Regional optimal allocation for reducing waste loads via artificial neural network and particle swarm optimization: a case study of ammonia nitrogen in Harbin, northeast China.

Abstract

Cutting external waste loads can improve water quality. Allocation for reducing waste loads should consider changing variables, such as river flows and pollutant emissions. A particle swarm optimization (PSO) method and coupling artificial neural network (ANN) models have been applied to optimize reduction rates of ammonia nitrogen (NH3-N) loads from sewage outlets in Harbin, northeast China. For the planned water quality functional section (WQFS), the NH3-N concentration is related to emitted pollutant loads and can be well predicted by ANN linkage models. Further, NH3-N load reduction rates of all outlets are optimized by PSO with the water quality standard target. The highest NH3-N concentrations occur in January and February, a typical low-flow period in Harbin. The results delivered optimum NH3-N reduction rates for the five outlets, for January and February 2011. All predicted NH3-N concentrations after the reduction meet the water quality standard. The results indicate that the outlet with the highest NH3-N load has the biggest reduction rate in each WQFS, and outlets in the WQFS with higher background NH3-N concentrations need to cut more NH3-N loads. Decision-makers should not only focus on the outlet with the highest NH3-N emission load, but also ensure that the NH3-N concentration of upper WQFS meets the water quality goal.