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

Abstract

(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 PM 10 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 PM 10 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 PM 10 pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.Implications: The PM 10 (particulate matter with an aerodynamic diameter 10 µm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m 3 , was higher than the 65 µg/m 3 recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM 10 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 PM 10 spatial distribution. The results indicated that the artificial neural network was an effective method for PM 10 pollution modeling.

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

“…The presented technique by Taheri Shahraiyni et al [136,137] reduces the need for additional in-situ measurement data, and enables a low-cost method for spatial prediction of PM 10 , which is suitable for policy making. Although the MLR technique is often utilized for LUR model development, Zhang et al [126] used MLP for spatial simulation of the annual PM 10 concentration in the urban core area of Taiyuan, China. The intercept of MLR in the LUR approach implies the background concentration values [60], [113] but Chen et al [120] found that the intercept of the MLR model for PM 10 in Jinan, China, is higher than the background values.…”

Characteristics of hopanoid hydrocarbons in ambient PM10 and motor vehicle emissions and coal ash in Taiyuan, China

“…Compared to other cities (e.g., Beijing, Tianjin, and Xian) in the north, air pollution levels are higher and more hazardous in Taiyuan (Ensor 2011;Xu and Zhang 2004;Bi et al 2007;Meng et al 2007). Annual average PM concentration with aerodynamic diameter less than or equal to 10 lm (PM 10 ) declined from about 200 to \150 lg/m 3 from 2001 to 2008 (Zhang et al 2013). Despite this decline, ambient PM 10 concentrations remain above the Chinese standard for residential areas (Ensor 2011).…”

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

“…Collection of such continuously varying information is quite difficult for large-scale applications. Moreover, employment of these models in real-world problems with huge amount of data is very time-consuming (Chaloulakou et al 2003 ; Kumar and Goyal 2011 ; Zhang et al 2013 ). Deficiencies of deterministic models have led the statistical methods to be more popular in real-world problems (Chen et al 2013 ).…”