In this study, on the basis of the temperature data collected at 612 meteorological stations in China from 1961 to 2019, cold regions were defined using three indicators: an average temperature of <−3.0 °C during the coldest month; less than five months with an average temperature of >10 °C; and an annual average temperature of ≤5 °C. Spatial interpolation, spatial superposition, a trend analysis, and a spatial similarity analysis were used to obtain the spatial distribution of the cold regions in China from 1961 to 2019. Then, the areas of the cold regions and the spatial change characteristics were analyzed. The results reveal that the average area of the cold regions in China from 1961 to 2019 was about 427.70 × 10⁴ km², accounting for about 44.5% of the total land area. The rate of change of the area of the cold regions from 1961 to 2019 was −14.272 × 10⁴ km²/10 a, exhibiting a very significant decreasing trend. On the basis of the changes between 1991–2019 and 1961–1990, the area of China’s cold regions decreased by 49.32 × 10⁴ km². The findings of this study provide references for studying changes in the natural environment due to climate change, as well as for studying changes on a global scale.

As a major agricultural province of China, Shandong province has long ranked first in agricultural growth value among all of the provinces; at the same time, it is also the province that is most affected by dry-hot wind. Therefore, it is of great significance to study the spatial zoning of the risks of dry-hot wind in this province. Based on meteorological, slope, and altitude data, and the principle of disaster risk assessment, this study uses a weighted comprehensive evaluation method, analytic hierarchy process, and ARC-GIS spatial analysis to study the spatial zoning of the risks of dry-hot wind in Shandong province. The results show that the high-risk regions of dry-hot wind are concentrated in the north-central portion of the province, the medium-risk regions are in the peripheral areas, and the low-risk regions are located mainly in the west, southwest, and east. Exposure of disaster-bearing bodies is high in the south and low in the north, while vulnerability to disaster-bearing bodies is high in the west and low in the east. The more developed areas in the east show high disaster prevention and mitigation capability, whereas this is weak in the west. In summary, dry-hot wind risk in Shandong province varies significantly by area. The medium- and high-risk areas are mainly in the west and central portions of the province.

Snow depth is a critical parameter for characterizing snow dynamics and water resources, and its accurate inversion is essential for hydrological processes, climate studies, and disaster prevention in cold regions. Based on long-term daily ground meteorological observation data from the hydrological years 1961 to 2015 at two meteorological stations in Mohe and Mishan, Heilongjiang Province, China, this study integrates physical parameters of snow density and snow albedo from the ERA5-Land reanalysis data to systematically compare the performance of three machine learning and three deep learning models in retrieving daily snow depth. Four feature combination schemes were designed to evaluate the contributions of meteorological factors, lagged snow depth terms, and snow physical parameters. The results indicate that, for both machine learning and deep learning models, the first-order lagged value of snow depth is the most important variable determining prediction accuracy. In terms of model performance, machine learning methods excelled, with XGBoost performing particularly outstandingly, achieving optimal prediction accuracy and stability under the best feature combination (coefficient of determination, R², reaching 0.989; root mean square error, RMSE, of 1.19 cm). Among deep learning methods, 1D CNN demonstrated strong local feature extraction capabilities, achieving prediction accuracy comparable to the best-performing machine learning model (R² of 0.9878, RMSE of 1.26 cm). Notably, models specifically designed for time-series data, such as LSTM (R² of 0.9848, RMSE of 1.41 cm), and the more complex 1D CNN-LSTM hybrid model (R² of 0.9803, RMSE of 1.60 cm) did not show significant advantages in this study. This indicates that model complexity and predictive performance are not simply positively correlated. Through comprehensive analysis of data from both stations, this study demonstrates that a prediction framework centered on historical snow depth as the core driving factor, combined with key meteorological elements, is highly robust. Although the inclusion of ERA5-Land snow physical parameters did not significantly improve model accuracy, it provides important insights for the future development of hybrid models that integrate physical mechanisms with data-driven approaches. The findings offer an effective solution for reconstructing long-term snow depth time series and hold significant application value for simulating cryospheric hydrological processes and studying climate change.

Microplastics have become a ubiquitous environmental pollutant, widely distributed across various media within natural ecosystems and human living environments. Currently, scholars have primarily focused on the characteristics of microplastics in natural environments, while research into microplastics within snow cover in agricultural areas remains scarce. This study focuses on the Songnen Plain in Northeast China, where 25 sampling points were selected in February 2023. Through laboratory analysis and modelling techniques, it examines the spatial distribution characteristics and potential sources of microplastics within snow cover on agricultural fields. Results indicate the following: (1) The average abundance of microplastics in snow cover on the Songnen Plain was 1451.07 N·L⁻¹. Fragment-type microplastics predominated, with over 86.38 ± 6.62% of particles concentrated within the 20–50 μm size range. A total of 27 types of microplastic polymers were detected, with polyamide (PA) being the predominant polymer type, while polyethylene (PE), polytetrafluoroethylene (PTFE), and polylactic acid (PLA) were the secondary predominant polymer types. Potential sources of microplastics in snow cover on the Songnen Plain encompass agricultural production activities, emissions from industrial buildings, domestic waste inputs, road traffic sources, and textiles and wastewater. (2) Microplastic abundance in agricultural areas is 1–4 orders of magnitude higher than in natural areas, and comparable to or 1 order of magnitude higher than in urban snow. The particle size of microplastics in farmland snow cover is comparable to that in natural areas, being slightly smaller than that in urban snow. The morphology of microplastics in snow cover farmland is essentially identical between natural and urban areas. The composition of microplastics in snow cover farmland differs from that in natural areas, with polyamide (PA) being the most prevalent component, whereas polyethylene terephthalate (PET) dominates in natural areas. Compared to urban areas, the proportion of polytetrafluoroethylene (PTFE) and polylactic acid (PLA) in snow microplastics is higher in agricultural regions.