LEADER 00000nam  2200385   4500 
001    AAI3133628 
005    20050630133815.5 
008    050630s2004                        eng d 
020    0496808677 
035    (UnM)AAI3133628 
040    UnM|cUnM 
100 1  Zhou, Liming 
245 10 Advanced receptor model research for Pittsburgh Air 
       Quality Study (PAQS) 
300    132 p 
500    Source: Dissertation Abstracts International, Volume: 65-
       05, Section: B, page: 2518 
500    Adviser:  Philip K. Hopke 
502    Thesis (Ph.D.)--Clarkson University, 2004 
520    To quantify the sources that are influencing the 
       particulate matter (PM) level in Pittsburgh area, size 
       distribution and composition data characterizing the 
       airborne particles measured by the Pittsburgh Air Quality 
       Study (PAQS) were used as inputs to multivariate receptor 
520    PMF2 was used for solving the bilinear model with particle
       size distribution data. For short periods, the transition 
       of the size distribution from the source to the receptor 
       can be thought close to constant and the assumptions of 
       the multivariate receptor models are satisfied. For the 
       nucleation events followed with particle growth events, 
       those data representing particle growth were excluded. 
       Five factors were separated. Two factors, local traffic 
       and nucleation are clear sources but each of the other 
       factors appears to be a mixture of several sources that 
       cannot be further separated 
520    A new method has been developed to utilize chemical 
       composition data with multiple time resolution. The source
       information in the high time resolution have been kept in 
       the source contributions without averaging or 
       interpolation. Wind direction data were used in 
       combination with source contributions to locate the 
       sources, which benefits from the high temporal resolution.
       Six sources were identified, secondary sulfate, secondary 
       nitrate, crustal, traffic, steel mill and coke plant 
520    The relationships between the size distribution data and 
       the composition data were investigated by Partial Least 
       Square (PLS). Three latent variables summarized both data 
       sets and proved the linearity between the two data sets. 
       The three latent variables were associated with traffic 
       and local combustion sources, secondary aerosol, and coal-
       fired power plants, respectively. The size distribution, 
       particle composition and gas composition data were 
       combined and analyzed by PMF. Eleven sources were 
       identified: secondary nitrate I and II, remote traffic, 
       secondary sulfate, lead, diesel traffic, coal-fired power 
       plant, steel mill, nucleation, local traffic and coke 
590    School code: 0049 
590    DDC 
650  4 Engineering, Chemical 
650  4 Environmental Sciences 
650  4 Engineering, Environmental 
690    0542 
690    0768 
690    0775 
710 20 Clarkson University 
773 0  |tDissertation Abstracts International|g65-05B 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/