Application of statistical distribution of PM10 concentration in air quality management in 5 representative cities of China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To estimate the frequency of daily average PM10 concentrations exceeding the air quality standard (AQS) and the reduction of particulate matter emission to meet the AQS from the statistical properties (probability density functions) of air pollutant concentration.</p><p><b>METHODS</b>The daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi'an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the lognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method.</p><p><b>RESULTS</b>The daily PM10 average concentration in the 5 cities was fitted using the lognormal distribution. The exceeding duration was predicted, and the estimated PM10 emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AQS.</p><p><b>CONCLUSION</b>Air pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.</p>

PM10 as Predictor of Ventilation Efficiency of Houses in Relation to Health Effect.

Concerns for the high concentration of particulates in the ambient air of Jakarta had been associated with respiratory health effects. Accordingly, the high concentration of indoor air particulate in homes was also recognized as a potential health hazard to the household. This paper was based on findings in a cross-sectional study in homes of a village, Jakarta done for a dissertation of a doctoral degree in Public Health. In relation to health aspect, ventilation effectiveness was more predicted by the variation of indoor particulates concentrations (as PM10) than the physical characteristic of the houses. Besides, respiratory symptoms rates among children under-five were positively associated to PM10 concentrations. Except for the house dampness factor, no physical features of the houses such as sizes of windows, rooms, and the like, contributed to the variability of health of the occupants. This research suggested that PM10 concentration was a better indicator for a healthy house than the physical characteristics of the house. As such, the most sensitive and specific level of PM10 concentration to predict the development of respiratory symptoms was 70µg/m3. This cutoff concentration of PM10 agreed with the guideline value set on the level of 70 µg/m3 for the thoracic particles by the World Health Organization.