超局部空气污染映射:一个可扩展的转移学习LUR方法,用于移动监控
Zhendong Yuan1, Jules Kerckhoffs1, Hao Li2
1Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands.
Environmental science & technology
|July 31, 2024
概括
这项研究表明,转移学习模型可以在没有城市特定数据的情况下绘制当地空气污染的地图. IDW_Coral模型有效估计了阿姆斯特丹的二氧化和超细颗粒水平,这对于大规模空气质量监测非常有用.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 地理空间分析的研究.
背景情况:
- 在缺乏本地监测站的地区,绘制超局部空气污染的地图具有挑战性.
- 土地利用回归模型 (LUR) 是空气质量评估的常用模型.
- 转移学习为数据稀缺地区提供了潜在的解决方案.
研究的目的:
- 评估基于无监督转移学习的LUR模型,用于超局部空气污染映射.
- 用其他城市的数据评估CORrelation ALignment (Coral) 和IDW_Coral模型的有效性.
- 确定在没有本地测量的情况下估计阿姆斯特丹空气污染的可行性.
主要方法:
- 使用Coral和IDW_Coral模型使用无监督转移学习.
- 应用模型从哥本哈根和鹿特丹到阿姆斯特丹接受了移动监控数据的培训.
- 估计每年平均空气污染 (NO2和UFP) 在50m公路段.
- 与局部安装的LUR模型 (AMS_SLR) 进行模型性能比较.
主要成果:
- 对于NO2,IDW_Coral在阿姆斯特丹直接应用哥本哈根/罗德丹LUR模型的表现优于NO2.
- IDW_Coral实现了MAE (4.47 μg/m3) 和RMSE (5.36 μg/m3),与160天的本地模型相比.
- 至少20天的移动监控可能足以获得特定城市的见解.
- IDW_Coral的预测显示,与UFP (r=0.71) 和NO2 (r=0.72) 的现有混合效应模型有很强的相关性.
结论:
- 无监督转移学习,特别是IDW_Coral,在没有进行本地测量的情况下有效地绘制超局部空气污染.
- 该方法显示了大规模空气质量评估和移动监测的经济效率的巨大潜力.
- 这种方法可以解决全球空气污染监测数据缺口.
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