Using Image Processing Technique for the Studies on Temporal Development of Air Quality

Nowadays visual information becomes more and more important in almost all areas of our life. This information is represented and processed digitally. Digital image processing is ubiquitous, with applications ranging from television to tomography, from photography to printing, from robotics to remote sensing. In this study, we developed an algorithm to convert multispectral image pixel values acquired by an Internet Video Surveillance camera into quantitative values of concentrations of particulate matter with diameter less than 10 micrometers (PM10). This algorithm was based on the regression analysis of relationship between the measured reflectance components from a surface material and the atmosphere. The newly developed algorithm can be applied to compute the PM10 values. These computed PM10 values were compared to other standard values measured by a DustTrakTM meter. The correlation results showed that this newly develop algorithm produced a high degree of accuracy as indicated by high correlation coefficient (R²) of 7566 and low root-mean-square-error (RMS) values of plusmn3.8306 mug/m³. A program was written by using Microsoft Visual Basic 6.0 to download still images automatically from the camera via the internet and utilized the newly developed algorithm to determine PM10 concentration automatically and continuously. This study indicates that the technique of using Internet Video Surveillance camera images can be a useful tool for monitoring temporal development of air quality.