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Industrial pollution monitoring system using iot and embedded system
Industrial pollution monitoring system using iot and embedded system

ABSTRACT

In the last several years, air pollution has risen steadily in urban environments. Cities like Gurugram, Faisalabad, Delhi, Beijing are few of the world’s most polluted cities and have seen a dangerous rise in air pollution levels. Forecasting is important because of the human, ecologic and economic toll of pollution, and is a useful investment at individual and community levels. Accurate forecasting will help us plan in advance, decreasing the effects on health and the costs associated. Local weather conditions strongly affect air pollution levels. Generating deterministic models to study air pollutant behavior in environmental science research is often not very accurate because they are complex and need simulation at the molecular interaction level. : Air pollution which is detrimental to people’s health is a wide spread problem across many countries around the world. Developing better air quality prediction approaches is an important research issue. Here comes machine learning to the rescue with high computing facilities to predict air pollution. We proposed a system using machine learning which predicts the air quality efficiently.

INTRODUCTION

With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants, especially in fast developing countries like India and China. Air pollution is one of the main detriments to human health. According to World Health Organization, 7 million people are at health risk due to air pollution . It is a leading risk factor for majority of health problems like asthma, skin infections, heart issues, throat and eye diseases, bronchitis, lungs cancer and respiratory system’s diseases. Besides the health problems related to air pollution, it also poses a serious threat to our planet. Pollution emissions from the sources like vehicles and industry is the underlying cause of greenhouse effect, CO2 emissions are amongst the foremost contributors to the greenhouse phenomenon. Climate change has been widely discussed at the global forums and has remained a burning issue for the world since last two decades as a result of increased smog and ozone damage Exposure to air pollution can affect everyone, but it can be particularly harmful to people with a heart disease or a lung condition, elderly people and children. Studies show that long-term exposure to fine particulate air pollution or traffic-related air pollution is associated with environmental-cause mortality, even at concentration ranges well below the standard annual mean limit value. Damages produced by air pollution include its impact on the depletion of the ozone layer, which plays a significant role in preserving the planet from ultraviolet radiation. Air pollution also causes acid rain that is harmful to soil, trees, and wildlife. Urban smog and global climate change are primarily caused by the pollution of air. Also, air pollution problem can directly impacts humans and it can be the source for long- and short-term health effects with the most affected people being children and the elderly. The reverse impacts of exposure to the air pollutants on health are tremendous such as short term effects to the eyes, throat, and nose as well as causing headaches, upper respiratory infections, and allergic reactions. Long-term effects may include brain damage, lung cancer, liver damage, and heart disease. Therefore, building an early warning system, which provides precise forecast and also alerts health alarm to local inhabitants will provide valuable information to protect humans from damage by air pollution. Air quality is an active topic at many social and political scales around the world. It is a significant concern for governments, environmentalists, and even data scientists who are raising awareness about this growing global problem. The availability of the massive amount of data in recent years enables better predictions of air quality using machine learning techniques. Therefore we proposed a system using machine learning which predicts the air quality efficiently.

MOTIVATION

Monitoring air quality is one of the best ways to prevent the harmful effects of air pollution. Having the information about the quality of air can lead to formulating suggestions and data driven recommendations to mitigate the possible harmful effects it can bring.

Objective

The main aim of our project is efficient air quality prediction using machine learning

 

 

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