Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City, China

Primary fine particulate matters with a diameter of less than 10 mu m (PM10) are important air emissions causing human health damage. PM10 concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM10 pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM10 yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM10 yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM10 from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM10 pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies. Implications: The PM10 (particulate matter with an aerodynamic diameter 10 m) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m(3), was higher than the 65 g/m(3) recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM10 yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM10 spatial distribution. The results indicated that the artificial neural network was an effective method for PM10 pollution modeling.