No abstract

No abstract

No abstract

We review statistical methodology for estimating mean concentrations of potentially toxic pollutants in water, for small samples that are not normally distributed and often contain substantial numbers of nondetects, i.e. samples that are only known to be below some set of fixed thresholds. Maximum likelihood estimation (MLE) and regression on order statistics (ROS) are two main approaches that dominate the literature, with transformation bias under non-normality that increases with the severity of censoring being the main problem. We consider exact maximum likelihood estimators in conjunction with the Box-Cox transformation and propose the Quenouille-Tukey Jackknife as a method for bias reduction and variance estimation. Exact maximum likelihood estimators resulting from the expectation-maximization (EM) algorithm are exhibited in a simple heuristic form that also provides estimated values for the nondetects as subsidiary outputs. We show in simulationsthatthetwo main approaches perform well for the log-normal and gamma distributions as long as the jackknife is employed to reduce bias. Bias corrections to MLE used in the literature are shown to correct in the wrong direction under severe censoring. The jackknife is also used for estimating the variance of the both the MLE and ROS estimators. Robustness is improved by searching a class of power transformations (Box-Cox) for the best approximating normal distribution. We conclude that both the exact MLE and ROS procedures can be useful under varying experimental conditions. Limited simulations indicate that the ROS procedure is unbiased and has a smaller variance than the MLE under the log-normal distribution and is robust. The MLE performed better in simulations involving the gamma as the underlying distribution. We also compare the estimators for the mean and variance that one obtains from typical sets of water quality data, analyzing for copper, alumnium, arsenic, chromium, nickel, and lead.

The objective of this study was to evaluate correlations between annual average daily traffic (AADT) and storm water runoff pollutant concentrations generated from California Department of Transportation (Caltrans) highway sites. Analyses of data collected from the Caltrans Cyear (1997-01) highway runoff characterization program revealed that, in general, pollutant concentrations from urban highways were higher than those found from non-urban highways. For a limited number of pollutants, however, the concentrations from norrurban highways were found to be higher than the concentrations from urban highways. No direct linear correlation was found between highway runoff pollutant event mean concentrations (EMCs) and AADT. However, through multiple regression analyses, it was shown that AADT has an influence on most highway runoff constituent concentrations, in conjunction with factors associated with watershed characteristics and pollutant build-up and wash oft The other noticeable factors shown ' (530) 7537030, e-mail: claua@lrr~a.corn to influence the accumulation of pollutants on highways were antecedent dry period, drainage area, maximum rain intensity, and land use.Keywords: Annual average daily traffic (AADT), highway runoff, linear regression model, multiple regression model, and pollutants.The California Department of Transportation (Caltrans) is engagd in a multi-year program of research and monitoring pertaining to the environmental effects of stormwater quality from transportation facilities. Part of Caltrans storm water quality research and monitoring program involves the characterization of highway runoff (Kayhanian et al., 2001). These monitoring studies were principally undertaken (i) to comply with the statewide National Pollution Discharge Elimination System (NPDES) storm water permit requirements, (ii) to address legal requirements, (iii) to aid in developing new treatment systems, (iv) to develop runoff load models, and (v) to fill data gaps in stormwater runoff characterization for statistical analysis. The information presented in this paper is based on a 4-year highway stormwater runoff characterization study that was undertaken during the 1997-01 rainy seasons from October through April.Caltrans monitoring data are analyzed on a regular basis to assess runoff characteristics. One question that is frequently asked i s whether a correlation exists between annual average daily traffic (AADT) and the concentrations of highway runoff pollutants. The current paper addresses this issue. METHODS Sampling ProceduresRepresentative highway sites and storm events were selected for event-based monitoring. There are a wide range of parameters that can potentially affect the quality of stormwater discharges including geographic location, climatic/ecologic conditions, hydrologic conditions, land use, and AADT. The highway sites were selected to represent the full range of physical parameters. In addition, the sites were selected as potential monitoring sites based on the ability of the sampling t...