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<p>This study evaluates mid-term calibration strategies for MONICA, a compact low-cost multi-sensor device for urban air quality monitoring, in the context of the upcoming EU Air Quality Directive 2024/2881. A key objective is to optimize the trade-off between calibration duration and long-term monitoring performance, balancing statistical robustness with practical deployment constraints. This question is particularly relevant in mid-latitude regions, where seasonal variability may introduce biases if calibration and observation periods are misaligned. Over a three-and-a-half-month winter co-location with reference-grade instruments, we assessed three models—Multiple Linear Regression (MLR), Random Forest (RF), and Generalized Additive Models (GAM)—across three pollutants: PM<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(_{2.5}\)</EquationSource></InlineEquation>, PM<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(_{10}\)</EquationSource></InlineEquation>, and NO<InlineEquation ID="IEq3"><EquationSource Format="TEX">\(_2\)</EquationSource></InlineEquation>. MLR emerged as the most stable model for temporal extrapolation, while RF and GAM, although accurate short-term, showed performance degradation outside the training range. A two-week calibration period was sufficient for PM, whereas NO<InlineEquation ID="IEq4"><EquationSource Format="TEX">\(_2\)</EquationSource></InlineEquation> required only one week. Although sensor accuracy declines over time-especially for NO<InlineEquation ID="IEq5"><EquationSource Format="TEX">\(_2\)</EquationSource></InlineEquation>—the MONICA system remains effective in tracking temporal trends and identifying regulatory exceedances. These results support the development of efficient and scalable low-cost sensor networks, offering practical insights for planning reliable air quality monitoring campaigns.</p> Graphical abstract <p></p>

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Mid-term Performance and Calibration of Multi-sensor Low-Cost Systems for Air Quality Monitoring in Urban Environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Sofia Fellini,
  • Davide Gallione,
  • Vincenzo Vaccaro,
  • Nicole Mastromatteo,
  • Grazia Fattoruso,
  • Marina Clerico,
  • Pietro Salizzoni

摘要

This study evaluates mid-term calibration strategies for MONICA, a compact low-cost multi-sensor device for urban air quality monitoring, in the context of the upcoming EU Air Quality Directive 2024/2881. A key objective is to optimize the trade-off between calibration duration and long-term monitoring performance, balancing statistical robustness with practical deployment constraints. This question is particularly relevant in mid-latitude regions, where seasonal variability may introduce biases if calibration and observation periods are misaligned. Over a three-and-a-half-month winter co-location with reference-grade instruments, we assessed three models—Multiple Linear Regression (MLR), Random Forest (RF), and Generalized Additive Models (GAM)—across three pollutants: PM\(_{2.5}\), PM\(_{10}\), and NO\(_2\). MLR emerged as the most stable model for temporal extrapolation, while RF and GAM, although accurate short-term, showed performance degradation outside the training range. A two-week calibration period was sufficient for PM, whereas NO\(_2\) required only one week. Although sensor accuracy declines over time-especially for NO\(_2\)—the MONICA system remains effective in tracking temporal trends and identifying regulatory exceedances. These results support the development of efficient and scalable low-cost sensor networks, offering practical insights for planning reliable air quality monitoring campaigns.

Graphical abstract