RESEARCH ON REAL-TIME SOURCE APPORTIONMENT METHOD OF VOCs BASED ON BP NEURAL NETWORK

XIE Wei; XU Jiao; LIN Zhiguo; YAN Di; ZHANG Yinglei

doi:10.13205/j.hjgc.202212031

Volume 40 Issue 12

Nov. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(12): 231-238

XIE Wei, XU Jiao, LIN Zhiguo, YAN Di, ZHANG Yinglei. RESEARCH ON REAL-TIME SOURCE APPORTIONMENT METHOD OF VOCs BASED ON BP NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(12): 231-238. doi: 10.13205/j.hjgc.202212031

Citation:

XIE Wei, XU Jiao, LIN Zhiguo, YAN Di, ZHANG Yinglei. RESEARCH ON REAL-TIME SOURCE APPORTIONMENT METHOD OF VOCs BASED ON BP NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(12): 231-238. doi: 10.13205/j.hjgc.202212031

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RESEARCH ON REAL-TIME SOURCE APPORTIONMENT METHOD OF VOCs BASED ON BP NEURAL NETWORK

doi: 10.13205/j.hjgc.202212031

1. Zhenjiang New Area Ecological Environment and Emergency Management Bureau, Zhenjiang 212000, China;
2. Beijing Capital Air Environmental Science & Technology Co., Ltd, Beijing 100176, China

Received Date: 2022-04-18
Available Online: 2023-03-23

Abstract

Abstract

In order to improve the efficiency of VOCs source apportionment work, a PBP model based on BP neural network was proposed, which could analyze VOCs monitoring data in real time. The model was validated with the test dataset, and the results showed that it had the same accuracy as PMF model. Besides, it had the advantages of no limitation from the volume of data and fast computing speed. The PBP model was applied to the hourly resolution online VOCs dataset of an industrial zone in the Yangtze River Delta for 10 months, and the hourly resolution source apportionment results were obtained. The main sources of VOCs at the monitoring site were in the sequence of industrial process source (25%)>coal combustion source (20%)>gasoline vehicle exhaust source (18%)>solvent use source (15%)>mixed diesel vehicle and vessel exhaust source (12%)>fuel volatilization source (6%)>biogenic source (5%). Combined with the wind speed and direction data, the main emission directions of each source were identified, which provided a scientific basis for precise manage and control of VOCs sources. Therefore, it had a strong application potential in source apportionment.
- volatile organic compounds (VOCs),
- online observation,
- BP neural network,
- PMF,
- source apportionment

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References(48)

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