The fast development of large-scale intensive animal husbandry has led to an increased proportion of atmospheric pollution arising from livestock and poultry housing. Atmospheric pollutants, including particulate matter (PM), ammonia (NH₃), hydrogen sulfide (H₂S), and greenhouse gases (GHG), as well as other hazardous materials (e.g., gases, bacteria, fungi and viruses), have significant influences upon the local atmospheric environment and the health of animals and nearby residents. Therefore, it is imperative to develop livestock and poultry housing mitigation strategies targeting atmospheric pollution, to reduce its negative effects on the ambient atmosphere and to promote sustainable agricultural production. In this paper, we summarize the various strategies applied for reducing outlet air pollutants and purifying inlet air from mechanical ventilated livestock and poultry housing. This review highlights the current state of knowledge on the removal of various atmospheric pollutants and their relative performance. The potential optimization of processes and operational design, material selection, and other technologies, such as electrostatic spinning, are discussed in detail. The study provides a timely critical analysis to fill the main research gaps or needs in this domain by using practical and stakeholder-oriented evaluation criteria.

Large-scale open burning of straw residues causes seasonal and severe atmospheric pollution in Northeast China. Previous studies focused on the causes or assessment of atmospheric pollution in a single city. However, studies conducted on the interaction range, degree and policy control of pollutant transport on a large scale are still to be performed. In this study, we propose combined control of straw burning by dividing region the straw burning in Northeast China in recent 20 years, determining the transport routes between main cities, and analyzing the interaction characteristics of straw burning under different scenarios. The fire point data suggest that the most intense straw burning years in Northeast China in the past 20 years occurred in the range from 2014 to 2017, mainly after the autumn harvest (October–November) and before spring cultivation (March–April). The burning areas were concentrated in the belt of Shenyang-Changchun-Harbin, the border of the three provinces and Eastern-Inner Mongolia, and the surrounding area of Hegang and Jiamusi City. The lower number of fire points before 2013 indicates that high-intensity burning has not always been the case, while the sharp decline after 2018 is mainly due to scientific control of straw burning and increased comprehensive utilization of straw. Compared with S2, the PM_2.5 concentrations increased by 6.2% in S3 and 18.7% in S4, indicating that burning in three or four provinces at the same time will significantly increase air pollution and exert a regional transmission effect. Straw burning in Northeast China is divided into six main regions based on correlation analysis and satellite fire monitoring. Under typical S3, the case analysis results indicate that there is regional transmission interaction between different cities and provinces, focusing on multi-province border cities, and it is affected by Northwest long airflow, and Southeast and Northeast short airflow. These results provide scientific and technological support for implementing the joint prevention and control plan for straw incineration in Northeast China.

Inspired by the natural epidermis of animals and plants with antioxidant and antibacterial properties, the aim of this research was to characterize and analyze the effects of the chitosan concentrations on properties of glycerol plasticized chitosan (GPC) film and to investigate the suitability of sensory evaluation and bionic electronic nose (b-electronic nose) detection to assess the freshness of ground beef packaged in the GPC film. The increase in chitosan concentration resulted in an increase in solubility value, total color differences and color intensity of chitosan films. The water vapor permeability (WVP) of the GPC films decreased with the increasing of the chitosan concentration and then increased at higher chitosan concentrations. Longer storage time led to poorer freshness of the ground beef and the GPC film could keep beef samples fresher and delay the deterioration of the beef. Both the traditional sensory evaluation and b-electronic nose technology were fit for evaluating the quality and shelf-life of ground beef, which could advantageously be applied in the future for analyzing other bionic food packaging materials.

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R² of 0.87 and an RMSE of 1.68, a 23% improvement in R² compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management.