Air pollution significantly affects human health and the environment. It is caused by the emission of diverse pollutants into the atmosphere. Most of the measurements for air quality are done through on-ground sensors that are single points and do not cover well a given territory. Currently, there are space missions that are aiming to complement these on-the-ground systems with more synoptic views. The management and processing of remote sensing and on-ground sensor data have become increasingly complex, requiring temporal and spatial analysis to extract meaningful information and identify patterns and trends of vast amounts of data from various sources. Data Cubes framework helps overcome processing challenges, effectively managing and analyzing large volumes of data in multiple dimensions, such as spatial, spectral, and temporal. The article presents a scalable EO processing and visualization service designed for Data Cubes to explore and analyze multidimensional array data obtained from the Sentinel 5P satellite by performing shared-memory parallel simulations. The service provides a comprehensive understanding of the studied region over a specific period through statistical analysis and visualization. A case study was conducted over the territory of Armenia from September 2018 to August 2019 to evaluate the performance and capabilities of the service.