The development of low-cost sensors and novel calibration algorithms offer new opportunities to supplement existing regulatory networks to measure air pollutants at a high spatial resolution and at hourly and sub-hourly timescales. We use a random forest model on data from a network of low-cost sensors to describe the effect of land use features on local-scale air quality, extend this model to describe the hourly-scale variation of air quality at high spatial resolution, and show that deviations from the model can be used to identify particular conditions and locations where air quality differs from the expected land-use effect. The conditions and locations under which deviations were detected conform to expectations based on general experience.

中文翻译：

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低成本传感器网络和土地利用回归：在南加州以高时空分辨率内插二氧化氮浓度

低成本传感器和新型校准算法的开发为补充现有监管网络提供了新的机会，以高空间分辨率和每小时和亚小时的时间尺度测量空气污染物。我们对来自低成本传感器网络的数据使用随机森林模型来描述土地利用特征对局部尺度空气质量的影响，扩展该模型以描述高空间分辨率下空气质量的小时尺度变化，以及表明与模型的偏差可用于识别空气质量与预期土地利用效应不同的特定条件和位置。检测到偏差的条件和位置符合基于一般经验的预期。