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interpolation step, all of these data can be used with varying degrees of success to 
estimate the total spatial distribution (to the limit of interpolator pixel size) of a water 
quality parameter. 
Step 2—Interpolation 
Interpolation can place data collected at various spatial densities on a common footing. 
On the one hand, this capability is advantageous because data collected at different 
spatial densities are available for the criteria assessment process. On the other hand, it 
can be misleading to accept interpolated surfaces from different data sources as equiv¬ 
alent without qualifying each interpolation with a measure of the estimation error 
associated with each data type. Clearly, an interpolation based on hundreds of points per 
segment (such as cruise track data) more accurately reflects the true non-attainment 
percentage when compared to an interpolation based on two or three points per segment 
(such as a fixed-station data). Of the various types of interpolation algorithms available 
and reviewed, kriging is best positioned to address this issue (STAC 2006). Kriging 
offers advantages over inverse distance weighting in that it provides the best assessment 
of the estimation error associated with interpolation, but has not been implemented to 
date. Other methods, such as interpolating polynomials, splines, and locally weighted 
regression methods, should also be explored. 
Step 3—Temporal Aggregation of Interpolations 
Depending on the interpolation method and the statistics available, it may be 
possible to calculate the probability of exceedance of the temporal mean at each 
point given the likely variance and the value(s) observed during the period. This step 
is necessary to calculate probabilities in the following step. 
Step 4—Pointwise Compliance 
Determining the percent attainment of each grid cell from each interpolation seems 
simple. If the estimated value for a grid cell is above (chlorophyll a) or below 
(dissolved oxygen, water clarity) the criterion, then that cell is not in attainment. 
While interpolation allows for standardization of many types of data, pointwise 
attainment determination allows for standardization of many criteria. Because attain¬ 
ment is determined at moments in time and points in space, it is possible to vary the 
criterion in time and space. If different levels of a water quality constituent are 
acceptable in different seasons, then the criterion can vary seasonally. It is possible 
to implement different criteria over space for a segment that bridges, for example, 
oligohaline and mesohaline salinity regimes. It might even be possible to let the 
criterion be a continuous function of some ancillary variable such as temperature or 
salinity, although this situation requires that such data exist for every interpolator 
cell. The only requirement is that the final attainment determination be “yes” or “no” 
for each interpolator cell. 
chapter ii 
Refinements to the Chesapeake Bay Water Quality Criteria Assessment Methodology 
