Evaluation of machine-based major risk factors during mechanized harvesting operations in forestry

Forestry is among the dangerous work classes due to its working conditions. Especially fatal accidents that may occur during production activities take an important place in this regard. There are studies on occupational accidents in forestry and solutions are sought for the prevention of accidents. However, in addition to occupational accidents, there are risks (physical, chemical, biological, ergonomic, etc.) that are often ignored and can cause serious health problems in long periods if repeated. Increasing mechanization, especially in the field of forestry, has brought with it various risks. On the other hand, the number of scientific researches and studies on this subject is limited. Although the mechanical tools used during the production of wood raw materials provide positive effects on production speed and efficiency, they may adversely affect the health of the operator and worker due to the potential risks it causes in the working environment. The basis of machine-based production works is the use of engine power. Engine power is mostly derived from fossil fuels. Therefore, situations such as the release of waste gases to the environment during operation are encountered. In addition, noise caused by motor movements and particle and dust exposure caused by wood raw material or ground interaction in machine production are important conditions. Considering the studies on this subject, the main risk factors can be classified as noise, particulate matter and gas. Although these factors do not have significant effects on human health in short-term operations, they cause serious health problems especially on machine operators in long-term operations. In this respect, it is very important to examine these risk factors in terms of occupational health and safety. In this study, measurement of some risk factors (noise and particulate matter) related to production machines used during forestry activities, development of a multi-measuring device (TriSensor 4.0) using open source Arduino platform and related factors with Multi-Criteria Decision Making Methods (Fine Kinney and Fuzzy Fine Kinney). risk assessments are intended. Within the scope of the study, noise and particulate matter measurements were made on mechanical vehicles (harvester, loader, overhead line, agricultural tractor, logging truck) used in forestry works carried out in various regions. Measurements were made with industrial measuring devices and TriSensor 4.0 multi-measuring device developed in the study, and the results were compared. TriSensor 4.0 device with GPS unit can simultaneously measure noise, particulate matter and CO and record location information along with measurement values. Risk assessment studies are widely carried out in many areas. Especially in dangerous business lines, it becomes inevitable to carry out these studies in order to prevent fatal accidents. In this study, it is planned to make a risk assessment of noise and dust factors originating from machinery, which do not have fatal risks in the short term but may cause significant health problems in the long term. Risk assessment studies are applied in many business lines and there are quite different risk assessment methods. Accordingly, the risk assessment method is based on the working environment, the work done, etc. situations are reviewed and selected. In this study, the risk assessment study was carried out by integrating the Fine Kinney Method and the Fuzzy Logic Method. According to the results, it is seen that the average noise values obtained from the production machines in both devices are in the range of 66-90 dBA, which is 2nd degree effective on human health. Looking at the results, the lowest average values are obtained from the MOZ500 skyline, while the highest values are from the URUS MIII skyline. The values were lower and close to each other due to the similarity of the working environment conditions of other production vehicles and the presence of operator cabins. When evaluated in terms of particulate matter risk (PM2.5), it was determined that the highest exposure value (>200 µg/m3) was obtained in New Holland TT55B model agricultural tractor measurements and the working environment was very unhealthy in terms of particulate matter. In studies where MAN TGA logging truck, URUS MIII skyline and Turkish-FIAT 80-66 agricultural tractor were used, it was found that the exposure values of the operators were in the category of unhealthy (65.5-150.4 µg/m3). In other mechanical production tools, PM2.5 exposure values are included in the group that does not pose significant hazards to human health and is considered unhealthy for sensitive groups. When the measurement values of the TriSensor 4.0 multi-measuring device developed in the study and industrial measurement devices were compared, it was determined that there was no significant difference between the measurement values of noise and particulate matter, and that the developed device was functional. When the risk scores obtained from risk assessment studies with noise and particle data were compared, it was seen that the Fine Kinney risk assessment results were generally lower than the Fuzzy Fine Kinney results.