Air pollution is a significant cause of health problems and disease worldwide.Considering the rapid urbanisation at a global scale in recent decades, resulting in more and more people in urban areas, cities deserve special attention in this regard.In this paper, we use air quality measurement data from 2010 to 2023 in the four largest Norwegian cities (Oslo, Bergen, Trondheim, and Stavanger) and correlate it with the evolution of population densities for the same period.The empirical analysis focuses on nitrogen dioxides (NO2) and particular matter (PM2.5 and PM10) as critical pollutants in urban areas to verify whether their concentrations are affected by the increase in population densities for individual municipalities.In addition, we also correlate the data on air pollutants with different natural indicators such as temperature, air pressure, humidity, wind, and the rate of motorisation in the cities of interest.

With the advancement of air pollution management, low-cost sensors are increasingly being used in air quality monitoring, but the data quality of these sensors is still a major source of concern.In this paper, data from five air monitoring stations in Sofia were compared to data from fixed low-cost PM sensors.The values of atmospheric pressure from low-cost sensors and the effects of relative humidity were investigated.A two-step model was created to refine the calibration process for low-cost PM sensors.At first, we calibrated the sensors with five separate supervised machine learning models and then the ANNfinal model with anomaly detection completed the results.The ANN-final model improved the R 2 values of the PM10 determined by low-cost sensors from 0.62 to 0.95 as compared to standard instruments.In conclusion, the two-step calibration model proved to be a positive solution to addressing low-cost sensor efficiency issues.