Air quality is an important aspect of both human health and climate change. In recent years, air quality forecasts have received a lot of attention and multiple attempts with different methods have been applied to achieve this task. Many pollutants have been utilised for air quality research, the most common ones being \(PM_{2.5}\) , \(PM_{10}\) and \(NO_2\) . Although various techniques have been used for air quality prediction, the need for more granular and reliable pollutant concentration data has been investigated on a smaller scale, especially in the case of interpolation methods with Internet of Things (IoT) sensors for data collection. In this study, the analysis of spatial patterns of multiple air pollutants ( \(PM_{2.5}\) , \(PM_{10}\) and \(NO_2\) ) has been assessed by collecting data at multiple locations in a case study area with Aeroqual devices and by utilising three interpolation techniques (Inverse Distance Weighted (IDW), Ordinary Kriging and Radial Basis Function). Each method achieved high accuracy in predicting pollution concentrations in new test locations and performance was evaluated using Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). IDW emerged as the best-performing interpolation technique for most of the pollutants with the lowest RMSE and MSE scores.