The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small numberof these stations can be deployed in a country. To improve the spatial resolution of the air monitoring process, an interesting idea is to develop data-driven models to predict air quality based on readily available data. In this paper, we investigate the correlations between air pollutants concentrations and meteorological and road traffic data. Using machine learning, regression models are developed to predict pollutants concentration. Both linear and non-linear models are investigated in this paper. It is shown that non-linear models, namely Random Forest (RF) and Support Vector Regression (SVR), better describe the impact of traffic flows and meteorology on the concentrations of pollutants in the atmosphere. It is also shown that more accurate prediction models can be obtained when including some pollutants’ concentration as predictors. This may be used to infer the concentrations of some pollutants using those of other pollutants, thereby reducing the number of air pollution sensors.

中文翻译：

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使用气象和交通相关功能进行空气质量预测的机器学习

空气中污染物的存在直接影响我们的健康，并对环境造成不利影响。因此，空气质量监测至关重要。获取和维护准确的空气质量站的高昂费用意味着在一个国家中只能部署少量这样的站。为了提高空气监测过程的空间分辨率，一个有趣的想法是开发基于数据的模型，以基于随时可用的数据来预测空气质量。在本文中，我们研究了空气污染物浓度与气象和道路交通数据之间的相关性。利用机器学习，开发了回归模型来预测污染物浓度。本文研究了线性和非线性模型。结果表明，非线性模型 即随机森林（RF）和支持向量回归（SVR），可以更好地描述交通流量和气象学对大气中污染物浓度的影响。还表明，当将某些污染物的浓度包括在内时，可以获得更准确的预测模型。这可用于使用其他污染物的浓度推断某些污染物的浓度，从而减少空气污染传感器的数量。