Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
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DOI：
10.1016/j.envint.2019.105022
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发表时间：
2019-10-01
影响因子：
11.8
通讯作者：
Kim, Sun-Young
中科院分区：
文献类型：
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作者：
Lim, Chris C.;Kim, Ho;Kim, Sun-Young
最近的研究表明，移动采样可以提高土地利用回归（LUR）模型的空间粒度。部署低成本（< 300 美元）空气质量传感器的移动采样活动可能会提供一种廉价且实用的方法来测量和模拟空气污染浓度水平。在这项研究中，我们开发了韩国首尔街道细颗粒物 (PM2.5) 浓度水平的 LUR 模型。 10 名志愿者共享 7 个 AirBeam（一种低成本（每台 250 美元）、基于智能手机的粒子计数器），在为期约三周、横跨 5 条路线的活动中收集了 169 小时的数据，而地理空间数据则从 OpenStreetMap（一个开源的、众包生成的地理数据集）中提取。我们在构建 LUR 模型时应用并比较了三种统计方法 - 线性回归 (LR)、随机森林 (RF) 和结合多种机器学习算法的堆叠集成 (SE) - 得出的交叉验证 R-2 值分别为 0.63、0.73 和 0.80，并识别了多个污染“热点”。高 R-2 值表明，采用移动采样与多个低成本空气质量监测仪结合的研究设计可用于以高空间分辨率表征城市街道空气质量，并且机器学习模型可以进一步提高模型性能。鉴于本研究设计的成本效益和易于实施，类似的方法可能特别适合公民科学和社区活动，或缺乏空气质量数据和现有空气监测网络的地区，例如发展中国家。
Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost ( < $300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R-2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R-2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.