The possibilities of using Big Data technologies in solving problems of processing data on atmospheric air pollution

The main objective of the article is to substantiate the possibility of using Big Data technologies in the field of atmospheric air monitoring. In the form of a diagram, a model for processing big data obtained from measuring meteorological gas analysis stations using the PySpark library for further experimental studies is presented. The factors accompanying the use of Big Data in the field of atmospheric air monitoring are derived, and the performance of the Pandas and PySpark libraries is compared. The obtained results will allow us to further rely on the derived factors and use the most optimal data processing technologies to build predictive machine learning models in the field of analyzing the level of atmospheric air pollution. Consistent use of big data and machine learning methods will ensure clean and healthy air for future generations through more effective predictive analytics. This article is valuable for students and specialists in the field of information technology, in particular, in the field of data processing and machine learning.