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     

Role of glycine on sulfuric acid-ammonia clusters formation: Transporter or participator

Glycine(Gly) is ubiquitous in the atmosphere and plays a vital role in new particle formation(NPF).However,the potential mechanism of its on sulfuric acid(SA)-ammonia(A)clusters formation under various atmospheric conditions is still ambiguous.Herein,a(Gly)_x·(SA)_y·(A)_z(z≤x+y≤3) multicomponent system was investigated by using density functional theory(DFT) combined with Atmospheric Cluster Dynamics Code(ACDC) at different temperatures and precursor concentrations.The results show that Gly,with one carboxyl(-COOH) and one amine(-NH_2) group,can interact strongly with SA and A in two directions through hydrogen bonds or proton transfer.Within the relevant range of atmospheric concentrations,Gly can enhance the formation rate of SA-A-based clusters,especially at low temperature,low [SA],and median [A].The enhancement(R) of Gly on NPF can be up to 340 at T=218.15 K,[SA]=10~4,[A]=10~9,and [Gly]=10~7 molecules/cm~3.In addition,the main growth paths of clusters show that Gly molecules participate into cluster formation in the initial stage and eventually leave the cluster by evaporation in subsequent cluster growth at low [Gly],it acts as an important "transporter" to connect the smaller and larger cluster.With the increase of [Gly],it acts as a "participator" directly participating in NPF.     

基于SVR的长江经济带水环境承载力评价

基于DPSIRM模型构建区域水环境承载力评价指标体系,并基于SVR模型构建了区域水环境承载力评价模型,利用交叉验证法对SVR模型参数进行优化选择,进一步提高模型预测精度.运用该模型研究了长江经济带2009~2018年的水环境承载力演变趋势及空间差异,结果表明：长江经济带水环境承载力等级整体呈现升高趋势,其中上游城市群承载等级由II级（超载）提升到了IV级（弱可承载）;中游城市群承载等级由II级（超载）提升到了IV级（弱可承载）;下游长三角区域承载等级由I级（重超载）提升到了IV级（弱可承载）.将评价结果与熵权-TOPSIS法的评价结果相比较,相同率达到91.7%,说明SVR模型评价区域水环境承载力可行,评价结果可靠.以下游区域为例,分别对其6个子系统的承载力进行剖析,并运用单因素轮换OAT法对各子系统内的评价指标进行敏感性分析,便于决策者识别指标敏感性.     

博斯腾湖水质矿化度模型及预测研究

根据博斯腾湖１９８５～１９９５年水质监测数据和出入湖水量等水文数据观测值，采用水质扩散模型和盐量平衡关系推导出博斯腾湖大湖区（简称博湖）水质矿化度模型，并预测了几种情况下博湖水质矿化度及近几年变化趋势，分析了影响博湖水质矿化度的主要因素，为博湖的近期和远期环境保护规划、环境管理等提供科学依据．     

大气污染物影响因素的灰色关联分析

运用灰色系统理论，对产生和影响城市大气主要污染物的相关因素，利用灰色关联分析方法进行了量化分析研究，根据研究结果，提出了控制大气污染的防治对策．     

西藏公路建设过程中的生态保护

针对西藏独特的高原生态环境特征，分析了公路建设的生态影响，提出了不同生态系统类型的生态恢复和保护措施。     

预应力锚索在公路边坡支护中的应用

随着高等级公路的出现及山区高速公路的大量建设,公路边坡问题日益突出,预应力锚索加固技术作为一项新兴技术,在公路边坡支护中有着广阔应用前景。本文根据国内外诸多的设计施工实践,综合论述了预应力锚索结构构造和原理、设计、施工原则和施工监测,应用效果和优越性,对有关理论、工艺问题进行了探讨,为进一步推广这些新技术提供了理论参考和实践经验。     

ASM1中废水和污泥组分浓度的测定方法研究

对呼吸计量法应用于校准ASM1作了简明的阐述。在呼吸速率与模型组分关系的基础上,介绍了ASM1中废水与污泥各组分浓度的测定方法,并指出模型组分细化的意义。     

公路建设项目环境保护投资界定

阐述了对公路建设项目环境保护投资进行界定的目的、意义，并对其现状进行了分析，在此基础上，提出了对公路建设项目环境保护投资划分的具体原则和建议。     

主成份分析法在大气环境质量综合评价中的应用

介绍了主成份分析法评价大气环境质量的基本原理，并对某市的大气环境质量进行了综合评价。最后，与其它方法的评价结果进行了对比分析，进一步证实了主成份分析法的科学性、合理性。