Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods.

Objective: The aim was to evaluate and optimize the performance of sensor monitors in measuring PM and PM under typical emission scenarios both indoors and outdoors.

Method: Parallel measurements and comparisons of PM and PM were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation.

Results: Indoor particles were measured in a range of 0.8-370.7 μg/m and 1.9-465.2 μg/m for PM and PM, respectively, while the outdoor ones were in the ranges of 1.0-211.0 μg/m and 0.0-493.0 μg/m, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM/PM ratios, and measurement time lengths (months) for both PM and PM, in indoor (R: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m and 4.56 μg/m, respectively) and outdoor environments (R: 0.90 and 0.80, RMSE of 5.61 μg/m and 17.54 μg/m, respectively), respectively.

Conclusions: Sensor monitors could provide reliable measurements of PM and PM with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM/PM could improve the validation performance.