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In this study, a solar-powered multipoint network monitoring method was used to record dust-particle concentrations and meteorological indicators in the Anjialing open-pit coal mine in the Pingshuo mining area. The factors influencing the concentrations of particulate matter of different maximum diameters (PM2.5, PM10, and total suspended particulates; TSPs) and the regularity of the spatial distribution were examined. The results show that the highest dust concentration and thus the most serious dust pollution occur in winter, and the lowest dust concentration is found in summer. There are peaks in dust concentration in December and January to February, and the pollution is more serious at these times. On a given day, the pollution is higher between 11:00 and 13:00, but it does not exceed the 24 h air concentration limits specified in the Chinese Ambient Air Quality Standard (GB3095-2012). It was found that the PM2.5 and PM10 concentrations are positively correlated with humidity and air pressure, and they are negatively correlated with wind speed, temperature, and noise. The TSP concentration is positively correlated with temperature and negatively correlated with humidity. The results of this study provide theoretical guidance and a reference for the distribution law of dust concentration in open-pit coal mines.

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With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research.