Letters M Smarter monitoring Dear Editor: The total maximum daily load (TMDL) process is overwhelming state environmental agencies, resulting in TMDLs that are sometimes nothing more than paper exercises having little scientific credibility. One important component of the TMDL process is water quality monitoring. It provides information essential for performing TMDL assessments, modeling water quality impacts, and listing waters not meeting standards. Despite monitoring’s importance, however, water quality data needed to support the TMDL program are often unavailable because of funding limitations, a situation that will only become worse as federal and state budgets are cut further. This lack of data is resulting in unjustified water body listings that are often based on personal evaluations, delays in assessments, inappropriate controls, and unintended consequences (1). Also an issue, but even less well understood is the need for monitoring to refine the water quality criteria that are fundamental to the TMDL process. EPA’s 1986 report found, for example, that naturally occurring dissolved oxygen concentrations may fall below target water quality criteria levels because of a naturally occurring combination of low flow, high temperature, and natural oxygen demand (2). Natural background variability for nutrients, sediment, and temperature in many reference watersheds and smaller feeder streams can similarly complicate the assessment process, and in some southeastern U.S. low-flow creek waters, dissolved oxygen standards are not always met because of naturally occurring organic inputs and limited reaeration capability. The emerging problem resulting from neglect of this issue is that water bodies not meeting state standards because of naturally occurring conditions must nevertheless be treated as
impaired, regardless of the underlying causes, because it is difficult to delist them. A more complete data record is needed that addresses this natural variability problem and assesses and improves the validity and usefulness of water quality standards. If states fail to address this issue, and instead always assume impairment without considering natural patterns and variability in water quality, resources will be diverted to achieve water quality criteria that are unachievable. More focused monitoring could avoid the problem of water bodies being unnecessarily listed. Rather than robotically monitoring to determine where water criteria are not being met, monitoring needs to be designed to describe water quality patterns, both naturally occurring and those associated with management activities, so that appropriate standards are set and reasonable, high-priority listings of impaired water bodies are established. In support of this objective, research conducted at Tufts University in the late 1990s addressed the challenges of smarter monitoring network designs and provided statistical approaches that can maximize the collection of more definitive data (3). RAY WHITTEMORE Principal Research Engineer NCASI Northeast Regional Center Lowell, MA 978-323-4606 978-323-4599 (Fax)
References (1) Whittemore, R.; Ice, G. Environ. Sci. Technol. 2001, 35 (11), 249A–255A. (2) Simcox, A. Design of Stream Sampling Networks and a GIS Method for Assessing Spatial Bias. Ph.D. Thesis, Tufts University, Medford, MA, 1998. (3) Ambient Water Quality Criteria for Dissolved Oxygen; EPA 440/5-86-003; U.S. Environmental Protection Agency, Office of Water Regulations and Standards, U.S. Government Printing Office: Washington DC, 1986. SEPTEMBER 1, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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