lowing mixing of the wastewater load with the ambient upstream load. As organic nitrogen declines by hydrolysis, the nitrification process begins (if a sufficient "seed" population of nitrifying bacteria is present), ammonia is oxidized to nitrite, and nitrite is quickly oxidized to nitrate. In the figure, nitrite and nitrate are shown combined as the sum (NO2 — N + NO3- N) of these two inorganic forms of the nitrogen cycle. As the sequential reactions of the nitrogen cycle proceed downstream, the concentration of total nitrogen (total N) remains unchanged to maintain the mass balance of the reactions between the organic and inorganic forms of nitrogen. In these sequential oxidation reactions of nitrification, the nitrogenous oxygen demand (NBOD) consumes oxygen faster than it can be replenished by atmospheric reaeration, and thus, oxygen drops.

The combined effect of the carbon and nitrogen reactions causes a characteristic critical low DO zone identified by a "sag" in the spatial distribution of oxygen (Figure 3-6c). Two key features of the "oxygen sag" curve are especially important for the purposes of this study:

• The magnitude of the minimum DO concentration.

• The distance downstream from a waste discharge affected by "degradation" and "active decomposition."

In designing the screening methodology to detect the "worst case" for oxygen from a spatial perspective, it is important to recognize that water quality monitoring stations located immediately downstream of wastewater inputs will most likely be within the zones of "degradation" or "active decomposition" but not necessarily at the minimum, or critical, location of the sag. For monitoring stations located considerably farther downstream from a wastewater discharge location, it is less likely that the station will be within the "degradation" or "active decomposition" zones of the river. It is more likely, rather, that the station(s) will be located in the "recovery" zone. For any stream or river, the actual locations that mark the beginning and end of these zones are highly variable. The spatial pattern of oxygen shown in Figure 3-6c is dependent on a number of factors, including streamflow and river velocity (travel time), depth, water temperature, the type and makeup of effluent discharged, the magnitude of the wastewater discharge load, and the degree of turbulent mixing. Rather than attempting to select stations that are located in the exact sag zone, which would undoubtedly show the sharpest downstream DO signal but in the smallest area of the waterbody, the opposite approach was taken. That is, location of the station relative to the sag zone is purposely not controlled or selected, thereby allowing representation of far larger spatial areas but at the possible sacrifice of the downstream DO signal strength.

The question originally posed in Chapter 1 is broad-based: How have the nation's water quality conditions changed since implementation of the 1972 CWA's mandate for secondary treatment as the minimum acceptable technology for POTWs? The focus of the analysis is on detecting improvements in water quality conditions downstream of POTWs in the nation as a whole, not just areas immediately below outfalls. Consequently, when the term "worst-case DO data" is used in this document, it should be taken to refer to data collected primarily during times of high water temperature and low-flow conditions (i.e., "worst-case" from a temporal perspective). Spatially, no screens were developed for selecting monitoring stations located at the deepest part of the sag curve, nor even for stations in the sag curve itself. The only screening rule applied was that the water quality station had to be downstream from a point source. Thus, a station might be anywhere from within a few yards to hundreds of miles below any particular outfall. As a result, the data sets developed for the comparative before- and after-CWA analysis contain a mix of DO data from within and outside DO sag curves.

The Role of Spatial Scale in this Analysis

Recall that the objectives for this portion of the study are as follows:

• Develop before- and after-CWA data sets made up of DO summary statistics derived from monitoring stations that inherently contain a response "signal" linking point source discharges with downstream water quality.

• Calculate a DO summary statistic (tenth percentile) for each station for each before- and after-CWA time period, and then aggregate station data at sequentially larger spatial scales (reaches, catalog units, and major river basins).

• Conduct an analysis of all spatial units having both a before- and an after-CWA summary statistic, and then document the direction and magnitude of the changes in worst-case (summer, mean tenth percentile) DO concentration.

• Assess the change in the point source discharge/downstream DO signal over progressively larger spatial scales.

The use of spatial scale is a key attribute of this analysis. Detection of positive change in signal at large (river basin) as well as small (stream reach) scales would provide evidence that the CWA's technology and water quality-based controls yielded broad as well as localized benefits (i.e., reaches both within and beyond the immediate sag curve have benefitted from the CWA). If true, therefore, the second leg of the three-legged stool approach would provide further support for the claim that the effluent control regulations of the CWA were a broad success.

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