Machine Learning-Based Assessment of Air Quality and Its Impact on Respiratory Health in Urban Environments
Keywords:
Air pollution, urban air quality, Linear Regression, Random Forest Regression, Gradient Boosting Regression, Pollutant concentrations, meteorological conditions, geographical attributes,Abstract
Air pollution, primarily driven by emissions of nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5), presents a critical environmental concern with significant implications for public health. Air quality in urban areas is critical in public health, particularly respiratory illnesses. This study uses advanced machine learning algorithms to analyze the relationship between air quality parameters and respiratory health outcomes. Specifically, we employ Linear Regression, Random Forest Regression, and Gradient Boosting Regression algorithms to assess and model the impact of air quality on respiratory health in urban settings. Our analysis utilizes a comprehensive dataset collected from various urban areas, encompassing data on air quality factors such as pollutant concentrations, meteorological conditions, and geographical attributes. Concurrently, we compile information on respiratory health indicators, including hospital admissions, prevalence of respiratory diseases, and symptom reports, from healthcare records and surveys.
Linear Regression is employed to establish baseline relationships between individual air quality parameters and respiratory health outcomes, providing insight into the linear associations between pollutants and health. Random Forest Regression then captures non-linearities and interactions within the data, accommodating complex relationships in urban environments. Additionally, Gradient Boosting Regression enhances predictive accuracy by iteratively learning and improving the model's ability to capture intricate dependencies in the data. Our findings reveal nuanced insights into the connections between air quality and respiratory health, highlighting the importance of considering linear and non-linear relationships. The Linear Regression model identifies straightforward associations between specific pollutants and health outcomes, while Random Forest Regression uncovers intricate interactions and non-linear dependencies. Gradient Boosting Regression, with its ensemble learning approach, further enhances our predictive capabilities, enabling us to assess respiratory health based on air quality data accurately.This research contributes to a better understanding of the factors contributing to respiratory health issues in urban areas. It underscores the significance of employing machine learning algorithms to analyze and predict health outcomes in complex urban environments. This study's outcomes can inform public health interventions and policies to improve air quality and mitigate respiratory health risks in urban populations.
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