Estimation of Emissions of γ-Hexachlorcyclohexane from the Great Lakes–St. Lawrence Ecosystem Using a Coupled Efficient Kalman Filter–Atmospheric Transport–Soil Model

Abstract

An efficient linear Kalman filter has been combined with a coupled atmospheric transport and soil–air exchange model to determine organochlorine pesticides emissions on the regional scale. In this study, results of γ-HCH emissions from the Great Lakes–St. Lawrence ecosystem, estimated from the coupled model, are presented and discussed. A source receptor technique is used to identify a priori the locations of emission sources of γ-HCH, the emissions are then updated through a Kalman filtering procedure which minimizes the weighted difference between the predicted mixing ratios from the coupled model and the measured concentrations over the Great Lakes–St. Lawrence river region. Two experiments using the inverse algorithm are carried out. In the first experiment, the coupled atmospheric transport and soil–air exchange model is implemented to predict γ-HCH air and soil concentrations. Emissions are then updated every 12 days using the updated soil concentrations and emission factors. However, the updated emissions are not input into the coupled atmospheric transport and soil–air exchange model. On the other hand, in the second experiment the updated emissions are fed back to the coupled model, so that the model is reinitialized in each 12 days. The results from the inverse technique for the year 1995 have been compared with grided γ-HCH emission inventory in Canada, generated by emission factors. It is shown that the estimated emissions of γ-HCH are consistent with the measured emissions. It is found that the St. Lawrence valley has larger emissions of γ-HCH than the Great Lakes region, indicating an opposite distribution to the emission usage inventory, but in agreement with the measured γ-HCH concentration.