Projects per year
Air pollution is known to be harmful for human health and environments. The official air quality monitoring stations have been established across many smart cities around the world. Unfortunately, these monitoring stations are sparsely located and consequently do not provide high resolution spatio- temporal air quality information. This paper demonstrates how a dense sensor network deployment offers significant advantages in providing better and more detailed air quality information. We use data from a dense sensor network consisting of 126 low- cost sensors (LCSs) deployed in a highly populated district in Nanjing downtown, China. Using data obtained from 13 existing reference stations installed in the same district, we propose three LCSs validation methods to evaluate the performance of LCSs in the network. The methods assess the reliability, accuracy tests, and failure and anomaly detection performance. We also demonstrate how the reliable data generated from the sensor network provides deep insights into air pollution information at a higher spatio-temporal resolution. We further discuss potential improvements and applications derived from dense deployment of LCSs in cities.
Fields of Science
- 113 Computer and information sciences
- 1172 Environmental sciences
- 114 Physical sciences