NATIONWIDE MS4 STORMWATER PHASE 1 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 data from a representative number of NPDES (National Pollutant Discharge Elimination System) MS4 (municipal separate storm sewer
system) stormwater permit holders. The initial version of this database, the National Stormwater Quality Database (NSQD, version 1.1) is currently being completed. These stormwater quality data and site descriptions are being collected and reviewed to describe the characteristics of national
stormwater quality, to provide guidance for future sampling needs, and to enhance local stormwater management activities in areas having limited data.
The monitoring data collected over nearly a ten-year period from more than 200 municipalities throughout the country have a great potential
in characterizing the quality of stormwater runoff and comparing it against historical benchmarks. This project is creating a national database of stormwater monitoring data collected as part of the existing stormwater permit program, providing a scientific analysis of the data, and providing
recommendations for improving the quality and management value of future NPDES monitoring efforts.
Each data set is receiving a quality assurance/quality control review based on reasonableness of data, extreme values, relationships among parameters, sampling methods, and a review of
the analytical methods. The statistical analyses are being conducted at several levels. Probability plots are used to identify range, randomness and normality. Clustering and principal component analyses are utilized to characterize significant factors affecting the data patterns. The master
data set is also being evaluated to develop descriptive statistics, such as measures of central tendency and standard errors. Regional and climatic differences are being tested, including the influences of land use, and the effects of storm size and season, among other factors. The data will
be used to develop a method to predict expected stormwater quality for a variety of significant factors and will be used to examine a number of preconceptions concerning the characteristics of stormwater, sampling design decisions, and some basic data analysis issues. Some of the issues that
are being examined with this data include: the occurrence and magnitude of first-flushes, the effects of different sampling methods (the use of grab sampling vs. automatic samplers, for example) on stormwater quality data, trends in stormwater quality with time, the effects of infrequent wrong
data in large data bases, appropriate methods to handle values that are below detection limits, the necessary sampling effort needed to characterize stormwater quality, for example. This paper describes the data collected to date and presents some preliminary data findings.
When this National
Stormwater Quality Database (NSQD) is completed (populated with most of the NPDES stormwater monitoring data), the continued routine collection of outfall stormwater quality data in the U.S. for basic characterization purposes may have limited use. Some communities may have obviously unusual
conditions, or adequate data may not be available in their region. In these conditions, outfall monitoring may be needed. However, stormwater monitoring will continue to be needed for other purposes in many areas having, or anticipating, active stormwater management programs (especially when
supplemented with other biological, physical, and hydrologic monitoring components). These new monitoring programs should be designed specifically for additional objectives, beyond basic characterization. These objectives may include receiving water assessments to understand local problems,
source area monitoring to identify critical sources of stormwater pollutants, treatability tests to verify the performance of stormwater controls for local conditions, and assessment monitoring to verify the success of the local stormwater management approach (including model calibration and
verification). In many cases, the resources being spent for outfall monitoring could be more effectively spent to better understand many of these other aspects of an effective stormwater management program.
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