Comparison of real-time instruments and gravimetric method when measuring particulate matter in a residential building

Abstract

This study used several real-time and filter-based aerosol instruments to measure PM_2.5 levels in a high-rise residential green building in the Northeastern US and compared performance of those instruments. PM_2.5 24-hr average concentrations were determined using a Personal Modular Impactor (PMI) with 2.5 µm cut (SKC Inc., Eighty Four, PA) and a direct reading pDR-1500 (Thermo Scientific, Franklin, MA) as well as its filter. 1-hr average PM_2.5 concentrations were measured in the same apartments with an Aerotrak Optical Particle Counter (OPC) (model 8220, TSI, Inc., Shoreview, MN) and a DustTrak DRX mass monitor (model 8534, TSI, Inc., Shoreview, MN). OPC and DRX measurements were compared with concurrent 1-hr mass concentration from the pDR-1500. The pDR-1500 direct reading showed approximately 40% higher particle mass concentration compared to its own filter (n = 41), and 25% higher PM_2.5 mass concentration compared to the PMI_2.5 filter. The pDR-1500 direct reading and PMI_2.5 in non-smoking homes (self-reported) were not significantly different (n = 10, R² = 0.937), while the difference between measurements for smoking homes was 44% (n = 31, R² = 0.773). Both OPC and DRX data had substantial and significant systematic and proportional biases compared with pDR-1500 readings. However, these methods were highly correlated: R² = 0.936 for OPC versus pDR-1500 reading and R² = 0.863 for DRX versus pDR-1500 reading. The data suggest that accuracy of aerosol mass concentrations from direct-reading instruments in indoor environments depends on the instrument, and that correction factors can be used to reduce biases of these real-time monitors in residential green buildings with similar aerosol properties.

Implications: This study used several real-time and filter-based aerosol instruments to measure PM_2.5 levels in a high-rise residential green building in the northeastern United States and compared performance of those instruments. The data show that while the use of real-time monitors is convenient for measurement of airborne PM at short time scales, the accuracy of those monitors depends on a particular instrument. Bias correction factors identified in this paper could provide guidance for other studies using direct-reading instruments to measure PM concentrations.

Figure 1

Comparison of PM_2.5 24 hr average concentrations between pDR-1500 direct reading and pDR-1500 filter-based measurements. The regression is presented by the following equation: y = β₁(±SE)x + β₀(±SE) where β₁ and β₀ are regression coefficients, and SE is the standard error of β values.

Figure 2

Comparison of PM_2.5 24 hr average concentrations determined by PMI PM_2.5 filter-based measurements and pDR-1500 filter-based measurements. The regression is presented by the following equation: y = β₁(±SE)x + β₀(±SE) where β₁ and β₀ are regression coefficients, and SE is the standard error of β values.

Figure 3

Comparison of PM_2.5 24 hr average concentrations determined by PMI PM_2.5 filter-based measurements and pDR-1500 direct reading measurements. The regression is presented by the following equation: y = β₁(±SE)x + β₀(±SE) where β₁ and β₀ are regression coefficients, and SE is the standard error of β values.

Figure 4

Comparison of PM_2.5 24 hr average concentrations determined by PMI PM_2.5 filter-based measurements and pDR-1500 direct reading measurements, with data stratified according to the self-reported smoking status in investigated apartments. The regression is presented by the following equation: y = β₁(±SE)x + β₀(±SE) where β₁ and β₀ are regression coefficients, and SE is the standard error of β values.

Figure 5

Comparison of PM_2.5 1-hr average concentrations determined by pDR-1500 direct reading measurements and Aerotrak OPC estimate assuming particles are spherical and have a density of 1.68 g/cm³. The regression is presented by the following equation: y = β₁(±SE)x + β₀(±SE) where β₁ and β₀ are regression coefficients, and SE is the standard error of β values.

Figure 6

Comparison of PM_2.5 1-hr average concentrations determined by pDR-1500 and DustTrak DRX direct reading measurements. The regression is presented by the following equation: y = β₁(±SE)x + β₀(±SE) where β₁ and β₀ are regression coefficients, and SE is the standard error of β values.