Optimizing best management practices to control anthropogenic sources of atmospheric phosphorus deposition to inland...

Excessive phosphorus loading to inland freshwater lakes around the globe has resulted in nuisance plant growth along the waterfronts, degraded habitat for cold-water fisheries, and impaired beaches, marinas, and waterfront property. The direct atmospheric deposition of phosphorus can be a significant contributing source to inland lakes. The atmospheric deposition monitoring program for Lake Simcoe, Ontario, indicates roughly 20% of the annual total phosphorus load (2010-2014 period) is due to direct atmospheric deposition (both wet and dry deposition) on the lake. This novel study presents a first-time application of the genetic algorithm (GA) methodology to optimize the application of best management practices (BMPs) related to agriculture and mobile sources to achieve atmospheric phosphorus reduction targets and restore the ecological health of the lake. The novel methodology takes into account the spatial distribution of the emission sources in the airshed, the complex atmospheric long-range transport and deposition processes, cost and efficiency of the popular management practices, and social constraints related to the adoption of BMPs. The optimization scenarios suggest that the optimal overall capital investment of approximately $2M, $4M, and $10M annually can achieve roughly 3, 4, and 5 tonnes reduction in atmospheric P load to the lake, respectively. The exponential trend indicates diminishing returns for the investment beyond roughly $3M per year and that focusing much of this investment in the upwind, nearshore area will significantly impact deposition to the lake. The optimization is based on a combination of the lowest cost, most beneficial and socially acceptable management practices that develops a science-informed promotion of implementation/BMP adoption strategy. The geospatial aspect to the optimization (i.e., proximity and location with respect to the lake) will help land managers to encourage the use of these targeted best practices in areas that will most benefit from the phosphorus reduction approach.

Excessive phosphorus loading to inland freshwater lakes around the globe has resulted in nuisance plant growth along the waterfronts, degraded habitat for cold water fisheries, and impaired beaches, marinas and waterfront property. This novel study presents a first-time application of the Genetic Algorithm methodology to optimize the application of best management practices related to agriculture and mobile sources to achieve atmospheric phosphorus reduction targets and restore the ecological health of the lake. The novel methodology takes into account the spatial distribution of the emission sources in the airshed, the complex atmospheric long-range transport and deposition processes, cost and efficiency of the popular management practices and social constraints related to the adoption of BMPs.