Field evaluation of particulate matter measurements using tapered element oscillating microbalance in a layer house

The tapered element oscillating microbalance (TEOM) is one type of continuous ambient particulate matter (PM) monitor. Adsorption and desorption of moisture and semivolatile species may cause positive or negative artifacts in TEOM PM mass measurement. The objective of this field study was to investigate possible uncertainties associated with TEOM measurements in the poultry operation environment. For comparisons of TEOM with filter-based gravimetric method, four instruments (TEOM-PM10, low-volume PM10 sampler TEOM-PM2.5, and PM2.5 speciation sampler) were collocated and tested inside a poultry house for PM2.5 and PM10 (PM with aerodynamic equivalent diameter < or =2.5 and < or =10 microm, respectively) measurements. Fifteen sets of 24-hr PM10 concentrations and 13 sets of 24-hr PM2.5 measurements were obtained. Results indicate that compared with filter-based gravimetric method, TEOM gave significantly lower values of both PM10 and PM2.5 mass concentrations. For PM10, the average ratio of TEOM to the gravimetric method was 0.936. For PM2.5, the average ratio of TEOM to the gravimetric method was 0.738. Particulate matter in the poultry houses possibly contains semivolatile compounds and moisture due to high levels of relative humidity (RH) and gas pollutants. The internal heating mechanism of the TEOM may cause losses in mass through volatilization. To investigate the effects of TEOM settings on concentration measurements, the heaters of two identical TEOMs were set at 50 degrees C, 30 degrees C, or no heating at all. They were collocated and tested for total suspended particle (TSP), PM10, and PM25 measurements in layer house for 6 weeks. For all TSR PM10, and PM2.5 measurements, the internal TEOM temperature setting had a significant effect (P < 0.05). Significantly higher PM mass concentrations were measured at lower temperature settings. The effects of environmental (i.e., temperature, RH, NH3 and CO2 concentrations) and instrumental (i.e., filter loading and noise) parameters on PM measurements were also assessed using regression analysis.