Li Junfeng, Chen Jiaqi, You Ran, He Qingqing
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2025 Sep 5;25(17):5527. doi: 10.3390/s25175527.
PM pollution is still serious in densely populated cities with frequent traffic activities, and it continues to threaten public health. Therefore, it is urgent that we obtain ultrahigh-resolution data that can reveal its complex spatiotemporal variation characteristics, supporting more refined environmental governance and health risk prevention and control. This study first carried out ground monitoring based on low-cost sensors combined with observation results, which were corrected with the national environmental monitoring station data. This study also introduced multi-source auxiliary variables and constructed a machine learning model through the stacking ensemble learning method. The model combines corrected low-cost sensor data with high-resolution prediction factors to achieve ultrahigh-spatiotemporal-resolution prediction of PM at 100 m × 100 m spatial resolution and hourly temporal resolution. The results show that the constructed model shows good prediction ability in 5-fold cross validation, with an overall R of 0.93 and a root mean square error (RMSE) of 3.09 μg/m. The spatiotemporal analysis based on the prediction results further revealed that the PM concentration in the city showed significant variation characteristics at both the ultra-local scale and the short-term scale, reflecting the high heterogeneity of urban air pollution. In addition, by comparing and analyzing the monitoring data of a national environmental monitoring station that were not used in the correction, it was found that the corrected low-cost sensor data significantly reduced the prediction uncertainty, reducing the RMSE from 72.068 μg/m to 16.759 μg/m, verifying its effectiveness in high spatiotemporal resolution air quality monitoring. This shows that low-cost sensors are expected to make up for the problem of insufficient spatial coverage in traditional national environmental monitoring stations, supporting the successful assessment of urban-level air pollution and health risk management, and therefore having broad application prospects.
在交通活动频繁的人口密集城市中，颗粒物污染仍然严重，并且持续威胁着公众健康。因此，迫切需要获取能够揭示其复杂时空变化特征的超高分辨率数据，以支持更精细化的环境治理和健康风险防控。本研究首先基于低成本传感器进行地面监测，并结合观测结果，利用国家环境监测站数据进行校正。本研究还引入多源辅助变量，并通过堆叠集成学习方法构建机器学习模型。该模型将校正后的低成本传感器数据与高分辨率预测因子相结合，实现了在100米×100米空间分辨率和每小时时间分辨率下对颗粒物的超高时空分辨率预测。结果表明，所构建的模型在五折交叉验证中表现出良好的预测能力，总体R值为0.93，均方根误差（RMSE）为3.09微克/立方米。基于预测结果的时空分析进一步表明，城市中的颗粒物浓度在超局部尺度和短期尺度上均呈现出显著的变化特征，反映出城市空气污染的高度异质性。此外，通过对校正过程中未使用的国家环境监测站监测数据进行比较分析，发现校正后的低成本传感器数据显著降低了预测不确定性，将RMSE从72.068微克/立方米降至16.759微克/立方米，验证了其在高时空分辨率空气质量监测中的有效性。这表明低成本传感器有望弥补传统国家环境监测站空间覆盖不足的问题，支持成功评估城市层面的空气污染和健康风险管理，因此具有广阔的应用前景。
