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Peer-reviewedCC BYInternational Journal of Applied Earth Observations and Geoinformation

Quantifying anisotropic impacts of LCZ landscape patterns on NO

  • Chongyang Li1
  • Meiqin Che2
  • Jieqiong Luo3
  • Yueqian Cao2
  • Zhenda Sun4
  • Zhongfeng Pan5
  • Youwen Sun6
  • Cheng Liu7
7 affiliations
  1. 1Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China; Science Island Branch, Graduate School of USTC, Hefei, 230026, China
  2. 2School of Transportation and Civil Engineering, Nantong University, Nantong, 226019, China
  3. 3School of Transportation and Civil Engineering, Nantong University, Nantong, 226019, China; Corresponding authors.
  4. 4School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China; Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China
  5. 5Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China; Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China
  6. 6Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China; Corresponding authors.
  7. 7Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230026, China
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Abstract

Urban landscape patterns strongly affect air quality, yet existing studies often neglect anisotropic processes, rely on arbitrary fixed-radius buffers, and lack standardized classification frameworks. To overcome these limitations, we developed a novel analytical framework that integrates high-accuracy Local Climate Zone (LCZ) classification, multi-scale spatial modeling, and a shape-adaptive influence zone. Central to this framework is the Adaptive Directionally Weighted Sector Influence Zone (ADWSIZ), a new method that explicitly captures the directional heterogeneity of landscape effects on pollutants. Applied to station-based NO 2 measurements in the Yangtze River Delta (YRD) urban agglomeration, we used multi-scale Geographically Weighted Ridge Regression (GWRR) to identify a 4000 m circular buffer as the optimal modeling scale, revealing a clear decoupling from the intrinsic static LCZ pattern scale (approximately 1000 m). Building upon this optimal distance, the ADWSIZ model was developed to refine the influence zone’s shape. The application of ADWSIZ improved model performance (Adjusted R 2 : 0.464 → 0.506 ; RMSE: 2.950 → 2.834 µg/m 3 ) and reduced residual spatial autocorrelation. Our findings highlight that the anisotropic influence of features like industrial land and compact high-rise fabrics is a critical, previously underexplored factor in explaining NO 2 variation. This study establishes a replicable and transferable methodology that advances the conceptualization of influence zones from simple distance-based buffers to scale- and shape-adaptive forms, offering a robust tool for refining spatial coupling analyses between urban morphology and air pollutants.

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Cite

BibTeXRIS
BibTeX
@article{li2026quantifying,
  title = {{Quantifying anisotropic impacts of LCZ landscape patterns on NO}},
  author = {Chongyang Li and Meiqin Che and Jieqiong Luo and Yueqian Cao and Zhenda Sun and Zhongfeng Pan and Youwen Sun and Cheng Liu},
  journal = {International Journal of Applied Earth Observations and Geoinformation},
  year = {2026},
  month = oct,
  doi = {10.1016/j.jag.2026.105627},
  url = {https://doi.org/10.1016/j.jag.2026.105627},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Quantifying anisotropic impacts of LCZ landscape patterns on NO
AU  - Chongyang Li
AU  - Meiqin Che
AU  - Jieqiong Luo
AU  - Yueqian Cao
AU  - Zhenda Sun
AU  - Zhongfeng Pan
AU  - Youwen Sun
AU  - Cheng Liu
PY  - 2026
DA  - 2026/10/30
JO  - International Journal of Applied Earth Observations and Geoinformation
DO  - 10.1016/j.jag.2026.105627
UR  - https://doi.org/10.1016/j.jag.2026.105627
AB  - Urban landscape patterns strongly affect air quality, yet existing studies often neglect anisotropic processes, rely on arbitrary fixed-radius buffers, and lack standardized classification frameworks. To overcome these limitations, we developed a novel analytical framework that integrates high-accuracy Local Climate Zone (LCZ) classification, multi-scale spatial modeling, and a shape-adaptive influence zone. Central to this framework is the Adaptive Directionally Weighted Sector Influence Zone (ADWSIZ), a new method that explicitly captures the directional heterogeneity of landscape effects on pollutants. Applied to station-based NO 2 measurements in the Yangtze River Delta (YRD) urban agglomeration, we used multi-scale Geographically Weighted Ridge Regression (GWRR) to identify a 4000 m circular buffer as the optimal modeling scale, revealing a clear decoupling from the intrinsic static LCZ pattern scale (approximately 1000 m). Building upon this optimal distance, the ADWSIZ model was developed to refine the influence zone’s shape. The application of ADWSIZ improved model performance (Adjusted R 2 : 0.464 → 0.506 ; RMSE: 2.950 → 2.834 µg/m 3 ) and reduced residual spatial autocorrelation. Our findings highlight that the anisotropic influence of features like industrial land and compact high-rise fabrics is a critical, previously underexplored factor in explaining NO 2 variation. This study establishes a replicable and transferable methodology that advances the conceptualization of influence zones from simple distance-based buffers to scale- and shape-adaptive forms, offering a robust tool for refining spatial coupling analyses between urban morphology and air pollutants.
SN  - 1569-8432
SN  - 1872-826X
ER  -