An analysis of numerical models of air pollutant exposure and vegetation response

A number of empirical (statistical, regression oriented) and mechanistic (process oriented) models are presently available to examine the relationship between air pollution stress and plant response. These models have their strengths and weaknesses. In all these models, a major concern is the numerical definition of the pollutant exposure kinetics (dose). At present there are no numerical definitions of dose which make satisfactory biological sense. A key issue is the existence of a biological time clock where plants respond differently to the pollutant stress at different stages of their growth. On the other hand, policy makers and regulatory personnel prefer a simple approach which would facilitate implementation and administration of ambient air quality standards. Long-term air pollutant averaging techniques create artifacts due to the non-normal distribution of ambient concentrations. A more appropriate approach may be the use of 'median' and 'percentiles' computed from short duration pollutant concentrations. Such an approach would be free of the influence of the non-normal distribution, but would require the development of appropriate exposure-response models. Any transfer of results from unit level models to regional level leads to 'scaling error'. There is no general agreement among researchers on how to deal with the scale problem. While this situation persists, any policy formulated on regional impact assessment must acknowledge the uncertainty.