Identification of VOC emission hotspots in industrial parks by high-spatiotemporal-resolution sensor networks
Industrial emissions are a significant contributor to volatile organic compound (VOC) pollution. However, timely and accurate tracking of high-emitting plants within industrial parks remains a challenge. Here, we deployed three high-density VOC sensor networks across a package printing industrial park (103 sites/km<sup>2</sup>), a fine chemical industrial park (8.57 sites/km<sup>2</sup>), and an urban area in central China. These networks enabled the identification of VOC pollution characteristics and sources in regions. The hourly average VOC concentrations in the package printing industrial park (320 ± 262 ppb) and the fine chemical industrial park (155 ± 62 ppb) were 1.62 to 2.75 times and 1.27 to 1.70 times higher compared to the urban area. Pollution levels in these two industrial parks were more severe than other reported industrial parks, primarily due to the higher VOC emission factors or higher VOC emission fluxes associated with these industries. Importantly, by integrating VOC concentration contour maps with meteorological data, major polluting plants in the package printing industrial park and the fine chemical industrial park were identified. These were further validated through downwind tracing of VOC species using gas chromatography-mass spectrometry. The results highlight that a VOC sensor network is an effective tool for real-time monitoring of VOC variations and the precise identification of high-pollution plants across various industrial parks, which can provide valuable insights for the refined control and management of VOC pollution emissions.
Authors
- Jingkun Jiang (ORCID: https://orcid.org/0000-0001-6172-190X)
- Shengao Jing
- Zizhen Ma (ORCID: https://orcid.org/0000-0002-1583-7220)
- Zehui Li (ORCID: https://orcid.org/0000-0002-9651-9063)
- Yan Tan (ORCID: https://orcid.org/0000-0001-7559-0779)
- Chun Chen (ORCID: https://orcid.org/0000-0002-3608-6972)
- Yingjie Xu (ORCID: https://orcid.org/0000-0002-8461-6974)
- Yanhui Wang (ORCID: https://orcid.org/0000-0002-8322-4581)
- Ruixue Du
- X. Fan (ORCID: https://orcid.org/0000-0003-1223-8270)
- Zhiyuan Liu (ORCID: https://orcid.org/0000-0002-7709-2543)
- Huawei Zhang (ORCID: https://orcid.org/0000-0002-0071-6186)
- Xue Li (ORCID: https://orcid.org/0000-0002-3445-142X)
- Ziyi Wang (ORCID: https://orcid.org/0009-0000-0729-4740)
- Jinping Cheng (ORCID: https://orcid.org/0000-0002-6750-6562)
- Yuhan Huang (ORCID: https://orcid.org/0009-0001-7671-7687)
- Xiaocan Bai
- Long Ma (ORCID: https://orcid.org/0000-0003-1900-0329)
Institutions
- Commercial Aircraft Corporation of China (China) (CN)
- Qingdao University of Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
- Beijing Municipal Ecological and Environmental Monitoring Center (CN)
- State Key Joint Laboratory of Environment Simulation and Pollution Control (CN)
- Shanghai Academy of Environmental Sciences (CN)
- Qingdao University of Technology (CN)
- Henan Normal University (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Environment International
- Published
- 2025-06-21
- DOI
- https://doi.org/10.1016/j.envint.2025.109628
- Citations
- 1
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.80