Many scientific work needs to analyze the environmental data which are usually collected by wireless sensor networks（WSNs）deployed in research areas. The integrity and accuracy of the collected data determine the reliability of the research results. However, data loss and error usually occur during the process of data collection, which affect the availability of collected data. Therefore, it is necessary to reconstruct the environmental data from the incomplete and erroneous sensory data. Based on the low-rank feature of the environmental data, an efficient data reconstruction approach via matrix completion with structural noise (DRMCSN) is proposed by formulating data reconstruction problem as a L2,1-norm regularized matrix completion model. Finally, experimental results on a real dataset demonstrate that the proposed approach can not only effectively reconstruct the environmental data, but also recognize the sensor nodes that collect erroneous data.