SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

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     

加强环境统计工作的几点建议

分析了当前环境统计工作中存在的一些问题,提出了对策和建设.     

高锰酸盐指数测定中校正系数K值的变化验证

运用数理统计方法,验证了高锰酸盐指数测定的校正系数K值在4～5h内不会随时间和水样的变化而变化,K值不必每个样品都测定。     

浅谈如何做好“十一五”环境统计工作

环境统计工作存在的问题：指标体系设置不合理、数据时效性差、数据质量不高、统计人员流动性大，采取的措施：精简完善指标体系、规范工作程序和审核办法、抓好统计范围、污染源监测及数据核查提高数据质量、加强统计队伍建设等。     

浅析环境统计工作中存在的问题及改进方法

针对环境统计存在的问题，分析产生的原因，并提出解决环境统计工作问题的方法。     

广东省"十五"环境统计改革设想

在国家环保总局"十五"环境统计改革基础上,结合各省经验和广东省实际,提出了广东省环境统计改革设想,逐步实行季报、月报,并将环境统计、排污申报合二为一。     

矿井重大灾害危险性评价的改进灰色统计法

用灰色统计理论［１］研究了矿井四大灾害即冒顶、水灾、火灾和瓦斯爆炸的危险性预评价问题，提出了一种新的评价方法──灰色统计综合法。同时，从应用的角度出发对灰色统计的算法作了改进，改进后的算法便于记忆和应用。     

INTEGRATED RIVER BASIN OPTIMIZATION: MODELING ECONOMIC AND HYDROLOGIC INTERDEPENDENCE1

ABSTRACT: This paper presents an integrated optimal control model that optimizes economic performance of reservoir management in watersheds in which there are significant economic and hydrologic interdependencies. The model is solved using the General Algebraic Modeling System (GAMS). Results show that application of this model to New Mexico's Rio Chama basin can increase total system benefits over historical benefits by exploiting complementarities between hydroelectricity production, instream recreation, and downstream lake recreation.     

推广工伤保险对我国生产事故统计的影响

我国安全生产统计工作所依托的行政手段还不能完全适应变化中的市场经济环境 ,政府安全生产管理不到位 ,监管不严 ,各类事故的少报、瞒报、不报现象相当普遍 ,极大地影响了伤亡事故统计的准确性。2 0 0 4年我国已实施新的《工伤保险条例》 ,笔者认为 ,会在一个较长的时期内对生产事故统计带来较明显的影响 ,使生产事故造成的伤亡人数统计变得更为全面和科学 ,这种影响必然会对原有统计偏差的修正 ,有利于今后加强和完善对安全生产的宏观决策和管理 ,会从根本上促进安全生产水平的提高。