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<p>Effective air quality monitoring is essential for ensuring urban environmental comfort and protecting public health, especially within smart-city frameworks. In Lisbon, the performance of low-cost NO₂ and PM<sub>10</sub> sensors was assessed using a novel Correlation-Distance Index (CDI) coupled with Principal Component Analysis (PCA) weighting, alongside a demographic-weighted spatial association technique, benchmarked against reference monitoring stations. The analysis began by quantifying the inverse relationship between sensor–sensor correlation and Euclidean distance. This metric was then refined through the integration of PCA-derived weights and local population density, resulting in a composite CDI-PCA score assigned to each sensor. Spatial clustering of these scores identified zones of underperforming sensors, particularly in densely populated downtown and western neighborhoods. When these clusters were compared with census-block population data, there was a statistically significant association between CDI patterns and population density (χ²=406.5, <i>p</i> &lt; 0.001). High-performing sensors were 22.5% more enriched in densely populated areas, indicating that demographic context provides meaningful spatial structure for network optimization, even though the modest effect size suggests the influence of multiple underlying factors. Robustness was verified through seasonal subsampling and leave-one-out cross-validation, confirming that distance-decay patterns and cluster boundaries remain consistent across environmental conditions and are not driven by individual outliers. The results demonstrate that coupling multivariate spatial statistics with demographic context supports targeted sensor calibration and redeployment strategies. Prioritizing high-population areas and dynamically adjusting sensor densities can improve real-time data accuracy and promote more equitable air quality monitoring.</p>

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Spatial clustering in air quality data accuracy: assessment of low-cost sensors in Lisbon using a novel correlation-distance index

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月29日

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  • Sina Ataee,
  • Myriam Lopes,
  • Maria Isabel Nunes,
  • Sónia Gouveia,
  • Mohammad Vahidi Borji,
  • Helder Relvas

摘要

Effective air quality monitoring is essential for ensuring urban environmental comfort and protecting public health, especially within smart-city frameworks. In Lisbon, the performance of low-cost NO₂ and PM10 sensors was assessed using a novel Correlation-Distance Index (CDI) coupled with Principal Component Analysis (PCA) weighting, alongside a demographic-weighted spatial association technique, benchmarked against reference monitoring stations. The analysis began by quantifying the inverse relationship between sensor–sensor correlation and Euclidean distance. This metric was then refined through the integration of PCA-derived weights and local population density, resulting in a composite CDI-PCA score assigned to each sensor. Spatial clustering of these scores identified zones of underperforming sensors, particularly in densely populated downtown and western neighborhoods. When these clusters were compared with census-block population data, there was a statistically significant association between CDI patterns and population density (χ²=406.5, p < 0.001). High-performing sensors were 22.5% more enriched in densely populated areas, indicating that demographic context provides meaningful spatial structure for network optimization, even though the modest effect size suggests the influence of multiple underlying factors. Robustness was verified through seasonal subsampling and leave-one-out cross-validation, confirming that distance-decay patterns and cluster boundaries remain consistent across environmental conditions and are not driven by individual outliers. The results demonstrate that coupling multivariate spatial statistics with demographic context supports targeted sensor calibration and redeployment strategies. Prioritizing high-population areas and dynamically adjusting sensor densities can improve real-time data accuracy and promote more equitable air quality monitoring.