Assessment and development of air quality forecasting models by the combination of PCA and ANN at metropolis Kolkata, India

PM10, PM2.5, SO2, NO2, Pb, As and Ni were monitored at Jadavpur, Kolkata over the time span of October 2016 to March 2017. The air quality of the study area was assessed in terms of air quality index (AQI) which is calculated on the assessed concentration of the pollutants. The result of the study showed significant seasonal variance and the ambient air of the study area is highly polluted in terms of particulate matters, NO2 and metallic pollutants. AQI study reveals that PM2.5 was found responsible for this degraded air quality of Kolkata. Principal component analysis (PCA) was used to identify possible contributors i.e. i) construction work, windblown dust, road dust and earth crust (variance- 28.98%); ii) automobile exhaust (variance- 20.47%); iii) brake wear source, road dust and tire wear (variance- 18.07%) of air pollution in the study area. Multiple linear regression (MLR) and feedforward backpropagation artificial neural network (FFBPANN) were used in combination with PCA to predict the future PM2.5 concentrations. Several performance indexes were used to assess the degree of accuracy and the magnitude of error in the model predicted values. Overall performance index establishes the fact that PCA-FFBP-ANN model most accurately predicts the future PM2.5 concentrations.