Impact of sampling techniques on measured stormwater quality data for small streams.

Autor: Harmel RD; Austin Community College, 11617 River Oaks Tr., Austin, TX 78753, USA. daren.harmel@ars.usda.gov, Slade RM Jr, Haney RL
Jazyk: angličtina
Zdroj: Journal of environmental quality [J Environ Qual] 2010 Sep-Oct; Vol. 39 (5), pp. 1734-42.
DOI: 10.2134/jeq2009.0498
Abstrakt: Science-based sampling methodologies are needed to enhance water quality characterization for setting appropriate water quality standards, developing Total Maximum Daily Loads, and managing nonpoint source pollution. Storm event sampling, which is vital for adequate assessment of water quality in small (wadeable) streams, is typically conducted by manual grab or integrated sampling or with an automated sampler. Although it is typically assumed that samples from a single point adequately represent mean cross-sectional concentrations, especially for dissolved constituents, this assumption of well-mixed conditions has received limited evaluation. Similarly, the impact of temporal (within-storm) concentration variability is rarely considered. Therefore, this study evaluated differences in stormwater quality measured in small streams with several common sampling techniques, which in essence evaluated within-channel and within-storm concentration variability. Constituent concentrations from manual grab samples and from integrated samples were compared for 31 events, then concentrations were also compared for seven events with automated sample collection. Comparison of sampling techniques indicated varying degrees of concentration variability within channel cross sections for both dissolved and particulate constituents, which is contrary to common assumptions of substantial variability in particulate concentrations and of minimal variability in dissolved concentrations. Results also indicated the potential for substantial within-storm (temporal) concentration variability for both dissolved and particulate constituents. Thus, failing to account for potential cross-sectional and temporal concentration variability in stormwater monitoring projects can introduce additional uncertainty in measured water quality data.
Databáze: MEDLINE