In recent years, the PM2.5 (particulate matter with a mean aerodynamic diameter of 2.5 micrometers or less) pollution has become a very serious problem in China. Currently, there are three types of monitoring approaches: government-led monitoring, Wireless Sensor Networks (WSN) approaches and Participatory Urban Sensing (PUS). There are three limitations in these state-of-the-art research approaches: a) the coarse-grained limitation of government-led monitoring, b) the deployment and maintenance cost of WSN approaches, c) the "Black Hole" problem and the "Black Time Window" problem of PMTI-based approaches in PUS. How to overcome these three main limitations is the biggest challenge. To address these limitations, we need a new way to collect PM2.5 data. Nowadays, IoT (Internet of Things) smart devices sold to various customers could steadily and directly collect the PM2.5 data in vast urban areas, but how to obtain wide-spread real PM2.5 data from tens of thousands of smart devices is another challenge. While no existing work has addressed these two challenges, how to address them is an open problem. In this paper, we propose PMViewer, a novel PUS approach to address these two challenges. PMViewer's data are collected from tens of thousands of smart devices called AirBox through a crowdsourcing approach. We aim to offer a fine-grained spatial-temporal resolution for the public to monitor the urban PM2.5 pollution near their locations. PMViewer scrawls data from AirBox's vendor server and parses the data to generate a map view to display real-time urban PM2.5 measurements. In this study, we design, implement and evaluate PMViewer. Evaluation results show that PMViewer efficiently and economically addressed these two challenges described above.