Current applications of the critical loads concept are geared primarily toward targeting emission control strategies at a national and international level. Maps of critical loads for freshwaters have been produced in grid form based on water samples of representative sites within each grid square. However, the water chemistry data required to calculate freshwater critical loads are not always readily available at a national level and maps are therefore limited to catchments where such data exist. This paper describes the development of an approach that uses nationally available secondary data to predict freshwater critical loads for catchments lacking the appropriate water chemistry information. An empirical statistical model is calibrated using data from 78 catchments throughout Scotland. Water chemistry for each catchment has been determined. Each catchment is characterized according to a number of attributes. Redundancy analysis of these data shows clear relationships between catchment attributes and the critical load derived from the water chemistry. The key variables that explain most of the variation in critical load relate to soil, geology and land use within the catchment. Using these variables as predictors in a regression analysis, the critical load can be predicted across a broad gradient of sensitivity (R-adj(2) = 0.81). The predictive power of the model was maintained when different combinations of explanatory variables were used. This accords the approach a degree of flexibility in that model parameterization can be geared toward availability of secondary data. There are limitations with the model as presently calibrated. However, the approach offers considerable scope for environmental managers to undertake national inventories of catchment sensitivity and specific assessments of individual catchments. References: 39
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