用深度学习提升低成本空气质量传感器的校准精度
A temporal deep learning framework for calibration of low-cost air quality sensors
- 采用LSTM捕捉时间依赖性，比传统方法更精准建模污染变化
- 校准后PM2.5误差仅9.1%，符合监管标准要求
- 适合城市空气质量监测网络部署与长期数据校正
低成本空气品质传感器（LCS）为密集城市监测提供了经济可行方案，但受限于传感器漂移、环境交叉敏感及设备间差异等问题。本文提出一种基于长短期记忆网络（LSTM）的深度学习校准框架，利用英国牛津市OxAria网络的共位参考数据，对PM₂.₅、PM₁₀和NO₂进行校准。相比独立处理每条观测的随机森林（RF）基线，该方法通过序列学习捕捉时间依赖性和延迟环境影响，在训练、验证和测试集上均取得更高的R²值。特征集融合时间滞后参数、谐波编码和交互项，提升在未见时间窗口上的泛化能力。与等效性表格工具3.1对比验证表明，校准后结果满足监管要求，扩展不确定性分别为：NO₂ 22.11%、PM₁₀ 12.42%、PM₂.₅ 9.1%。
原文摘要 · Abstract (English)
Low-cost air quality sensors (LCS) provide a practical alternative to expensive regulatory-grade instruments, making dense urban monitoring networks possible. Yet their adoption is limited by calibration challenges, including sensor drift, environmental cross-sensitivity, and variability in performance from device to device. This work presents a deep learning framework for calibrating LCS measurements of PM$_{2.5}$, PM$_{10}$, and NO$_2$ using a Long Short-Term Memory (LSTM) network, trained on co-located reference data from the OxAria network in Oxford, UK. Unlike the Random Forest (RF) baseline, which treats each observation independently, the proposed approach captures temporal dependencies and delayed environmental effects through sequence-based learning, achieving higher $R^2$ values across training, validation, and test sets for all three pollutants. A feature set is constructed combining time-lagged parameters, harmonic encodings, and interaction terms to improve generalization on unseen temporal windows. Validation of unseen calibrated values against the Equivalence Spreadsheet Tool 3.1 demonstrates regulatory compliance with expanded uncertainties of 22.11% for NO$_2$, 12.42% for PM$_{10}$, and 9.1% for PM$_{2.5}$.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。