Dust Suppression in Urban Environments: An Integrated Approach with Road Sweeper Robots

Abstract

Urban areas especially those with heavy traffic and industrial activity, are more vulnerable to dust pollution. The vision, weather, and health of city dwellers may all be negatively impacted by dust. To lessen the impact of dust and enhance air quality, efficient methods of dust suppression are required. It presents a comprehensive strategy for urban dust reduction combining neural networks, Internet of Things (IoT) sensors, and road sweeper robots. Sensors and cameras on board the road sweeper robots allow them to monitor on dust levels and road conditions, allowing them to optimize their sweeping settings as needed. In order to provide the robots information on the air quality, the IoT sensors are placed along the highways. By considering environmental, traffic, and weather variables, neural networks evaluate sensor data and determine the best dust suppression approach to use. Through the use of simulations and tests, method is both practical and successful it may greatly enhance urban air quality by reducing dust emissions.