Forthcoming Articles http://geoscien.neigae.ac.cn EN-US http://geoscien.neigae.ac.cn/EN/current.shtml http://geoscien.neigae.ac.cn 5 2, and the per capita village industrial land is 43.52 m²/person, both of which have a significant positive correlation in spatial distribution. 2) At the regional level, the scale of village industrial land in the eastern region is significantly higher than in other regions, and both the Northeast and Eastern regions have a per capita village industrial land scale exceeding 60 m²/person. The Theil index indicates that the differences within the four major regions dominate the formation of national differences. At the provincial level, only Hebei and Shandong have industrial land scales exceeding 2×10⁵hm², and Qinghai has the largest internal differences in village industrial land. 3) At the national level, rural permanent population, topographic relief and rural per capita disposable income factor have a greater driving force on the scale of village industrial land. At the regional level, urbanization rate, topographic relief and rural per capita disposable income have a greater impact on the scale of village industrial land in the western region, while other regions are mainly affected by rural resident population, topographic relief, and cultivated land area. The interactive detection shows that the scale of village industrial land in various regions is also affected by multiple factors, but there are heterogeneity characteristics. Focusing on the needs of rural industrial revitalization in the new era, this study analyzes the characteristics and trends of rural development in various regions, and puts forward policy suggestions to promote the integration of village industrial land and intensive and economical utilization.]]> μ increases with the increase of groundwater depth H. When the groundwater depth exceeds the maximum rising height of capillary water, the change of complete specific yield is small and can be approximately regarded as a constant. 3) The average groundwater depth of interdune land in the southern edge of Gurbantunggut Desert is 8.80 m. The complete specific yield under the condition of zero surface flux is 0.36, the average releasing specific yield under the condition of deep buried groundwater evaporation is 0.13, and the average charging specific yield under the condition of lateral leakage recharge is 0.17. The results of this study can provide a new idea for the determination of soil specific yield under the condition of deep buried groundwater.]]> Pinus; Fern spore (15.67%-55.29%) is next, dominated by Dicranopteris, Pteris and Polypodiaceae; Then there is herbaceous pollen (9.31%-41.17%), among which the main pollen taxa are Poaceae, Amaranthaceae/Chenopodaceae and Asteraceae. The lowest content of 0.28%-16.01% was found in broad-leaved trees, among which the main pol- len taxa are Chestnut, Quercus, Fabaceae and Euphorbiaceae; The topsoil pollen assemblages can basically re- flect the general characteristics of the vegetation around the depression. 2) The content of herbaceous pollen in paddy field was the highest, reaching 41.17%, mainly in rice-type Gramineae (≥40 μm); Secondary forest, abandoned land and maize field have higher fern spore content, reaching 55.29%, 44.50% and 40.50%, respect- ively. 3) PCA analysis could better distinguished paddy field, maize field, secondary forest, orange forest, and abandoned land, but mulberry forest was not well distinguished, which might be related to the fact that mul- berry genus is wind-borne pollen, as well as the sampling time and pollen preservation condition. 4) The con- centration of pollen in non-agricultural land is significantly higher than that in agricultural land, which may be related to it's upper vegetation cover and difference in topsoil disturbance. The results of this study can provide a reference basis for ecological restoration, Quaternary paleo-environment, and paleo-vegetation reconstruc- tion studies in Karstic desertification areas of Guangxi.]]> SSR was 37.54 MJ/m2 every 10 years, accounting for 0.75% of the multi-year average. The change in maximum temperature was directly influenced by changes in surface solar radiation. In the average situation of Northeast China from 1970 to 1989, the decrease in surface solar radiation weakened the increasing trend of maximum temperature, resulting in a decrease of 0.49℃ every 10 years. From 1993 to 2019, the increase in surface solar radiation strengthened the increasing trend of max- imum temperature in Northeast China, with an increase of approximately 0.09℃ every 10 years. Further ana- lysis revealed that changes in surface solar radiation altered the average temperature and diurnal temperature range by changing the maximum temperature. From 1970 to 1989, it weakened the increasing trend of average temperature in Northeast China and reduced the diurnal temperature range, while it strengthened them from 1993 to 2019.]]> 2 to 8 913.83 hm² and then decreased to 8 001.89 hm² and the number of patches increased from 26 148 to 30 987, and the rate of settlement evolution was 8.11 times of that before relocation, From the natural development model to the accelerated development model; In space, the difference of spatial distribution of settlements is significant, the local aggregation was significant and the whole distribution is small-scale, low-density and high-density multi-band distribution along the valley and hilly area, and a multi-core distribution with the town as the center. The evolution of settlements is mainly influenced by the combined effects of economic activities, policies, topographic slope and urbanization, and the five factors of distance to settlements, slope conditions, distance to scenic spots, townships and urban areas are the main drivers of settlements evolution. Strong changes in rural settlements were mainly influenced by policy factors in 2015 —2020 that contributed to the complexity of rural settlements in the mountainous of Karst, mainly because the implementation of the policy of relocation for poverty alleviation promoted the evolution of rural settlements in Karst mountainous areas, accelerated the development or demise of settlements, and led to the development of settlements in the right direction. Therefore, in the process of rural revitalization, the optimization of settlements should be adjusted according to the diversity of settlements. We should optimize the spatial pattern of rural settlements in accordance with local conditions, strengthen the development of villages themselves, and promote the development of rural revitalization. This study can provide a reference for the optimization of the spatial layout of rural settlements in Karst mountainous areas and the effective implementation of the rural revitalization strategy.]]> 2 to 8 913.83 hm² and then decreased to 8 001.89 hm² and the number of patches increased from 26 148 to 30 987, and the rate of settlement evolution was 8.11 times of that before relocation, From the natural development model to the accelerated development model; In space, the difference of spatial distribution of settlements is significant, the local aggregation was significant and the whole distribution is small-scale, low-density and high-density multi-band distribution along the valley and hilly area, and a multi-core distribution with the town as the center. The evolution of settlements is mainly influenced by the combined effects of economic activities, policies, topographic slope and urbanization, and the five factors of distance to settlements, slope conditions, distance to scenic spots, townships and urban areas are the main drivers of settlements evolution. Strong changes in rural settlements were mainly influenced by policy factors in 2015 —2020 that contributed to the complexity of rural settlements in the mountainous of Karst, mainly because the implementation of the policy of relocation for poverty alleviation promoted the evolution of rural settlements in Karst mountainous areas, accelerated the development or demise of settlements, and led to the development of settlements in the right direction. Therefore, in the process of rural revitalization, the optimization of settlements should be adjusted according to the diversity of settlements. We should optimize the spatial pattern of rural settlements in accordance with local conditions, strengthen the development of villages themselves, and promote the development of rural revitalization. This study can provide a reference for the optimization of the spatial layout of rural settlements in Karst mountainous areas and the effective implementation of the rural revitalization strategy.]]> 2.5 has become increasingly prominent. The atmosphere particulate pollution occurred frequently in Northeast China over past years which is one of the main regions of air pollution. The spatial distribution of near-surface PM_2.5 stations is sparse, and time series of data are short. Therefore, using the near-surface PM_2.5 concentration data can not analyse the variation of reginal air pollution. Based on near-surface PM_2.5 concentration data, MODIS 10 km aerosol optical depth (AOD), ERA5 reanalysis meteorological data and digital elevation model (DEM), multiple linear regression (MLR), linear mixed effects (LME) model and random forest model (RF) were selected to estimate PM_2.5 concentration in Northeast China from 2014 to 2018. The accuracies of three models were evaluated by means of ten-fold cross validation. On that basis, the optimal model was used to simulate daily PM_2.5 concentration in Northeast China from 2009 to 2018. The results showed that: 1) Correlation coefficients (R²) between the estimated and observed PM_2.5 concentration by three models were ranked as RF>LME>MLR. The RF model had the highest accuracy. 2) The R² of the estimated and observed PM25 concentration by RF model was higher than 0.93 in different seasons and months. It was feasible to estimate PM_2.5 concentration in Northeast China by RF model. 3) The annual average PM_2.5 concentration showed interannual trend of first increasing then decreasing in Northeast China from 2009 to 2018. And it had seasonal variation characteristics of winter>spring>autumn> summer. In addition, the average PM25 concentration decreased gradually from southwest to northeast, that was shown as Liaoning>Jilin>Heilongjiang. By establishing a long time-series PM2.3 concentration dataset, this study contributes to estimate the spatial-temporal distribution of PM_2.5 concentration, and also may be useful to analyse the weather change characteristics and formation mechanism of heavy pollution in Northeast China.]]> 9 to 22.25x10⁹ yuan; The flood potential losses rose from 0.39x10⁹ to 5.42x10⁹ yuan, and the expected annual flood damage (EAD) is 0.49x10⁹ yuan/year. These results indicate it is necessary to take comprehensive flood adaptation measures to cope with the potentially increasing flood risk. The framework proposed in this study can provide insights into community-based flood risk analysis.]]>