# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
###### Abstract  
The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment. air pollution; fuzzy logic; Kampala Air Quality Index 202115 June 20219 July 2021  
According to the Air Visual's World Air Quality Report, Kampala city is among the cities with the most polluted air in Africa [9]. Air pollution has a great effect on health, alone causing millions of hospitalizations every year. Major sources of air pollution in Kampala city include dust from unpaved roads and open burning of waste by individuals as a way of managing uncollected waste, which introduces dangerously large amounts of pollutants into the air via combustion. There are also massive amounts of dirty air coming from many factories and power plants around the Kampala area [10, 11]. Another big contributing source of air pollution is vehicular emissions coming from many imported second-hand vehicles; the Uganda National Environment Management Authority (NEMA) estimates that more than 140,000 litres of fuel are burnt by idling cars every day in Kampala city because of ever-growing road traffic [12]. A report compiled by Global Burden of Disease project indicates that exposure to dirty air is the fifth highest ranking risk issue for death, answerable for 4.2 million deaths from heart condition and stroke, carcinoma, chronic respiratory organ unwellness, and metabolic process infections; conjointly, an extra 254,000 deaths were owing to exposure to gas and its impact on chronic respiratory organ disease around the world. In Africa alone, there was an estimated one million deaths [13]. Therefore, it's a very important health factor to measure the quality of air. Information on air quality concentrations in a particular region and its health effects is usually presented via the Air Quality Index (AQI). The AQI presents air quality concentrations in a more understandable form to the public. It is a public information tool designed to help individuals in a particular society understand the effects of air quality on both health and the environment; it is a generalized way of describing the quality of air around the universe. In this paper, the AQI is based on Kampala standards for air quality, which follow the Environmental Protection Agency (EPA) standards [14]. The values of the Kampala Air Quality Index are divided into six groups: good, moderate, unhealthy for sensitive groups, unhealthy, very unhealthy, and hazardous. Different colors are assigned to each group. Thus, in this paper, the AQI prediction model for Kampala city is designed using the fuzzy logic inference system. In predicting the quality of air, traditional methods, such as clustering analysis, regression analysis and variance analysis, have been greatly used but these methods do not give the desired measurements in predicting the air quality, due to the non-linear relationship between pollutant datasets.