Air Quality Data Acquisition Through Temporal Data Modeling

Abstract

The paper presents a novel temporal data model for acquiring air quality data. The proposed model uses the free and open-source Postgres database with tuple time stamping, resulting in rapid and effective data extraction. The conventional relational database SQL server for data extraction is used in this paper as the foundation for an air quality temporal database model. The air quality data set collected for MDU Rohtak (source: Central Control Room for Air Quality Management - All India) is utilized to test and determine the outcomes of the proposed model. Valid time is used as the time dimension for capturing data values, together with tuple timestamping. The Postgres "tsrange" datatype is used to record the timestamp. To eliminate data duplication, the dataset is cleaned up and normalized. Using the air quality temporal data model, AQI data at various time points is effectively extracted. The present model data extraction is compared with the conventional relational data model using SQL Server database. The air quality data extraction process takes a remarkably minimal amount of time, with an average of 23% lesser time than SQL server database. It takes only a few milliseconds to extract the AQI data, and the results of the experiment are ready immediately after that. The suggested model is helpful in decision-making and offers decision-makers useful information and the tool they need to enhance air quality. The results are relevant to the study of the AQI components by different pollution management agencies like central and state pollution control boards, national green tribunal and Ministry of the Environment for efficient air pollution control measures.