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     

基于时序NDVI数据的鄱阳湖流域常绿覆被季节性变化特征

选择位于红壤丘陵区的鄱阳湖流域作为研究对象，利用1 km×1 km分辨率的时序SPOT4 VEGETATION数据，对流域内典型土地覆被--常绿覆被的绿度值、峰值、谷值、年均NDVI(NDVI-I)和NDVI年内极差(NDVI MM)等特征值进行了提取。在此基础上，探讨了不同常绿覆被类型的NDVI指数年内季节变化规律。结果表明：时序NDVI指数基本上能够较好地刻画不同常绿覆被类型之间的差异性，植被指数NDVI特征值随覆被的类型及其生长状态有规律地变化，即NDVI年均值和最小值基本上按“常绿阔叶林＞常绿针阔叶混交林＞常绿针叶林＞常绿针叶-落叶混交林”的顺序变化；典型常绿阔叶林的NDVI指数年内变化曲线基本上没有大的起伏波动；常绿针叶林以及常绿针阔叶混交林占主导地位的常绿混交林NDVI指数年内变化比较和缓，但常在8月和11月有所波动；以常绿针叶林为主、但有较多落〖JP2〗叶林混杂其中的常绿混交林，其NDVI指数年内变化曲线基本上呈和缓的单峰型波动。     

北京南部城区PM2.5中碳质组分特征

为了解《大气污染防治行动计划》实施后北京市大气PM_2.5中碳质组分特征,于2017年12月至2018年12月在北京污染较重的南部城区进行了PM_2.5连续采样,对其中的有机碳（OC）和元素碳（EC）进行了全面研究.结果表明,北京大气PM_2.5、OC和EC浓度变化范围分别为4.2~366.3、0.9~74.5和0.0~5.5 μg ·m^-3,平均浓度分别为（77.1±52.1）、（11.2±7.8）和（1.2±0.8）μg ·m^-3,碳质组分（OC和EC）整体占PM_2.5的16.1%.OC质量浓度季节特征表现为：冬季[（13.8±8.7）μg ·m^-3] > 春季[（12.7±9.6）μg ·m^-3] > 秋季[（11.8±6.2）μg ·m^-3] > 夏季[（6.5±2.1）μg ·m^-3],EC四季质量浓度水平均较低,范围为0.8~1.5 μg ·m^-3.二次有机碳（SOC）年均质量浓度为（5.4±5.8）μg ·m^-3,四季贡献比例范围为45.7%~52.3%,年均贡献为48.2%,凸显了二次形成的重要贡献.随污染加重,尽管OC和EC贡献比例均降低,但浓度水平却成倍升高,OC和EC浓度在严重污染天分别是空气质量为优天的6.3和3.2倍.与非供暖时段相比,供暖时段PM_2.5、OC和SOC浓度分别增加了14.4%、47.9%和72.1%,体现了OC对供暖季PM_2.5污染的重要贡献.PSCF分析表明,位于北京西南的山西省和河南省部分区域是PM_2.5和OC的主要潜在源区,且PM_2.5潜在源区更为集中;EC的PSCF高值（>0.7）区域较少,主要位于北京南部,如山东省和河南省部分地区,且北京市及周边地区贡献明显.     

基于遥感尾气监测的柴油车气态污染物排放因子计算方法研究

采用遥感尾气测试系统实测了柴油车在实际道路工况下的CO、HC和NO排放特征,修正了排放因子的计算方法,并与车载排放测试系统（PEMS）实测结果进行了验证,获得了实测车辆的CO、HC和NO排放因子.测试结果显示,在各种遥感监测的工况下柴油车尾气中均含有较高浓度的氧气,未考虑氧气影响的燃烧方程反演获得的各污染物体积浓度计算值与PEMS实测值的偏差较大,且氧气浓度越大,偏差越大.经过氧气修正的燃烧方程反演计算的尾气浓度与PEMS实测值吻合度大幅提升,适用于实际工况下遥感检测车辆尾气的反演计算.修正算法得到CO、HC和NO的排放因子离散性较小,精确度较高,可以为量化柴油车尾气排放贡献提供科学依据.     

Development of a static test apparatus for evaluating the performance of three PM2.5 separators commonly used in China

PM_(2.5) separator directly affects the accuracy of PM_(2.5) sampling.The specification testing and evaluation for PM_(2.5) separator is particularly important,especially under China's wide variation of terrain and climate.In this study,first a static test apparatus based on polydisperse aerosol was established and calibrated to evaluate the performance of the PM_(2.5) separators.A uniform mixing chamber was developed to make particles mix completely.The aerosol concentration relative standard deviations of three test points at the same horizontal chamber position were less than 0.57%,and the particle size distribution obeyed logarithmic normal distribution with an R~2 of 0.996.The flow rate deviation between the measurement and the set point flow rate agreed to within ± 1.0% in the range of -40 to 50℃.Secondly,the separation,flow and loading characteristics of three cyclone separators(VSCC-A,SCC-A and SCC112) were evaluated using this system.The results showed that the 50% cutoff sizes(D_(50)) of the three cyclones were 2.48,2.47 and 2.44 μm when worked at the manufacturer's recommended flow rates,respectively.The geometric standard deviation(GSD) of the capture efficiency of VSCCA was 1.23,showed a slightly sharper than SCC-A(GSD = 1.27),while the SCC112 did not meet the relevant indicator(GSD = 1.2 ± 0.1) with a GSD = 1.44.The flow rate and loading test had a great effect on D_(50),while the GSD remained almost the same as before.In addition,the maintenance frequency under different air pollution conditions of the cyclones was summarized according to the loading test.     

保定市大气污染特征和潜在输送源分析

保定市是京津冀地区重要城市之一.为了解保定市大气污染物质量浓度特征和潜在输送源,对保定市国控点2017年1月1日-12月31日PM₁₀、PM_2.5、SO₂、NO₂、O₃、CO等常规大气污染物数据进行分析,并利用TrajStat后向轨迹模型进行区域传输研究.结果表明:①ρ（PM₁₀）、ρ（PM_2.5）、ρ（SO₂）、ρ（NO₂）分别为（138±96）（84±66）（29±23）和（50±24）μg/m3,与2016年相比分别下降5.9%、9.1%、25.5%和13.1%;ρ（CO）较2016年下降了14.0%;ρ（O₃）较2016年增长了25.2%.ρ（PM₁₀）、ρ（PM_2.5）、ρ（NO₂）和ρ（O₃）分别超过GB 3095-2012《环境空气质量标准》二级标准限值的0.97、1.40、0.25和0.34倍,ρ（SO₂）和ρ（CO）未超标.②除ρ（O₃）外,其他污染物质量浓度均呈冬季最高、夏季最低的季节性特征,其中,冬季PM_2.5污染最为严重,春季PM_2.5~10（粗颗粒物）污染严重.③空气质量模型源解析结果显示,保定市ρ（PM_2.5）约60.0%~70.0%来自本地污染源排放.后向轨迹结果表明,在外来区域传输影响中,保定市主要受到西北方向气团（占比为21.7%~60.0%）远距离传输和正南方向气团（占比为34.8%~50.5%）近距离传输的影响.④PSCF（潜在源贡献因子分析法）和CWT（浓度权重轨迹分析法）分析表明,除保定市及周边区县本地污染贡献外,位于太行山东麓沿线西南传输通道的邯郸市、邢台市、石家庄市是影响保定市PM_2.5的主要潜在源区.研究显示,PM_2.5为保定市大气中的主要污染物,并呈冬季高、夏季低的变化特征,其主要来自西北远距离输送和南部近距离传输.      

基于野外原位抽注试验和同位素技术的含水层反硝化速率研究

反硝化作用是地下水硝酸盐污染去除最重要的过程.由于水文地质条件和水文地球化学环境的复杂性和不确定性,精准测定含水层反硝化速率是反硝化过程的研究难点.选取潮白河冲洪积扇中部中国环境科学研究院地下水创新野外基地作为研究区,基于野外原位试验和15N同位素示踪法提出一种含水层反硝化速率的测定方法.该方法综合体现了研究区实际水文地质条件和水文地球化学环境对反硝化作用的影响,并充分考虑了硝酸盐在含水层中稀释弥散作用对计算结果的影响.结果表明:①潮白河冲洪积扇中部某地地下26~28 m处于还原环境,含水介质以粉细砂为主,ρ（NO₃-N）平均值为2.77 mg/L.②地下26~28 m反硝化速率在349.52~562.99 μg/（kg·d）（以N计,下同）之间,平均值为450.31 μg/（kg·d）.通过与研究区含水介质、采样深度和硝酸盐背景值相似的国内外案例对比研究,初步评估结果处于合理区间.③测试结果具有一定不确定性,主要来自忽略中间产物NO₂-和NO的计算方法、扰动采样方法、N₂O的操作规范程度及采样频率等方面.研究方法为测定含水层硝酸盐速率研究提供了新的思路,研究结果可为地下水中硝酸盐转化过程机理研究、地下水硝酸盐污染修复及风险管控提供关键的理论支撑数据.      

弹射起飞和拦阻着陆冲击试验方法探讨

目的研究舰载机机载装备弹射起飞和拦阻着陆冲击试验的工程实现方法。方法从GJB 150.18A—2009、MIL-STD-810F/G及其提供的实测数据出发,分析弹射起飞和拦阻着陆冲击载荷特征,对几种常用试验方法的工程实现进行探讨和比较。结果弹射起飞和拦阻着陆冲击具有瞬态、交变、低频、大位移、高速度等特征。阻尼正弦波方法的实施路径比较明确,复杂波形再现方法实施路径比较模糊且复杂,超长脉冲半正弦波方法具有多种局限性,前两种方法的速度、位移都较大。结论阻尼正弦波方法较复杂波形再现方法更容易实现,但两者都需要大位移、高速度的专用冲击试验设备和适当的波形处理技术,超长脉冲半正弦波方法在前面两种标准推荐方法能够实现时,不建议使用。     

机载产品盐雾试验结果评定判据分析与探讨

针对当前机载产品盐雾试验标准中缺少明确、可量化评估指标,试验结果评定判据粗略笼统且以定性评价为主,已不能满足产品发展和使用环境扩展需求等现状,在总结国内外现行盐雾试验标准的结果评定要求基础上,分析当前机载产品盐雾结果评定判据存在的问题与不足,从管理和技术两个层面提出改进试验结果评定可操作性的建议,指导机载产品盐雾试验实施与评价。