Huaibei Plain Research Articles

Hydrogeochemical characteristics and agricultural suitability of shallow groundwater quality in a concentrated coalfield area of Huaibei Plain, China.

Groundwater is one of the chief water sources for agricultural activities in an aggregation of coal mines surrounded by agricultural areas in the Huaibei Plain. However, there have been few reports on whether mining-affected groundwater can be adopted for agricultural irrigation. We attempted to address this question through collecting 71 shallow groundwater samples from 12 coal mining locations. The Piper trilinear chart, the Gibbs diagram, the proportional coefficient of major ions, and principal component analysis were examined to characterize the source, origin, and formation process of groundwater chemical composition. The suitability for agricultural irrigation was evaluated by a final zonation map that establishes a comprehensive weighting model based on analytic hierarchy process and criteria importance though the intercriteria correlation (AHP-CRITIC). The results revealed that the groundwater was classified as marginally alkaline water with a predominant cation of HCO3- and anion of Na+. Total hardness, total dissolved solids, sulfate (SO42-), sodium (Na+), and fluoride (F-) were the primary ions that exceeded the standard. The results also indicated that the dominant hydrochemical facies were Ca-HCO3 and Na-Cl. The dissolution of carbonate, silicate, sulfate minerals, along with cation exchange, were the main natural drivers controlling the hydrogeochemical process of groundwater. The zonation map suggested that 43.17%, 18.85%, and 37.98% of the study area were high, mediate, and low suitability zones, respectively. These results from this study can support policymakers for better managing groundwater associated with a concentration of underground coal mines.

Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage.

Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0-20 cm, the XGBoost model achieved the optimal result (R2 = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up.

Micro-sprinkling irrigation and topsoil compaction improve seedling quality of winter wheat in the Huaibei Plain of China

Micro-sprinkling irrigation and topsoil compaction improve seedling quality of winter wheat in the Huaibei Plain of China

Studies on Root Growth, Yield and Resilience of Winter Wheat Under Waterlogging Control in Huaibei Plain, China

Studies on Root Growth, Yield and Resilience of Winter Wheat Under Waterlogging Control in Huaibei Plain, China

Unveiling antibiotic contamination in surface water: A study of the Huaihe River Basin's Huaibei Plain, a significant Chinese herbal medicine planting region

The abuse of antibiotics has caused the accumulation of antibiotic residues in environmental media, threatening the ecosystem and human health. Many studies on the distribution of aqueous antibiotics have been reported. However, the pollution status of antibiotics in the environment in Chinese herbal medicine planting areas is rarely comprehensively clarified, resulting in the lack of updated pollution data and conducive suggestions for ecological cultivation and sustainable development of Chinese herbal medicine. Thus, we comprehensively investigated the distribution, profiles, sources, and risks of the antibiotics in the surface water of an important tributary of the Huaihe River Basin, located in Bozhou City, a significant Chinese herbal medicine planting region. Solid-phase extraction coupled with an ultra-performance liquid chromatography-tandem mass spectrometer (SPE-UPLC-MS) was utilized to detect the antibiotics in the water. 27 kinds of antibiotics were identified with total concentrations ranging from 75.01 to 1737.99 ng·L−1, with doxycycline (DC) and doxycycline hydrochloride (DCH) possessed the highest concentration. And DC, DCH, oxilinic acid (OA), sulfamethoxazole (SMZ), clarithromycin (CLA), and roxithromycinum (ROX) were the main antibiotics detected in this basin. Correlation analysis and principal component analysis (PCA) indicated that animal husbandry was the primary source of antibiotics. Furthermore, the ecological risk assessment revealed that certain antibiotics could seriously threaten the survival of aquatic organisms, implying that local Chinese herbal medicines might be at similar growth risk. The drinking risk assessment showed that antibiotics in the water posed low risks for human, and children faced a greater drinking risk than adults. The study can help to facilitate the management of aqueous antibiotic pollution for the ecological cultivation and safe production of Chinese herbal medicine.

Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42− while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3−. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl−, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R2, MSE and MAE of RF, SVM, BPNN were 0.43–0.70, 0.16–0.49 and 0.25–0.44. Overall, BPNN showed the best prediction performance and had higher R2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

Multidimensional diversity of birds in small and medium-sized lakes in Huaibei Plain

Multidimensional diversity of birds in small and medium-sized lakes in Huaibei Plain

Quantitative source identification and determination of natural background levels for typical ions in groundwater of the Huaibei Plain, China

Quantitative source identification and determination of natural background levels for typical ions in groundwater of the Huaibei Plain, China

Hydrochemistry, quality, and integrated health risk assessments of groundwater in the Huaibei Plain, China.

Groundwater is an essential freshwater resource utilized in industry, agriculture, and daily life. In the Huaibei Plain (HBP), where groundwater significantly influences socio-economic development, information about its quality, hydrochemistry, and related health risks remains limited. We conducted a comprehensive groundwater sampling in the HBP and examined its rock characteristics, water quality index (WQI), and potential health risks. The results revealed that the primary factors shaping groundwater hydrochemistry were rock dissolution and weathering, cation exchange, and anthropogenic activities. WQI assessment indicated that only 73% of the groundwaters is potable, as Fe2+, Mn2+, NO3-, and F- contents in the water could pose non-carcinogenic hazards to humans. Children were more susceptible to these health risks through oral ingestion than adults. Uncertainty analysis indicated that the probabilities of non-carcinogenic risk were approximately 57% and 31% for children and adults, respectively. Sensitivity analysis further identified fluoride as the primary factor influencing non-carcinogenic risks, indicating that reducing fluoride contamination should be prioritized in future groundwater management in the HBP.

Quantitative Diagnosis of Water Resources Carrying Capacity Obstacle Factors Based on Connection Number and TOPSIS in Huaibei Plain

To further quantitatively assess the water resources carrying capacity (WRCC) system and analyze and identify the regional water resources carrying state and the physical mechanism of the state change, WRCC and obstacle factor diagnosis were carried out. In this paper, we proposed the mobility matrix to determine the connection number components, considered the dynamic attributes of the difference degree coefficient, and calculated it using the semi-partial subtraction set pair potential and triangular fuzzy number, so as to construct the quantitative diagnosis method of regional WRCC obstacle factors based on the connection number and TOPSIS. The results applied to six cities in the Huaibei Plain showed that the WRCC fluctuated around grade 2 and was in a poor state, which was mainly due to the insufficient support force; the water resources carrying state of the six cities gradually improved from 2011 to 2018, but the state became worse in 2019, which was related to the low precipitation in that year, the reduction in water resources, and the high degree of water resource utilization. The WRCC of Fuyang and Huainan was worse than that of the other four cities; over the 9 years, the average grades of Fuyang and Huainan were 2.26 and 2.43, while those of Huainan, Bozhou, Suzhou, and Bengbu were 2.19, 2.12, 2.05, and 2.05, respectively. The key obstacles limiting the improvement in the WRCC of the Huaibei Plain were per capita water resources, annual water production modulus, per capita water supply, vegetation coverage ratio, utilization ratio of water resources, water consumption per 104 yuan value-added by industry, and population density. In time, the key obstacle factors in neighboring years generally tended to have similarity, and conversely appeared as a difference; in space, neighboring regions showed similarity and conversely presented as a difference. The results of this study can offer technical support and a decision-making basis for water resources management in the Huaibei Plain. The method constructed in this paper is extremely interpretive, easy to calculate, highly sensitive, and reliable in application results, which opens up a new perspective for the rational determination of the connection number and the difference degree coefficient and provides a new intelligent way to determine the state of a complex set pair system and its causal mechanism analysis and diagnosis of obstacle factors.

Assessment of Heat Risk of Winter Wheat Cropping Based on Long-Term Meteorological Data

The frequency of heat events is likely to increase due to global climate change, posing an increasing risk to wheat production. To optimize crop management strategies for coping with future climates, it is crucial to quantify the high-temperature occurrence during cropping seasons. Here, sixty-six years (1955~2020) of meteorological data during wheat reproductive growth were collected from six meteorological stations in the Huaibei Plain of Anhui Province. These data were analyzed to quantify the pattern and characteristics of post-anthesis heat stress for wheat crops. Five levels of annual mean daily maximum temperature (Tmax) were defined, from normal to extreme temperatures. Six crop developmental phases of winter wheat, i.e., phase i to phase vi, were divided from flowering to maturity. The data suggest an annual mean temperature of 17~24 °C from flowering to maturity, with an annual effective cumulative temperature ranging from 725 °C d to 956 °C d. The mean temperature and effective cumulative temperature increased as crop growth progressed, along with more frequent heat events during phase ii (8~14 days after anthesis) and phase iii (15~21 days after anthesis). We also found that the frequency of extremely high temperatures (≥33 °C) from 1990 to 2020 was significantly greater than that from 1957 to 1990. Interestingly, it was found that the intensity of post-anthesis night temperatures also increased with crop growth, i.e., from phase i to phase vi. Wheat grain yield increased with increasing effective accumulative temperature and Tmax, but it started to decline when thresholds of effective accumulative temperature and Tmax were reached. Overall, these findings could provide guidelines for winter wheat cropping in the Huaibei Plain, China, or similar climate and cropping regions.

Improving wheat seedling quality through deep ploughing and soil compaction at sowing in lime concretion black soil.

The straw incorporation in lime concretion black soil compromises the emergence and quality of winter wheat seedlings in Huaibei Plain, China, lowering the potential of wheat productivity. To overcome the disadvantage, a two-year field experiment was conducted in 2017-18 and 2018-19 to investigate the effects of different tillage modes on seedling emergence and subsequent seedling growth, and final grain yield (GY) in winter wheat. The modes are rotary tillage with compaction after sowing (RCT), rotary tillage after deep ploughing (PT) and rotary tillage after deep ploughing with compaction after sowing (PCT), with the traditional rotary tillage (RT) method as the control. Compared to RT, greater soil moisture content (SMC) at the seedling stage was observed in deep ploughing or compaction treatment, and the highest SMC was achieved in PCT; the time of reaching the maximum number of seedlings was 1 d sooner in RCT or PT, and 3 d in PCT; the seedling number in RCT, PT and PCT was significantly increased by 32.6%, 34.5% and 61.5% respectively. The population size, shoot and root growth of winter wheat in ploughing mode was significantly enhanced than that of rotary treatment at the over-wintering stage; compared to no compaction after sowing, plant growth in compaction treatments was significantly promoted with greater plant population size and height of seedlings. At harvest, GY in RCT, PT and PCT was significantly improved by 5.87%, 10.8% and 16.4%, respectively, compared to RT and the highest GY was achieved in PCT by up to 8, 350.1 kg ha-1 due to the increased spike number. In conclusion, the seedling quality in the straw incorporation practice was improved through rotary after deep ploughing and compaction after sowing for lime concretion black soil in Huaibei Plain, China or a similar soil type.

Interaction between the hydrochemical environment, dissolved organic matter, and microbial communities in groundwater: A case study of a vegetable cultivation area in Huaibei Plain, China

Intensive vegetable planting has a profound impact on the surrounding aquatic environment. The self-purification ability of groundwater is poor, and it is difficult to return groundwater to its original state once polluted. Therefore, it is necessary to clarify the impact of intensive vegetable planting on groundwater. This study selected the groundwater of a typical intensive vegetable planting base in the Huaibei Plain of China as the research object. This work analyzed the content of major ions, the dissolved organic matter (DOM) composition, and the bacterial community structure in groundwater. Redundancy analysis was used to explore the interactions between the major ions, the DOM composition, and the microbial community. The results showed that under the influence of intensive vegetable planting, the F− and NO3−-N contents in groundwater were significantly increased; the excitation–emission matrix combined with parallel factor analysis identified four fluorescent components (C1 and C2 were humus-like components, while C3 and C4 were protein-like components), which mainly consisted of protein-like components. Proteobacteria was the dominant phylum (mean = 69.27 %), followed by Actinobacteriota (mean = 7.25 %) and Firmicutes (mean = 4.02 %), which together explained over 80 % of the total abundance; and TDS, pH, K+, and C3 were the main influencing factors affecting the microbial community structure. This study provides a better understanding of the impact of intensive vegetable cultivation on groundwater.

Rapid Urbanization Increased the Risk of Agricultural Waterlogging in the Huaibei Plain, China

The drainage modulus is an important indicator in the drainage system design of farmlands. Changes in the drainage modulus determine the effectiveness of drainage projects, and thus agricultural production. Thus, in this research, the trends in the drainage modulus of the Huaibei Plain, China were examined. The drainage modulus was estimated using the average draining method at 16 meteorological stations located in different areas of the Huaibei Plain during the period of 1960–2017. The trends of the drainage modulus were investigated using the Mann–Kendall test and Sen’s slope estimator. The periodicities of the drainage modulus were investigated using wavelet analysis. The major environmental factors affecting the drainage modulus were investigated using the contribution rate method. The results showed that the mean drainage modulus (q1, q3) had increasing trends and significant 2.4-year and 2.5-year periodicities, respectively. An increase in building lots was the main factor that influenced the variability in the drainage modulus. Rapid urbanization increased the risk of agricultural waterlogging. These results provide important references for scientific planning in agriculture and farmland drainage engineering.

Environmental controls on carbon and water fluxes of a wheat-maize rotation cropland over the Huaibei Plain of China

Environmental controls on carbon and water fluxes of a wheat-maize rotation cropland over the Huaibei Plain of China

The Effects of Drought in the Huaibei Plain of China Due to Climate Change

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area’s precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen’s Slope in the baseline and future periods with all RCPs.

Construction of a Time-Variant Integrated Drought Index Based on the GAMLSS Approach and Copula Function

Construction of an integrated drought index is a fundamental task to conducting drought disaster risk management and developing drought resistance planning strategies. Given the evident non-consistent features during the drought evolution process, firstly, the GAMLSS approach was utilized to construct multiple combination scenarios of time-variant parameters and corresponding probability distribution functions. Then, the time-variant comprehensive drought index integrating the variable characteristics of precipitation and soil moisture was established by means of the copula function. Finally, the reliability of the time-variant comprehensive drought index was verified through its application in frequency analysis and return period determination of drought hazard system in Huaibei Plain, China. The application results demonstrated that: (1) The variation of the time-variant integrated drought indicator presented strong consistency with both soil moisture and precipitation during historical years in Huaibei Plain. (2) The overall variation of the drought hazard system characterized by drought duration and severity presented a gradual mitigation trend from west to east and north to south in Huaibei Plain, which agrees with the geographic differences and water resources availability distribution features. (3) Drought recognition results, including the frequency of drought events and typical drought processes with extreme grades, are in agreement with the practical statistics and observed data series. On the whole, the proposed time-variant integrated drought indicator is capable of extracting complex variation characteristics within the drought hazard evolution process, and can be further applied in drought monitoring, recognition and assessment research fields.

Assessment of Agricultural Drought Vulnerability Based on Crop Growth Stages: A Case Study of Huaibei Plain, China

Climate change can lead to and intensify drought disasters. Quantifying the vulnerability of disaster-affected elements is significant for understanding the mechanisms that transform drought intensity into eventual loss. This study proposed a growth-stage-based drought vulnerability index (GDVI) of soybean using meteorological, groundwater, land use, and field experiment data and crop growth model simulation. The CROPGRO-Soybean model was used to simulate crop growth and water deficit. Four growth stages were considered since the sensitivity of soybean to drought is strictly related to the growth stage. The GDVI was applied to the Huaibei Plain, Anhui Province, China, with the goal of quantifying the spatiotemporal characteristics of soybean drought vulnerability in typical years and growth stages. The results show that: (1) The sensitivity of leaf-related parameters exceeded that of other parameters during the vegetative growth stage, whereas the top weight and grain yield showed a higher sensitivity in the reproductive growth stage; (2) A semi-logarithmic law can describe the relationship between the drought sensitivity indicators and the GDVI during the four growth stages. The pod-filling phase is the most vulnerable stage for water deficit and with the highest loss upper limit (over 70%); (3) The 2001 and 2002 seasons were the driest time during 1997−2006. Fuyang and Huainan Cities were more vulnerable to drought than other regions on the Huaibei Plain in 2001, while Huaibei and Suzhou Cities were the most susceptible areas in 2002. The results could provide effective decision support for the categorization of areas vulnerable to droughts.

Hydrochemical Characteristics, Ion Source Analysis and Irrigation Water Quality Evaluation of Rivers in Huaibei Plain

Hydrochemical Characteristics, Ion Source Analysis and Irrigation Water Quality Evaluation of Rivers in Huaibei Plain

Experimental Study on Prevention and Control of Ground Fissures in Coal Mining Subsidence in Huaibei Plain of China

The purpose of this study was to explore the effective prevention and control method of ground fissures in plain coal mining subsidence. Firstly, the model experiment was carried out, and then two typical working faces, working face A (WFA) and working face B (WFB), in the Huaibei plain mining area were selected for the case study of the moisturization method. Model experimental results show that water content had a significant effect on cohesive soil fissure development, and the results of the case study show that the humidification method could effectively reduce the development degree of ground fissures. Therefore, this study provided a new approach for the effective prevention and control of ground fissures in plain coal mining subsidence.