This project had two major data analysis tasks. The first task was to perform advanced factor analysis using Positive Matrix Factorization (PMF) on three sets of IMPROVE data, Crater Lake National Park (CRLA), Lassen Volcano National Park (LAVO), and San Gorgonio National Wilderness (SAGO). Two of these IMPROVE sites, CRLA and LAVO, are at relatively high altitude and the objective is to separate and quantify the influence of Asian dust on the observed mass concentrations. Among the sources resolved at the two sites, six are common. These six sources exhibit not only similar chemical compositions, but also similar seasonal variations at both 3 sites. The Asian dust represented by Al, Ca, Fe, NO , S, K, and Ti. with strong seasonal variation; secondary sulfate with a high concentration of S and strong seasonal variation correlated with the Asian dust; wood smoke represented by organic carbon (OC), elemental 3; carbon (EC) and K; sea salt with the high concentrations of Na, S and NO nitrate dominated by 3 NO and motor vehicle with high concentrations of OC, EC and dust elements. A incinerator source with the presence of Cu and Zn also was resolved from Crater Lake site. Generally, most of the sources at these two sites showed similar chemical composition profiles and seasonal variation patterns. The source profile of Asian Dust resolved from this study agreed reasonably well with the source characteristics found in other Asian Dust studies.
The third site is downwind of Los Angeles (SAGO) and the primary objective is to determine if gasoline and diesel emissions can be separately identified and quantified using these chemically speciated data. The results demonstrate the feasibility of separating diesel/gasoline emission profiles based on concentration data including OC/EC fractions. Also in the analysis of these data, two crustal factors were identified with one being associated with local suspended soil and the other being associated with transported Asian desert dust. The second task is to expand the existing capabilities of Aerosol Time-of-Flight Mass Spectrometry by ascertaining our ability to apply factor analysis to separate diesel from gasoline motor vehicle emissions and to develop and test calibration models that permits the estimation of the composition of the bulk ambient aerosol composition from single particle data. In the study of data from Fresno, 52 samples were created to build a calibration model. Compared with an earlier study (Fergenson, et al., 2001), significant improvements were obtained in this work, which fully demonstrated the ability of the calibration model based on ART-2a and PLS to estimate the chemical composition from ATOFMS data and also provided a good base to testing the transferability of calibration models of neighbor sites. In addition, some important steps to building a successful calibration mode, like how to determine the PLS components number, are presented in detail, and the corresponding guidance is provided. In order to use single particle data obtained from ATOFMS measurements in PMF models, it is essential to have effective uncertainty estimates for the numbers of particles in each identified particle class in each time interval sample. An approach to developing these uncertainties is presented. Data from Fresno has been analyzed by PMF and results are presented. However, the interpretation of these results is currently incomplete and will require collaboration with Prof. Prather of the University of California, San Diego over the next several months to produce final, interpreted results.
For questions regarding this research project, including available data and progress status, contact: Research Division staff at (916) 445-0753
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