Smoke exposure levels prediction following laboratory combustion of Pinus koraiensis plantation surface fuel.

High concentrations of harmful gases released from forest fire will pose a short-term hazard to fire-fighters' cardiopulmonary function, even threaten their lives. In this study, laboratory experiments were conducted to examine the relationship between harmful gases concentrations and burning environment and fuel characteristics. In the experiments, fuel beds were created with controlled moisture contents and fuel loads; a wind tunnel device was used to conduct 144 trials, each with a specific wind speed. The easily predicted fire behavioral characteristics and the harmful gases concentrations such as CO, CO2, NOx, SO2 which were released during fuel combustion were measured and analyzed. The results showed that the influences of wind speed, fuel moisture content, and fuel load on the flame length are in accordance with the fundamental theory of forest combustion. The contributions by controled variables to the influence on the short-term exposure concentration of CO and CO2 can be ranked as fuel load > wind speed > fuel moisture. The R2 of the established linear model that was used to predict Mixed Exposure Ratio was 0.98. Our results can help protect the health and lives of forest fire-fighters and can be used by forest fire smoke management to guide fire suppression.

Effect of fire spread, flame characteristic, fire intensity on particulate matter 2.5 released from surface fuel combustion of Pinus koraiensis plantation- A laboratory simulation study.

PM2.5 is one of major pollutants emitted from forest fires. High PM2.5 concentration not only affects short-term human respiration health, but also poses a long-term threat to human cardiopulmonary functionality. Therefore, it is of great importance to quantitatively assess the PM2.5 released by forest combustion in forest fire studies. In this study we examine relationships between the PM2.5 concentration and environment and fuel characteristics laboratory experiments. In the experiments, fuel beds with controlled moisture contents and loads were first built; then 144 ignition experiments were conducted for various combinations of wind speeds using a wind tunnel device. Fire behavior characteristics and PM2.5 concentrations released from fuel combustion were measured and analyzed. The experimental results show that the relationship between fire characteristics, fire intensity and the influencing factors of wind speed, fuel moisture content, and fuel load can be explained by the fundamental theory of forest combustion. Although PM2.5 concentration rises with the increase of wind speed, the decrease of fuel moisture content, and the increase of fuel load, there appears to be a fuel load threshold for a given combination of wind speed and fuel moisture content that the increase of PM2.5 concentration decelerates quickly after the load passes the threshold value. After screening fire behavior characteristics that affect PM2.5 concentration, we found that fire line intensity and flame width are the ones with the strongest association with the concentration. With flame width as independent variable, we have built two regression models to predict PM2.5 and fire line intensity which are treated as dependent variable; the models have high accuracy with R2 = 0.92 for predicting PM2.5 and R2 = 0.97 for predicting fire line intensity. Study results can be used as reference to protect the health of forest fire fighters, and can be helpful for forest fire smoke management.

Spatial distribution of particulate matter 2.5 released from surface fuel combustion of Pinus koraiensis - A laboratory simulation study.

High concentration particulate matter 2.5 released from forest fires, in addition to direct burns and asphyxia, PM2.5 is one of the main pollutants which threaten the safety of forest fire fighter. Therefore, to assess spatial distribution of PM2.5, a simulation study was conducted. Fuel beds with different moisture contents and loads were constructed. 144 times burning experiments were carried out under different wind speeds by using wind tunnel device. PM2.5 particles at different spatial points were collected and calculated. The results show that, in the two of three variables interaction between wind speed, fuel load, and, except fuel moisture content, wind speed and fuel load are positively correlated with the PM2.5 concentrations. From PM2.5 concentration which collected at each point in the horizontal and vertical directions, the overall trend is that PM2.5 concentration increases along the horizontal downwind direction (C and D higer than A and B) and the vertical upward direction (A and C higer than B and D) Based on BP neural network, the spatial distribution model of PM2.5 concentration with single hidden layer was established. The prediction accuracy of modeling samples and validation samples is balanced when hidden layer node is 5. This study will help to make reference for PM2.5 occupational exposure standards, forest fire smoke management and forest fire management in China.