Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min, and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution

Bulot

Russell

Rezaei

et al. 2020

“…The PM 2.5 sensor, PMS3003, has been evaluated by several research teams in laboratory environments. For example, Kelly et al [ 18 ] obtained an R 2 of 0.73–0.97 in wind tunnels, and Sayahi et al [ 37 ] obtained an R 2 > 0.978 for 242 sets of PMS3003 in a controlled chamber. These results indicated the good performance of PMS3003 compared to research-grade instruments.…”

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network

Wang

Lung

Liu

2020

Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM2.5 LCSs from July 2017 to December 2018. Three candidate models were evaluated: Multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The model-corrected PM2.5 levels were compared with those of GRIMM-calibrated PM2.5. RFR was superior to MLR and SVR in its correction accuracy and computing efficiency. Compared to SVR, the root mean square errors (RMSEs) of RFR were 35% and 85% lower for the training and validation sets, respectively, and the computational speed was 35 times faster. An RFR with 300 decision trees was chosen as the optimal setting considering both the correction performance and the modeling time. An RFR with a nighttime pattern was established as the optimal correction model, and the RMSEs were 5.9 ± 2.0 μg/m3, reduced from 18.4 ± 6.5 μg/m3 before correction. This is the first work to correct LCSs at locations without monitoring stations, validated using laboratory-calibrated data. Similar models could be established in other countries to greatly enhance the usefulness of their PM2.5 sensor networks.

“…Subsequently, at least seven publications have presented results from laboratory evaluations for various PM LCSs [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Among the sensors evaluated, Plantower sensors, which have a relatively low cost (~35 USD), consistently performed well in terms of the intra-precision among themselves and their precision compared to various research-grade instruments [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. It was found that Plantower sensors performed better than Shinyei ones because Plantower sensors are designed with a fan that draws in air and a laser light source [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Laboratory Evaluations of Correction Equations with Multiple Choices for Seed Low-Cost Particle Sensing Devices in Sensor Networks

Wang

Lung

Liu

et al. 2020

To tackle the challenge of the data accuracy issues of low-cost sensors (LCSs), the objective of this work was to obtain robust correction equations to convert LCS signals into data comparable to that of research-grade instruments using side-by-side comparisons. Limited sets of seed LCS devices, after laboratory evaluations, can be installed strategically in areas of interest without official monitoring stations to enable reading adjustments of other uncalibrated LCS devices to enhance the data quality of sensor networks. The robustness of these equations for LCS devices (AS-LUNG with PMS3003 sensor) under a hood and a chamber with two different burnt materials and before and after 1.5 years of field campaigns were evaluated. Correction equations with incense or mosquito coils burning inside a chamber with segmented regressions had a high R2 of 0.999, less than 6.0% variability in the slopes, and a mean RMSE of 1.18 µg/m3 for 0.1–200 µg/m3 of PM2.5, with a slightly higher RMSE for 0.1–400 µg/m3 compared to EDM-180. Similar results were obtained for PM1, with an upper limit of 200 µg/m3. Sensor signals drifted 19–24% after 1.5 years in the field. Practical recommendations are given to obtain equations for Federal-Equivalent-Method-comparable measurements considering variability and cost.