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1.
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 相似文献
2.
酸性法测定高锰酸盐指数加热时间的选择 总被引:1,自引:0,他引:1
张美萍 《环境监测管理与技术》1997,9(1):38-38
酸性法测定高锰酸盐指数,会因实验条件的差异产生不同的结果。对不同加热时间,加热方式等因素进行了试验,结果表明加热时间以水浴加热沸腾30min或电炉直接加热8min为宜。 相似文献
3.
本文通过目前比较常见的几种内外防腐方式及材料的性能和优缺点 ,对埋地钢质输水管道的防腐选择提供了参考意见 相似文献
4.
介绍了采用中和混凝 水解酸化 生物接触氧化 化学除磷工艺对可发性聚苯乙烯 (EPS)废水的处理工艺。在废水中的CODCr、TP、SS、NH3 N分别为 194 0mg L、112mg L、5 92mg L、2 3 6mg L的条件下 ,废水经过处理后 ,出水CODCr、TP、SS、NH3 N分别为 70mg L、0 38mg L、2 6mg L、0 32mg L ,均可达到《污水综合排放标准》中的一级标准 ,废水中CODCr、TP、SS、NH3 N的平均去除率分别为 96 4 %、99 3%、95 6 %、98 6 %。 相似文献
5.
王奇 《辽宁城乡环境科技》2005,25(5):23-25
根据1999年~2003年大连市区声环境质量的监测数据,采用两次残差修正GM(1,1)对2010年前声环境质量进行预测结果表明,修正后模型均为一级模型,进行中、短期预测精度高。预测结果可为城市噪声污染防治提供科学的依据。 相似文献
6.
根据参窝水库近几年的水质监测资料,对参窝水库的水体自净能力及水质变化规律进行了较深入的研究、计算出主要污染物在参窝水库的自净率,分析了参窝水库水质变化规律,为参窝水库的水污染防治提供了科学的依据。 相似文献
7.
8.
水解酸化+两级生物接触氧化处理高盐度水产品加工废水 总被引:1,自引:0,他引:1
介绍了“水解酸化+两级生物接触氧化”处理水产品加工废水的运行效果和工程实例,结果表明:对C1^-浓度平均6000mg/L的高盐度水产品加工废水,系统对COD、SS、氨氮的去除率分别超过了88%、90%、85%,出水COD、SS、氨氮分别低于100mg/L、70mg/L、15mg/L,出水完全可以达到《污水综合排放标准》(GB8978—1996)一级排放标准。 相似文献
9.
国内外环境监测分析的现状和发展 总被引:3,自引:0,他引:3
从实验室分析仪器和技术、与污染物总量控制有关的自动监测系统和技术,与应急事故监测有关的现场简易监测分析仪器和技术等方面概括介绍了环境监测分析仪器和技术的现状与发展,并概要介绍了日美及中国环境监测分析产业的现状与发展。 相似文献
10.
浮选剂污染河段大型底栖无脊椎动物的群落特征及其对水质的评价 总被引:2,自引:0,他引:2
利用大型底栖动物的群落结构及其行为反应对有毒化合物——浮选剂污染河段水质进行评价,并提出了受浮选剂污染的污水生物系统评价标准.结果表明,主要污染物丁基磺药对涡虫(Dugesia gonocephala)的24h LC_51为78ppm.根据受污河段底栖动物群落的变化和涡虫在各断面河水中的行为反应,浮选剂污水生物系统可以划分成α-多污带,β-多污带,α-中污带,β-中污带和寡污带。并对所提出的浮选剂污水生物系统进行了讨论。 相似文献