Reducing Urban Pollution and Health Risks with Big Data for Predictive Environmental Monitoring Learning

Abstract

Pollution in cities is one of the overriding environmental issues affecting people everywhere. Increased urbanization is causing more harm than good and the resultant damage is putting citizens in risk for a myriad of ailments including respiratory disorders, heart complications, and in more severe cases, even death. Most existing pollution control methods are always post-event, which leaves the urban planners and public health officials with scarce resources to rectify the problem before it actually happens. This paper looks into how IoT devices, satellites, and sensors transform environmental monitoring using big data and predictive analytics, concentrating particularly on urban pollution and related health issues. The rapid proliferation of big data technologies makes it possible to constantly monitor air and water quality in a city. With the help of machine learning algorithms, predictive models can be created single handedly and pollution levels can accurately be forecasted, leading to ample head room for necessary adjustments to be made. The review seeks to analyze the existing models case studies frameworks and technologies that have incorporated big data and predictive analytics in monitoring Hybrid architecture for the urban pollution predictive modeling in smart cities IoT system. The models pose several challenges like having unreliable integration, poor data quality, and others. This paper describes a complete solution that integrates big data analytics Internet of Things and machine learning in smart cities to tackle urban pollution and the public health consequences associated with it. The ultimate objective is to shape future studies and aid in the creation of more sophisticated as well as resilient data-centric management systems for pollution in cities.