Prediction of PM10 and TSP Air Pollution Parameters Using Artificial Neural Network Autoregressive, External Input Models: A Case Study in Salt, Jordan

Abstract

Air pollution in large cities has been a major and a serious environmental problem all over the world; hence, many countries initiated air quality management systems to monitor and control pollution rates around big cities. It was found that harmful emission into the air is a symbol for environmental force that affects seriously man's health, natural life and agriculture; thus leading to major loss on the nation's economy. Government in industrialized countries deployed many regulations to apply restrictions on emission limits thus reduce the levels of pollution in air and enforce the international standards for air quality levels. The main objective of this study is to develop a non-parametric Artificial Neural Network (ANN) models to predict both the Particulate Matters (PM10) and Total Suspended Particles (TSP) in Salt, Jordan. A data set collected around Al- Fuhais cement plant over one-year period (2006-2007) by eight monitoring stations were used in our study. The proposed ANN models considered the meteorological parameters: Temperature (Temp), Relative Humidity (RH), Wind Speed (WS) as inputs. We developed two Artificial Neural Network based AutoRegressive with eXternal (ANNARX) Input models to provide high performance modeling for the PM10 and the TSP air pollution parameters. Experimental results show that ANNARX can provide good modeling results using a limited number of measurements.