用微型传感器+机器学习实时识别四种污染源，成本低覆盖广。
Real-time Pollutant Identification through Optical PM Micro-Sensor
- 用XGBoost、LSTM和隐马尔可夫模型分析传感器数据序列
- 四类污染识别准确率超90%，支持实时判断
- 适合城市环保监测与低成本设备部署
空气污染仍是现代最紧迫的环境挑战之一，严重威胁人类健康、生态系统和气候。传统空气质量监测系统虽提供关键数据，但成本高、空间覆盖有限，难以实现污染物的实时识别。微传感器技术进步提升了数据采集能力，但仍缺乏高效溯源方法。本文探索将机器学习模型应用于光学微传感器数据，实现实时污染物分类。提出一种新框架，可区分四类污染场景：背景污染、灰烬、沙尘和蜡烛燃烧。评估了三种机器学习方法——XGBoost、长短期记忆网络（LSTM）和隐马尔可夫链（HMM）在序列建模与污染识别中的表现。结果表明，结合微传感器与机器学习技术能显著提升空气质量监测能力，为城市规划与环境保护提供可操作洞察。
原文摘要 · Abstract (English)
Air pollution remains one of the most pressing environmental challenges of the modern era, significantly impacting human health, ecosystems, and climate. While traditional air quality monitoring systems provide critical data, their high costs and limited spatial coverage hinder effective real-time pollutant identification. Recent advancements in micro-sensor technology have improved data collection but still lack efficient methods for source identification. This paper explores the innovative application of machine learning (ML) models to classify pollutants in real-time using only data from optical micro-sensors. We propose a novel classification framework capable of distinguishing between four pollutant scenarios: Background Pollution, Ash, Sand, and Candle. Three Machine Learning (ML) approaches - XGBoost, Long Short-Term Memory networks, and Hidden Markov Chains - are evaluated for their effectiveness in sequence modeling and pollutant identification. Our results demonstrate the potential of leveraging micro-sensors and ML techniques to enhance air quality monitoring, offering actionable insights for urban planning and environmental protection.
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