## 2 Monitoring Project Overview
### Analysis of Experimental Data Processing  
#### 2.3.1 Data Pre-Processing  
During the dust data monitoring process, interruptions in data may occur due to equipment power failures, abnormal peaks caused by truck bumps on transportation roads, and other uncertain factors such as human error and natural occurrences. Some data values are missing or abnormal. Through data comparison, it was found that the dust concentration at Monitoring Point No.1 fluctuated significantly. The monitoring point may be located about 6 m from the center of the transportation road, near the side where many trucks pass by. Therefore, only monitoring point No.2 will be analyzed and predicted in the future. The research data used dust concentration data from Monitoring Point No.2, collected by the real-time online monitoring system. In order to improve data quality and facilitate the observation and analysis of the change pattern of each data over time, noise reduction was performed first to address existing data gaps and anomalies. Furthermore, employing a weighted average decreased the frequency of the data from every ten minutes to hourly intervals, resulting in approximately 250 sets in total. The specific steps for handling missing data and exceptions are as follows. 1. Missing data The presence of missing values not only complicates in-depth data analysis but also hinders the development of a stable and accurate model. The lack of dust concentration data is due to the monitoring instrument's power supply being turned off, resulting in missing data for the intervals of 12:00-13:00 on August 10 and 21:26-22:23 on August 11. Some data are missing, and this part of the data has been restored and repaired. There are three commonly used methods: average interpolation method, weight method, and direct deletion method. When the proportion of missing values is small, they can be either directly discarded or filled in manually. When the missing data are relatively significant to the overall dataset, it is necessary to use the average interpolation method to fill in the missing values. There are 12 instances of missing data at this time, and the gaps can be filled using the mean interpolation method. \[F(d,t)=\frac{F(d-1,\ t)+F(d-2,\ t)+F(d+1,\ t)+F(d+2,\ t)}{4} \tag{1}\]  
where \(F(d,t)\) represents the filled data value, \(d\) is the missing data, \(t\) is the corresponding collection point of the missing data, \(F(d-1,\ t)\) and \(F(d-2,\ t)\) represent the data at the same time as the previous day and the day before that of the data point, and \(F(d+1,\ t)\) and \(F(d+2,\ t)\) represent the data at the same time as the next day and the day after that of the data point. 2. Abnormal data The monitoring equipment is installed near the road. The abnormal fluctuations in data caused by trucks passing and road unevenness need to be eliminated or smoothed out. Due to the small sample size, a few outliers were identified using an intuitive observation \begin{table}
\begin{tabular}{c c c c} \hline
**Monitored Object** & **Monitoring Range** & **Resolution** & **Precision** \\ \hline PM10 & 0–1000 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) & 1 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) & \(\pm\)10 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) \\ PM2.5 & 0–1000 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) & 1 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) & \(\pm\)10 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{m}^{3}\) \\ Temperature & \(-\)40–1200 \(\mathrm{\SIUnitSymbolMicro C}\) & 0.1 \(\mathrm{\SIUnitSymbolMicro C}\) & \(\pm\)0.5 \(\mathrm{\SIUnitSymbolMicro C}\) \\ Humidity & 0–99\%RH & 0.1\%RH & \(\pm\)3\%RH \\ Wind speed & 0–70 \(\mathrm{\SIUnitSymbolMicro m}\)/s & 0.1 \(\mathrm{\SIUnitSymbolMicro m}\)/s & \(\pm\)0.3 \(\mathrm{\SIUnitSymbolMicro m}\)/s \\ Wind direction & 8 bearing & 1 bearing & — \\ Rainfall & 0–8 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{min}\) & 0.2 \(\mathrm{\SIUnitSymbolMicro m}\) & \(\leq\)\(\pm\)5\% \\ Noise & 30–130 dB & 0.1 dB & \(\pm\)0.5 dB \\ \hline \end{tabular}
\end{table}