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Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM2.5 concentrations, are still not fully understood or quantified. In this study, we conducted a detailed examination of the spatial and temporal patterns of straw burning in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire-point data from the Fengyun-3 satellite. We subsequently employed random forest (RF) models alongside Shapley Additive Explanations (SHAPs) to systematically evaluate the impact of various determinants, including straw burning (as indicated by crop fire-point data), meteorological conditions, and aerosol optical depth (AOD), on PM2.5 levels across spatial and temporal dimensions. Our findings indicated a statistically nonsignificant downward trend in the number of crop fires in Heilongjiang Province from 2015 to 2023, with hotspots mainly concentrated in the western and southern parts of the province. On a monthly scale, straw burning was primarily observed from February to April and October to November—which are critical periods in the agricultural calendar—accounting for 97% of the annual fire counts. The RF models achieved excellent performance in predicting PM2.5 levels, with R2 values of 0.997 for temporal and 0.746 for spatial predictions. The SHAP analysis revealed the number of fire points to be the key determinant of temporal PM2.5 variations during straw-burning periods, explaining 72% of the variance. However, the significance was markedly reduced in the spatial analysis. This study leveraged machine learning and interpretable modeling techniques to provide a comprehensive understanding of the influence of straw burning on PM2.5 levels, both temporally and spatially. The detailed analysis offers valuable insights for policymakers to formulate more targeted and effective strategies to combat air pollution. [ABSTRACT FROM AUTHOR]

In this study, M-HMX-based PBX (M-HMX-PBX) and N-HMX-based PBX (N-HMX-PBX) were prepared by industrial micron-sized HMX (M-HMX) and nano-sized HMX (N-HMX) via solution-water suspension method, respectively. The critical electronic excitation energy (Ec) of M-HMX particle and N-HMX particles was calculated by combining with field emission scanning electron microscopy (FE-SEM) and Image Pro Plus (IPP) software. Meanwhile, the average critical electron excitation energy ( E C ‾ ) of the entire particle group was calculated. Those results shown that E C ‾ of N-HMX was higher than that of M-HMX, which indicated that the stability of N-HMX was better than that of M-HMX. In addition, the reaction activation energies (Ea) also indicated that N-HMX and N-HMX-PBX were more stable than M-HMX and M-HMX-PBX, which were calculated by the Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods. The above results explained the reasons for the high thermal decomposition stability of N-HMX-PBX from the micro and macro perspectives. And those provided theoretical basis for exploring the application of N-HMX in improving the thermal stability of HMX-PBX. [ABSTRACT FROM AUTHOR]

ABSTRACT The molecular imprinting technique is a new method for preparing molecularly imprinted polymers (MIPs) with specific molecular recognition sites for certain target molecules. In this study, a novel, facile preparation method was presented, called 'seed precipitation polymerization,' for the synthesis of MIPs via surface imprinting and a support matrix. In the polymerization process, kaempferol was used as the template molecule, methacrylic acid as the functional monomer, nano-TiO2 as the support, azodiisobutyronitrile as the initiator, and ethylene glycol dimethacrylate as the crosslinker in acetonitrile solvent. The synthesized T-MIP and MIP were analyzed by infrared spectroscopy and scanning electron microscopy. In addition, the obtained polymers were evaluated by adsorption isotherms and dynamic curves for their selective recognition properties for kaempferol. The results show that T-MIP shows regular spherical particles; the adsorption dynamic curves of T-MIP show that the adsorption capacity increases with time and reaches a maximum value and then finally reaches equilibrium, and the T-MIP exhibits a higher affinity for kaempferol than does the MIP. The adsorption follows pseudo-second-order kinetics, the Freundlich adsorption equation fits the experimental data well, and there is strong evidence for multiple-layer adsorption. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44888. [ABSTRACT FROM AUTHOR]

Open biomass burning (OBB) is a significant source of air pollutants, profoundly impacting regional air quality and global climate change. However, due to the lack of high-resolution burned area products, limited biomass data resolution, and inappropriate emission factors (EFs), current OBB emission inventories have significant uncertainties. In this study, we integrated the FireCCI51 burned area product (250 m), high-resolution gridded biomass data, localized EFs, and various statistical survey data to compile a finer-resolution (250 m) and long-term (2001-2020) inventory of 11 pollutants for Heilongjiang Province (an important agricultural and forestry region in China). The results indicated that the annual average OBB emissions in Heilongjiang Province were 12.26, 63.70, 931.60, 17,203.35, 16.09, 171.25, 39.83, 71.22, 184.33, 129.16, and 6.35 Gg for BC, CH 4 , CO, CO 2 , NH 3 , NMVOC, NOx, OC, PM 10 , PM 2.5 , and SO 2 , respectively. Taking PM 2.5 as an example, emissions from cropland, forest, and grassland burning accounted for 73%, 26%, and 1% respectively. Geographically, emissions were primarily concentrated in the western, southwestern, and northeastern regions of Heilongjiang. The peak PM2.5 emissions exhibited notable seasonal variation, with maxima occurring in April and October. This is primarily due to the extensive burning of straw during the spring and autumn ploughing seasons. The results of this study provide a detailed multi-year inventory crucial for atmospheric transport models and can support the development of effective pollution control strategies and greenhouse gas mitigation measures., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)