Department of Environmental Medicine, New York School of Medicine, New York, NY, United States of America.
Graduate School of Public Health, Seoul National University, Seoul, South Korea.
Environ Int. 2019 Oct;131:105022. doi: 10.1016/j.envint.2019.105022. Epub 2019 Jul 27.
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 (PM) 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 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R 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.
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）空气质量传感器的移动采样活动可能提供一种廉价且实用的方法来测量和建模空气污染浓度水平。在这项研究中，我们为韩国首尔的街道级细颗粒物（PM）浓度水平开发了 LUR 模型。通过十名志愿者在五个路线上进行了大约三周的活动，收集了 169 小时的数据，这些志愿者共享七个 AirBeams，这是一种低成本（每个 250 美元）、基于智能手机的粒子计数器，同时从 OpenStreetMap 提取了地理空间数据，这是一个开源和众包的地理数据集。我们应用并比较了三种统计方法来构建 LUR 模型 - 线性回归（LR）、随机森林（RF）和堆叠集成（SE），将多个机器学习算法结合在一起 - 分别导致交叉验证 R 值为 0.63、0.73 和 0.80，并确定了几个污染“热点”。高 R 值表明，采用移动采样和多个低成本空气质量监测器的研究设计可以应用于以高空间分辨率表征城市街道级空气质量，并且机器学习模型可以进一步提高模型性能。鉴于这种研究设计的成本效益和易于实施性，类似的方法可能特别适合公民科学和社区为基础的努力，或者在缺乏空气质量数据和现有的空气监测网络的地区，如发展中国家。
