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     

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介绍我国对水污染控制认识的发展和深化，阐述了污水生物处理方面的研究和新进展，包括在微生物方面的研究和新反应器、新工艺的发展。同时，讨论了新型、高效化学混凝剂和絮凝剂的开发成功，使化学法用于城市污水的处理成为可能。并提出任何一种新的处理工艺和反应器都各有其优缺点和适用条件，不能认为某一种就是最好，最先进的，可以无条件适用各种情况的。     

滤膜法测定空气中氟化物的改进

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浅析分布式防火墙技术

随着计算机网络的不断发展,现有的防火墙体系已不能满足要求,发展新的网络防火墙技术已非常重要.分布式防火墙具有明显的防护优势,了解分布式防火墙技术,并在实际应Ζ用中用好它,已成为保障我们网络安全的必要.     

生态示范区可持续发展灰色评估系统设计应用

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孔道结构对柴油机DPF压降及再生特性的影响

针对微粒捕集器（DPF）内部碳烟及灰分颗粒特征,运用AVL-Fire软件建立了六边形孔道结构柴油机微粒捕集器模型。 针对不同排气流量、进口温度、孔密度、碳烟和灰分沉积量,对六边形孔道及四边形孔道DPF压降特性和碳烟再生特性进行分析,并研究灰分分布形式对不同孔道形状DPF的影响。 结果表明：排气质量流量越大,进口温度越高,不同孔道结构的压降敏感性增大;与传统四边形孔道DPF相比,当碳烟沉积量较低时,六边形孔道DPF压降损失较高;随着碳烟沉积量的增加,六边形孔道DPF压降损失较低,且碳烟承载量较大;灰分在DPF孔道表面层状分布可以有效阻止碳烟深床捕集模式,降低压降损失;六边形孔道DPF能够有效提高碳烟及灰分容量,且碳烟捕集及再生效率较高,再生速率较快,热应力较小,可以降低DPF主动再生频率,延长使用寿命。     

不同气氛下微粒捕集器的离线再生

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