An inexpensive light-scattering particle monitor: field validation

Abstract

We have developed a small, light, passive, inexpensive, datalogging particle monitor called the "UCB" (University of California Berkeley particle monitor). Following previously published laboratory assessments, we present here results of tests of its performance in field settings at high particle concentrations. We demonstrate the mass sensitivity of the UCB in relation to gravimetric filter-based PM(2.5) mass estimates as well as commercial light-scattering instruments co-located in field chamber tests and in kitchens of wood-burning households. The coefficient of variation of the unadjusted UCB mass response in relation to gravimetric estimates was 15%. Although requiring adjustment for differences in sensitivity, inter-monitor performance was consistently high (r(2) > 0.99). Moreover, the UCB can consistently estimate PM(2.5) mass concentrations in wood-burning kitchens (Pearson r(2) = 0.89; N = 99), with good agreement between duplicate measures (Pearson r(2) = 0.94; N = 88). In addition, with appropriate cleaning of the sensing chamber, UCB mass sensitivity does not decrease with time when used intensively in open woodfire kitchens, demonstrating the significant potential of this monitor.

Fig. 1

Test chamber (1.2 m diameter × 1 m height) set up in Mexican field station showing placement of UCBs and cyclone during testing.

Fig. 2

Responses of 19 UCBs and DustTrak during chamber tests.

Fig. 3

Correlation of mean UCB peak response with DustTrak during 4 chamber tests (N = 19).

Fig. 4

Correlation of UCB response with PM_2.5 Teflon gravimetric filters collected during 4 tests.

Fig. 5

Comparison between UCB and gravimetric PM_2.5 mass concentrations (N = 99) from Guatemala.

Fig. 6

Correlation between duplicate (N = 88) UCB measurements.

Fig. 7

Frequency distribution of PM_2.5 concentrations from the UCB and gravimetric measurements in (a) open-fire kitchens (N = 50) and (b) chimney-stove kitchens (N = 49) in Guatemala. The error bars in the “Total” represent one standard deviation from the mean.