The impact of constituent ions of acid mist on assimilation and stomatal conductance of Norway spruce prior and post mid-winter freezing

Norway spruce seedlings were sprayed twice weekly with one of a range of artificial mists at either pH 2.5, 3.0 or 5.6, for three months. The mists consisted of either (NH4)2SO4 (pH 5.6), NH4NO3 (pH 5.6), water (pH 5.6), HNO3 (pH 2.5), H2SO4 (pH 2.5). In late December 1988 and early January 1989 the light response of assimilation and stomatal conductance were assessed in the laboratory following a 4-day equilibration period at 12 degrees C. The intact trees were then subjected to a mild (-10 degrees C), brief (3 h) frost in the dark and the recovery of light saturated assimilation (Amax) was followed during the subsequent light period. The same trees were then subjected to a second 3 h (-18 degrees C) frost. The recovery of Amax during the next day was followed. All ion-containing mists stimulated Amax and apparent quantum yield relative to control trees, irrespective of pH. The mists containing SO4 made stomatal conductance unresponsive to light flux density and caused the stomata to lock open. Frosts of -10 degrees C and -18 degrees C did not inhibit the Amax of control trees for longer than 200 min into the light period. In contrast, the ion-containing mists exerted a significant inhibitory effect upon the recovery of Amax. Nitric acid inhibited Amax to 35% of the pre-frost value, whilst the remaining treatments inhibited Amax between 15% and 40% of the pre-frost value. It is concluded that SO4 causes increased mid-winter frost sensitivity and NO3 ameliortes this effect. The results are discussed in relation to forest decline.     

Probability analyses of combining background concentrations with model-predicted concentrations

In order to calculate total concentrations for comparison to ambient air quality standards, monitored background concentrations are often combined with model predicted concentrations. Models have low skill in predicting the locations or time series of observed concentrations. Further, adding fixed points on the probability distributions of monitored and predicted concentrations is very conservative and not mathematically correct. Simply adding the 99th percentile predicted to the 99th percentile background will not yield the 99th percentile of the combined distributions. Instead, an appropriate distribution can be created by calculating all possible pairwise combinations of the 1-hr daily maximum observed background and daily maximum predicted concentration, from which a 99th percentile total value can be obtained. This paper reviews some techniques commonly used for determining background concentrations and combining modeled and background concentrations. The paper proposes an approach to determine the joint probabilities of occurrence of modeled and background concentrations. The pairwise combinations approach yields a more realistic prediction of total concentrations than the U.S. Environmental Protection Agency's (EPA) guidance approach and agrees with the probabilistic form of the National Ambient Air Quality Standards.

Implications: EPA's current approaches to determining background concentrations for compliance modeling purposes often lead to “double counting” of background concentrations and actual plume impacts and thus lead to overpredictions of total impacts. Further, the current Tier 1 approach of simply adding the top ends of the background and model predicted concentrations (e.g., adding the 99th percentiles of these distributions together) results in design value concentrations at probabilities in excess of the form of the National Ambient Air Quality Standards.