Air Pollution Mapping with Sensor-based Methodology

the purpose of this study is to develop a sensor-based methodology(S-BM) for mapping air pollution (AP) related to Gaseous Composition of the Atmosphere in a specific area. It uses a drone equipped with sensors to identify the current composition of the air. After self-identifying the locations with specific distances in a specific area, the drone can go to those locations automatically and obtain sensor readings related to the gas percentages at those locations. After that the data is then transmitted to a computer program which analyzes (cluster analysis methodology), the data and then maps the air pollution in that specific area. Our results provide important informa-tion on how to measure, manage and atmospheric pollution mapping (APM). It also helps to identify air-polluted areas that need to be addressed quickly, and, thereby, it helps to save the atmosphere. We hope to program to get the sensor reading sand analyze the data with a suitable methodology and predict the condition of the atmosphere in the specific area. We hope to use cluster analysis and other analysis methodologies and technologies to this function. We need a dataset to train the model that can do the air quality prediction (AQP) of the relevant area. For that, we surfed the internet and found some datasets regarding the air pollution level of some major countries and their capitals. We think we will be able to make the model by using these datasets and predict the air pollution level of a specific area clearly. In addition, we are going to predict the future AP levels in a specific area by analyzing the current gas percentages of some specific gas components in the atmosphere like CO, CO2, SO2 and NH3 etc.