Applications of optimization in the field of process systems engineering often involve dealing with nonlinearity and uncertainty, necessitating the Solution Of Stochastic nonlinear programming (SNLP) problems. However, the existing algorithms to solve such problems suffer from various limitations. The L-shaped BONUS algorithm, which is an integration of the BONUS (Better Optimization of Nonlinear Uncertain Systems) algorithm and the sampling-based L-shaped method, has been recently proposed to overcome some of these problems. It is shown to have desirable computational properties. This work further investigates the properties of the algorithm by applying it to the environmental problem of pollutant (nutrient) trading in the Christina River Basin. With environmental concerns being heightened, pollution abatement-related decisions are important for industry, making efficient solution techniques invaluable. The results confirm the computational efficiency of the L-shaped BONUS algorithm. Simultaneously, interesting aspects of the environmental trading problem are also explored, pointing toward the scope of implementing stochastic programming techniques for better decision making in the field of industrial ecology.