无需参考站即可校准低成本空气质量传感器，提升监测准确性。
Veli: Unsupervised Method and Unified Benchmark for Low-Cost Air Quality Sensor Correction
- 基于变分推断构建去耦表示，分离真实污染值与传感器噪声
- 在23,737个传感器数据上验证，对漂移和异常行为有强鲁棒性
- 首次提供统一基准数据集，适合环境监测与边缘计算研究者
城市空气污染每年导致数百万人过早死亡，亟需高精度且可扩展的空气质量（AQ）监测。低成本传感器（LCS）虽具可扩展性，但易受漂移、校准误差和环境干扰影响。为此，我们提出Veli（无参考变分估计通过潜在推理），一种无需与参考站共位的无监督贝叶斯模型，利用变分推断校正LCS读数，突破部署瓶颈。Veli构建了LCS读数的解耦表征，有效分离真实污染物浓度与传感器噪声。为支持模型训练与评估，我们还推出了当前最大规模的空气质量传感器基准数据集AQ-SDR，包含来自多个区域的23,737个LCS与参考站数据。Veli在分布内及分布外场景均表现优异，能有效应对传感器漂移与异常行为。模型与数据集代码将在论文发表后公开。
原文摘要 · Abstract (English)
Urban air pollution is a major health crisis causing millions of premature deaths annually, underscoring the urgent need for accurate and scalable monitoring of air quality (AQ). While low-cost sensors (LCS) offer a scalable alternative to expensive reference-grade stations, their readings are affected by drift, calibration errors, and environmental interference. To address these challenges, we introduce Veli (Reference-free Variational Estimation via Latent Inference), an unsupervised Bayesian model that leverages variational inference to correct LCS readings without requiring co-location with reference stations, eliminating a major deployment barrier. Specifically, Veli constructs a disentangled representation of the LCS readings, effectively separating the true pollutant reading from the sensor noise. To build our model and address the lack of standardized benchmarks in AQ monitoring, we also introduce the Air Quality Sensor Data Repository (AQ-SDR). AQ-SDR is the largest AQ sensor benchmark to date, with readings from 23,737 LCS and reference stations across multiple regions. Veli demonstrates strong generalization across both in-distribution and out-of-distribution settings, effectively handling sensor drift and erratic sensor behavior. Code for model and dataset will be made public when this paper is published.
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