Use Of passive samplers to characterize the spatial heterogeneity of coarse particle mass concentration and composition in Cleveland, OH

Eric J. Sawvel

doi:10.17077/etd.m4xe16dx

The overall goals of this dissertation are: 1) to better quantify the spatial heterogeneity of coarse particulate matter (PM10-2.5) and its chemical composition; and 2) to evaluate the performance (accuracy and precision) of passive samplers analyzed by computer-controlled scanning electron microscopy with energy-dispersive X-ray spectroscopy (CCSEM-EDS) for PM10-2.5. For these goals, field studies were conducted over multiple seasons in Cleveland, OH and were the source of data for this dissertation. To achieve the first goal, we characterized spatial variability in the mass and composition of PM10-2.5 in Cleveland, OH with the aid of inexpensive passive samplers. Passive samplers were deployed at 25 optimized sites for three week-long intervals in summer 2008 to characterize spatial variability in components of PM10-2.5. The size and composition of individual particles were determined using CCSEM-EDS. For each sample, this information was used to estimate PM10-2.5 mass and aerosol composition by particle class. The highest PM10-2.5 means were observed at three central industrial urban sites (35.4 Μg m-3, 43.4 Μg m-3, and 47.6 Μg m-3), whereas lower means were observed to the west and east of this area with the lowest means observed at outskirt suburban background sites (12.9 Μg m-3 and 14.7 Μg m-3). Concentration maps for PM10-2.5 and some compositional components of PM10-2.5 (Fe oxide and Ca rich) show an elongated shape of high values stretching from Lake Erie south through the central industrial area, whereas those for other compositional components (e.g., Si/Al rich) are considerably less heterogeneous. The findings from the spatial variability of coarse particles by compositional class analysis, presented in Chapter II of this dissertation, show that the concentrations of some particle classes were substantially more spatially heterogeneous than others. The data suggest that industrial sources located in The Flats district in particular may contribute to the observed concentration variability and heterogeneity. Lastly, percent relative spatial heterogeneity (SH%) is more consistent with spatial heterogeneity as visualized in the concentration surface maps compared to the coefficient of divergence (COD). The second goal was achieved by assessing the performance of passive samplers analyzed by CCSEM-EDS to measure PM10-2.5 (Chapter III) and investigating potential sources of variability in the measurement of PM10-2.5 with passive samplers analyzed by CCSEM-EDS (Chapter IV). Data for these analyses were obtained in studies conducted in summer 2009 and winter 2010. The precision of PM10-2.5 measured with the passive samplers was highly variable and ranged from a low coefficient of variation (CV) of 2.1% to a high CV of 90.8%. Eighty percent of the CVs were less than 40%. This assessment showed the CV for passive samplers was greater than that recommended by the United States Environmental Protection Agency (EPA) guidelines for the Federal Reference Method (FRM). Several CV values were high, exceeding 40% indicating substantially dissimilar results between co-located passive samplers. The overall CV for the passive samplers was 41.2% in 2009 and 33.8% in 2010. The precision when high CVs > 40% (n = 5 of 25) were excluded from the analysis was 24.1% in 2009 and 18.2% for 2010. Despite issues with precision, PM10-2.5 measured with passive samplers agreed well with that measured with FRM samplers with accuracy approaching EPA Federal Equivalent Method (FEM) criteria. The intercept was 1.21 and not statistically significant (p = 3.88). The passive to FRM sampler comparison (1:1) line fell within the 95% confidence interval (CI) for the best-fit linear regression and was statistically significant (p < 0.05). However, several data points had large standard deviations resulting in high variability between co-located passive samplers (n = 3), which extend outside of the 95% CI's. The passive sampler limit of detection (LOD) for the CCSEM method was 2.8 Μg m-3. This study also showed certain samples had higher CVs and that further investigation was needed to better understand the sources of variability in the measurement of PM10-2.5 with passive samplers. Sources of variability observed in the measurement of PM10-2.5 with passive samplers analyzed by CCSEM were explored in Chapter IV of this dissertation. This research suggests mass concentrations greater than 20 Μg m-3 for week long samples are needed on the passive sampler substrate to obtain overall CVs by mass less than 15%. It also suggests that greater than 55 particle counts within a compositional class are needed to reduce analytical CVs to less than 15%. Another finding from this study was increasing the concentration from 6.2 to 10.6 Μg m-3 increases the CCSEM analytical precision by mass 38% and by number 75% for random orientation. Also certain compositional classes appeared problematical for precision of passive sampler measurements. For example, the presence of salt plus moisture introduces challenges for CCSEM analysis through the wetting of salt crystalline particles which dissolve creating a displaced dry deposition pattern of particles upon subsequent evaporation. This process can falsely elevate or reduce the particle count and alter its distribution on the sampling media.

Use Of passive samplers to characterize the spatial heterogeneity of coarse particle mass concentration and composition in Cleveland, OH

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