Environmental Science & Technology southwest Florida between Gullivan Bay and Florida Bay.12 These areas were not included in further analysis because the noise from the interferences was greater than the signal from chl-a. Average ChlRS-a was low in the FP, higher in the WFS, and increased from low to high along the AC (Figure 3). Coefficient of variation within a segment was relatively uniform at 63% across most coastal segments. Higher values occurred near Panama City (segment 12) where C.V. was 142%. C.V. was also high (138%) near West Palm Beach where conditions fluctuated between low ChlRS-a to the south and higher values to the north. These means and C.V. described the reference condition between 1997 and January 1, 2010. The 50th and 75th percentiles for each trailing 3-year period were calculated, resulting in 11 50th percentile and 11 75th percentile values for each segment (Figure 4A). Criteria values Figure 4. (A) Trailing 3-year cumulative distribution functions for ChlRS-a in segment 22 (outside Tampa Bay) for 1998 through 2009. The estimate of the 90th percentile of medians and upper quartiles (75th percentile), which are 2.37 and 3.10, respectively, could be used as criteria values. (B) Computed criteria values for all 76 coastal water segments. Insufficient data prevent computations for segments 35 and 72. were selected as the 90th percentile from the 11 50th percentiles and 11 75th percentiles values. Criteria for the upper quartile (i.e., 75th percentile) averaged 50% (±26%) higher than criteria values for median ChlRS-a (Figure 4B). Criteria were relatively consistent within regions of the coast, particularly the FP and southeast AC. Higher values occurred adjacent to the pass of major estuaries (i.e., segment 1 and 2 near Pensacola Bay). Criteria for ChlRS-a was more heterogeneous in the WFS region than in other regions. To ensure that future water quality remains consistent with the reference period, new data for 3-year assessment periods could be evaluated against these criteria using a binomial test.22 This Article test involves recoding 8-day composite observations, during a 3-year assessment period, into those exceeding the criteria and those not exceeding the criteria, then testing statistically if the number exceeding the criteria is more than would be expected, with a specified Type I error rate (α = 0.1). Applied to criteria developed in this study, 50% of 8-day composite observations would be expected to exceed the criteria value for the median and 25% of 8-day composite observations to exceed the criteria value for the upper quartile. The binomial test was applied to data in rolling 3-year intervals during the reference period, confirming that the criteria were not exceeded in any of the 3- year intervals. As we have noted, the objective of numeric criteria is to protect aquatic life uses from anthropogenic nutrient loading to the coastal zone. K. brevis blooms contributed to variability in ChlRS-a, but typically initiate 70 km off-shore25 independent of anthropogenic nutrients.6 Therefore, we considered whether observations affected by K. brevis blooms should be removed from the reference condition. Analyses of cumulative distributions showed they were minimally affected by inclusion or removal of observations affected by K. brevis. Even though coastal segment 22 near Tampa Bay (Figure S5A of the SI) had one of the greatest influences from K. brevis, the 90th percentile decreased by only 12% when observations flagged for K. brevis were removed (Figure S5B of the SI). Historical records reported observations of K. brevis blooms in the Gulf of Mexico prior to the 1950s26,27 and do not suggest that K. brevis blooms have increased in frequency or biomass.28 Natural fluctuations in K. brevis did not prevent detection of water quality changes that could occur due to anthropogenic nutrient loading. One approach, therefore, could be to regard K. brevis blooms as a natural part of the reference condition and therefore not exclude flagged observations. There were limited data in coastal waters for TN and TP, which EPA identified as key causal variables in the context of numeric criteria development. This precluded development of criteria for TN and TP. However, it was still important to evaluate if ChlRS-a responded to nutrients coming from land sources. We evaluated relationships between coastal segment 8- day averaged ChlRS-a and river discharge, used as a proxy for nutrient loading from land since data quantifying nutrient loading was not available. Regressions indicated significant relationships (p < 0.01) between river discharge and ChlRS-a in the adjacent coastal segment (Figure S6 and Table S1 of the SI). These relationships occurred with 7 of the tested river systems. Larger regression slopes for the FP suggest rivers in this region had a larger influence on coastal ChlRS-a than rivers on the WFS and AC. FP river discharge magnitude was greater than the WFS and AC. The combined discharge of the Escambia and Yellow Rivers, both of which empty into Pensacola Bay, averaged 249 m3 s−1, whereas the other major FP river, the Choctawhatchee River, averaged 168 m3 s−1. For the four rivers with the lowest regression slopes (Table S1 of the SI), average discharge ranged from 6.7 m3 s−1 in the Hillsborough River to 61 m3 s−1 in the Caloosahatchee River. On the AC, the St. John’s River discharge averaged 254 m3 s−1 and had a regression slope similar to the FP. These relationships were similar to those observed for other coastal waters in the Gulf of Mexico.29 ■ DISCUSSION This approach could also be transferred to other satellites such as the Moderate Resolution Imaging Spectroradiometer 920 dx.doi.org/10.1021/es2014105 | Environ. Sci. Technol. 2012, 46, 916−922