Derek G. Williamson

Uncertainty analysis in air dispersion modeling

Abstract The Taylor series approach for uncertainty analyses is advanced as an efficient method of producing a probabilistic output from air dispersion models. A probabilistic estimate helps in making better-informed decisions when compared to results of deterministic models. In this work, the Industrial Source Complex Short Term (ISCST) model is used as an analytical model to predict pollutant transport from a point source. First- and second-order Taylor series approximations are used to calculate the uncertainty in ground level concentrations of ISCST calculations. The results of the combined ISCST and uncertainty calculations are then validated with traditional Monte Carlo (MC) simulations. The Taylor series uncertainty estimates are a function of the variance in input parameters (wind speed and temperature) and the model sensitivities to input parameters. While the input variance is spatially invariant, sensitivity is spatially variable; hence the uncertainty in modeled output varies spatially. A comparison with the MC approach shows that uncertainty estimated by first-order Taylor series is found to be appropriate for ambient temperature, while second-order Taylor series is observed to be more accurate for wind speed. Since the Taylor series approach is simple and time-efficient compared to the MC method, it provides an attractive alternative.

Review of Historical Street Dust and Dirt Accumulation and Washoff Data

Many complex models that utilize continuous simulation (SWMM, HSPF, SLAMM, SIMPTM, etc.) require information pertaining to the accumulation rate of pollutants …

Sources of Pollutants in Urban Areas (Part 1) - Older Monitoring Projects

Information concerning source area runoff characteristics during wet weather events can be very important when developing stormwater management plans that inco…

A knowledge-based system for the recycling of non-hazardous industrial residuals in civil engineering applications

: It is estimated that 4.6 billion tons of non-hazardous solid waste materials are produced annually in the USA. The potential reuse for a portion of the materials in the construction of highways and roads suggests that valuable benefits in terms of economic and environmental gains are possible. This paper describes the development of a prototype computer-assisted tool or expert system to help manufacturers assess and analyze their industrial residuals as potential road construction material. This represents an expansion in the application of intelligent systems to domains where a few, hard-to-find technical reports have represented the main source of expertise available to practitioners for years. The system, developed through the use of an object-oriented software shell, Level5 Object, was designed in a user-friendly Windows environment which allows users with little or no computer training to effectively evaluate material residuals.

Collection and Examination of a Municipal Separate Storm Sewer System Database

The University of Alabama and the Center for Watershed Protection were awarded an EPA Office of Water 104(b)3 grant in 2001 to collect and evaluate stormwater …

Nonparametric Statistical Tests Comparing First Flush and Composite Samples from the National Stormwater Quality Database

The University of Alabama and the Center of Watershed Protection, as part of an EPA 104(b)3 project, has collected and reviewed phase I NPDES (National Polluta…

Sources of Pollutants in Urban Areas (Part 2) - Recent Sheetflow Monitoring

Two research projects that examined source area sheetflows that were conducted in the 1990s are high-lighted in this chapter. These are a comprehensive project…

Use of subsampled traffic data to estimate roadway emissions, including conversion to MOBILE6 vehicle classifications.

Abstract Air quality is degraded by many factors, among which the emissions from on‐road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy‐making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NOx), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NOx emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.

A Machine Learning Tool for Weighted Regressions in Time, Discharge, and Season

A new machine learning tool has been developed to classify water stations with similar water quality trends. The tool is based on the statistical method, Weighted Regressions in Time, Discharge, and Season (WRTDS), developed by the United States Geological Survey (USGS) to estimate daily concentrations of water constituents in rivers and streams based on continuous daily discharge data and discrete water quality samples collected at the same or nearby locations. WRTDS is based on parametric survival regressions using a jack-knife cross validation procedure that generates unbiased estimates of the prediction errors. One of the disadvantages of WRTDS is that it needs a large number of samples (n > 200) collected during at least two decades. In this article, the tool is used to evaluate the use of Boosted Regression Trees (BRT) as an alternative to the parametric survival regressions for water quality stations with a small number of samples. We describe the development of the machine learning tool as well as an evaluation comparison of the two methods, WRTDS and BRT. The purpose of the tool is to evaluate the reduction in variability of the estimates by clustering data from nearby stations with similar concentration and discharge characteristics. The results indicate that, using clustering, the predicted concentrations using BRT are in general higher than the observed concentrations. In addition, it appears that BRT generates higher sum of square residuals than the parametric survival regressions.

Monte Carlo Simulation in Sampling Techniques of Traffic Data Collection

Traffic volume counts are used in transportation planning, design, operation, and safety analyses. A new methodology establishes a statistical basis for comparing traffic volumes generated from different samples. Monte Carlo simulation was used to generate a cumulative probability function (CPF) of traffic volumes based on the fit-of-Weibull probability distribution to a particular traffic sample. A 90% confidence interval of the traffic volumes from a given traffic sample was obtained from the CPF and was used to compare different traffic samples. A case study was performed by using this methodology to determine if shorter time-frame data may be used to represent longer-time traffic. Results from the case study show that traffic at 20-min intervals may be used to represent 1-h traffic when moderate to high traffic volumes are considered. It was observed that subsamples failed to represent the 1-h traffic data for lower traffic volumes of selected vehicle types.