Air pollutants, such as particulate matter, negatively impact human health. Most existing pollution monitoring techniques use stationary sensors, which are typically sparsely deployed. However, real-world pollution distributions vary rapidly with position and the visual effects of air pollution can be used to estimate concentration, potentially at high spatial resolution. Accurate pollution monitoring requires either densely deployed conventional point sensors, at-a-distance vision-based pollution monitoring, or a combination of both. The main contribution of this paper is that to the best of our knowledge, it is the first publicly available, high temporal and spatial resolution air quality dataset containing simultaneous point sensor measurements and corresponding images. The dataset enables, for the first time, high spatial resolution evaluation of image-based air pollution estimation algorithms. It contains PM2.5, PM10, temperature, and humidity data. We evaluate several state-of-art vision-based PM concentration estimation algorithms on our dataset and quantify the increase in accuracy resulting from higher point sensor density and the use of images. It is our intent and belief that this dataset can enable advances by other research teams working on air quality estimation. Our dataset is available at https://github.com/implicitDeclaration/HVAQ-dataset/tree/master.

翻译：现有大多数污染监测技术使用固定式空气质量数据集,这些传感器通常部署很少,但实际世界污染分布迅速变化,空气污染的视觉影响可以用来估计浓度,可能达到高空间分辨率。准确的污染监测需要密集部署的常规点传感器、远距离视像污染监测,或两者相结合。本文的主要贡献是,根据我们的知识,这是第一个公开提供的高时间和空间分辨率空气质量数据集,包含同步点感应测量和相应图像。数据集首次能够对基于图像的空气污染估计算法进行高空间分辨率评估,其中包括PM2.5、PM10、温度和湿度数据。我们评估了一些基于视像的状态常规传感器、远程视像的污染监测算法,并量化由于高点感应密度和使用图像而提高的准确性。我们的意图和信念是,这一数据集能够使从事空气质量估计工作的其他研究小组取得进展。我们的数据集可在 https://githubab/implainQ.com/implyQata/https://gistreal-HmamamasalQ.