This Publication has to be referred as: AbstractTemperature inversions represent an important element of air pollution in cities situated in natural valleys, such as the Sarajevo Valley. Due to the strong thermal stratification, which acts as a natural lid over the city, air circulation is blocked and pollutants accumulate and stay for much longer time. As a consequence, air pollution in the residential area of the city increases dramatically. Investigation of the temperature inversions in Sarajevo Valley was, for the first time, performed using unmanned aerial vehicles.

Vertical temperature profiles represent a very important factor for various analytical and numerical studies, such as weather forecasts, air pollution models and CFD simulations. These temperature profiles are especially important during the winter periods, when temperature inversions occur. The cities in the natural valleys, such as the city of Sarajevo, B&H, are strongly affected by this phenomenon. In this paper, a method for quantitative characterization of vertical temperature profiles, which is based on the in-house developed data acquisition system and the unmanned aerial vehicle, is presented. Comprehensive calibration and verification procedure was performed and explained in details. Field measurements were focused on the winter period and extreme temperature inversion scenarios. The correlation with the air pollution in the city, for the same period, was discussed as well.

Air pollution with particulate matter (PM) represents major risk for humans' health. Important step towards the solution of the aforementioned problem is the correct measurement of PM concentrations in the air. Commodity (low-cost) sensors are very popular nowadays, since they are affordable and they fit well in the trend called Internet of Things (IoT). However, measurement of PM concentrations in the air is difficult task and low-cost sensors are unable to provide accurate readings out of the box. Thus, advanced calibration is necessary for any application of low-cost dust sensors. In this paper we propose calibration technique and discuss initial results.

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