300MW锅炉低NOx燃烧系统改造效果分析

针对日益严重的环境污染、锅炉热效率低等问题，阐述了燃煤锅炉炉内空间分级燃烧技术、煤粉浓淡高效分离技术改造项目和特点，提出了对燃烧器系统、一次风管道、二次风系统和空预器系统等系统进行具体的低氮改造措施。分析了300 MW燃煤锅炉低氮改造后的热经济性和安全性。经试验验证，1号锅炉低氮改造后，锅炉NOx 排放量大幅减少，排放浓度降到了280 mg／m3。     

室内空气和灰尘中全(多)氟烷基化合物的研究进展

全(多)氟烷基化合物(per(poly)fluoroalkyl substances,PFASs)在环境各个介质及人体样品中广泛被检出,近年,在室内空气和灰尘中也普遍发现PFASs.研究表明,室内空气中PFASs的含量普遍高于室外空气,室内空气和灰尘中的PFASs可能是室外空气的污染来源及人体暴露源,因此室内环境中PFASs成为环境领域的又一个研究热点.但目前为止,我国还没有开展室内空气中PFASs的相关研究,室内灰尘中PFASs的研究也相对较少.本文就室内空气和灰尘中PFASs的采样与分析方法、污染现状、来源分析及人体暴露等4个方面进行了综合阐述,以期为我国室内环境中PFASs的研究提供参考.     

杭州市环境空气中黑碳质量浓度变化特征

采用2013年环境空气自动监测数据,分析杭州市空气中黑碳质量浓度的变化规律,并对变化特征的产生原因进行探讨。结果表明:黑碳测定年均值为4.10μg/m3,日变化有明显双峰结构,峰值出现在早7时和晚8时左右;从季节看,黑碳质量浓度冬季高(5.20μg/m3)、夏季低(3.00μg/m3);黑碳质量浓度与NO2、CO、PM10、PM2.5显著相关,与O3、风速、气温呈负相关,降水对黑碳的清除作用明显。     

城市大气中挥发性有机化合物监测技术进展

挥发性有机物(VOCs)是臭氧及二次有机颗粒物(SOA)的主要前体物。近年来,我国逐步将VOCs纳入大气污染物控制体系。准确可靠的监测技术是大气VOCs研究及控制的重要前提保障。按照采样方法、分析方法 2个方面介绍并讨论了城市大气中VOCs的现有监测方法,较为详细地介绍了几类广泛采用的离线及在线监测技术,简要讨论了目前VOCs监测中存在的一些问题,展望了今后的发展趋势。     

湖北省2008年初低温雨雪冰冻过程气候特征分析

2008年初，湖北省出现了严重的低温雨雪冰冻灾害，直接经济损失高达110亿元以上，有必要对灾害期间的气候特征进行系统、科学的分析和总结。对湖北省76个气象站2008年1月12日～2月3日气温、降水（雪）、日照以及低温持续日数等要素进行时空间差异分析及历史与同期比较，并选取10个代表站历史上所有低温雨雪天气过程，对其过程持续低温日数、最长连续雨雪日数、过程极端低温进行比较分析。结果表明：（1）此次过程的平均气温异常偏低，该省大部为-1～-2℃，比常年同期偏低4～6℃，为各站历史同期最低，其中主要是最高气温异常偏低所致，但极端低温并不低；（2）降雪过程频繁，雨雪量异常偏多；（3）低温冰冻持续时间长，该省大部在16～22 d，位于历史第一；（4）日照异常偏少。持续而稳定的大气环流异常形势是湖北省大范围低温雨雪天气的直接原因。     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

对导热炉大气污染物排放执行标准的探讨

目前，相关环境标准中均未明确制定关于导热炉大气污染物的排放内容。因此，对于导热炉的大气污染排放，各环境管理及监测部门均没有统一的执行标准。本文从导热炉工作原理、污染物排放特点等方面进行了分析，对其执行标准提出了个人的观点。     

南宁城市大气污染对人体健康的危害及治理对策

南宁市大气属煤烟型污染 ,大气的主要污染物为SO2 、NOx、TSP、降尘 ,虽然随着环境管理和污染治理工作的加强 ,污染物浓度逐年下降 ,但是工业区大气污染仍然较重。污染物流行病学调查显示 :工业区癌症和呼吸系统疾病死亡率均高于全市平均水平 2倍左右 ,城区又高于郊区 2倍 ,大气污染综合指数与呼吸内科门诊就诊人数呈正相关。用邓聚龙的灰色系统理论分析得知污染物对癌症和呼吸系统疾病死亡率有关联 ,关联度从大到小排序为 :TSP >降尘 >SO2 >NOx ,最后提出了大气污染治理的对策。     

Effect of Mountain Relief and Industrial Air Pollution on Biometric Characteristics of Pine Stands

The effect of mountain relief and industrial air pollution on biometric parameters of pine stands was studied. The empirical–statistical models of the dependence of biometric characteristics on the parameters of forest sites were developed using raster modeling and multivariate analysis. The possibility of predicting changes in the biometric parameters at any site on the basis of these models is shown.     

Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003

Data collected from the five air-quality monitoring stations established by the Taiwan Environmental Protection Administration in Taipei City from 1994 to 2003 are analyzed to assess the temporal variations of air quality. Principal component analysis (PCA) is adopted to convert the original measuring pollutants into fewer independent components through linear combinations while still retaining the majority of the variance of the original data set. Two principal components (PCs) are retained together explaining 82.73% of the total variance. PC1, which represents primary pollutants such as CO, NO(x), and SO(2), shows an obvious decrease over the last 10 years. PC2, which represents secondary pollutants such as ozone, displays a yearly increase over the time period when a reduction of primary pollutants is obvious. In order to track down the control measures put forth by the authorities, 47 days of high PM(10) concentrations caused by transboundary transport have been eliminated in analyzing the long-term trend of PM(10) in Taipei City. The temporal variations over the past 10 years show that the moderate peak in O(3) demonstrates a significant upward trend even when the local primary pollutants have been well under control. Monthly variations of PC scores demonstrate that primary pollution is significant from January to April, while ozone increases from April to August. The results of the yearly variations of PC scores show that PM(10) has gradually shifted from a strong correlation with PC1 during the early years to become more related to PC2 in recent years. This implies that after a reduction of primary pollutants, the proportion of secondary aerosols in PM(10) may increase. Thus, reducing the precursor concentrations of secondary aerosols will be an effective way to lower PM(10) concentrations.