The overall goal of this doctoral dissertation is to develop a prototype instrument, a Portable Aerosol Collector and Spectrometer (PACS), that can continuously measure aerosol size distributions by number, surface area and mass concentrations over a wide size range (from 10 nm to 10 µm) while also collecting particles with impactor and diffusion stages for post-sampling chemical analyses.
To achieve the goal, in the first study, we designed, built and tested the PACS hardware. The PACS consists of a six-stage particle size selector, a valve system, a water condensation particle counter to measure number concentrations and a photometer to measure mass concentrations. The valve system diverts airflow to pass sequentially through upstream stages of the selector to the detectors. The stages of the selector include three impactor and two diffusion stages, which resolve particles by size and collect particles for chemical analysis. Particle penetration by size was measured through each stage to determine actual performance and account for particle losses. The measured d50 of each stage (aerodynamic diameter for impactor stages and geometric diameter for diffusion stages) was similar to the design. The pressure drop of each stage was sufficiently low to permit its operation with portable air pumps.
In the second study, we developed a multi-modal log-normal (MMLN) fitting algorithm to leverage the multi-metric, low-resolution data from one sequence of PACS measurements to estimate aerosol size distributions of number, surface area, and mass concentration in near-real-time. The algorithm uses a grid-search process and a constrained linear least-square (CLLS) solver to find a tri-mode (ultrafine, fine, and coarse), log-normal distribution that best fits the input data. We refined the algorithm to obtain accurate and precise size distributions for four aerosols typical of diverse environments: clean background, urban and freeway, coal power plant, and marine surface. Sensitivity studies were conducted to explore the influence of unknown particle density and shape factor on algorithm output. An adaptive process that refined the ranges and step sizes of the grid-search reduced the computation time to fit a single size distribution in near-real-time. Assuming standard density spheres, the aerosol size distributions fit well with the normalized mean bias (NMB) of -4.9% to 3.5%, normalized mean error (NME) of 3.3% to 27.6%, and R2 values of 0.90 to 1.00. The fitted number and mass concentration biases were within ± 10% regardless of uncertainties in density and shape. With this algorithm, the PACS is able to estimate aerosol size distributions by number, surface area, and mass concentrations from 10 nm to 10 µm in near-real-time.
In the third study, we developed a new algorithm–the mass distribution by composition and size (MDCS) algorithm–to estimate the mass size distribution of various particle compositions. Then we compared the PACS for measuring multi-mode aerosols to three reference instruments, including a scanning mobility particle sizer (SMPS), an aerodynamic particle sizer (APS) and a nano micro-orifice uniform deposit impactor (nanoMOUDI). We used inductively coupled plasma mass spectrometry to measure the mass of collected particles on PACS and nanoMOUDI stages by element. For the three-mode aerosol, the aerosol size distributions in three metrics measured with the PACS agreed well with those measured with the SMPS/APS: number concentration, bias = 9.4% and R2 = 0.96; surface area, bias = 17.8%, R2 = 0.77; mass, bias = -2.2%, R2 = 0.94. Agreement was considerably poorer for the two-mode aerosol, especially for surface area and mass concentrations. Comparing to the nanoMOUDI, for the three-mode aerosol, the PACS estimated the mass median diameters (MMDs) of the coarse mode well, but overestimated the MMDs for ultrafine and fine modes. The PACS overestimated the mass concentrations of ultrafine and fine mode, but underestimated the coarse mode. This work provides insight into a novel way to simultaneously assess airborne aerosol size, composition, and concentration by number, surface area and mass using cost-effective handheld technologies.