Modeling the numerical relationships between chronic ambient sulphur dioxide exposures and tree growth

Abstract

An exponential growth curve model was developed for Pinuscontorta Dougl. ex Loud. var. latifolia Engelm. × Pinusbanksiana Lamb, (lodgepole × jack pine) trees from basal area increment data collected from five ecologically analogous sampling locations (AI to AV) in the vicinity of a sulphur recovery sour gas processing plant emitting sulphur (S) gases (mainly SO2) in the West Whitecourt study area near the town of Whitecourt in west-central Alberta, Canada. The mean basal area increment growth declined by 1.2%, 1.4%, 0.8%, and 0.6% between 1959 and 1981 at sampling locations AI AII, AIII and AIV, respectively, in comparison to the reference sampling location, AV. Since 1974 there has been an increase in wood production at the impacted sites, AI to AIV. This was most likely the result of the significant and progressive reductions in total sulphur gas emissions from 1963 to 1981, of 58 403 to 6782 t S/year, respectively. A multivariate nonlinear, polynomial Fourier regression model was applied to explain the relationships between the ambient SO2 exposures at the five sampling locations and changes in pine tree basal area increment growth. The regression model included the ambient SO2 exposure parameters: (1) number of episodes (an episode is equivalent to single or successive occurrences of 0.5-h mean concentrations of ≥10 ppb); (2) cumulative integral of exposures (concentration with respect to time); and (3) peak episodal concentrations. The model parameters were estimated using the least squares approach. The MPF regression model captured the actual effects of the episodicity of SO2 exposures on radial tree growth of pine species and provided a high degree of forecasting power because of the use of the integral of the SO2 exposures. Peak episodal SO2 concentrations or the number of episodes appeared not to play as important a role in the model as the integral.