The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments’ ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

使用空气质量监测网络为城市政策提供信息至关重要，尤其是在城市人口暴露于前所未有的空气污染水平的情况下。然而，高成本限制了市政府大规模部署参考级空气质量监测器的能力；例如，只有 33 台参考级监测仪可用于印度德里全境，覆盖 1500 平方公里，拥有 1500 万居民。在本文中，我们描述了一种高精度时空预测模型，可用于导出细粒度污染地图。我们利用来自覆盖德里密集地区的 28 个定制设计的低成本便携式空气质量传感器的低成本监测网络两年的数据。该模型使用消息传递循环神经网络与传统的时空地质统计学模型相结合，在面对来自低成本传感器的高数据可变性和间歇性数据可用性（由于传感器故障、网络和电源问题）。使用来自参考级监测器的数据进行验证，我们的时空污染模型可以在 1 小时的时间窗口内进行预测，与我们的低成本监测器、参考级监测器相比，平均绝对百分比误差 (MAPE) 分别为 9.4、10.5 和 9.6% , 和组合监控网络。这些精确的细粒度污染传感地图为构建公民驱动的低成本监测系统提供了一种方法，该系统以细粒度检测危险的城市空气质量。