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Real-Time Estimation of the Urban Air Quality with Mobile Sensor System

机译:移动传感器系统实时估算城市空气质量

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摘要

Recently, real-time air quality estimation has attracted more and more attention from all over the world, which is close to our daily life. With the prevalence of mobile sensors, there is an emerging way to monitor the air quality with mobile sensors on vehicles. Compared with traditional expensive monitor stations, mobile sensors are cheaper and more abundant, but observations from these sensors have unstable spatial and temporal distributions, which results in the existing model could not work very well on this type of data. In this article, taking advantage of air quality data from mobile sensors, we propose an real-time urban air quality estimation method based on the Gaussian Process Regression for air pollution of the unmonitored areas, pivoting on the diffusion effect and the accumulation effect of air pollution. In order to meet the real-time demands, we propose a two-layer ensemble learning framework and a self-adaptivity mechanism to improve computational efficiency and adaptivity. We evaluate our model with real data from mobile sensor system located in Beijing, China. And the experiments show that our proposed model is superior to the state-of-the-art spatial regression methods in both precision and time performances.
机译:近来,实时空气质量估计已引起全世界越来越多的关注,这已经接近我们的日常生活。随着移动传感器的普及,出现了一种通过车辆上的移动传感器监测空气质量的新兴方法。与传统的昂贵的监控站相比,移动传感器更便宜,更丰富,但是从这些传感器获得的观测值的时空分布不稳定,这导致现有模型无法很好地处理此类数据。在本文中,我们利用移动传感器的空气质量数据,提出了一种基于高斯过程回归的非监测区域空气污染实时城市空气质量估算方法,重点是空气的扩散效应和累积效应。污染。为了满足实时需求,我们提出了一个两层的集成学习框架和一种自适应机制来提高计算效率和适应性。我们使用来自中国北京的移动传感器系统的真实数据评估模型。实验表明,我们提出的模型在精度和时间性能上均优于最新的空间回归方法。

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