With the increasing attention paid on air pollution issues in Taiwan, the Environment Protect Admiration has plan to set up more than ten thousand sensors in Taiwan to improve the resilience of future air quality forecasts and management. There are many types of sensor on Taiwan market including Edimax, LASS, Asus and etc. With the increase of sensors and the development of internet, people can assess information about PM2.5instantly. However, due to the design and diverse environment condition, there are limitation on PM2.5observation. Therefore, the purpose on this project are develop mathematical calibration model to conduct the quality inspection on sensor data, including analysis of the outlier of sensor data and its calibration model. Due to the vary on environment condition in Taiwan, we have developed calibration model on three regions including north, central and southern part of Taiwan to discuss the relationship between the difference of sensor and EPA station monitor data on PM2.5. When the sensor data observe 20-40μg/m^3, the difference is very small; while when the sensor data is below or above the difference, the difference will be significantly increased. In the quality inspection on sensor data, we develop 3 methods including temporal outlier, spatial outlier and spatio-temporal buffer outlier detection to consider the different of space time data of sensor in Taiwan. In temporal outlier, we able to detect the error cause by machine that observe abnormal data and sudden air pollutant events. Spatial outlier detection determines the sensor in a different environment; spatiotemporal buffer outlier detection method provides a comprehensive type of outlier Value detection and proposed. Through the above three methods and sensor data, the program successfully detects and proposes sensor outliers. Besides that, we also conduct data fusion estimation techniques on EPA monitor data and sensor data. Due to the different resolutions and characteristics of sensors and EPA stations in space or time, the data fusion estimation technique considers the above problems and uses the corrected sensor data and its nearby EPA monitoring stations data to estimate and integration, to provide more complete information on air pollutant. We also applied spatio-temporal feature extraction technique on air pollutant data to find the distribution of PM2.5.in Taiwan. In the spatio-temporal feature extraction technique, the PM2.5 data were extracted from the time and space characteristics of day and week to understand the changes of air quality in different regions. We identify the activities such as work commuting time in various regions in daily cycle feature and parks and camps affect the PM2.5. change in the region due to human activities due to weekend time on weekly cycle. In addition, the program also succeeded in identifying air pollution incidents in the extraction characteristics. Through spatio-temporal feature extraction techniques, PM2.5concentrations in different spatial and time periods can be identified and the information on air pollution control and prevention can be provided. In 2011, we had finish the retrospective of spatio-temporal PM2.5 distribution Taipei. Since there was no sensor observation data at that time, only few EPA monitoring stations data were used to develop model with land use data. In this research plan, we use north Taiwan as example. Beside explore the relationship between PM2.5 and land use data, we also include road length, air pollution emissions and climate data to establishes the relationship between various environmental variables and PM2.5. Through above data, we had developed 2233 of different variables, and explore the relationship between 2233 variables and PM2.5. We also develop visualization of space-time information on air quality monitoring and developed dynamic dashboard to enable users and decision makers to instantly understand the status of air and space products quickly and easily. Due to the work alignment, the work on the sensor data comparison operation, we still need to consider the formation of PM2.5 in our quality inspection process. For a complete assessment, we hope to continue the project. Consequential results of the fusion data can be followed up with the results of the health risk analysis to develop a map of the relevant health risk assessment.

Air quality sensor, credibility analysis, space-time features analysis