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     

Modelling nitrogen fluxes at the landscape scale

The distribution and impacts of different nitrogen pollutants are inextricably linked. To understand the problem fully, the interactions between the different pollutants need to be taken into account. This is particularly important when it comes to abatement techniques, since measures to reduce emissions of one nitrogen pollutant can often lead to an increase in another. This project represents a step towards greater understanding of these issues by linking together new and existing nitrogen flux models into a larger framework. The modelling framework has been constructed and some of the nitrogen flows between fields, farms and the atmosphere have been modelled for a UK study area for typical farm management scenarios.     

大气环境数据分析预测方法对比研究

以西安市2006年9月27日至2008年5月3日每日的SO2平均浓度时间序列为例,应用时间序列分析对前555个数据进行拟合,得到合适的时间序列模型ARIMA(1,1,2);利用神经网络中的BP神经网络和RBF神经网络对同样的样本进行训练,用这三种方法对2008年4月4日至2008年5月3日的SO2日均浓度值进行了预测,并用同样的方法分析预测了同期PM10日均浓度值,最后比较了它们的预测效果。结果表明,利用这三种方法进行浓度预测都是可行的,其中RBF神经网络法的预测误差最小,效果最好。     

天然河流纵向离散系数的最优化计算方法及其应用

提出了计算天然河流纵向离散系数的最优化方法，并用此法对岷江蕨溪段的试验数据进行处理，计算出了该段河流的纵向离散系数。     

镇江市大气环境承载力研究

根据前人对大气环境容量和环境承载力计算的对比分析,计算2012年镇江市的大气环境容量和大气环境承载力指数,通过对该市的气象条件和地理位置的分析,选取SO_2、NO_x和PM_(10)这3种主要大气污染物,并分析其环境承载状况。结果表明:句容市、丹阳市、杨中市处于高承载的状态,临界超载、严重超载和超载的3个区分别是润州区、京口区和镇江新区,针对这3个区提出相应改进措施。     

Prevention of an atmospheric storage tank bund failure

Secondary containment (bunds, drip trays, interceptors/sumps) is an integral part of many chemical, petrochemical, as well as agricultural, food and pharmaceutical facilities. Bunds are the built-in cases for accidental liquid substance spillage where there is a hazard that a substance could leak into the surroundings and the potential of health or environmental damage. The authors focus on the cases installed in large-volume atmospheric tanks storing hazardous chemicals in liquid form, where the bund failure occurred. In the first part of the study, a database of these accidents was created, numbering a total of 15 items. Attention was paid to the course of the accident, its causes and consequences. Several basic representative scenarios of bund failure have also been developed. The second part of the study is devoted mainly to the measures against the bund failure, such as risk assessment, technical and organizational measures. The aim of this study is to provide readers (including experts in the field of safety engineering, maintenance experts and building designers) with the information that can subsequently be used in the risk assessment of bund failure, possibly also for planning the construction of new bunds, or revitalization and maintenance of the existing ones.     

考虑油滴碰撞聚结的斜板除油过程模拟

考虑离散油滴在油田废水除油过程中发生的油滴碰撞聚结现象,模拟得出斜板除油器内全部油滴的动态信息,用于斜板除油器除油效率的计算.对矩形同向流斜板除油过程的模拟研究表明:油滴的碰撞聚结会增加斜板除油的效率;当废水的原始含油浓度增大时,斜板除油的效率会增大,碰撞聚结对除油效率提高的影响也越大;废水流动速度提高及斜板的倾斜角度增加均会使斜板的除油效率降低,但此时油滴碰撞聚结对除油效率的影响仍很明显.     

WRF-Fluent耦合模式的构建及其对城市大气扩散的精细化模拟

为了准确刻画城市环境中的复杂建筑结构对大气运动和污染物输送及扩散过程的影响,结合中尺度模式WRF和微尺度模式Fluent构建了单向耦合的WRF-Fluent模式,并将其应用于榆中县城区高架源排放情景下大气扩散的模拟研究.基于城市气象/示踪外场试验期间获取的气象数据对WRF模式的模拟性能进行验证,发现WRF能够为Fluent提供比较准确的随时间变化的气象驱动场.在此基础上,将榆中县城区三维几何模型嵌入到WRF-Fluent中,对城区内的流场及污染烟羽的时空演变过程进行精细化模拟,并将模拟结果与实测示踪物浓度数据进行比对.结果显示,WRF-Fluent模式可以很好地描述城区内的复杂三维风场结构以及污染烟羽随时间的动态变化特征,而且模拟的地面和城市冠层顶浓度最大值基本位于观测值的0.3~3倍之内,这表明构建的WRF-Fluent模式具有较好的模拟性能,可作为一种有效的工具应用于实际城市规划和空气质量改善以及环境风险评估等方面的研究.     

高分散MnOx/SAPO-34催化剂的制备及其低温NH3选择性催化还原NO性能

采用改进的溶胶-凝胶法制备一系列MnO_x/SAPO-34催化剂,考察了各制备参数对催化剂的结构及其低温氨选择性催化还原(NH_3-SCR)脱硝性能的影响,并通过X射线衍射、N_2吸附-脱附、透射电镜、X射线光电子能谱、NH_3程序升温脱附等手段对催化剂进行表征.结果表明,当制备参数为n(乙醇)/n(Mn)=15,n(H_2O)/n(Mn)=20,n(柠檬酸)/n(Mn)=1,Mn负载量为15%(质量分数),催化剂焙烧温度为350℃时,制备的高分散15%-MnO_x/SAPO-34-350℃催化剂具有最佳的低温SCR活性,在空速为45000 h~(-1)的条件下,且反应温度在120～240℃范围时均保持90%以上的NO转化率和接近100%的N_2选择性.MnO_x纳米颗粒高度分散在SAPO-34载体表面,平均粒径约为5.46 nm,纳米颗粒的表面效应使得该催化剂具备较大的比表面积,暴露出大量的活性位点和高活性的MnO_2(110)晶面,同时,高Mn~(4+)比例和更多的化学吸附氧以及适宜的表面酸强度和酸量也是15%-MnO_x/SAPO-34-350℃催化剂呈现最佳低温SCR活性的重要原因.     

Insights into multidimensional transport flux from vertical observation and numerical simulation in two cities in North China

This study represents the first quantitative evaluation of pollution transport budget within the boundary layer of typical cities in the Beijing-Tianjin-Hebei (BTH) region from the perspective of horizontal and vertical exchanges and further discusses the impact of the atmospheric boundary layer (ABL)-free troposphere (FT) exchange on concentration of fine particulate matter (PM_2.5) within the ABL during heavy pollution. From the perspective of the transport flux balance relationship, differences in pollution transport characteristics between the two cities is mainly reflected in the ABL-FT exchange effect. The FT mainly flowed into the ABL in BJ, while in SJZ, the outflow from the ABL to the FT was more intense. Combined with an analysis of vertical wind profile distribution, BJ was found to be more susceptible to the influence of northwest cold high prevailing in winter, while sinking of strong cold air allowed the FT flowing into the ABL influence the vertical exchange over BJ. In addition, we selected a typical pollution event for targeted analysis to understand mechanistic details of the influence of ABL-FT exchange on the pollution event. These results showed that ABL-FT interaction played an important role in PM_2.5 concentration within the ABL during heavy pollution. Especially in the early stage of heavy pollution, FT transport contributed as much as 82.74% of PM_2.5 within the ABL. These findings are significant for improving our understanding of pollution transport characteristics within the boundary layer and the effect of ABL-FT exchange on air quality.