The impact of long-range transport of yellow sand from Asian Continent to the Taipei Metropolitan Area (Taipei) not only deteriorates air quality but also poses health risks to all, especially the children and the elderly. As such, it is important to assess the enhancement of PM(10) during yellow sand periods. In order to estimate PM(10) enhancement, we adopted factor analysis to distinguish the yellow-sand (YS) periods from non-yellow-sand (NYS) periods based on air quality monitoring records. Eight YS events were identified using factor analysis coupling with an independent validation procedure by checking background site values, examining meteorological conditions, and modeling air mass trajectory from January 2001 to May 2001. The duration of each event varied from 11 to 132 h, which was identified from the time when the PM(10) level was high, and the CO and NOx levels were low. Subsequently, we used the artificial neural network (ANN) to simulate local PM(10) levels from related parameters including local gas pollutants and meteorological factors during the NYS periods. The PM(10) enhancement during the YS periods is then calculated by subtracting the simulated PM(10) from the observed PM(10) levels. Based on our calculations, the PM(10) enhancement in the maximum hour of each event ranged from 51 to 82%. Moreover, in the eight events identified in 2001, it was estimated that a total amount of 7,210 tons of PM(10) were transported by yellow sand to Taipei. Thus, in this study, we demonstrate that an integration of factor analysis with ANN model could provide a very useful method in identifying YS periods and in determining PM(10) enhancement caused by yellow sand.