Modelling the dry deposition of atmospheric pollutants from an aluminum smelter in the Kitimat Valley, British Columbia

Sulphur dioxide is an important atmospheric pollutant that can negatively impact the surrounding environment through the processes of wet and dry deposition. Although wet deposition can be measured directly in the field, dry deposition must be estimated from air concentrations and modelled dry deposition velocity. The versatile “big-leaf” deposition model is frequently used to estimate sulphur dioxide deposition velocity; however, the process of aggregating input variables for the model is time-consuming and challenges routine, long-term monitoring efforts. The overarching objective of the proposed research is to explore methodologies that could improve the workflow efficiency and long-term monitoring capabilities of the big-leaf deposition model. Long-term modelling analysis will be focused on the Kitimat Valley, a remote region in British Columbia that receives local sulphur dioxide emissions from an aluminum smelter owned and operated by Rio Tinto. The expected deliverables from this work will improve the efficiency and viability of long-term sulphur deposition monitoring across Canada. Moreover, these advancements stand to benefit monitoring programs that utilize the big-leaf model to estimate the dry deposition of other pollutant species.

Faculty Supervisor:

Julian Aherne

Student:

Partner:

BC Works Rio Tinto

Discipline:

Earth science

Sector:

Manufacturing; Mining

University:

Trent University

Program: