# Analysis of Chainsaw Emissions during Chestnut Wood Operations and Their Health Implications
## 2 Material and Methods
### Data Collection  
Noise measurements were conducted during the various silvicultural operations (FE, DE, and BU) on a sample of 30 trees. A total of 1260 observations were made, with 420 observations for each silvicultural operation. To capture a comprehensive range of scenarios encountered by operators, individuals with varying diametrical classes were included in the study. The dosimeter was directly affixed to the operator during chain-saw cutting operations to simulate the actual stress experienced by the auditory system (Figure 1). Such positioning ensured that data were acquired at a constant distance of 50 cm to 70 cm.  
Specifically, the index of exceedance of exposure limits was calculated, both based on the recorded average values and the corresponding medians (Table 1). The following formula was employed to determine the indices:  
\[i_{AV}=\frac{AV\left[\text{dB}\right]}{Lim\left[\text{dB}\right]} \tag{1}\]  
\[i_{MED}=\frac{MED\left[\text{dB}\right]}{Lim\left[\text{dB}\right]} \tag{2}\]  
where \(AV\) is the average value, \(MED\) refers to the median value of the variable, and \(Lim\) is the threshold established by law.  
For exhaust gas sampling, including CO, VOC, and C6H6, specialized clips were utilized to securely attach the instrumentation to the operator's belt (Figure 2). This approach yielded reliable results, as it ensured proximity between the gas analyser and the worker's mouth, which is a crucial requirement according to standards [7].  
Pollutants that are close to the operator's airway access are captured by a gentle current of air generated at the connecting tube port, to be transmitted to the sensor that provides data acquisition.  
In this case, 432 measurements were taken on a sample of 30 trees, including 144 for each silvicultural operation. The collected data were clustered into 8 groups (one for each  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & _DIAMETER_ (cm) & _AVERAGE_ & _MEDIAN_ & _MAX_ & _SD_ & _VARIANCE_ & _CV_ \\ \hline \multirow{3}{*}{FELLING} & 5 & 86.4 & 82.1 & 110.1 & 12.6 & 159.1 & 14.6 \\  & 7 & 88.4 & 83.8 & 111.3 & 87 & 162.1 & 14.4 \\  & 9 & 90.6 & 87 & 114.4 & 13 & 169.6 & 14.4 \\ \hline \multirow{3}{*}{DELIMBING} & 12 & 90.8 & 89.8 & 110.9 & 13.6 & 185.5 & 15 \\  & 16 & 92.3 & 89.7 & 115.9 & 13.7 & 188.6 & 14.9 \\  & 18 & 94.9 & 96 & 116.5 & 14 & 196.1 & 14.8 \\ \hline \multirow{3}{*}{BUCKING} & 10 & 90.7 & 90.5 & 108.9 & 12.4 & 154.1 & 13.7 \\  & 15 & 94.3 & 99.8 & 112.4 & 13.3 & 177.3 & 14.1 \\ \cline{1-1}  & 20 & 101.5 & 107.6 & 122 & 14.3 & 204.8 & 14.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for each type of machining and in relation to different diametric classes.  
Figure 1: Operating scheme of data collection using Noise sensor TES 1355.  
Figure 2: Operating scheme of data collection using gas emission sensor MultiRAE PGM-50.  
day of data acquisition). Instantaneous measurements of the three gas emission variables were acquired at 15 min intervals throughout each processing phase (FE, DE, and BU).  
In both cases, the descriptive statistics were calculated for each dataset, encompassing maximum values (V\({}_{\text{max}}\)), minimum values (V\({}_{\text{min}}\)), averages, and standard deviations for each time interval and work process. In particular, to estimate the variability of the parameters, the coefficient of variation was computed:  
\[\text{CV}(\%)=\frac{\sigma\left[\text{dB}\right]}{\left|\mu\right|\left[ \text{dB}\right]}\times 100 \tag{3}\]  
where \(\sigma\) is the standard deviation and \(\mu\) is the mean value of the variable.  
The frequency of exceeding limits is given by the following formula:  
\[f=100\times\left(\frac{Na}{Ntot}\right) \tag{4}\]  
where _Na_ are the readings above the threshold and _Ntot_ is the total number of events.