Tree height growth is sensitive to climate change; therefore, incorporating climate factors into tree height prediction models can improve our understanding of this relationship and provide a scientific basis for plantation management under climate change conditions. Mongolian pine (Pinus sylvestris var. mongolica) is one of the most important afforestation species in Three-North Regions in China. Yet our knowledge on the relationship between height growth and climate for Mongolian pine is limited. Based on survey data for the dominant height of Mongolian pine and climate data from meteorological station, a mixed-effects Chapman-Richards model (including climate factors and random parameters) was used to study the effects of climate factors on the height growth of Mongolian pine in Zhanggutai sandy land, Northeast China. The results showed that precipitation had a delayed effect on the tree height growth. Generally, tree heights increased with increasing mean temperature in May and precipitation from October to April and decreased with increasing precipitation in the previous growing season. The model could effectively predict the dominant height growth of Mongolian pine under varying climate, which could help in further understanding the relationship between climate and height growth of Mongolian pine in semiarid areas of China.

The change of soil organic carbon and its influencing factors after afforestation in sandy land should be taken into account. Here, the factors would be revealed which would influence the SOC dynamics to a depth of 100 cm during the development of Mongolian pine plantations in Horqin sandy land, northeast China. The chronosequence method was used to quantify the change of SOC in vertical distribution and influencing factors following conversion grassland to Pinus sylvestris var. mongolica forest in semi-arid sandy land, northeast China. Then the traditional statistical approaches were used to assessed the influence of the identified factors. Stand age played a major role in SOC dynamics. It took 38 years for SOC in 0–10 cm layer to recover to its initial level after afforestation, and 46 years for 10–20 cm layer. SOC accumulation increased with the age of Mongolian pine plantation. Over-mature forest fully embodied the advantage of SOC accumulation. In addition, the changes of SOC in 0–10 cm layer were also affected by TN, TP, TK and soil moisture, and those below 10 cm soil layers were related to the effects of TN, TP, TK, BD and CS.

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Aluminum alloys have been widely utilized in automobiles, aircraft, building structures, and high-speed railways industries due to their excellent structural and mechanical properties. Surface oxide film removal prior to aluminum alloy welding and old paint removal prior to repainting aluminum alloy surfaces are critical factors in ensuring the welding quality and service life of aluminum alloy products. Because of its unique advantages, such as environmental protection and precision control, laser-controlled cleaning has great application potential as a surface cleaning technology in removing oxide films and paint layers on aluminum alloy surfaces. In this paper, the mechanism of laser cleaning of oxide films and paint layers on aluminum alloy is discussed. Furthermore, the impact of various processing parameters such as laser beam power, energy density, scanning speed, and so on is analyzed in detail. After laser cleaning, the corrosion resistance, welding performance, adhesive performance, and other properties of the aluminum alloy are optimized. This paper also discusses several real-time detection technologies for laser cleaning. A summary and the development trend are provided at the end of the paper.

Height-diameter (H-D) models are important tools for forest management practice. Sandy Mongolian pine plantations (Pinus sylvestris var. mongolica) are a major component of the Three-North Afforestation Shelterbelt in Northern China. However, few H-D models are available for Mongolian pine plantations. In this paper we compared different equations found in the literature for predicting tree height, using diameter at breast height and additional stand-level predictor variables. We tested if the additional stand-level predictor variable is necessary to produce more accurate results. The dominant height was used as a stand-level predictor variable to describe the variation of the H-D relationship among plots. We found that the basic mixed-effects H-D model provided a similar predictive accuracy as the generalized mixed-effects H-D model. Moreover, it had the advantage of reducing the sampling effort. The basic mixed-effects H-D model calibration, in which the heights of the two thickest trees in the plot were included to calibrate the random effects, resulted in accurate and reliable individual tree height estimations. Thus, the basic mixed-effects H-D model with the above-described calibration design can be an accurate and cost-effective solution for estimating the heights of Mongolian pine trees in northern China.