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Open AccessArticle

Correction of Light Scattering-based Total Suspended Particulate Measurements through Machine Learning

1
China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China
2
School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
3
Shanghai Eureka Environmental Protection Hi-tech., Ltd., Shanghai 200090, China
4
Division of Atmospheric Sciences, Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA
5
Shanghai Environmental Monitoring Center, Shanghai 200235, China
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(2), 139; https://doi.org/10.3390/atmos11020139
Received: 17 November 2019 / Revised: 23 January 2020 / Accepted: 24 January 2020 / Published: 26 January 2020
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
Instruments based on light scattering used to measure total suspended particulate (TSP) concentrations have the advantages of fast response, small size, and low cost compared to the gravimetric reference method. However, the relationship between scattering intensity and TSP mass concentration varies nonlinearly with both environmental conditions and particle properties, making it difficult to make corrections. This study applied four machine learning models (support vector machines, random forest, gradient boosting regression trees, and an artificial neural network) to correct scattering measurements for TSP mass concentrations. A total of 1141 hourly records of collocated gravimetric and light scattering measurements taken at 17 urban sites in Shanghai, China were used for model training and validation. All four machine learning models improved the linear regressions between scattering and gravimetric mass by increasing slopes from 0.4 to 0.9–1.1 and coefficients of determination from 0.1 to 0.8–0.9. Partial dependence plots indicate that TSP concentrations determined by light scattering instruments increased continuously in the PM2.5 concentration range of ~0–80 µg/m3; however, they leveled off above PM10 and TSP concentrations of ~60 and 200 µg/m3, respectively. The TSP mass concentrations determined by scattering showed an exponential growth after relative humidity exceeded 70%, in agreement with previous studies on the hygroscopic growth of fine particles. This study demonstrates that machine learning models can effectively improve the correlation between light scattering measurements and TSP mass concentrations with filter-based methods. Interpretation analysis further provides scientific insights into the major factors (e.g., hygroscopic growth) that cause scattering measurements to deviate from TSP mass concentrations besides other factors like fluctuation of mass density and refractive index.
Keywords: light scattering; total suspended particulate (TSP); machine learning; hygroscopic effect light scattering; total suspended particulate (TSP); machine learning; hygroscopic effect
MDPI and ACS Style

Guo, Q.; Zhu, Z.; Cheng, Z.; Xu, S.; Wang, X.; Duan, Y. Correction of Light Scattering-based Total Suspended Particulate Measurements through Machine Learning. Atmosphere 2020, 11, 139.

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