Determining PM2.5 calibration curves for a low-cost particle monitor: common indoor residential aerosols.
Real-time particle monitors are essential for accurately estimating exposure to fine particles indoors. However, many such monitors tend to be prohibitively expensive for some applications, such as a tenant or homeowner curious about the quality of the air in their home. A lower cost version (the Dylos Air Quality Monitor) has recently been introduced, but it requires appropriate calibration to reflect the mass concentration units required for exposure assessment. We conducted a total of 64 experiments with a suite of instruments including a Dylos DC1100, another real-time laser photometer (TSI SidePak™ Model AM-510 Personal Aerosol Monitor), and a gravimetric sampling apparatus to estimate Dylos calibration factors for emissions from 17 different common indoor sources including cigarettes, incense, fried bacon, chicken, and hamburger. Comparison of minute-by-minute data from the Dylos with the gravimetrically calibrated SidePak yielded relationships that enable the conversion of the raw Dylos particle counts less than 2.5 μm (in #/0.01 ft(3)) to estimated PM2.5 mass concentration (e.g. μg m(-3)). The relationship between the exponentially-decaying Dylos particle counts and PM2.5 mass concentration can be described by a theoretically-derived power law with source-specific empirical parameters. A linear relationship (calibration factor) is applicable to fresh or quickly decaying emissions (i.e., before the aerosol has aged and differential decay rates introduce curvature into the relationship). The empirical parameters for the power-law relationships vary greatly both between and within source types, although linear factors appear to have lower uncertainty. The Dylos Air Quality Monitor is likely most useful for providing instantaneous feedback and context on mass particle levels in home and work situations for field-survey or personal awareness applications.
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA, 94305 USA and Department of Geography and Environmental Engineering, United States Military Academy, 745 Brewerton Rd, 6th Floor, West Point, NY, 10996 USA. philip.dacunto@usma.edu.
Philip J
PJ
Dacunto
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA, 94305 USA and Graduate School of Public Health, San Diego State University Research Foundation, Center for Behavioral Epidemiology and Community Health (CBEACH), 9245 Sky Park Court, Suite 230, San Diego, CA, 92123 USA.
Neil E
NE
Klepeis
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA, 94305 USA.
Kai-Chung
KC
Cheng
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA, 94305 USA.
Viviana
V
Acevedo-Bolton
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA, 94305 USA.
Ruo-Ting
RT
Jiang
Repace Associates, 6701 Felicia Lane, Bowie, MD, 20720 USA.
James L
JL
Repace
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA, 94305 USA.
Wayne R
WR
Ott
Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA, 94305 USA.
Lynn M
LM
Hildemann
D000336:Aerosols / Q000032:analysis; D016902:Air Pollution, Indoor / Q000032:analysis* / Q000706:statistics & numerical data; D002138:Calibration; D004784:Environmental Monitoring / Q000295:instrumentation* / Q000592:standards; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis* / Q000592:standards
D000336:Aerosols; D052638:Particulate Matter