Article: Identification and long-term temporal sequential change analysis of urban VOCs high-value areas based on GIS and remote sensing Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.8 pp.23 - 32 Abstract: This study systematically identifies key high-emission zones for volatile organic compounds within the Beijing-Tianjin-Hebei urban cluster by integrating geographic information systems spatial analysis with remote sensing inversion models, utilising long-term tropospheric monitoring instrument formaldehyde column concentration data (2005-2022) and Landsat land use data. We specifically developed a spatiotemporal weighted regression model to comprehensively analyse the spatial distribution patterns of volatile organic compounds. Results consistently revealed that urban areas exhibited average concentrations 3.4 times higher than natural background zones, with industrial clusters forming statistically significant emission hotspots. Long-term Theil-Sen trend analysis indicated an average annual decrease of 4.2% in volatile organic compound concentrations after 2013, systematically validating the effectiveness of clean air policies and providing a scientific basis for informed precise management of regional ozone precursors. Inderscience Publishers - linking academia, business and industry through research

Title: Identification and long-term temporal sequential change analysis of urban VOCs high-value areas based on GIS and remote sensing

Authors: Xiang Li; Xiang Wang; Wei Peng

Addresses: School of Architecture and Urban Planning, Nanjing University, Nanjing, 210000, China ' Jiangsu Provincial Academy of Building Research Co., Ltd., Nanjing, 210000, China ' Jiangsu Provincial Academy of Building Research Co., Ltd., Nanjing, 210000, China

Abstract: This study systematically identifies key high-emission zones for volatile organic compounds within the Beijing-Tianjin-Hebei urban cluster by integrating geographic information systems spatial analysis with remote sensing inversion models, utilising long-term tropospheric monitoring instrument formaldehyde column concentration data (2005-2022) and Landsat land use data. We specifically developed a spatiotemporal weighted regression model to comprehensively analyse the spatial distribution patterns of volatile organic compounds. Results consistently revealed that urban areas exhibited average concentrations 3.4 times higher than natural background zones, with industrial clusters forming statistically significant emission hotspots. Long-term Theil-Sen trend analysis indicated an average annual decrease of 4.2% in volatile organic compound concentrations after 2013, systematically validating the effectiveness of clean air policies and providing a scientific basis for informed precise management of regional ozone precursors.

Keywords: VOCs hotspots; GIS; remote sensing; long-term time-series analysis; emission hotspots.

DOI: 10.1504/IJRIS.2026.151723

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.8, pp.23 - 32

Received: 03 Nov 2025
Accepted: 24 Dec 2025
Published online: 17 Feb 2026 *

Title: Identification and long-term temporal sequential change analysis of urban VOCs high-value areas based on GIS and remote sensing

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