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     

在污染条件下生物的生态效应

主要就污染物对动物、植物及人体健康的危害，以及在污染条件下生物的适应与进化作一综述。     

从动力地质作用原理探讨沙漠化成因

大规模沙漠化从岩石记录中可以追溯到中生代中期，第四纪以来南北两半球各存在一条中纬度沙漠带，中国的沙漠化是这个全球沙漠化进程的一个组成部分。沙漠化是在地质演化过程中受内、外动力地质作用联合控制的地质事件。是岩石圈与大气圈、生物圈及水圈强烈作用在地壳表层形成的一种特殊地质现象。这种现象的形成与演化是漫长的和具有周期性的，不会因为局部的条件变化而发生整体意义上的突变。人类活动是局部的，在整个地球沙漠化进程中只是起到一个加速剂的作用。沙漠化过程可分为3个阶段，即物理风化与沙源积聚阶段、风沙作用阶段和沙漠化阶段。沙漠期后沙丘沙经过固化生草、胶结成岩阶段后即形成风成砂岩。     

我国农药废水的处理现状及发展趋势

随着我国农药产量的逐年提高，农药废水的处理形势也日益严峻，因此农药废水处理的新工艺、新方法成为科研工作者研究的热点。根据文献资料，概述了农药废水的种类、特点及其处理方法，主要包括物理法，化学法和生物化学法，以及近些年发展的磁分离法和超声波技术处理法，并叙述了目前我国有机磷、有机氯、有机硫等农药废水的处理现状及研究成果。同时指出了新技术、新工艺的研究及实行清洁生产制度将成为我国农药废水处理技术的发展趋势。     

欧亚大陆东南缘大陆地壳基底研究与展望

本文从不同方面和断代论述了中国东南地区的基底杂岩时代、性质、构造环境、边界变质条件、地球物理场及其反映的构造格局、断裂体系、中生代火山活动的基底构造及类型分区。在此基础上提出了东南地区的构造划分及构造演化阶段和格局。并提出了不同类型的地体及其分类、大陆裂解、增生的新概念。     

TEMPORAL CHANGES IN THE YADKIN RIVER FLOW VERSUS SUSPENDED SEDIMENT CONCENTRATION RELATIONSHIP1

ABSTRACT: Dynamic linear models (DLM) and seasonal trend decomposition (STL) using local regression, or LOESS, were used to analyze the 50‐year time series of suspended sediment concentrations for the Yadkin River, measured at the U.S. Geological Survey station at Yadkin College, North Carolina. A DLM with constant trend, seasonality, and a log₁₀ streamflow regressor provided the best model to predict monthly mean log₁₀ suspended sediment concentrations, based on the forecast log likelihood. Using DLM, there was evidence (odds approximately 69:1) that the log₁₀ streamflow versus log₁₀ suspended sediment concentration relationship has changed, with an approximate 20 percent increase in the log₁₀ streamflow coefficient over the period 1981 to 1996. However, sediment concentrations in the Yadkin River have decreased during the decade of the 1990s, which has been accompanied by a concomitant increase in streamflow variability. Although STL has been shown to be a versatile trend analysis technique, DLM is shown to be more suitable for discovery and inference of structural changes (trends) in the model coefficient describing the relationship between flow and sediment concentration.     

我国道路交通安全发展情景分析

以日本和美国为例 ,分析了发达国家道路交通事故高发期的发展历程 ,得出了道路交通事故的上升与国家经济发展和机动化增长有着直接关系的结论。笔者通过分析和预测我国经济和道路交通的发展情景发现 ,今后我国道路交通发展特征和日本道路交通高发期的情况十分类似。如果现有交通管理体制和技术措施没有大的变化 ,我国道路交通事故死亡人数到 2 0 2 0年可能会超过 2 3万人。为此 ,国家应尽快改善道路交通管理方面所存在体制上、行政上和技术上的不足 ,防止出现笔者所预测的道路交通事故极其严重的局面。     

拉伸加载下老化复合固体推进剂的损伤演化定量分析

目的 定量分析老化对拉伸加载下复合固体推进剂细观损伤演化的影响规律。方法 开展不同老化时间（0、32、74、98 d）下三组元端羟基聚丁二烯（HTPB）复合固体推进剂的热加速老化试验,以及老化后推进剂的微型哑铃形试验件在不同拉伸应变水平（0、5%、10%、20%、40%、50%）下的微CT扫描观测试验,并对扫描重构图像进行定量分析,获得老化对推进剂细观损伤演化的影响规律。结果 随拉伸应变增加,老化前后推进剂微CT图像的灰度均方差、平均孔隙率和图像的分形维数均呈非线性增加趋势,图像的灰度均值呈非线性减小趋势。在较低应变水平（<10%）下,上述参数受老化时间的影响不显著,但随拉伸应变增加（≥10%）和老化时间增长,灰度均方差、平均孔隙率、孔隙率增量和分形维数的数值整体增高,而灰度均值随老化时间的增长而降低,且不同老化时间下,分形维数与平均孔隙率之间存在正线性关系。结论 重构得到微CT图像的灰度均值、平均孔隙率和分形维数可作为表征参数,用于定量分析老化复合固体推进剂在拉伸载荷下的细观损伤和损伤的动态演化规律,推进剂的初始损伤和小应变下的损伤程度受老化的影响较弱,而老化时间对推进剂细观损...     

重复碰撞载荷下加筋板结构的动态响应研究

目的 探究船体加筋板结构在受到重复碰撞载荷作用下塑性变形的累计趋势,分析加筋板主要参数对损伤演化过程的影响,揭示高速、重复冲击下船体加筋结构的变化机理。方法 以一纵三肋加筋板为研究对象,建立基于能量法的刚塑性加筋板动态变形计算方法,以及基于Johnson-Cook本构关系的数值模拟方法,开展不同板架厚度、冲击能量以及冲击尺寸等参数对加筋板变形演化影响的研究。结果 对比分析了理论模型和数值方法,获得了不同参数下的加筋板的结构损伤演化规律。结论 理论解和数值解较为吻合。冲击能量增加后,加筋板的塑性变形量、塑性变形累积速度也随之增加。载荷集中程度越大,对于碰撞边界的破坏效应越大。     

基于MDE和分形优化SVM的周期来压预测

为了解决周期来压的预测问题,首先对已知支架周期来压荷载曲线使用多重差异进化算法(MDE)进行拟合,将每重拟合形成的单一正弦曲线与上次差余曲线(Ei)再作差余曲线(Ei+1)。将这些Ei图通过分形几何的盒子法计算维度和相关系数(r)。将每条Ei的维度、r和支架相对距离(L)作为输入值,对应的Ei的周期Ti、缩放系数Si和纵移系数Di作为目标值,使用支持向量机(SVM)进行训练。通过对维度和r规律的研究得到拟设置支架处荷载各Ei的维度和r,带入训练后的SVM模拟得到Ei的Ti、Si和Di,进而得到Ei的表达式。将上述Ei求和即为所求拟设置支架处的周期来压荷载。实例分析说明,该种方法预测结果可以大体反映支架周期来压的基本形式和变化规律。