衡阳市区和衡山背景站臭氧浓度变化规律的对比分析

选取衡阳市区和衡山背景站臭氧自动监测数据,分析两地的臭氧污染特征。对空气质量的优良率情况、臭氧作为首要污染物的变化情况、臭氧浓度的日变化特征、典型时段的浓度变化特征、臭氧浓度的月际变化特征和臭氧与PM_(2.5)的关联情况等进行了分析。结果表明,多云及阴雨天气时,衡阳市区的臭氧浓度日变化幅度大于衡山背景站。夏季,衡阳市区和衡山背景站的臭氧浓度的日变化特征规律差异较大,臭氧浓度分布比较分散,前者为典型的单峰形,后者则波动平缓。冬季,日变化幅度不大,但衡阳市区的臭氧浓度明显低于衡山背景站。衡山背景站和衡阳市区的臭氧基本同步变化,但日均值高于衡阳市区。     

非柔性连接石化储罐配管温差应力分析与对策

储罐配管在温差作用下产生热位移会导致管壁产生热应力，未采用柔性连接的配管由于热应力集中作用可能发生破漏。针对与石油化工储罐非柔性连接的配管的安全问题，应用ANSYS有限元分析软件模拟计算石化企业典型配管的热应力分布；根据不同类型配管的分析结果，提出了工程技术与管理方面的针对性安全措施。研究结果表明:温差效应对石化储罐配管热应力影响显著，随内外温差的增加，配管应力集中的区域增大，最大热应力值增大；补偿措施能够改善配管的柔性，降低配管应力；约束载荷限制配管热膨胀从而增大配管应力；三通管是多根配管接合的区域，这种复杂管道交汇处会形成明显应力集中。研究结果对于提高石油化工非柔性连接配管安全性有一定的指导意义。     

Mitigating Impact of Devils Lake Flooding on the Sheyenne River Sulfate Concentration

Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m³/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.     

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。     

基于SHERPA的环境空气质量减排情景模拟评估

分析了SHERPA综合评价模型的基本原理和主要建模理念,重点介绍了其在环境空气质量减排情景模拟评估方面的作用,以及在排放源与受体关系(SRR)方面的处理方法,比较了其与欧盟常用的其他情景模拟模型的优缺点。SHERPA模型的特点是空间灵活性较好,对于任何给定地点,可以快速评估不同地区对该研究地点空气质量的影响。SHERPA模型的3个主要功能为污染物来源分析、决策支持和情景模拟。基于SHERPA模型对法国环境空气中PM_(2.5)、PM_(10)和NO_2年均浓度进行污染来源分析、决策支持分析和减排情景模拟评估,展示了模型在环境治理措施优先级筛选和政府间联合治理措施协调建议方面的功能和作用,以期为中国环境空气质量预测预报、环境质量管理措施的制定和成效评估等环境服务与管理工作提供借鉴。     

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