Paper Key : IRJ************677
Author: Samruddhi Sainath Bhor,Apurva Anant Bhuwad,Vaishali Aajinath Payghan,Sandesh Santosh Ghule,Prof.shakil Tamboli
Date Published: 04 Apr 2025
Abstract
Urban air pollution from vehicle emissions is a serious problem that the project seeks to solve.By integrating Machine Learning (ML) and Internet of Things (IoT) technologies, this project seeks to develop a real-time monitoring and control system to mitigate vehicular pollu- tion. IoT sensors will be deployed on vehicles and in strategic urban locations to collect real- time data on pollutants such as nitrogen oxides (NOx), carbon monoxide (CO), and particulate matter (PM). This data will be analyzed using advanced ML algorithms to identify patterns, predict pollution levels, and recommend actionable measures to reduce emissions. The project will also incorporate engine model and fuel quality data to provide a comprehensive analysis of emission and traffic-related pollution. The expected outcome is an intelligent, data-driven system capable of providing timely insights and interventions, thereby contributing to cleaner air and healthier urban environments. This method promotes public health programs and sustainable urban design in addition to improving pollution monitoring and control. The project intends to solve the pressing problem of vehicle emissions causing urban air pollution.. By integrating Machine Learning (ML) and Internet of Things (IoT) technologies, this project seeks to develop a real-time monitoring and control system to mitigate vehicular pollution. IoT sensors will be deployed on vehicles and in strategic urban locations to collect real-time data on pollutants such as nitrogen oxides (NOx), carbon monoxide (CO), and particulate matter (PM). This data will be analyzed using advanced ML algorithms to identify patterns, predict pollution levels, and recommend actionable measures to reduce emissions. The project will also incorporate engine model and fuel quality data to provide a comprehensive analysis of emission and traffic-related pollution. The expected outcome is an intelligent, data-driven system capable of providing timely insights and interventions, thereby contributing to cleaner air and healthier urban environments. This method promotes public health programs and sustainable urban design in addition to improving pollution monitoring and control.Keywords: traffic management, nitrogen oxides (NOx), carbon monoxide (CO), fuel quality, engine model, IoT sensors, smart cities, public health, automobile pollution, machine learning (ML), and the internet of things (IoT). etc.I.INTRODUCTION Nowadays, the rapid growth in the number of vehicles has significantly contributed to increas- ing levels of air pollution, posing a serious threat to environmental and public health. The emissions from internal combustion engines, particularly those running on fossil fuels, release a variety of harmful pollutants, including carbon monoxide (CO), nitrogen oxides (NOx), par- ticulate matter (PM), and hydrocarbons (HC). These pollutants have been linked to a wide range of health problems, such as respiratory diseases, cardiovascular diseases, and even premature death. Therefore, controlling and reducing vehicular pollution has become a critical priority for urban planning and public health policies. Traditional methods for monitoring and control- ling vehicle emissions rely on periodic inspections and fixed-location air quality monitoring stations. While these methods provide valuable data, they are often limited in scope and unable to provide real-time, granular insights into vehicular pollution. This is where advancements in Machine Learning (ML) and the Internet of Things (IoT) offer promising solutions. By leverag- ing these technologies, it is possible to create a more dynamic, responsive, and effective system for monitoring and mitigating vehicular pollution.In order to continuously monitor emission levels, a network of sensors is installed on cars and along roadsides as part of the integration of IoT in pollution control.. These sensors collect vast amounts of data in real-time, which can be transmitted to central processing units. Here, machine learning algorithms can analyze the data to identify patterns, predict pollution levels, and recommend actionable insights. For example, machine learning models can forecast areas with heavy pollution, suggest the best routes to reduce emissions, and even identify cars that are exceeding emission standards in real time.Moreover, the use of ML can enhance predictive maintenance by analyzing engine per- formance and predicting potential failures that could lead to increased emissions. This proac- tive approach helps in maintaining vehicles in optimal condition, thereby reducing their overall environmental impact.In the context of urban environments, where traffic congestion and air quality are of significant concern, an IoT-enabled pollution control system can also integrate with smart city infrastructure. This integration can facilitate adaptive traffic management systems that optimize traffic flow based on real-time pollution data, reducing idle times and emissions. The proposed project, aims to design and implement a comprehensive system that uti- lizes these cutting-edge technologies to monitor, analyze, and control vehicular pollution.
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