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Conservation management usually carried out for a period of time to maintain the vegetation restoration of coal waste dumps after reclamation. However, the natural restoration of vegetation is faced with great challenges in semi-arid mining areas without management, due to the fragile ecological environment. Therefore, it is necessary to determine a reasonable management plan so that vegetation restoration can reach a stable state although the abandonment of the management. The objective was to explore the difference of vegetation restoration under different management modes in a typical semi-arid mining area. Two reclaimed coal waste dumps under different management measures, the north waste dump (ND) and the south waste dump (SD), were examined in the ShengLi coalfield in Inner Mongolia, China. The normalized difference vegetation index (NDVI) dataset based on Landsat series imagery was obtained using the Google Earth Engine (GEE) platform, and the landscape metrics were also calculated based on different vegetation coverage. The results proved that 3 years of management was not enough to stabilize vegetation restoration. A serious vegetation degradation occurred at the ND after the management stopped, with 40.1% of the pixels recorded a significant decrease (ρ = 0.05). The vegetation coverage became fragmented, and there was a tendency of succession to lower coverage. On the contrary, the vegetation restoration of SD was better under continuous management, and no significant degradation trend was observed. Furthermore, the results indicated that rainfall is the main influencing factor on vegetation restoration in semi-arid mining areas. The coal waste dump was more susceptible to weather change in natural restoration. By contrast, continuous management measures will resist such climate disturbances, even in dry years. This research will provide support for the formulation of the reclamation management plan of coal waste dumps in semi-arid mining areas. [ABSTRACT FROM AUTHOR]

Underground coal mining leads to land subsidence, and the situation is particularly serious in the Coal-Grain Complex in eastern China, causing the crop production to be reduced or to be taken out. Backfilling with Yellow River sediment is one of the effective methods to solve the land subsidence in this area, but a key issue is how to select the optimal soil reconstruction profile so that the crop yield after backfilling and reclamation is unaffected. The main purpose of this study is to verify the feasibility of selecting the optimal soil reconstruction profile by rapid monitoring of crop growth and judging soil quality with the aid of unmanned aerial vehicle systems (UAVs). A control treatment and 13 experimental treatments were established for the study area. The control treatment consisted of using 30 cm topsoil and 90 cm subsoil and the topsoil is a proxy for native (undisturbed) soil from the study sites. All other treatments consisted of using varying combinations of subsoil and sediment overlain by 30 cm of topsoil. The vegetation indices from the UAV multispectral images, and the plant height and vegetation coverage from the UAV RGB images were used for estimation of the winter wheat biomass in a random forest regression. The results showed that the random forest regression model yielded accurate estimation of the aboveground biomass. Furthermore, knowledge of plant height and vegetation coverage improved the accuracy of prediction such that crop growth was well characterized. The optimal soil profile consisted of 0.3 m topsoil + 0.2 m subsoil + 0.2 m sediment + 0.2 m subsoil + 0.3 m sediment. A fast and effective airborne monitoring method for soil quality was established, thus providing greatly improved monitoring efficiency. [ABSTRACT FROM AUTHOR]

In eastern China, coal mining has damaged a large amount of farmland, posing a great threat to food security. Backfilling with coal waste, fly ash, and sediments from rivers is an effective method to restore farmland. This study was conducted at the reclaimed area (RA) and the undisturbed area (UA) in Shandong Province, China. Soil and plant analyzer development (SPAD) of corn was selected as an indicator of crop growth. Multi-spectral data was obtained by the unmanned aerial vehicle equipped with a camera. By analyzing the correlation between SPAD and spectral bands, the common vegetation index is improved. Different regression methods were used to construct the SPAD inversion model. The distribution of corn SPAD was monitored to objectively evaluate reclamation technology. The results are as follows: (1) the vegetation index improved using the red-edge band has a higher correlation with SPAD, and the largest coefficient of determination (R2) value is 0.779; (2) the optimum inversion model for both jointing stage (R2 = 0.676) and milky stage (R2 = 0.661) is the linear regression model; the optimum model for both tasseling stage (R2 = 0.809) and filling stage (R2 = 0.830) is the partial least squares regression model; (3) the SPAD inversion map of RA and UA obtained by the optimum model shows that the corn grown in RA is slightly better than in UA. This study realized the rapid and efficient monitoring of the reclamation effects based on multi-spectral imagery and verified the feasibility of backfilling reclamation with Yellow River sediment in coal mining subsidence areas. [ABSTRACT FROM AUTHOR]

Underground coal mining inevitably causes land subsidence, while negatively impacting land and ecological environments. This is particularly severe in coal-grain overlap areas (CGOA) in eastern China, which have high groundwater levels. Mining subsidence has substantially altered the original topography, and raised the groundwater level, which threatens grain security in the region. Therefore, it is necessary to determine the damaged farmland area in the CGOA. The traditional method to define the range of coal mining disturbance is usually based on surface subsidence. However, this fails to consider the multidimensional impacts of coal mining on the ecology, which is considered unreasonable. Therefore, this paper introduces a low-cost, fast, and non-destructive method for land damage assessment in a typical CGOA in eastern China, using maize aboveground biomass (AGB) as estimated from an unmanned aerial vehicle (UAV). There were three key results from the survey. (1) underground coal mining caused significant ecological problems in the study area, including subsidence (approximately 6 m) and the degradation of vegetation (maize AGB in a range of 192.73–1338.06 g/m2). In addition, the degradation of maize was affected by subsidence (0.61** Pearson coefficient found between the AGB and surface elevation). (2) An UAV combined with multispectral and digital cameras, allowed precise estimation of the AGB and the red-edge chlorophyII index (CIrededge) combined with the elevation factor had the best explanatory power using the random forest (RF) method (R2 = 0.96, RMSE = 65.03 g/cm2). (3) The maize AGB could be used to assess land damage affected by underground coal mining, which accounted for 82.12% of the study area. The results of the study could provide a reference for land damage assessments in the CGOA, while also providing a guide for land reclamation and agricultural management decisions in the region. [ABSTRACT FROM AUTHOR]

The extraction of underground ore body inevitably causes a large amount of land subsidence. Current reclamation technologies in China mainly focus on stable subsided land, which means most of the affected lands are submerged into water because of the high groundwater table in some areas, leading to the loss of soils and inefficient reclamation. Therefore, a new technology for reclaiming unstable subsiding land is being studied for restoring farmland as much as possible, based on a case study in northern Anhui, China. In consideration of the mining plan, subsidence processes in various stages were analyzed and some related factors such as vertical subsidence, post-mining slope, water area, and land use condition were also simulated. Due to mining activities, useful farmland has gradually decreased to merely 14.4 % of the pre-mining area. In this study, the following stages were modeled from pre-mining to post-mining: (1) percentage of farmland was 100 % in stage (a) (pre-mining), (2) 72.5 % in stage (b), (3) 67.3 % in stage (c), and (4) 14.4 % in stage (d) (post-mining). The results show that 86.6 % of cultivated land was submerged into water and lost its capacity for cultivation after coal mining. Reclamation plans for stages (b), (c), and (d) were made by a traditional reclamation method called 'Digging Deep to Fill Shallow'. Based on scenario simulation of reclamation, the farmland reclamation percentages were improved to 78.3, 73.3, and 40.70 %, respectively. Taking the percentage of reclaimed farmland as the preferred standard, concurrent mining and reclamation for stage (b) and (c) could increase farmland reclamation percentages to 37.6 and 32.6 %, respectively, compared with the farmland reclamation percentage of post-mining [reclaiming the land in stage (d)]. The results reveal that optimum reclamation time should be at stage (b). Therefore, under current technical conditions, concurrent mining and reclamation could enhance the quantity of cultivated land and provide better land protection and food security in the mined areas with high groundwater table. [ABSTRACT FROM AUTHOR]

Chinese cabbage ( Brassica rapa L. ssp. pekinensis) is one of the most important vegetables in China. Genetic dissection of leaf mineral accumulation and tolerance to Zn stress is important for the improvement of the nutritional quality of Chinese cabbage by breeding. A mapping population with 183 doubled haploid (DH) lines was used to study the genetics of mineral accumulation and the growth response to Zn. The genetic map was constructed based on 203 AFLPs, 58 SSRs, 22 SRAPs and four ESTPs. The concentration of 11 minerals was determined in leaves for 142 DH lines grown in an open field. In addition shoot dry biomass (SDB) under normal, deficient and excessive Zn nutritional conditions were investigated in hydroponics experiments. Ten QTLs, each explaining 11.1–17.1% of the Na, Mg, P, Al, Fe, Mn, Zn and Sr concentration variance, were identified by multiple-QTL model (MQM) mapping. One common QTL was found affecting SDB under normal, deficient and excessive Zn nutritional conditions. An additional QTL was detected for SDB under Zn excess stress only. These results offer insights into the genetic basis of leaf mineral accumulation and plant growth under Zn stress conditions in Chinese cabbage. [ABSTRACT FROM AUTHOR]

We assess the associations between personality traits and co-occurrence of depressive symptoms and high BMI from adolescence to early adulthood. We employed a nationally representative cohort in China from 2010 to 2020 year. We included adolescents aged 10–19 years without depressive symptoms and unhealthy weight status (obesity, overweight, or thinness) at baseline and excluded those without any measurement of depressive symptoms or BMI at follow-ups. We assessed baseline personality traits in 7 dimensions of conscientiousness, openness, neuroticism, agreeableness, extraversion, self-esteem, and responsibility. We also assessed the combined effects of these 7 dimensions of personality traits by generating individual-level personality trait risk scores based on the weighted sum of all these 7 dimensions of personality traits. We measured the co-occurrence of depressive symptoms and high BMI using both a single measurement of depressive symptoms and BMI at the last follow-up and repeated measurements of them over 10 years. We used the multinomial logistic regression models to examine the exposure-outcome associations. At baseline, we included 1778 individuals (mean age: 14.4 year; female: 853 (48.0%)). At follow-ups, we observed increased risk of co-occurrence of depressive symptoms and high BMI per 1-SD increase in neuroticism score (1.95–2.38 odds ratio) or 1-SD decrease in self-esteem and conscientiousness (0.63–0.80 odds ratio; all P values < 0.05); we observed no evidence of associations between openness, agreeableness, extraversion, or responsibility and the risk of co-occurrence of depressive symptoms and high BMI (all P values > 0.05). For the combined effects of the 7 dimensions of personality traits, we found an elevated risk of co-occurrence of depressive symptoms and high BMI per 1-SD increase in the personality trait risk scores (OR (95% CI), single measurement at the last follow-up: 2.01, 1.66 to 2.43; trajectory classification using the repeated measurements 2.30, 1.55 to 3.42; average level using the repeated measurements: 2.27, 1.93 to 2.67). In this national cohort in China, personality traits were found to be associated with the co-occurrence of depressive symptoms and high BMI from adolescence to early adulthood. These findings highlight the importance of stratifying individuals based on their personality traits and providing targeted interventions for those at risk of comorbid depression and obesity. [ABSTRACT FROM AUTHOR]