TICLab Research Laboratory, International University of Rabat, Rabat 11103, Morocco.
ENSIAS, Mohammed V University in Rabat, Rabat 10100, Morocco.
Int J Environ Res Public Health. 2022 Mar 6;19(5):3095. doi: 10.3390/ijerph19053095.
Air pollution exposure has become ubiquitous and is increasingly detrimental to human health. Small Particulate matter (PM) is one of the most harmful forms of air pollution. It can easily infiltrate the lungs and trigger several respiratory diseases, especially in vulnerable populations such as children and elderly people. In this work, we start by leveraging a retrospective study of 416 children suffering from respiratory diseases. The study revealed that asthma prevalence was the most common among several respiratory diseases, and that most patients suffering from those diseases live in areas of high traffic, noise, and greenness. This paved the way to the construction of the MOREAIR dataset by combining feature abstraction and micro-level scale data collection. Unlike existing data sets, MOREAIR is rich in context-specific components, as it includes 52 temporal or geographical features, in addition to air-quality measurements. The use of Random Forest uncovered the most important features for the understanding of air-quality distribution in Moroccan urban areas. By linking the medical data and the MOREAIR dataset, we observed that the patients included in the medical study come mostly from neighborhoods that are characterized by either high average or high variations of pollution levels.
空气污染无处不在，对人类健康的危害日益严重。细颗粒物（PM）是最有害的空气污染形式之一。它很容易渗透到肺部，引发多种呼吸道疾病，特别是在儿童和老年人等弱势群体中。在这项工作中，我们首先利用对 416 名患有呼吸道疾病的儿童进行的回顾性研究。研究表明，哮喘在几种呼吸道疾病中最为常见，而且大多数患有这些疾病的患者生活在交通、噪音和绿化程度较高的地区。这为 MOREAIR 数据集的构建铺平了道路，该数据集结合了特征抽象和微观尺度数据收集。与现有数据集不同，MOREAIR 包含丰富的特定于上下文的组件，因为它除了空气质量测量外，还包括 52 个时间或地理位置特征。随机森林的使用揭示了理解摩洛哥城市地区空气质量分布的最重要特征。通过将医疗数据和 MOREAIR 数据集联系起来，我们观察到，参与医学研究的患者主要来自以下两类社区：要么污染水平的平均值较高，要么污染水平的变化幅度较大。
