Mineral exploitation and energy consumption contribute to significant environmental pollution, threatening ecological stability and human health, especially in large coal mining areas. To evaluate environmental risks in the Yulin coal mining area, a study was carried out on polycyclic aromatic hydrocarbons (PAHs) in a representative river across different seasons. Multivariate statistical analysis and a positive matrix factorization (PMF) model were employed for source identification and quantitative allocation. To assess ecological-health risks linked to PAH concentrations and source orientation, an integrated methodology was applied, combining Monte Carlo simulation (MCS), PMF, and risk assessment models. Among all PAHs, BaA, BaP, BbF, InP, and DahA exhibited elevated concentrations, surpassing USEPA threshold values at many sampling sites. Seasonal variations were observed, with ∑PAH concentrations averaging 185.11 and 128.22 ng∙L-1 in the dry and wet seasons, respectively. PAHs in Kuye River were primarily influenced by coking/petroleum, coal combustion, traffic emissions, and fuel-wood burning, contributing 37.39%, 34.78%, 14.40%, and 13.44%, respectively, to total concentrations. Ecological risks associated with these source-specific PAHs were classified as moderate, mainly attributed to BaA derived from coal combustion sources. Non-carcinogenic risks for all population groups were within safe limits based on the USEPA hazard quotient threshold. In contrast, under conservative exposure assumptions, the average total carcinogenic risks for all six population groups associated with source-specific PAHs exceeded the USEPA acceptable risk level, with a relatively high probability of risk exceedance. Females aged 2 -15 years showed the highest estimated risks, and dermal exposure was estimated as the primary pathway. In addition, traffic emission sources and BaP were inferred as potential priorities for controlling carcinogenic risks. These results are screening-level estimates based on default parameters and require validation using local data. The findings highlight the importance of probabilistic risk assessment methods based on specific pollution sources and provide a scientific basis for comprehensive PAH risk assessment and prevention strategies in coal mining areas.

The accumulation of heavy metals (HMs) from coal resource exploitation and coal-related industries poses serious ecological and health risks in the hinterland river sediments of Shenfu coal field in Northern China. Using data from 59 samples, the geo-accumulation index and ecological health risks of HMs were assessed probabilistically using Monte Carlo Simulation. Two coupling models were constructed in this study, integrating both the Positive Matrix Factorization and ecological health risk models to identify the risk levels of pollution sources in the Kuye River. The risk of HMs during the wet season was explored due to the lower concentrations and a broader range of pollution sources compared to the dry season. The Igeo value of Hg was greater than 1 in 69% of the samples, suggesting moderately contaminated sediments. According to the source-oriented coupling models (PMF-RI/HRA), coal-mining sources contributed to the overall ecological risk by 48.79%, primarily due to Hg (98.50%). Industrial sources (51.48%) were the largest contributors to carcinogenic risk, with Ni corresponding to the target HM. This indicated that different HMs originating from distinct pollution sources were responsible for ecological or carcinogenic health risks. The probabilistic health risk evaluation results indicated that children faced higher risks than adults, with over 94.07% of carcinogenic risks exceeding thresholds. Traffic sources followed, contributing 34.41% to ecological and 41.09% to carcinogenic risks. The results highlight the priority sources and target HMs based on specific sources in the mixed ‘mining-industrial-traffic’ areas, providing valuable insights for environmental protection and the development of risk prevention strategies in a typical energy industry area.

Taking Kuye River， a typical river in the Yulin National Energy and Chemical Base as the research object， 16 polycyclic aromatic hydrocarbons （PAHs） concentrations were measured from 59 water samples collected in the dry and wet seasons. The seasonal distribution characteristics of PAHs were analyzed， and the positive definite matrix factorization model （PMF） was used to analyze the PAHs pollution sources. By combining PMF and the human health risk assessment model （HHR）， the PMF-HHR risk source quantitative analysis coupling model was established， and the contribution of various pollution sources to population health risk was calculated. The results showed that 10 and 16 PAHs were detected in the dry and wet seasons， respectively. The concentration of ∑PAHs in the dry season was higher than that in the wet season， and the ranges of ρ∑PAHs in the dry season were 54.36-369.94 ng·L-1， with an average value of 185.11 ng·L-1， and low ring （2-3 ring） PAHs was the dominant compound， accounting for 89.55% of ∑PAHs on average. During the wet season， the ranges of ρ∑PAHs were 50.06-278.16 ng·L-1， with an average value of 128.22 ng·L-1， mainly middle-low ring （2-4 ring） PAHs， of which the average proportion of low ring （2-3 ring） was 33.22%， and that of the middle ring （4 ring） was 51.41%. PAHs in the Kuye River analyzed by the PMF model mainly came from coking and petroleum emissions （37.39%）， coal combustion （34.78%）， traffic emission （14.40%）， and fuel-wood combustion （13.44%）. The coking and petroleum source and coal combustion source were the main factors affecting the PAHs concentration in the study area. The non-carcinogenic risk of PAHs in the study area could be ignored， but the carcinogenic risk exceeded the significant threshold by 2-5 times. The average contribution rate of pollution sources to carcinogenic risk by the PMF-HHR model was as follows： traffic emissions （36.75%） > coking and petroleum emissions （30.15%） > coal combustion （17.17%） > fuel-wood combustion （15.93%）. Traffic emission sources and BaP were priority control sources and monomer for PAHs carcinogenic risk in the Kuye River. The contribution of the same pollution source to the PAHs concentration and health risk was different. Quantitative analysis PAHs pollution source risk was the key to pollution mitigation and risk control in energy and chemical industry area. It is suggested that the risk source quantitative analysis model should be applied to environmental risk management to reduce human health risk more effectively.

Stock trading signal prediction is very important for investors’ trading decisions. However, since the stock market is a complex and nonlinear system, stock trading is frequent and complex. Human beings cannot integrate all the relevant information in time and make the right decisions by their brains alone. Machine learning can mimic the brain, learn from experience, and discover the connection between different things, thus realizing correct prediction and decision-making. Therefore, this study innovatively proposes a fusion of interpretable embedded multicriteria feature cross-selection engineering to capture effective features. Meanwhile, an optimized neural network prediction model is proposed where the Bayesian (BO) algorithm assumes the task of searching for hyperparameter combinations. The methods are as follow: (1) Daily stock prices are categorized into four types of key points for stock trading signals based on the time series extreme point algorithm. (2) A more comprehensive range of impact factors is constructed. Starting from the stock’s historical trading data, based on the stock’s trend, volatility, and turnover flow, five categories of technical indicators are constructed: Overlap Study, Momentum Indicator, Momentum Indicator, Volatility Indicator, and Price Conversion. (3) To construct a feature cross-selection method with multiple feature screening criteria to find the optimal feature influencing factors from different evaluation dimensions. (4) The hyper-parameters of the Artificial Neural Network (ANN) are optimized using Bayesian optimization algorithm. The optimized ANN is then used to model the data and obtain predictions. Twenty stocks were randomly selected from Shanghai Stock Exchange and Shenzhen Stock Exchange as experimental data to verify the validity of the model. The accuracy of the model proposed in this paper is 54.83

Accurate exchange rate forecasting is important for the better realization of international economic transactions and international currency investments. However, it is challenging to forecast exchange rates due to their high volatility, nonlinearity, and noisy characteristics. This paper creatively proposes a novel interpretable deep learning multi-dimensional integration framework for foreign exchange rate forecasting based on shallow and deep feature selection and snapshot ensemble technology. The main factors affecting exchange rates are considered at a deep and shallow level, and the feature selection project is implemented. A cluster-based multidimensional learning paradigm is proposed. The framework clusters data and constructs predictive sub-models and then embeds snapshot ensemble techniques into models to improve the robustness of single models. Finally, the results are nonlinearly integrated by a deep learning model. In addition, three datasets and twelve comparative models were used to demonstrate the performance of the models. The empirical results show that the proposed model has the smallest mean absolute percentage error (0.407859%, 0.47134%, and 0.470167%, respectively).

Identifying the sources of heavy metals (HMs) in river sediments is crucial to effectively mitigate sediment HM pollution and control its associated ecological risks in coal-mining areas. In this study, ecological risks resulting from different pollution sources were evaluated using an integrated method combining the positive matrix factorization (PMF) and the potential ecological risk index (RI) model. A total of 59 sediment samples were collected from the Kuye River and analyzed for eight HMs (Zn, Cr, Ni, Cu, Pb, As, Cd, and Hg). The obtained results showed that the sediment HM contents were higher than the corresponding soil background values in Shaanxi Province. The average sediment Hg content was 3.42 times higher than the corresponding background value. The PMF results indicated that HMs in the sediments were mainly derived from industrial, traffic, agricultural, and coal-mining sources. The RI values ranged from 26.15 to 483.70. Hg was the major contributor (75%) to the ecological risk in the vicinity of the Yanjiata Industrial Park. According to the PMF-based RI model, coal-mining activities exhibited the strongest impact on the river ecosystem (48.79%), followed, respectively, by traffic (34.41%), industrial (12.70%), and agricultural (4.10%) activities. These results indicated that the major anthropogenic sources contributing to the HM contents in the sediments are not necessarily those posing the greatest ecological risks. The proposed integrated approach in this study was useful in evaluating the ecological risks associated with different anthropogenic sources in the Kuye River, providing valuable suggestions for reducing sediment HM pollution and effectively protecting river ecosystems.

The research aimed to quantify the lake area dynamics, evaluate the changes in distance and rate of lake shorelines quantitatively and spatially and investigate the key factors influencing the Hongjiannao Lake (HL) area shrinkage. The study used remote sensing (RS) data from Landsat TM/ETM+ and OLI images and Google Earth Engine (GEE), a cloud platform for obtaining the lake surface area and island information from 1987 to 2023. A modified normalized difference water index (MNDWI) was applied to water area extraction. Digital Shoreline Analysis System (DSAS) was employed to assess net shoreline movement (NSM) and depict the lake shoreline length and rate changes. Furthermore, the water level was derived by ASTER GDEM V2 using the waterline method and lake boundaries. Six climatic features (temperature, precipitation, potential and actual evaporation, aridity index, and actual water difference) were investigated to find the driving factors of lake area shrinkage by correlation and factor analysis. The results reveal that during 1987-2023, the HL area has undergone four separate phases: stable (1987-1997), shrinkage (1998-2015), growth (2016-2019), and reduction (2020-2023). The most substantial negative change (-7.45%) in the HL area was observed in 1998. NSM analysis demonstrates that the lake has experienced both expansion and shrinkage at various times and locations. According to Water Balance Method, the water volume of HL exhibited variations, ranging from-0.1895 to-0.009 km(3). The average yearly change in lake volume, water level, and area displayed similar characteristics with high inconstancy. Correlation and factor analysis of lake area and climatic factors demonstrate that higher precipitation, low temperatures, less potential evaporation level, lower actual evaporation rates, and more minor differences in water levels are associated with an increase in lake area. In contrast, the opposite conditions lead to a reduction in lake size.

PurposeWith the rapid development of the financial market, stock index futures have been the one of important financial instruments. Predicting stock index futures accurately can bring considerable benefits for investors. However, traditional models do not perform well in stock index futures forecasting. This study put forward a novel hybrid model to improve the predictive accuracy of stock index futures.Design/methodology/approachThis study put forward a multivariate deep learning framework based on extreme gradient boosting (XGBoost) for stock index futures price forecasting. First, the original sequences were decomposed into several sub-sequences by variational mode decomposition (VMD), and these sub-sequences were reconstructed by sample entropy (SE). Second, the gradient boosting decision tree (GBDT) was used to rank the feature importance of influential factors, and the top influential factors were chosen for further prediction. Next, reconstructed sequence and the multiple factors screened were input into the bidirectional gate recurring unit (BiGRU) for modeling. Finally, XGBoost was used to integrate the modeling results.FindingsFor the sake of examining the robustness of the proposed model, CSI 500 stock index futures, NASDAQ 100 index futures, FTSE 100 index futures and CAC 40 index futures are selected as sample data. The empirical consequences demonstrate that the proposed model can serve as an effective tool for stock index futures prediction. In other words, the proposed model can improve the accuracy of stock index futures.Originality/valueIn this paper, an innovative hybrid model is proposed to enhance the predictive accuracy of stock index futures. Meanwhile, this method can be applied in other financial products prediction to achieve better forecasting results.

Accurate exchange rate forecasting is of great importance for foreign exchange investment, hedging foreign exchange risk and international economic transactions. However, it is extremely challenging to make accurate forecasts for exchange rates because of their high volatility, nonlinearity, and non-stationarity. Based on this, this paper proposes a multifactor clustering integration paradigm for exchange rate prediction. From the classical theory of exchange rate determination, a comprehensive library of factors affecting the exchange rate is constructed. A two-stage feature engineering is constructed to capture the stable structure of features. In order to enable features to be learned adequately and to improve algorithmic efficiency and predictive performance, a novel clustering integration paradigm is constructed to improve the stability of the model. The framework builds different predictive sub-models for different data, and then embeds Bayesian optimization algorithms into the bidirectional deep neural networks. Finally, the output results of different sub-models are integrated using nonlinear integration techniques. In addition, the superiority of the proposed model is verified using eight comparative models. The results of the empirical analysis show that the average percentage error of our proposed model is the lowest among all the comparative models (0.412648 %, 0.515348 %, and 0.329892 % on the three datasets, respectively). Compared to the standard LSTM, the average percentage error is at least 88 % lower, proving the effectiveness of the proposed model. It can help investors to make better decisions in the international financial markets.

This study investigated the spatial distribution, pollution source, and ecological risk of polycyclic aromatic hydrocarbons (PAHs) in the Kuye River, which is a typical river in the mining area of China, 16 priority PAHs were quantitatively detected at 59 sampling sites by high-performance liquid chromatography-diode array detector-fluorescence detector. The results showed that the ∑PAHs concentrations in the Kuye River were in the range of 50.06–278.16 ng/L. The PAHs monomer concentrations were in the range 0–121.22 ng/L, of which chrysene had the highest average concentration (36.58 ng/L), followed by benzo[a]anthracene and phenanthrene. In addition, the 4-ring PAHs showed the highest relative abundance in the 59 samples, ranging from 38.59 to 70.85

煤矿废水是造成周边水环境污染的重要因素,通过分析乌兰木伦河石圪台断面水质含量特征、驱动因子和污染趋势,为矿区生态环境建设提供科学依据.从2015年-2021年石圪台断面的历年21项监测数据来看:化学需氧量、氨氮、总磷、高锰酸盐指数、生化需氧量和氟化物6项指标超标率较高,其中高锰酸盐指数、生化需氧量和化学需氧量是影响水质的主要驱动因子;7年期间水质变化趋势显著变好.并结合石圪台断面处的工业排污口、污染源等因素进行溯源分析,对于改善该断面水质具有良好的监督管理作用.

The aim of this study was to assess the occurrence level, spatial distribution, pollution source, and ecological risk of polycyclic aromatic hydrocarbons (PAHs) in the Kuye River of the northern Shaanxi mining area. In total, 16 priority PAHs were quantitatively detected at 59 sampling sites using a high-performance liquid chromatography-diode array detector in series with a fluorescence detector. The results showed that the ρ(ΣPAHs) in the Kuye River ranged from 50.06 to 278.16 ng·L-1, with an average value of 128.22 ng·L-1. The PAHs monomer concentrations ranged from 0 to 121.22 ng·L-1, of which Chrysene had the highest concentration, with average values of 36.58 ng·L-1, respectively, followed by benzo(a)anthracene and phenanthrene. The detection rate of each monomer was more than 70%, of which 12 monomers revealed detection rates of 100%. In addition, the 4-ring PAHs showed the highest relative abundance in the 59 samples, ranging from 38.59% to 70.85%. The PAHs concentrations revealed significant spatial variation in the Kuye River. Moreover, the highest PAHs concentrations were mainly observed in coal mining, industrial, and densely populated areas. Compared with those in other rivers in China and worldwide, the PAHs concentrations in the Kuye River showed a medium pollution level. On the other hand, the positive definite matrix factorization (PMF) and diagnostic ratios were used to quantitatively assess the source apportionment of PAHs in the Kuye River. The results showed that coking and petroleum emissions, coal combustion, fuel-wood combustion, and automobile exhaust emissions contributed to the PAHs concentrations in the industrial areas of the upper reach by 34.67%, 30.62%, 18.11%, and 16.60%, and coal combustion, fuel-wood combustion, and automobile exhaust emissions contributed in the downstream residential areas by 64.93%, 26.20%, and 8.86%. In addition, the results of the ecological risk assessment showed low ecological risks of naphthalene and high ecological risks of benzo(a)anthracene, respectively, whereas the remaining monomers revealed medium ecological risk. Among the 59 sampling sites, only 12 belonged to low ecological risk areas, whereas the remaining sampling sites were at medium to high ecological risks. Moreover, the water area near the Ningtiaota Industrial Park showed a risk value close to the high ecological risk threshold. Therefore, it is urgent to formulate prevention and control measures in the study region.

In this study,59 sediment samples were collected from Kuye River in the hinterland of the northern Shaanxi mining area, and the contents of 8 kinds of heavy metals in the sediments including Zn, Cr, Ni, Cu, Pb, As, Cd and Hg were determined. The positive matrix factorization（ PMF） model was used to quantitatively analyze the sources of heavy metals,and the ecological risk contribution rate of each pollution source was analyzed.The results showed that:（1）The contents of heavy metals in sediments all exceeded the background value of soil in Shaanxi Province, among which the average value of Hg was 3.42 times of the background value, and the samples with high total concentration were mainly located around the industrial parks in the middle and upper reaches.（2）The comprehensive potential ecological risk index ranged from 26.15 to 483.70, with the largest contribution of Hg（mainly from coal mining sources）, contributing up to 75% in some sample sites, followed by Cd（from traffic sources）; 28.8% of the sample sites in the study area had relatively serious ecological risks.（3） PMF source analysis showed that heavy metals in sediments in the study area mainly came from industrial sources, traffic sources, agricultural pollution sources and coal mining sources.（4）PMF-RI risk source analysis results showed that coal mining sources（48.79%） had the greatest impact on the ecosystem, followed by transportation sources（34.41%） and industrial sources（12.70%）, and agricultural pollution sources had the lowest contribution rate（4.10%）. PMF-RI model comprehensively considered factors such as the toxicity coefficient of pollutants on the basis of the contribution concentration of pollution sources analyzed by PMF, which made the analysis results more reasonable. Thus, it is suggested that quantitative assessment of ecological risk sources should be carried out for heavy metals in river sediments in mining areas, and appropriate risk prevention measures should be taken in time.

黄河陕西府谷段流经毛乌素沙漠边缘,生态环境脆弱,具有独特的结构和功能,准确了解地表水体中污染物的污染状况、污染来源是解决环境问题的关键.从2015年～2021年碛塄断面的历年监测数据来看,高锰酸盐指数、COD、BOD5、溶解氧四项指标出现超标,水质定量变化趋势为显著变好.通过分析碛塄断面对应的汇水范围内入河排污口、重点污染源对断面水质的影响,结合上下游、左右岸的行业特征、面源分布,进行溯源分析,研究对于改善黄河中游水质和河流规划目标具有良好的支撑作用.

[目的]分析陕北矿区饮用水源地尤家峁水库的水质变化特征和驱动因素,为中国同类地区饮用水的保护和处理提供技术依据.[方法]于2013-2019年对榆林市饮用水源地尤家峁水库水质进行连续监测,获取了20项水质指标数据,并采用主成分分析法(PCA)和PCA-熵值结合法对监测数据进行分析.[结果]①浑浊度、色度、锰含量是影响尤家峁水库水质的主要驱动因子,高锰酸盐指数、氨氮次之.水质净化的首要任务是除锰.②尤家峁水库2014年水质最差,2015,2016年水质较好;年内冬季水质最好,夏季最差,与降水量、气温变化有明显的相关性.③PCA是一种切实可行的水质主要驱动因子识别方法,与PCA-熵值结合法的计算结果基本一致.[结论]锰污染是尤家峁水库水质恶化的主导因素,夏季锰浓度明显升高,应相应调整水处理工艺或增大药剂投加量,以满足民生饮水安全.

With the increase of mining depth and the extension of mining level, the hydrogeological conditions are more complex, the water head pressure of the floor water filled aquifer with supply conditions is more and more large, and a series of problems such as the connection of mining coal seam and thick limestone caused by regional fault seriously restrict the environmental safety. Therefore, in the actual production process, it is of great practical significance to grasp the water rich distribution law of the water filled aquifer and predict the water drainage of the coal seam floor to ensure the effective prevention and control of mine water disaster and the safe mining of coal. According to the type and characteristics of coal mine water filled aquifer, in the case of lack of hydrogeological drilling this paper establishes a set of relatively systematic evaluation index system of coal mine water filled aquifer's water yield, and applies PNN neural network theory and method to establish a new practical model conforming to the water yield law of the aquifer to study the water filled aquifer's water yield in the study area Make new comments. The results show that the coupling model based on the distribution of sedimentary (microfacies) and PNN neural network is more accurate than the traditional information fusion method. Therefore, this evaluation method is reasonable and effective in the evaluation of water yield of sandstone fracture aquifer in Zhiluo formation.

针对国内外部分水厂出现原水季节性锰、色度超标,出厂水存在异臭味的问题,以陕北榆林高新区水厂为例,在现有工艺基础上,提出KMnO4与PAC联用的处理工艺.首先通过室内模拟试验,确定药剂投加顺序、投加量和预氧化时间等最佳工艺参数,结果表明,先投加KMnO4预氧化5min之后再投加PAC,KMnO4和PAC的最优投加量分别为0.3 mg/L、5 mg/L,相应滤后水的浊度、色度、锰质量浓度分别为0.28 NTU、6度、＜0.05 mg/L.在模拟试验的基础上进行现场投加,结果表明,7-10月KMnO4和PAC质量浓度的生产运行投加量分别为0.3 ～0.6 mg/L、5.0 ～9.0 mg/L,其他季节为0.15 ～0.25 mg/L、2.5～4.5 mg/L;出水浊度、色度、锰质量浓度分别为0.18 ～0.49 NTU、＜5度、＜0.05mg/L,且有效去除了异臭味.KMnO4氧化产物水合二氧化锰对Mn2有很好的吸附作用,因此模拟试验中KMnO4最优投加量低于理论值;而且KMnO4具有助凝作用,减少了PAC的投加量,使单方水的生产成本降低了4％.KMnO4与PAC联用技术在净水厂工艺改造中具有很好的适用性,该研究成果可为原水出现突发性、季节性锰超标的水厂提供经验借鉴.

陕北榆林高新区水厂原水夏季锰、色度明显超标,出厂水存在异臭味问题,主成分分析显示锰是水质变化的主要驱动因子.应用KMnO4预氧化技术可有效去除出厂水锰、色度、浊度和异臭味.小试试验确定KMnO4和PAC最佳投加量为夏季0.25 mg/L和6 mg/L,其他季节0.15 mg/L和3 mg/L.并通过进一步现场运行实践确定了KMnO4和PAC的生产运行投加量.同时,技改后可降低单位水生产成本4％.

The northern Shaanxi province of China has severe water shortages, especially in coal mining areas, and it is very important to calculate the riverine ecological instream flows (EIFs) and analyse the runoff profit‐loss situation. Using the Kuye River as a case study, the EIF was calculated for different years and seasons using the instream flows rate (IFR) method and compared with the Tennant and the minimum monthly average flow (MAF) methods. The recommended value of the Kuye River EIF was obtained by an analysis of the results of these three methods. The river runoff profit‐loss situation associated with the EIF was also calculated and the main reason for the loss explained. The Kuye River EIF was calculated to be 1.69 to 11.14 m3/s by the IFR method, 1.94 to 8.50 m3/s by the Tennant method, and 3.81 to 10.87 m3/s by the MAF method. Based on these results, the EIF annual recommended value of the Kuye River was 4.00 m3/s for the 1961–2010 period. The wet season (July–October), average season (March–June), and dry season (November–following Feb) EIFs were 6.50, 3.50, and 2.00 m3/s, respectively. The Kuye River had a large surplus runoff within the EIF prior to1999, but from 1999 to 2010, the runoff and EIF were very close and the April to June average runoff did not meet the EIF. The main factors that affected the river runoff were rainfall, temperature, water and soil conservation, coal mining, and water consumption for industry and domestic use, with coal mining becoming a more important factor since 1999. This case study provides important technical support and guidance for the ecological restoration of the Kuye River basin, and the concept can be applied to other similar coal mining areas.

Northern Shaanxi Province of China has been affected by severe water shortages, especially in coal mining areas. A systematic method of identifying the influencing factors and their significance on river runoff is essential for paving the way towards environmental protection. Based on stepwise regression analysis, this paper analyzed the runoff influence factors of the Kuye River for 1961-1979, 1980-1998, 1999-2016 and 1961-2016, and calculated the runoff reduction caused by various factors in different periods. We found that the main influence factors on Kuye runoff are disparate in different periods. In 1961-2016, the main factors were rainfall, temperature, soil and water conservation measures, and coal mining. Compared with the base period (1961-1979), the reduction in Kuye runoff stood at 21,569x10(4) m(3) per year in 1980-1998. In 1999-2016, runoff reduction of 52,992x10(4) m(3) was attributable to water conservation measures (57%), temperature (21%), and coal mining (25%)-but was also partially offset by the rainfall increase. These findings could serve as important references for the ecological restoration of river ecosystems in other coal-mining areas.