DETECTION AND PREDECTION OF AIR POLLUTION USING ML MODELS

Governments in both developed and developing countries are fully aware that air quality control is a crucial responsibility that must be completed. Conditions such as weather and traffic congestion, fossil fuel burning, and industrial features such as power plant emissions all have a substantial impact on environmental contamination and are thus considered to be environmental polluting factors. In terms of influence on air quality, particulate matter (PM 2.5) is the most significant of all the particulate matter that can be measured, and it deserves more attention than it now receives. Human health may be negatively affected when there is an excess of ozone in the air, which is conceivable when the amount of ozone is high in the atmosphere. No amount of emphasis can be placed on how vital it is to monitor its concentration in the atmosphere on a regular basis in order to effectively control it. In this study, logistic regression is used to determine if a data sample is contaminated or not polluted, based on the distribution of the data sample data. It is possible to estimate future levels of PM2.5 using authoregression, which is a statistical method that is based on previously gathered data. Being aware of the amount of PM2.5 that will be present in the air in the following years, months, or weeks allows us to work toward lowering its concentration to levels lower than those considered to be hazardous. Based on a data collection that includes daily atmospheric conditions in a certain city, this technique was developed to attempt to anticipate PM2.5 levels and identify air quality in a given place