Prediction of airborne nanoparticles at roadside location using a feedeforward artificial neural network

Özet

Accurate prediction of nanoparticles is essential to provide adequate mitigation strategies for air quality management. On the contrary to PM10, SO2, O3, NOx and CO, nanoparticles are not routinelyemonitored by environmental agencies as they are not regulated yet. Therefore, a prognostic supervised machine learning technique, namely feedeforward artificial neural network (ANN), has been used with a back epropagation algorithm, to stochastically predict PNCs in three size ranges (N5e30, N30e100 and N100 e300 nm). Seven models, covering a total of 525 simulations, were considered using different combina-tions of the routinelyemeasured meteorological and five pollutants variables as covariates. Each model included different numbers of hidden layers and neurons per layer in each simulation. Results of sim-ulations were evaluated to achieve the optimum correspondence between the measured and predicted PNCs in each model (namely Models, M1eM7). The best prediction ability was provided by M1 when all the covariate variables were used. The model, M2, provided the lowest prediction performance since all the meteorological variables were omitted in this model. Models, M3eM7, that omitted one pollutant covariate, showed prediction ability similar to M1. The results were within a factor of 2 from the measured values, and provided adequate solutions to PNCs' prognostic demands. These models are useful, particularly for the studied site where no nanoparticles measurement equipment exist, for determining the levels of particles in various size ranges. The model could be further used for other locations in Kuwait and elsewhere after adequate longeterm measurements and training based on the routinelyemonitored environmental data.