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High concentration particulate matter 2.5 released from forest fires, in addition to direct burns and asphyxia, PM 2.5 is one of the main pollutants which threaten the safety of forest fire fighter. Therefore, to assess spatial distribution of PM 2.5 , 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. PM 2.5 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 PM 2.5 concentrations. From PM 2.5 concentration which collected at each point in the horizontal and vertical directions, the overall trend is that PM 2.5 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 PM 2.5 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 PM 2.5 occupational exposure standards, forest fire smoke management and forest fire management in China. [Display omitted] • PM 2.5 released from Pinus koraiensis leaves and twigs burning were quantified. • The spatial distribution of PM 2.5 was investigated. • BP neural network model had been established to predict spatial distribution of PM 2.5. • Research will make reference for PM 2.5 occupational exposure standards in fire fighting. [ABSTRACT FROM AUTHOR]

The decline of conventional petroleum resources has shifted attention to the exploration of shale oil resources. The shale oil potential of organic-rich shales is a critical focus, which is controlled by organic matter (OM) abundance, thermal maturity, kerogen type, and thickness. However, some commonly-used indicators to characterize the shale oil potential often yield inconsistent results. Hence, to precisely assess the shale oil potential, in this study, combined with the rock-eval pyrolysis, organic petrography, total organic carbon (TOC) content, organic elements, vitrinite reflectance (Ro , %), and gas chromatography-mass spectrometry (GC-MS), a new evaluation index for shale oil potential (SOPI) was proposed to evaluate the shales of Permian Lucaogou Formation in southeastern Junggar basin. Several geochemometric methods including principal component analysis (PCA), grey correlation analysis (GCA), hierarchical cluster analysis (HCA), and analytical hierarchy process (AHP) were used to calculate the SOPI. PCA serves to transform multiple shale oil evaluation metrics into a few integrated metrics (main principal component, PC) that reflect the practical geological significance (OM abundance, kerogen type, thermal maturity, and dark mudstone thickness). Then, the weights of these PC with practical geological significance were determined by GCA. Based on this, SOPI was obtained by which the quality of Permian Lucaogou shales in different areas was quantitatively determined. The SOPI values were examined by the HCA, which indicates a high coincidence rate of 89.4%. The AHP method also confirmed the credibility of the weights obtained from GCA with a good result of the consistency test. The results of shale oil potential evaluation based on the SOPI are consistent with the previous unilateral shale oil evaluation based on uni- and bivariate evaluation parameters, the distribution characteristics of shale oil resources volume, and the lithological distribution proposed by previous research. The comprehensive analysis suggests that, in the six areas of the southeastern Junggar Basin, the Qitai area exhibits the highest shale oil exploration potential, and its shale oil potential even exceeds that of the Jimusaer area, followed by the Miquan and Mulei areas. The shale oil potential in the Chaiwopu and Shiqiantan areas are the lowest. • The oil-generation potential of organic-rich shales is a critical focus controlled by several factors. • The new shale oil potential index (SOPI) was proposed based on chemometric theories. • Chemometric theories used to calculate the SOPI include PCA, GCA, HCA, and AHP. • The evaluation results based on the SOPI are consistent with uni- and bivariate parameters. • The Qitai area has the highest shale oil exploration potential in the SE Junggar Basin. [ABSTRACT FROM AUTHOR]

[Display omitted] PM 2.5 is one of major pollutants emitted from forest fires. High PM 2.5 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 PM 2.5 released by forest combustion in forest fire studies. In this study we examine relationships between the PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 concentration decelerates quickly after the load passes the threshold value. After screening fire behavior characteristics that affect PM 2.5 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 PM 2.5 and fire line intensity which are treated as dependent variable; the models have high accuracy with R2 = 0.92 for predicting PM 2.5 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. [ABSTRACT FROM AUTHOR]