Seasonal prediction of particulate matter over the steel city of India using neural network models

The particulate matter (PM) concentration forecast is an important component to evaluate the air quality over any region of the world. The results are vital when it comes to health concern issues related to air pollution in developing countries like India and China. The present study is focused on the prediction of future concentrations of respirable suspended particulate matter (RSPM, also called PM2.5) and suspended particulate matter (SPM, also called PM10) using the artificial neural networks (ANN) over tropical inland industrial site, Rourkela. Rourkela, popularly known as steel city of India is situated at the heart of a rich mineral belt and is endowed with many small, medium and large scale industries. This study aims to develop a seasonal ANN modelling approach for prediction of RSPM and SPM for the year 2013 by using the datasets of meteorology and RSPM and SPM concentrations covered from 2009 to 2013. Four neural network models namely, wavelet-based multi-layer perceptron feed forward neural network (WMLPNN), wavelet-based recurrent neural network, multi-layer perceptron feed forward neural network (MLPNN) and recurrent neural network (RNN) are used. The networks are trained using daily data from 2009 to 2012 on a seasonal basis, and daily predictions are performed for 2013 using seasonal based trained model. Five meteorological variables [temperature (T), relative humidity (RH), boundary layer height (BLH), surface pressure (SP), wind direction (WD) and wind speed (WS)] along with the Daubechies wavelet decomposed coefficients are considered as predictor variables in the models. A lagged scheme is introduced in wind speed input vector and networks are trained and tested up to 3 days lagged wind speed variable which greatly improved the prediction accuracy. WMLPNN is found to be outperforming all other tested schemes by providing promising results among all the tested models with lag in pre-monsoon, monsoon and winter seasons and without lag in wind speed for the post-monsoon season for both RSPM and SPM, elucidating the importance of predictor wind speed in the models. The values of R2 varied from 0.6 to 0.91 for RSPM and 0.7 to 0.98 for SPM and RMSE values varied from 0.09 to 0.14 for RSPM and 0.03 to 0.13 for SPM for WMLPNN model. The proposed WMLPNN model in the present study appeared to be reliable and promising for the prediction of PM2.5 and PM10 by providing aid in passing the alerts and notices for the betterment of living beings.