Global ambient air pollution, a transboundary challenge, is typically
addressed through interventions relying on data from spatially sparse and
heterogeneously placed monitoring stations. These stations often encounter
temporal data gaps due to issues such as power outages. In response, we have
developed a scalable, data-driven, supervised machine learning framework. This
model is designed to impute missing temporal and spatial measurements, thereby
generating a comprehensive dataset for pollutants including NO$_2$, O$_3$,
PM$_{10}$, PM$_{2.5}$, and SO$_2$. The dataset, with a fine granularity of
0.25$^{\circ}$ at hourly intervals and accompanied by prediction intervals for
each estimate, caters to a wide range of stakeholders relying on outdoor air
pollution data for downstream assessments. This enables more detailed studies.
Additionally, the model's performance across various geographical locations is
examined, providing insights and recommendations for strategic placement of
future monitoring stations to further enhance the model's accuracy.

通过数据驱动的监督机器学习框架填补空缺的时间和空间测量来生成全面的污染物数据集，为依赖室外空气污染数据进行下游评估的各方提供了具有 0.25 度细粒度和小时间隔的数据，同时为每个估计值提供了预测区间，从而支持更详细的研究。还通过检查模型在不同地理位置上的表现，为将来监测站的战略放置提供了见解和建议，以进一步提高模型的准确性。