  Since Particulate Matters (PMs) are closely related to people's living and
health, it has become one of the most important indicator of air quality
monitoring around the world. But the existing sensor-based methods for PM
monitoring have remarkable disadvantages, such as low-density monitoring
stations and high-requirement monitoring conditions. It is highly desired to
devise a method that can obtain the PM concentration at any location for the
following air quality control in time. The prior works indicate that the PM
concentration can be monitored by using ubiquitous photos. To further
investigate such issue, we gathered 1,500 photos in big cities to establish a
new AQPDCITY dataset. Experiments conducted to check nine state-of-the-art
methods on this dataset show that the performance of those above methods
perform poorly in the AQPDCITY dataset.
