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Identification of Significant Factors Affecting Stormwater Quality Using the National Stormwater Quality Database

Alexander Maestre and Robert E. Pitt (2006)
University of Alabama; University of Alabama
DOI: https://doi.org/10.14796/JWMM.R225-13
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Abstract

The normal approach to classify urban sites for estimating stormwater characteristics is based on land use. This approach is generally accepted because it is related to the activity in the watershed, plus many site features are generally consistent within each land use. Two drainage areas with the same size, percentage of imperviousness, ground slope, sampling methods, and stormwater controls will produce different stormwater concentrations if the main activity in one watershed is an automobile manufacturing facility (industrial land use) while the other is a shopping center (commercial land use) for example. There will likely be higher concentrations of metals at the industrial site due to the manufacturing processes, while the commercial site may have higher concentrations of PAHs (polycyclic aromatic hydrocarbons) due to the frequency and numbers of customer automobiles entering and leaving the parking lots.

Previous studies indicated that there are significant differences in stormwater constituents for different land use categories (Pitt et al. 2004). This is supported for other databases like NURP (EPA 1983), CDM (Smullen and Cave, 2002) and USGS (Driver et al., 1985). The main question to be addressed in this chapter is if there is a different classification method that better describes stormwater quality, possibly by also considering such factors as geographical area (EPA Rain Zone), season, percentage of imperviousness, watershed area, type of conveyance, controls in the watershed, sampling method, and type of sample compositing, and possible interactions between these factors.

This chapter presents several approaches to explain the variability of stormwater quality using the National Stormwater Quality Database (NSQD). Several analyses had been performed using this database (Maestre et al. 2004, 2005a). Maestre et al. (2005b) for example, has shown that ignoring the non-detected observations can adversely affect the mean, median and standard deviations of the dataset, and the resulting statistical test results. The calculations presented in this chapter used the censored observations using the Cohen’s maximum likelihood method.

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PAPER INFO

Identification

CHI ref #: R225-13 872
Volume: 14
DOI: https://doi.org/10.14796/JWMM.R225-13
Cite as: CHI JWMM 2006;R225-13

Publication History

Received: N/A
Accepted: N/A
Published: February 15, 2006

Status

# reviewers: 2
Version: Final published

Copyright

© 2006 CHI. Some rights reserved.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

The Journal of Water Management Modeling is an open-access (OA) publication. Open access means that articles and papers are available without barriers to all who could benefit from them. Practically speaking, all published works will be available to a worldwide audience, free, immediately on publication. As such, JWMM can be considered a Diamond, Gratis OA journal.

All papers published in the JWMM are licensed under a Creative Commons Attribution 4.0 International License (CC BY).

JWMM content can be downloaded, printed, copied, distributed, and linked-to, when providing full attribution to both the author/s and JWMM.


AUTHORS

Alexander Maestre

University of Alabama, Tuscaloosa, AL, USA
ORCiD:

Robert E. Pitt

University of Alabama, Tuscaloosa, AL, USA
ORCiD:


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