The statistical characters of PM10 concentration in Taiwan area

Abstract The concentrations of air pollutants varied inherently with meteorological conditions and pollutant emission level. From the statistical properties (probability density) of air pollutants, it is easy to estimate how many times the exceedance compared with air quality standards occurs. In this paper, three distributions (lognormal, Weibull and type V Pearson distribution) were utilized to simulate the PM10 concentration distribution in Taiwan areas. Air quality data of three monitoring stations, Hsin-Chu, Sha-Lu and Gian-Jin, were taken to compare the characters of PM10 concentrations during a five-year period (1995–1999). Two parametric estimating methods, method of moments and method of least squares, were used to estimate the parameters of these three theoretic distributions. Therefore, the exceedance frequency of air pollutant concentration and emission source reduction can be predicted from these theoretic distributions. These results show that the lognormal is the best distribution to represent the PM10 daily average concentration. Between these two parametric estimation methods, the method of least squares has more accurate results than the moments method. The PM10 concentration distributions of Hsin-Chu and Sha-Lu stations are all unimodal distributions, but the distribution of Gian-Jin is a bimodal distribution. The measured PM10 concentrations of Gian-Jin station were divided into two seasons, and the parameters were computed individually. The reproduced bimodal distribution, which combined with the two unimodal distributions, agrees well with measured data. This result shows that the distribution type of PM10 concentration varied greatly in different areas, and could be influenced by local meteorological conditions in different seasons. In addition, the probabilities exceeding the air quality standard (PM10>125 μg m−3) and emission sources reduction of PM10 concentration to meet the air quality standard for Hsin-Chu, Sha-Lu and Gian-Jin stations are predicted successfully.