## 1 Introduction  
Air pollution is one of the major factors affecting the health of people, leading to one in nine deaths worldwide [1]. Short-term and long-term health risks, attributed to exposure to air pollution, are very critical, for example, breathing problems, cardiovascular diseases and lung cancer [2; 3; 4; 5]. Platforms for monitoring air pollution have played an important role in dealing with the pollution problem, where the Unmanned Aerial Vehicles (UAVs) appear to be one of the most disruptive tools for air pollutant monitoring applications [6; 7; 8; 9; 10; 11], showing great advantages with respect to common methods like terrain stations or satellite imaging. These advantages include high spatial resolution, low cost, security, flexibility, multitasking and ease of deployment [12]. The UAV-based platforms are particularly useful due to their ability to monitor specific areas that could be inaccessible or hazardous for humans, such as congested industrial buildings where the pollutant release cannot be measured at ground levels. Since the air pollutant monitoring using UAVs could be ineffective with an arbitrary exploration of the spaces in the polluted environment, an algorithm, which guides the navigation of the vehicle using online and historical measurements, could be a very useful tool. The location of the pollutant source could be estimated, if the monitoring is concentrated in the most critical areas [13; 14; 15; 16]. We require an algorithm that, in the first place, detects the plume of the pollutant and then concentrates the exploration in the most promising areas, where the UAV has estimated the source could be, using online and historical measurements. Release of pollutants into the atmosphere has several consequences for potentially many people living in the proximity of its source. Air pollution does not affect only urban areas but also rural. In urban areas, traditional methods of air pollution monitoring (fixed monitoring stations) are being replaced with mobile crowdsensing sensors that are small enough to be carried around by users, or installed in different vehicles [17]. However, this technique is not feasible for rural environments, since the traffic of people and vehicles is scarce. This makes the use of the UAV to locate the air pollutant source important, given its advantage of effectiveness and being able to reach poorly accessible areas [18]. Correct application of the protective measures requires the quick location of the source. However, the localization of the air pollutant concentration using a UAV is challenging, since the environment is very complex. Most of the time, the search region is very large and the effectiveness in locating the source depends not only on the motion of the UAV but also on the unknown and time varying pollutant distribution. The problem of the UAV-based pollutant source identification depends on two steps [19]:  
1. The air pollutant plume localization and
2. The tracing of the pollutant plume towards the source. The pollutant plume localization implies that the UAV navigates in the targeted environment without useful information about the contaminant levels. A suitable approach could be to perform a deterministic planned navigation in the environment until finding the air pollutant plume. However, it becomes unfeasible in environments with an area much longer that the propagation of the plume, and the limited autonomy of the UAV. Once the plume is located, the UAV will be able to trace it towards the positions with the highest concentration of the pollutant. Some UAV-based solutions for pollutant plume tracing have been studied that are based mainly on gradient algorithms [13; 20]. These algorithms require at least two spatially separated measurements, which are often obtained by using multiple sensors on-board the vehicle or by using multiple measurements separated in time and space with a single sensor.