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

The aim was to evaluate and optimize the performance of sensor monitors in measuring PM_2.5 and PM₁₀ under typical emission scenarios both indoors and outdoors.

Parallel measurements and comparisons of PM_2.5 and PM₁₀ 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.

Indoor particles were measured in a range of 0.8-370.7 μg/m³ and 1.9-465.2 μg/m³ for PM_2.5 and PM₁₀, respectively, while the outdoor ones were in the ranges of 1.0-211.0 μg/m³ and 0.0-493.0 μg/m³, 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_2.5/PM₁₀ ratios, and measurement time lengths (months) for both PM_2.5 and PM₁₀, in indoor (R²: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m³ and 4.56 μg/m³, respectively) and outdoor environments (R²: 0.90 and 0.80, RMSE of 5.61 μg/m³ and 17.54 μg/m³, respectively), respectively.

Sensor monitors could provide reliable measurements of PM_2.5 and PM₁₀ 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_2.5/PM₁₀ could improve the validation performance.