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Journals / Atmospheric Pollution Research / 2019 / Cilt: 10 - Sayı: 6

Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset

Qi-Xiang CHEN
Chun-Lin HUANG
Yuan Yuan XIAO
Qian-Jun MAO
He-Ping TAN
Pages
1843–1851
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results show that the average determination coefficient (R2) ofthe ACM-MRM (0.52) was greater than that of the MRM (0.44), while the root mean square error (RMSE) andmean absolute percent error (MAPE) of the ACM-MRM (3.74 μg/m3 and 34.91%, respectively) were lower thanthe values obtained with the MRM (4.09 μg/m3 and 37.64%, respectively).

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, the influence of aerosol type classification on estimating surface PM2.5 concentrations was assessedby using ground-based aerosol optical depth (AOD) at 22 ground-based observation points in the United States.To clarify the influence, a two-stage model consisting of a multiple regression model (MRM) and an aerosolclassification model (ACM) was proposed to estimate surface PM2.5 concentrations, and results from a traditionalMRM and the new ACM-MRM were compared.
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Key Findings & Data:Results show that the average determination coefficient (R2) ofthe ACM-MRM (0.52) was greater than that of the MRM (0.44), while the root mean square error (RMSE) andmean absolute percent error (MAPE) of the ACM-MRM (3.74 μg/m3 and 34.91%, respectively) were lower thanthe values obtained with the MRM (4.09 μg/m3 and 37.64%, respectively).
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Conclusion & Impact:Further analysis demonstrated that aerosol type changes had adverse influenceson the estimation of short-term PM2.5 concentrations and the introduction of ACM can effectively restrain theadverse influences when aerosols change frequently.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

In this study, the influence of aerosol type classification on estimating surface PM2.5 concentrations was assessedby using ground-based aerosol optical depth (AOD) at 22 ground-based observation points in the United States.To clarify the influence, a two-stage model consisting of a multiple regression model (MRM) and an aerosolclassification model (ACM) was proposed to estimate surface PM2.5 concentrations, and results from a traditionalMRM and the new ACM-MRM were compared. Results show that the average determination coefficient (R2) ofthe ACM-MRM (0.52) was greater than that of the MRM (0.44), while the root mean square error (RMSE) andmean absolute percent error (MAPE) of the ACM-MRM (3.74 μg/m3 and 34.91%, respectively) were lower thanthe values obtained with the MRM (4.09 μg/m3 and 37.64%, respectively). The use of ACM improved the estimationof daily PM2.5 concentrations in different regions and different seasons by reducing the deviationscaused by aerosol type changes. Further analysis demonstrated that aerosol type changes had adverse influenceson the estimation of short-term PM2.5 concentrations and the introduction of ACM can effectively restrain theadverse influences when aerosols change frequently.

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Cite This Article

CHEN, Q., HUANG, C., XIAO, Y. Y., MAO, Q., TAN, H. (2019). Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset. Atmospheric Pollution Research, Cilt: 10 - Sayı: 6, 1843–1851. https://ajindex.com/articles/351464

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