Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in low and middle-income countries have to rely on a sparse allocation of them. In this paper, we investigate the use of Gaussian Processes for both nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. In particular, we focus on the city of Kampala in Uganda, using data from AirQo's network of sensors. We demonstrate the advantage of removing outliers, compare different kernel functions and additional inputs. We also compare two sparse approximations to allow for the large amounts of temporal data in the dataset.

翻译：空气污染的监测对人口整体健康至关重要。然而，能够测量空气质量的设备成本高昂，许多中低收入国家的城市只能依赖稀疏的传感器布局。本文研究了高斯过程在两类任务中的应用：对无传感器区域的当前空气污染进行即时预报，以及对传感器位置处的未来空气污染进行预测。我们重点以乌干达坎帕拉市为例，使用AirQo传感器网络的数据展开分析。通过对比不同核函数与附加输入，我们展示了剔除异常值的优势。此外，为处理数据集中大量的时序数据，我们还比较了两种稀疏近似方法。