## 1 Introduction  
In recent years, the process of industrialization and urbanization has been accelerating, but the problems of air pollution and climate change are becoming more and more prominent, seriously affecting people's daily life and physical health [1; 2; 3]. The control and traceability of air pollution require the collection of information on polluting gases [4; 5]. Commonly used air pollutant monitoring equipment is mainly divided into base station air quality monitoring stations and portable air-quality monitors [6; 7]. However, the former has poor flexibility and low coverage density, while the latter has a small measurement range, is limited by the measurement environment, and requires large labor costs [8]. The quadrotor is a powered autonomous aircraft that relies on the sensors and micro-control units it carries for flight control, data acquisition and information processing. It is widely used in many fields, such as photogrammetry, search and rescue missions, farmland monitoring, fire monitoring, military detection and power line inspection detection. In environmental monitoring [9], it can be used as a carrier to carry a gas sensing device to complete the collection of pollution information in three-dimensional space for specific polluted areas, such as industrial parks, sewage treatment plants, etc. In order to obtain environmental information, mobile sensing robots equipped with gas sensors must navigate to sampling locations in the monitored area to make the necessary measurements, and higher resolution sampling covering the entire space will obviously provide real data closer to underlying environmental phenomena. However, UAVs in this field have limited onboard energy, which limits the number of data samples collected and the associated coverage. In related literature, this determination of an optimal path involving all points in a given region of interest is often referred to as the Coverage Path Planning problem (CPP). In previous work, most UAVs flew in a \"Z\" or lawnmower mode at a single altitude during CPP missions and did not consider whether the robot had enough power to cover the entire area. In an atmospheric environment detection mission, because the distribution of different gases at different heights is different [10; 11], it is necessary for the UAV to perform a large amount of data measurement of the entire space, rather than a single height plane. [PERSON] [12] uses a pyramid-shaped spiral flight path for atmospheric environment detection. While it can collect gas data at different altitudes, it can only collect data from space boundary points and cannot fully cover samplings from the space region, and therefore cannot reconstruct the gas distribution throughout the three-dimensional space. CPPs can be divided into two categories [13] according to the way the regions are decomposed. One type is called grid-based decomposition, which discretizes the coverage area into a grid and then plans a path to visit each grid cell. Another type is called cell decomposition, which divides the coverage area into different units as needed. These units are then overlaid with a zig-zag flight, and finally the traveling salesman problem on adjacent decomposition units is solved to determine the sequence of access units. Methods based on grid decomposition include a spiral spanning tree [14], a wavefront algorithm based on distance transforms and an adaptive D* path planning method [15]. These methods decompose the area that needs to be covered into grid cells, such as triangles, squares [16], and hexagons, and then sample these grid cells. This paper uses this idea; the difference is that we use the hexagonal prism as the basic unit, and then use the center of the unit as the sampling point, so as to collect data samples with balanced spatial distribution in the entire study area, rather than only the data of a certain height plane. When planning access paths for grid cells, the traveling salesman problem (TSP) [17] is widely used by directly connecting all target locations.