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Air pollution of fine particles with diameters less than 10 micrometers (PM10) is a major concern in many countries due to their ability to penetrate further into our lungs to cause adverse health effects. PM10 also have a significant influence on climate change and visibility. Due to the high cost to set-up air pollution monitoring stations, there are limited numbers of these stations in use. As a result, researchers do not have good temporal development and spatial distribution of the air pollutant readings over a city. This paper is to report upon the usage of an internet surveillance camera to record the temporal development and to map the spatial distribution of air quality concentration. An internet surveillance camera was used to quantify air quality with our own developed algorithm, which is based on the regression analysis of the relationship between measured reflectance components from a surface material and the atmosphere. A newly developed algorithm was applied to compute the temporal development of PM10 values. Advanced Land Observing Satellite (ALOS) images were used to map air quality concentration of the study area. The algorithm was developed based on the aerosol characteristics in the atmosphere to measure PM10 spatial distribution. These PM10 values were compared to other standard values measured by a DustTraktrade meter. The correlation results of both techniques showed that these newly developed algorithms produced a high degree of accuracy as indicated by high correlation coefficient (R2) and low root-mean-square-error (RMS) values.