Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this paper propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale.

翻译：高效空气质量遥感是最近任何智能城市中提供的一项基本服务,大部分是由难以安装和维护的部署很少的空气质量监测站(AQMS)所推动的,总体空间变化严重影响到远离这些预先部署的公共基础设施的地点的空气质量监测。为了减轻这一影响,我们在本文件中提议了一个名为AQuaMohoo的框架,这个框架可以说明从AQI标签的低成本热湿度计(作为唯一的物理感测装置)获得的数据,并辅之以更多公开爬行的当地时空信息。在其核心方面,AQuaMoho利用一套现成的空间特征,使用基于LSTM的模型,利用时间关注,进一步提升说明的整体质量。我们从对两个不同城市的透彻研究中看到,AQuaMoho可以极大地帮助个人对空气质量数据进行注意。