Coefficients Of Confidence Intervals Research Articles

Attributes and uncertainty of dissolved zinc data from a mined catchment

This study evaluated the attributes and uncertainty of non-point source pollution data derived from synoptic surveys in a catchment affected by inactive metal mines in order to help to identify and select appropriate methods for data analysis/reporting and information use. Dissolved zinc data from the Upper Animas River Basin, Colorado, USA, were the focus of the study. Zinc was evaluated because concentrations were highest relative to national water quality criteria for brown trout, and zinc had the greatest frequency of criteria exceedances compared with other metals. Data attributes evaluated included measurement and model error, sample size, non-normality, seasonality and uncertainty. The average measurement errors for discharges, concentrations and loadings were 0·15, 0·1 and 0·18, respectively. The 90 and 95% coefficients of confidence intervals for mean concentrations based on a sample size of four were 0·48 and 0·65, respectively, and ranged between 0·15 and 0·23 for sample sizes greater than 40. Aggregation of data from multiple stations decreased the confidence intervals significantly, but additional aggregation of all data increased them as a result of increasing spatial variability. Unit area loading data were approximately log-normal. Concentration data were right-skewed but not log-normal. Differences in median concentrations were appreciable between snowmelt and both storm flow and baseflow, but not between storm flow and baseflow. Differences in unit area loadings between all flow events were large. It was determined that the average concentration and unit area loading values should be estimated for each flow event because of significant seasonality. Time weighted values generally should be computed if annual information is required. The confidence in average concentrations and unit area loadings is dependent on the computation method used. Both concentrations and loadings can be significantly underestimated on an annual basis when using data from synoptic surveys if the first flush of contaminants during the initial snowmelt runoff period is not sampled. The ambient standard for dissolved zinc for all events was estimated as 1600 μg l−1 using the 85th percentile of observed concentration data, with a 90% confidence interval width of 200 μg l−1. Copyright © 1999 John Wiley & Sons, Ltd.

Statistical Analysis of Historical Climate Data Sets

Abstract The problem of determining confidence intervals for climatic signals using data sets with spatial and temporal sampling inhomogeneities is solved by a four-step process. First, the actual data set is analysed to determine autoregressive models which are consistent with the actual data at daily, monthly and annual time scales. Second, these models are used to generate artificial, but realistic, data sets which reproduce selected statistical properties of the actual data. Third, these artificial data sets are sampled by Monte Carlo techniques to determine certain confidence interval coefficients appropriate to different fields, geographical regions, and averaging periods. Fourth, these confidence interval coefficients are used to place error bars on climatic signals derived from the actual data set. The technique is illustrated by the analysis of historical sea surface temperature and sea level pressure data in the eastern tropical Pacific Ocean.