2.1.1 Generation of Calibration Curves for Cigarette Smoke:
Cigarette smoke-specific calibration curves for the Airbeam 1 and 2 PM2.5 sensors were created in a laboratory setting via the direct comparison of the output of the low cost Airbeam sensors with simultaneous 1-min readings produced by a factory-calibrated Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet (Thermo Environmental Instruments, Waltham, MA). The PDR-1500 unit is a widely used instrument and shown to be reliable from previous studies.14-22 Over the course of the 2-year period, our low-cost sensors were calibrated four times using the same PDR-1500 unit, where the internal filter was checked to control the real-time measurements gravimetrically. The Airbeam 1 and 2 devices utilize two low-cost sensors: The Shinyei PPD60PV and Plantower PMS 7003 infra-red light scattering particle sensors, respectively. The PDR 1500 unit was zeroed with particle-free air prior to each run.
To perform the calibration, 8-12 Airbeam units were placed into an airtight stainless-steel chamber, where temperature is room temperature and humidity matches the building’s at below 50%, with access ports permitting the introduction of cigarette smoke or HEPA filtered air. The PDR-1500 was connected to a sampling port for measuring the PM2.5 concentrations inside the chamber. This instrument has both an inlet and outlet where tubes are connected to inject cigarette smoke into the chamber; the PDR-1500 was not placed inside the chamber to prevent contamination resulting from its enclosure with cigarette smoke. A smoking machine (Borgwaldt, Hamburg, Germany) was used to inject fresh mainstream cigarette smoke using 3R4F reference cigarettes into the chamber until the PDR-1500 registered a particle mass concentration greater than 1,000 µg/m3. A high concentration value such as 1,000 µg/m3 exceeds the upper limit for PM2.5 values for both low-cost particle sensor types. Airbeam 1 and Airbeam 2 sensors have different saturation points at 80 µg/m3 and 200 µg/m3, respectively (i.e., the light scattering derived PM2.5 output plateaus), ensuring the decreasing PM2.5 calibration curve would begin above their detection ceiling (approximately 180 µg/m3 and 800 µg/m3, respectively). After cigarette smoke generation was stopped, the sample pump and internal filter of the PDR-1500 slowly removed cigarette smoke from the chamber which was replaced by HEPA-filtered room air. The resulting time-dependent decrease in PM2.5 was used to develop the calibration curve. The start times of the Airbeam units and PDR-1500 particulate matter readings were synchronized, and the 1 min outputs were recorded beginning above the nominal upper detection limit and continued until the PDR-1500 values stabilized in the low single digit µg/m3 range. Each run lasted approximately one hour.
Readings from each Airbeam (X-axis) were matched by synchronized timestamp with the corresponding values from the PDR-1500 (Y-axis). Using Excel, a unique calibration equation for each Airbeam unit was calculated by linear regression up to 80 µg/m3 which was the expected upper limit for indoor PM2.5. Polynomial regression models were also generated; however, the output of these models was linear up to 80 µg/m3, which strengthened our decision to use linear models. Each unique equation and accompanying R value was recorded and assigned to the unit by serial number. Because of differences in the sensor type’s output, for consistency, we calculated calibration coefficients using the total PM reading from Airbeam 1 sensors, and the PM10 output from Airbeam 2 sensors. Both sensor types use an algorithm based on an internal equation to generate PM output; Airbeam 1 sensors do not have the split for PM1, PM2.5 and PM10 values. The calibration coefficient for cigarette smoke was developed as a multiplication factor to correct the Airbeam PM2.5 output and calculated as:
Calibration Coefficient = slope of the PDR-1500 (Y-axis) vs Airbeam (X-axis) calibration curve
To assess the effect of particle composition on the calibration curve, the Airbeam devices were also calibrated using airborne particles in the NYC subway system. As in the cigarette smoke calibration procedure, the output of four Airbeam 1’s and four Airbeam 2’s was compared to the PDR 1500 PM2.5 output and a calibration coefficient was calculated for subway PM2.5.
2.1.2 Field Sampling Periods
We calibrated 51 low-cost particle sensors (Airbeam 1 generation N=29; Airbeam 2 generation N=22) at 4 different timepoints over a 2-year period spanning from 2019 to 2021. After each laboratory calibration, the Airbeam units were deployed in a large, natural experiment evaluating the impact of new smoke-free housing (SFH) policies on air quality in public housing units every 6 months.11,23 Due to the onset of the COVID-19 pandemic, we were unable to perform Airbeam sensor calibration 24 months post-SFH policy implementation (April-September 2020). A calibration technical error for select Airbeam 2 sensors occurred at 30 months post-SFH policy (December-March 2021), leading to their exclusion from data analysis at that timepoint. We then calibrated all 51 Airbeam sensors at 36 months post-SFH policy (May-September 2021) to obtain a final calibration coefficient.
2.1.3 Data Analysis:
We descriptively tabulated the mean (SD) calibration coefficients at four different 6-month timepoints over a 2-year period from 2019 to 2021 for the two different Airbeam sensor types. We performed independent t-tests to measure statistically significant differences in calibration coefficient means between particle sensor types, and also characterized the between-and-within variability for calibration coefficient measurements. Because the light scattering properties of airborne particles are influenced by particle composition, we compared the mean (SD) calibration coefficients for cigarette smoke and subway PM2.5 using an independent t-test. Lastly, we used a difference-in-difference (DID) approach to compare within-group changes between Airbeam 1 and Airbeam 2 sensors across four different calibration timepoints. Regression models included fixed effects for particle sensor type (Airbeam 1 vs Airbeam 2 sensors) and data collection timepoints (12, 18, 30 and 36 months post-SFH policy implementation24). We adjusted for the clustering of individual Airbeam IDs and repeated measures overtime. Model-based mean differences with 95% confidence intervals were calculated for each particle sensor type over time. P-values were reported after implementation of the independent t-tests, with a significance level set at p<0.05, using a two-sided test. All analyses were performed using SAS statistical software, version 9.4 (SAS institute).
We examined the individual time trends in calibration coefficient measurements for low-cost particle sensors over a 2-year period, grouped by particle sensor type (Supplemental Figure S1), and descriptively categorized all low-cost particle sensors that were taken out of circulation over the 2-year period (Supplemental Table S1). We then examined the correlation between the number of unique instances of use for individual Airbeam sensors, and their final calibration coefficients at the end of the 2-year period (Supplemental Table S2 and Supplemental Figure S2).