## 2 Monitoring Project Overview
### Analysis of Experimental Data Processing
#### 2.3.2 Test Date Analysis  
1. Analysis of Distribution Patterns  
Dividing the 24 h of a day into 6 periods of 4 h each, the violin plot is combined with the box plot and the kernel density plot [34]. This analysis examines the distribution of temperature, humidity, noise, wind speed, and other variables during different periods of the day, as depicted in Figure 2.  
The temperature index data in Figure 2a indicate that the temperature is low at 0:00-4:00, 4:00-8:00, and 8:00-12:00, generally distributed between 15 and 30 \({}^{\circ}\)C, and gradually increases at 12:00-16:00, reaching 30-40 \({}^{\circ}\)C. A small amount of data show that the temperature ranges between 20 and 30 \({}^{\circ}\)C until it rises to 40 \({}^{\circ}\)C at 16:00-20:00 in the 5 th period, which represents the peak period of temperature difference change. Subsequently, the temperature gradually decreases at 20:00-24:00. The data overall indicate that the highest temperature reaches 41.2 \({}^{\circ}\)C, while the lowest temperature recorded is 13.9 \({}^{\circ}\)C over a period of ten days. The humidity violin plot in Figure 2b depicts the overall distribution as \"slender\". The data distribution density is low, and there is a large fluctuation, indicating significant changes in humidity at different times. In the first, second, and third periods, the humidity data are mainly concentrated between 40% and 80% relative humidity (RH), with a small amount of data exceeding 80% RH or falling below 40% RH. In the 4 th and 5 th periods, the time is between 12:00 and 20:00, and the humidity gradually decreases to below 40% RH. The data reveal that the maximum humidity reaches 91.6% RH, while the minimum humidity drops to 11% RH over a period of ten days. In Figure 2c, the distribution of noise data is associated with mine production operations. In a typical outsourcing or self-operated common operating environment, the noise intensity ranges from 55 to 65 dB. At 4:00-6:00, the noise intensity gradually decreases in the second period due to the shutdown of outsourcing and the shift to self-operated production. Some of the noise data in the third, fourth, and fifth periods are minimal. There may be two reasons. First, lunch break is from 12:00 to 12:30 and from 17:30 to 18:00, and work is paused for half an hour. Secondly, in the early morning of August 11, it raned throughout the day, causing the outdoor party to suspend production and only operate in a self-contained manner. The trend of increasing noise can be observed in time periods 3, 4, and 5 in Figure 2c. Figure 2d shows the wind speed violin diagram. Most of the data are concentrated in the range of 0-2.5 m/s. However, in the second period, there are outliers in the wind speed from 4:00 to 8:00, where it increases from 2 m/s to 13.6 m/s, and the data fluctuate greatly. At this time, there is a light rainfall. Figure 2: The data distribution status of each influence index and the density of distribution across different time periods. Analysis of the relationship  
The concentration of dust is influenced by numerous factors. A single factor can visually display the density and distribution of the data, but it needs to be combined with the change in dust concentration to better reflect the relationship between dust concentration and its variables. Figure 3 illustrates the correlation between individual variables, including temperature, humidity, noise, rainfall, wind speed, wind direction, and dust concentration. Figure 3: The variation law of each influencing factor and dust concentration data. From Figure 3, it is evident that there is a strong correlation between the PM2.5 and PM10 data, and their change patterns are essentially identical. The PM2.5 levels are kept within the range of 10-130 \(\upmu\)g/m\({}^{3}\), while the PM10 levels are generally maintained between 20 and 140 \(\upmu\)g/m\({}^{3}\). It can be seen from Figure 3a that there is a significant negative correlation between dust concentration and temperature. As the temperature increases, the dust concentration decreases.