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CLOSURE and nitrification must occur to some extent. In our mind, the only question is how reliable is this mechanism, that is, are nitrifiers sloughed on a relatively consistent basis? To answer this question, we believe that the term "consistent" must be defined on an operational rather than a theoretical basis. That is, do models derived based on this assumption possess sufficient predictive capability to be useful? That is the approach used in this research and is the reason for presenting the Duck Creek and Rowlett Creek data, which indicate that models derived on this basis are useful.The discussors request presentation of the NR data from the Duck Creek plant. We thank them for this in that these data were present in the version of the paper originally submitted to WEF, but they were taken out of the paper in response to review comments. Figure A of this discussion presents the NR data for Duck Creek, which demonstrate that the NR based on the contact basin only was consistently less than 1.0 and that the NR based on the entire suspended growth reactor was also frequently less than 1.0. These results indicate that nitrification would not have occurred in the suspended growth reactor at Duck Creek if nitrifiers were not being seeded into it from the upstream trickling filter. These results demonstrate the utility of the model and support its underlying assumptions from an operational perspective.The discussors also ask whether the authors are aware of any further data supporting the proposed hypothesis. A complete response to this question would be an entire technical paper by itself. Since the original version of the paper was prepared and originally presented at the WEF Conference in Washington D.C., the authors have been made aware of observations by others that are similar to the Duck Creek and Rowlett Creek wastewater treatment plants. For example, a pilot plant at Windsor, Ontario, Canada has clearly demonstrated nitrification in a TF/SC facility at conditions that were not achievable in a companion activated sludge process (that is, one without an upstream trickling filter accomplishing combined carbon oxidation and nitrification).The authors thank the discussors for their thoughtful comments on our work. In this closure we will respond directly to the four questions that they have asked. This closure also provides the opportunity to correct a typographical error contained in the original paper. That error is in Equation (8) which, unfortunately is the critical equation of the paper. The error is in the second term of the equation and involves a minus sign which was omitted. The correct equation is listed below.To Discussion of: Process and kinetic analysis of nitrification in coupled trickling filter/activated sludge systems, 65, 750 (1993); Discussion by D. S. Parker, J. T. Richards, 66, 934 (1994).In their introduction the discussors indicate that they used the IAWPRC activated sludge model, with seeding, to analyze the first months of operation of the Duck Creek TF/SC plant. The authors point...

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Summary Uncertainties associated with legacy data contribute to the spatial uncertainty of predictions for soil properties such as pH. Examples of potential sources of error in maps of soil pH include temporal variation and changes in land use over time. Prediction of soil pH can be improved with a linear mixed model (LMM) to analyse factors that contribute to uncertainty. Probabilities from conditional simulations in combination with agronomic critical thresholds for acid‐sensitive species can be used to identify areas that are likely, or very likely, to be below these critical thresholds for plant production. Because of rapid changes in farming systems and management practices, there is a need to be vigilant in monitoring changes in soil acidification. This is because soil acidification is an important factor in primary production and soil sustainability. In this research, legacy data from south‐western Victoria (Australia) were used with model‐based geostatistics to produce a map of soil pH that accommodates a variety of error sources, such as the time of sampling, seasonal variation, differences in analytical method, effects of changes in land use and variable soil sample depth in legacy data. Spatial covariates that are representative of soil‐forming factors were used to improve predictions. To transform spatial prediction and estimates of error in soil pH into more informative and usable maps with more information content, simulations from the conditional distribution were used to compute the probability of a soil's pH being less than critical agronomic production thresholds at each of the prediction locations. These probabilities were mapped to reveal areas of potential risk. Highlights Can maps of soil pH be improved by accounting for temporal variation and change in land use? First example of taking account of temporal variability in sampling for pH in spatial models. Key factors for uncertainty in spatial prediction include time of sampling and sample depth. Accuracy improved by accounting for additional sources of error combined with conditional simulations.