标题： 基于地理加权回归的空气质量低成本传感器校准 显示英文标题

标题： Geographically Weighted Regression for Air Quality Low-Cost Sensor Calibration

摘要： 本文重点探讨了使用地理加权回归（GWR）方法来校正空气质量低成本传感器的测量数据。这些传感器在当前城市尺度高分辨率空气质量监测的时代具有重要意义，但需要使用参考分析仪进行校准。NO2的结果提供了对估计的GWR模型和估计系数的空间内容的评论。该研究使用了安特卫普市公开的SensEURCity数据集，这尤其相关，因为它包括9个参考站和34个微传感器，这些传感器在同一地点部署在城市内。 显示英文摘要

摘要： This article focuses on the use of Geographically Weighted Regression (GWR) method to correct air quality low-cost sensors measurements. Those sensors are of major interest in the current era of high-resolution air quality monitoring at urban scale, but require calibration using reference analyzers. The results for NO2 are provided along with comments on the estimated GWR model and the spatial content of the estimated coefficients. The study has been carried out using the publicly available SensEURCity dataset in Antwerp, which is especially relevant since it includes 9 reference stations and 34 micro-sensors collocated and deployed within the city.