decomposition method was applied to research the factors affecting the changes in air pollution emissions in China. Based on 1995–2009 data from the World IO Database, we combine China’s (Import) Noncompetitive IO Table and the Environmental Account Table. The results indicate that emission intensities represent the most important factor for reducing air pollution emissions in China. In contrast, economies of scale and the intermediate input product structure constitute the major causes for acceleration in the growth of air pollution emissions in China. From the perspective of final demand, the economic scale effect caused by investment demand is the main reason for this accelerated growth in China’s air pollution emissions in recent years. Consumption-driven economic growth is cleaner, while investment-driven economic growth is dirtier. This study constructed a structure decomposition model based on the input-output tables, which is suitable for studying the driving forces of various economic indicators, such as energy, carbon dioxide, and economic growth. At the same time, this method is helpful for analyzing the factors that influence changes in economic indicators that result from different economic pull modes, such as the final demand mode. However, the model does have limitations; for example, it does not consider the difference between general trade and processing trade in exports.     

Forest biomonitoring of the largest Slovene thermal power plant with respect to reduction of air pollution

The condition of the forest ecosystem in the vicinity of the largest Slovene power plant [the ?o?tanj Thermal Power Plant (?TPP)] was monitored during the period 1991–2008 by determining the total concentration of sulphur, ascorbic acid and chlorophyll in Norway spruce needles. After 1995, the introduction of cleaning devices at the ?TPP dramatically reduced the former extremely high SO₂ and dust emissions. The most significant findings of this comprehensive, long-duration survey are as follows: (1) the chosen parameters are suitable bioindicators of stress caused by air pollution in Norway spruce needles; they reflect both spatial and temporal variations in air pollution as well as the degree of efficiency of the cleaning devices; (2) observations show that the physiological condition of Norway spruce in northern Slovenia has significantly improved since 1995, when the first desulphurization device at ?TPP was built, together with a reduction in the area influenced by pollution from ?TPP; (3) metabolic processes in spruce needles react to air pollution according to the severity of the pollution and the length of exposure; exposure to high SO₂ ambient levels and/or spread over a long duration can damage the antioxidant defence mechanisms of spruce trees as well as diminishing the concentration of ascorbic acid; (4) a reduction in the exposure to air pollution improves the vitality of the trees (e.g. higher concentrations of total (a?+?b) chlorophyll), as well as restoring their defence capabilities as shown by higher concentrations of ascorbic acid; and (5) forest monitoring should be continued and focused on integrating the effects of multiple stressors, which can additionally affect a forest ecosystem.     

Receptor model-based source apportionment of particulate pollution in Hyderabad, India

Air quality in Hyderabad, India, often exceeds the national ambient air quality standards, especially for particulate matter (PM), which, in 2010, averaged 82.2?±?24.6, 96.2?±?12.1, and 64.3?±?21.2 μg/m³ of PM₁₀, at commercial, industrial, and residential monitoring stations, respectively, exceeding the national ambient standard of 60 μg/m³. In 2005, following an ordinance passed by the Supreme Court of India, a source apportionment study was conducted to quantify source contributions to PM pollution in Hyderabad, using the chemical mass balance (version 8.2) receptor model for 180 ambient samples collected at three stations for PM₁₀ and PM_2.5 size fractions for three seasons. The receptor modeling results indicated that the PM₁₀ pollution is dominated by the direct vehicular exhaust and road dust (more than 60 %). PM_2.5 with higher propensity to enter the human respiratory tracks, has mixed sources of vehicle exhaust, industrial coal combustion, garbage burning, and secondary PM. In order to improve the air quality in the city, these findings demonstrate the need to control emissions from all known sources and particularly focus on the low-hanging fruits like road dust and waste burning, while the technological and institutional advancements in the transport and industrial sectors are bound to enhance efficiencies. Andhra Pradesh Pollution Control Board utilized these results to prepare an air pollution control action plan for the city.     

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality.     

上海市轨道交通与地面交通换乘场站局地空气污染调查与评价

对上海市轨道交通与地面交通换乘场站局地环境空气质量进行了调查,结果表明,万体馆站空气污染较重,大气污染达到警戒水平,PM10及NO2超标频率及超标量较高。中山公园监测点PM10及CO超标较多。对照点张江高科点及静安点的环境空气质量相对较好,上海大气质量指数能够达到大气环境质量标准,污染物单因子超标轻微。在地面交通与轨道交通的换乘场站,集聚的机动车所排放的尾气,是造成该地区环境空气质量恶化的重要原因之一。