Air Quality Assessment In The University Of The Philippines Diliman Campus Through The Integration Of Small Sensors, Satellite Data, And Kriging Interpolation Techniques

Air quality monitoring is an essential procedure to ensure that pollutant levels remain within safe limits and do not pose a threat to public health, particularly for vulnerable populations. The deployment and maintenance of stationary air quality monitoring stations can be expensive, especially when a large number is required to create a comprehensive network. As a result, there has been growing interest in utilizing small, low-cost sensors that are easier to deploy and provide a more flexible and cost-effective alternative. In addition to these sensors, satellite systems have become valuable tools for air quality monitoring, offering high temporal resolution data that facilitates the assessment of air pollution over larger areas. This study looks into data fusion techniques to combine data from both stationary and mobile low-cost sensors with satellite data to analyze the air quality at the University of the Philippines, Diliman campus. Seven small sensors were deployed across the university, a mixed-use area with both vegetation and buildings, to measure pollutant concentrations, such as particulate matter. Satellite data from MODIS, Sentinel-5P, and ERA5 reanalysis were used to monitor aerosol optical depth (AOD), sulfur dioxide (SO2), nitrogen dioxide (NO2), and meteorological conditions. The time-series analysis focused on a three-day period during which mobile air quality data from an e-trike were collected around the university. The data from these mobile sensors, along with the stationary sensor measurements, were used to estimate PM2.5 concentrations across the campus. Kriging interpolation, a geostatistical method that estimates unknown values based on the spatial correlation of known data points, was employed to generate smooth surfaces of PM2.5 concentration across the university.  Kriging interpolation was used on the stationary sensor dataset to predict the PM2.5 levels at the location of the mobile sensors at a given timeframe. Moreover, cokriging was also applied by incorporating multiple correlated variables, improving predictions by utilizing relationships between the primary variable (PM2.5) and secondary variables, such as aerosol optical depth or SO2 and NO2 concentrations. The results obtained from both Kriging and Cokriging methods were compared with data collected from mobile sensors to assess the air quality at the University of the Philippines, Diliman. The interpolated PM2.5 values were compared with the data from the mobile sensors (SEN55 and PMS7003) as ground truth, and a mean absolute percentage error (MAPE) of 43.00% to 57.23% was obtained. Initial results of cokriging with NO2 showed MAPE of 36.67% to 52.55%. Further work is expanding the dataset and refining the interpolation models to enhance the accuracy and reliability of air quality assessments across the university. By integrating more data and conducting additional tests, this approach can provide more comprehensive air quality monitoring at reduced costs and address data gaps.