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     

Multipoint source method in air pollution modelling of cold start emission

The paper presents a new method of air pollution modelling on a micro scale. For estimation of concentration of car exhaust pollutants, each car is treated as an instantaneous moving emission source. This approach enables us to model time and spatial changes of emission, especially during cold and cool start of an engine. These stages of engine work are a source of significant pollution concentration in urban areas. In this work, two models are proposed: one for the estimation of emission after cold start of the engine and another for the prediction of pollutant concentration. The first model (defined for individual exhaust gas pollutants) enables us to calculate the emission as a function of time after the cold or cool start, ambient temperature and average speed of motion. This model uses the HBEFA database. The second mathematical model is developed in order to calculate the pollutant dispersion and concentrations. The finite volume method is applied to discretise the set of partial differential equations describing wind flow and pollutant dispersion in the domain considered. Models presented in this paper can be called short-term models on a small spatial scale. The results of numerical simulation of pollutant emission and dispersion are also presented.     

溶剂法生产酞菁兰废水处理工艺

分析了煤油溶剂法生产酞菁兰过程中废水产生的节点及特征，介绍了以回收利用与末端处理相结合使废水减量化和达标排放的酞菁兰废水处理方法     

燃煤氮氧化物排放控制技术研究进展及相关思考

简述了燃煤氮氧化物的产生、迁移、转化规律及其对环境造成的危害，综述了燃煤氮氧化物排放控制技术研究进展和存在的问题，提出了可供参考的相关建议及解决的方案途径。     

活性炭性质对其吸附水中硝基苯性能的影响

通过对活性炭进行HNO3氧化及热处理改性,研究了活性炭性质对其吸附硝基苯性能的影响.以低温液氮(N2/77 K)吸附测定活性炭的比表面积和孔容、孔径分布;以Boehm滴定、零电荷点pHPZC的测定及元素分析定量表征活性发表面含氧官能团变化.结果表明:经改性后,活性炭比表面积及总孔容略有减小,表面性质发生较大变化.改性活性炭对硝基苯的吸附容量明显改变,吸附容量大小依次为:AC1′＞AC0′＞AC0＞AC1.经硝酸氧化后,比表面积下降、存在过多表面含氧官能团是导致AC1吸附硝基苯能力降低的主要原因;而AC1'表面适量酚羟基所提供的氢键吸附是其对硝基苯吸附量增加的主要原因.     

重型柴油车污染物排放量计算方法比较分析

目的 计算大功率大吨位级重型柴油车的污染物排放量.方法 根据《公路隧道通风设计细则》和世界道路协会(PIARC)2012年技术报告,分别计算32 t重型柴油车的污染物排放量和稀释污染物所需的通风量,对比分析两种计算方法的差异.结果 对《公路隧道通风设计细则》中柴油车的车型系数和海拔高度系数提出建议.根据世界道路协会(PIARC)2012年技术报告,在0～2000 m低海拔地区,国产32 t柴油车的CO、NOx和烟尘排放量分别为88.6、166.0 m3/(h·veh)和84.2 m2/(h·veh),如果考虑NOx的空气污染,稀释单辆国产32 t柴油车排放污染物所需空气量约为33000 m3/h;如果不考虑NOx的空气污染,所需空气量约为28000 m3/h.结论 结合工程实际,建议大功率大吨位级重型柴油车的污染物排放量根据世界道路协会(PIARC)2012年技术报告进行计算.     

上海市机动车排污状况与污染控制战略

通过对上海市中心城区机动车行驶工史现状的主要特点及发展趋势的分析，计算出中心城区１９９５年机动车尾气排放的ＣＯ、ＮＭＨＣ和ＮＯｘ负荷，分别占区域内机动车和固定源产排放总量的７６％、９３％和４４％，据预测，到２０１０年，中心城区内机动车排出的ＣＯ、ＮＭＨＣ和ＮＯｘ负荷，将分别占区域中机动车和固定源排放总量的９４％、９８％和７５％，因此，针对机动车排污所面临的严峻挑战，需要采取加强机动车检查与维修（Ｉ     

降低硫黄回收装置烟气CO排放浓度研究

介绍了造成中国石化齐鲁分公司硫黄回收装置烟气CO排放浓度高的原因,并制定了相应的改造方案。对装置目前可实施的运行参数进行了优化调整,个别装置CO排放可达100 mg/m 3以下,其他装置等待检修时改造实施。     

Solid phase extraction of methomyl and thiodicarb from environmental water with an activated carbon cartridge and their determination by HPLC

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粉煤灰分选工艺流程的改进

利用闲置设备进行流程改进，提高了粉煤灰的利用率，解决了制砖所需的细骨料。每年节省运灰费用２００多万元，具有良好的经济效益和环境效益