A BP NEURAL NETWORK MODEL OF AEROSOL SCATTERING HYGROSCOPIC GROWTH FACTOR

TONG Jingzhe; NI Changjian; MI Jiayuan; JIANG Mengjiao; ZHANG Ying; SHI Qiaoyu

doi:10.13205/j.hjgc.202307018

Volume 41 Issue 7

Jul. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(7): 131-137,165

TONG Jingzhe, NI Changjian, MI Jiayuan, JIANG Mengjiao, ZHANG Ying, SHI Qiaoyu. A BP NEURAL NETWORK MODEL OF AEROSOL SCATTERING HYGROSCOPIC GROWTH FACTOR[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 131-137,165. doi: 10.13205/j.hjgc.202307018

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TONG Jingzhe, NI Changjian, MI Jiayuan, JIANG Mengjiao, ZHANG Ying, SHI Qiaoyu. A BP NEURAL NETWORK MODEL OF AEROSOL SCATTERING HYGROSCOPIC GROWTH FACTOR[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 131-137,165. doi: 10.13205/j.hjgc.202307018

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A BP NEURAL NETWORK MODEL OF AEROSOL SCATTERING HYGROSCOPIC GROWTH FACTOR

doi: 10.13205/j.hjgc.202307018

1. College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, China;
2. Chengdu Plain Urban Meteorology and Environment Observation and Research Station of Sichuan Province, Chengdu 610225, China;
3. Institute of Plateau Meteorology, China Meteorology of Administration, Chengdu 610072, China

Received Date: 2022-10-18

Abstract

Abstract

The complexity of aerosol components, structure and morphology challenges the applicability of statistical models for aerosol scattering hygroscopic growth factor under high humidity conditions. Based on the hourly observational data of nephelometer and aethalometer, as well as the simultaneous monitored data of environmental meteorology from October to December, 2017 in Chengdu, the aerosol scattering hygroscopic growth factor was calculated by optical synthesis method. The relative humidity (RH), C_BC, C_BC/C_{PM_2.5}, C_PM₁/C_{PM_2.5} and C_{PM_2.5}/C_PM₁₀ were used as input factors (C_BC, C_PM₁, C_{PM_2.5} and C_PM₁₀ representing mass concentrations of BC, PM₁, PM_2.5 and PM₁₀ respectively), and a BP neural network model of aerosol scattering hygroscopic growth factor was proposed. The comparison results of multiple models showed that:the corresponding determination coefficient (R²) of the univariate quadratic polynomial model, bivariate model, multivariate GAM model and BP neural network model for the aerosol hygroscopic growth factor were 0.650, 0.744, 0.792 and 0.870 respectively, and the corresponding determination coefficient (R²) for the simulated values under high humidity conditions of RH>85% were 0.538, 0.638, 0.685 and 0.749, respectively. The BP neural network model of aerosol scattering hygroscopic growth factor achieved the best fitting effect and the simulation error of aerosol scattering hygroscopic growth factor under high humidity condition (RH>85%) was significantly reduced.
- aerosol,
- scattering hygroscopic growth factor,
- artificial neural network,
- BP model,
- Chengdu

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References(40)

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