使用超局部移动监测数据,对NO2的长期平均小时度进行LUR建模
Zhendong Yuan1, Youchen Shen1, Gerard Hoek1
1Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
The Science of the total environment
|February 28, 2024
概括
移动监控有效地绘制每小时二氧化 (NO2) 污染的地图. 地理和时间加权回归 (GTWR) 模型对动态空气质量评估有希望.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 空气质量监测 空气质量监测
背景情况:
- 移动监测活动擅长捕捉空气污染物度的空间变化.
- 然而,它们在创建动态的,每小时的空气污染地图中的实用性仍未得到充分探索.
研究的目的:
- 调查使用移动测量的可行性,以估计每天每小时的长期平均二氧化 (NO2) 度.
- 在这种情况下,评估时空土地利用回归 (LUR) 模型的性能.
主要方法:
- 利用了来自阿姆斯特丹的10个月的移动NO2数据.
- 将两个时空LUR方法 (时空Kriging和GTWR) 与两个经典的空间LUR模型进行了比较.
- 根据长期固定站点测量验证的模型性能.
主要成果:
- 移动测量通常与固定站点数据保持一致,但由于收集不确定性,会出现偏差.
- GTWR模型表现出卓越的性能,平滑偏差,并实现了0.49的R2和6.33μg/m3的MAE.
- 移动监控捕获的时空变化是重建每小时空气污染图的关键.
结论:
- 移动NO2测量可以用来创建每小时的空气污染地图.
- 这些地图允许动态暴露评估,以考虑时空空间人类活动模式.
- GTWR显示了从移动数据改善空气质量绘图准确性的潜力.
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