Calibration of sharp cut impactors for indoor and outdoor particle sampling

Citation
Wa. Turner et al., Calibration of sharp cut impactors for indoor and outdoor particle sampling, J AIR WASTE, 50(4), 2000, pp. 484-487
Citations number
15
Categorie Soggetti
Environment/Ecology,"Environmental Engineering & Energy
Journal title
JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION
ISSN journal
10962247 → ACNP
Volume
50
Issue
4
Year of publication
2000
Pages
484 - 487
Database
ISI
SICI code
1096-2247(200004)50:4<484:COSCIF>2.0.ZU;2-F
Abstract
A low-flow rate, sharp cut point inertial impaction sampler was developed i n 1986 that has been widely used in PM exposure studies in the United State s and several other countries. Although sold commercially as the MS&T Area Sampler, this sampler is widely referred to as the Harvard Impactor, since the initial use was at the Harvard School of Public Health. Impactor nozzle s for this sampler have been designed and characterized for hows of 4,10, 2 0, and 23 L/min and cut points of 1, 2, 5, and 10 mu m An improved method f or determining the actual collecting efficiency curve was developed and use d for the recent impactor calibrations reported here. It consists of placin g a multiplet reduction impactor inline just downstream of the vibrating or ifice aerosol generator to remove the multiplets, thus allowing only the si nglet particle s to penetrate through to the impactor being calibrated. This paper documents the techniques and results of recent nozzle calibratio ns for this sampler and compares it with other size-selective inertial impa ctors. In general, the impactors were found to have sharp cutoff characteri stics. Particle interstage losses for all of the impactors were very low, w ith the exception of the 10-mu m cut size 20 L/ min impactor, which had gre ater losses due to the higher flow rate. All of the 2.5-mu m cut nozzle lab oratory calibrations compare favorably to the U.S. Environmental Protection Agency (EPA) WINS-96 fine particle mass (PM2.5) impactor calibration data.