Assessment Of Spatial Hazard And Impact Of PM10 Using Machine Learning

Air pollution is one of the major threats to human health and environment. When some substances in the atmosphere exceed a certain concentration it becomes harmful to the ecological system and the normal conditions of human existence. Particulate matter (PM) refers to small solid or liquid particles floating in the air. These small particles can move deeper into the respiratory tract, including the lungs this can lead to cough, asthma attacks, high blood pressure, heart attack, stroke and so on. Particulate matter is considered as the air pollutant of greatest concern to health. So as a first step to understand the seriousness of the issue is to monitor PM concentration. For the spatial hazard modeling of PM10 productive machine learning models such as Mixture Discriminant Analysis (MDA), Bagged Classification and Regression Trees (Bagged CART), Random Forest (RF), with accuracy 0.87, 0.92 and 0.93 respectively are used. However, these models cannot give accurate results with large samples and to overcome this eXtreme Gradient Boosting (XGBoost) method is used.