We compared the performance of a low-cost (∼$500), compact optical particle counter (OPC, OPC-N2, Alphasense) to another OPC (PAS-1.108, Grimm Technologies) and reference instruments. We measured the detection efficiency of the OPCs by size from 0.5 to 5 μm for monodispersed, polystyrene latex (PSL) spheres. We then compared number and mass concentrations measured with the OPCs to those measured with reference instruments for three aerosols: salt, welding fume and Arizona road dust. The OPC-N2 detection efficiency for monodispersed was similar to the PAS-1.108 for particles larger than 0.8 μm (minimum of 79% at 1 μm and maximum of 101% at 3 μm). For 0.5-μm particles, the detection efficiency of OPCN2 was underestimated at 78%, whereas PAS-1.108 overestimated concentrations by 183%. The mass concentrations from the OPCs were linear (r ≥ 0.97) with those from the reference instruments for all aerosols, although the slope and intercept were different. The mass concentrations were overestimated for dust (OPC-N2, slope = 1.6; PAS-1.108, slope = 2.7) and underestimated for welding fume (OPC-N2, slope = 0.05; PAS-1.108, slope = 0.4). The coefficient of variation (CV, precision) for OPC-N2 for all experiments was between 4.2% and 16%. These findings suggest that, given site-specific calibrations, the OPC-N2 can provide number and mass concentrations similar to the PAS-1.108 for particles larger than 1 μm.

Recently, inexpensive (<$300) consumer aerosol monitors (CAMs) targeted for use in homes have become available. We evaluated the accuracy, bias, and precision of three CAMs (Foobot from Airoxlab, Speck from Carnegie Mellon University, and AirBeam from HabitatMap) for measuring mass concentrations in occupational settings. In a laboratory study, PM2.5 measured with the CAMs and a medium-cost aerosol photometer (personal DataRAM 1500, Thermo Scientific) were compared to that from reference instruments for three aerosols (salt, welding fume, and Arizona road dust, ARD) at concentrations up to 8500 μg/m3. Three of each type of CAM were included to estimate precision. Compared to reference instruments, mass concentrations measured with the Foobot (r-value = 0.99) and medium-cost photometer (r-value = 0.99) show strong correlation, whereas those from the Speck (r-value range 0.88 – 0.99) and AirBeam (0.7 – 0.96) were less correlated. The Foobot bias was (−12%) for ARD and measurements were similar to the medium-cost instrument. Foobot bias was (< −46%) for salt and welding fume aerosols. Speck bias was at 18% salt for ARD and −86% for welding fume. AirBeam bias was (−36%) for salt and (−83%) for welding fume. All three photometers had a bias (< −82%) for welding fume. Precision was excellent for the Foobot (coefficient of variation range: 5% to 8%) and AirBeam (2% to 9%), but poorer for the Speck (8% to 25%). These findings suggest that the Foobot, with a linear response to different aerosol types and good precision, can provide reasonable estimates of PM2.5 in the workplace after site-specific calibration to account for particle size and composition.

No abstract

Development of an air quality monitoring network with high spatio-temporal resolution requires installation of a large number of air pollutant monitors. However, state-of-the-art monitors are costly and may not be compatible with wireless data logging systems. In this study, low-cost electro-chemical sensors manufactured by Alphasense Ltd. for detection of CO and oxidative gases (predominantly O and NO) were evaluated. The voltages from three oxidative gas sensors and three CO sensors were recorded every 2.5 sec when exposed to controlled gas concentrations in a 0.125-m acrylic glass chamber. Electro-chemical sensors for detection of oxidative gases demonstrated sensitivity to both NO and O with similar voltages recorded when exposed to equivalent environmental concentrations of NO or O gases, when evaluated separately. There was a strong linear relationship between the recorded voltages and target concentrations of oxidative gases (R > 0.98) over a wide range of concentrations. Although a strong linear relationship was also observed for CO concentrations below 12 ppm, a saturation effect was observed wherein the voltage only changes minimally for higher CO concentrations (12-50 ppm). The nonlinear behavior of the CO sensors implied their unsuitability for environments where high CO concentrations are expected. Using a manufacturer-supplied shroud, sensors were tested at 2 different flow rates (0.25 and 0.5 Lpm) to mimic field calibration of the sensors with zero air and a span gas concentration (2 ppm NO2 or 15 ppm CO). As with all electrochemical sensors, the tested devices were subject to drift with a bias up to 20% after 9 months of continuous operation. Alphasense CO sensors were found to be a proper choice for occupational and environmental CO monitoring with maximum concentration of 12 ppm, especially due to the field-ready calibration capability. Alphasense oxidative gas sensors are usable only if it is valuable to know the sum of the NO and O concentrations.

Noise is a pervasive workplace hazard that varies spatially and temporally. The cost of direct-reading instruments for noise hampers their use in a network. The objectives for this work were to: (1) develop an inexpensive noise sensor (<$100) that measures A-weighted sound pressure levels within ±2 dBA of a Type 2 sound level meter (SLM; ∼$1,800); and (2) evaluate 50 noise sensors for use in an inexpensive sensor network. The inexpensive noise sensor consists of an electret condenser microphone, an amplifier circuit, and a microcontroller with a small form factor (28 mm by 47 mm by 9 mm) than can be operated as a stand-alone unit. Laboratory tests were conducted to evaluate 50 of the new sensors at 5 sound levels: (1) ambient sound in a quiet office; (2) 3 pink noise test signals from 65-85 dBA in 10 dBA increments; and (3) 94 dBA using a SLM calibrator. Ninety-four percent of the noise sensors (n = 46) were within ±2 dBA of the SLM for sound levels from 65-94 dBA. As sound level increased, bias decreased, ranging from 18.3% in the quiet office to 0.48% at 94 dBA. Overall bias of the sensors was 0.83% across the 75 dBA to 94 dBA range. These sensors are available for a variety of uses and can be customized for many applications, including incorporation into a stationary sensor network for continuous monitoring of noise in manufacturing environments.