Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter

Abstract

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ-Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.

Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

“…As explained below, relative humidity larger than 70-80% contributes to particle growth with consequent erroneous reading of the particulate number counts. One of the solutions for this shortcoming consists of implementing a theory for the growth of particulates due to humidity when converting particulate numbers into mass concentrations [48,84,96].…”

“…Some high values (R 2 higher than 0.95) were reported for studies using a linear calibration, while MLR did not perform well (R 2 < 0.5). These results are misleading, since the good results with linear calibration are generally obtained by discarding LSC data obtained with relative humidity exceeding a threshold between 70 and 80%, above which humidity is responsible for particle growth [84,96]. This effect is more important for PM 10 than for PM 1 and PM 2.…”

“…5 . Other studies did not discard high relative humidity, they took into consideration the particle growth factor, either on mass concentration with an exponential calibration model ( [73,75,82]) with a median R 2 of 98 or using the Kölher theory on PM mass concentration [80], or directly for the particle beans of each OPC bin [96], leading to R 2 of about 0.80. Figure 3 shows a summary of all mean R 2 obtained from the calibration of SSys against reference measurements.…”

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region

“…These uncorrected PM 10 readings of the sensors have an offset from that of the reference measured concentrations. The potential effect of relative humidity on particle size measurements could be attributed to the offset in measurements of PM sensors [7,17,23,48,49].…”

Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments

“…Yet, concerns remain about the validation and quality control of those sensors (Castell et al, 2017) as few personal exposure studies have evaluated their performance in field deployment conditions (Rai et al, 2017). Typically, novel sensing platforms are exclusively evaluated in outdoor static co-locations with reference instruments and they only target small numbers of pollutants, most commonly ozone, nitrogen dioxide (Lin et al, 2015) and/or particulate matter (Holstius et al, 2014;Feinberg et al, 2018).…”