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Northeast China played an irreplaceable role in population migrations and cultural exchanges in East Asia during the Paleolithic. This paper collected 182 archaeological sites to analyse the characteristics of spatial-temporal variations of Paleolithic human activities in Northeast China, and explored the driving mechanisms behind these variations in combination with the paleoenvironmental and archaeological studies. During the Lower Paleolithic, constrained by the relatively cold-dry climate and blocked by the Songnen paleo-lake basin, the population was sparse and distributed only near 40° N at the southern end of Northeast China. Upon entering the Middle Paleolithic, as humans developed greater tolerance to cold climates and adopted more sophisticated survival strategies with the application of small lithic tools, their numbers increased and geographical distribution expanded northward to 45° N at the central region of Northeast China. The Upper Paleolithic saw a considerable increase in population and expansion across almost all of Northeast China except for the eastern Inner Mongolia Plateau. We speculated that 50 ka and 30 ka BP were two key points, both of which may be associated with a significant increase in the frequency of migrations and communications among humans, and the widespread popularity of highly mobile microblade technology, respectively. In addition, the environmental analysis of archaeological sites revealed a continuous spread of Paleolithic humans to areas with higher elevations, steeper slopes, and lower temperatures. Meanwhile, they were becoming less reliant on water resources and were more widely conducting activities in open-air areas, showing that the environmental adaptability had been continuously improving over time. This study can provide a reference for the reconstruction of the migration history of early humans in East Asia, and is also of great significance for a comprehensive understanding of the evolutionary process of early human-environment interactions. [ABSTRACT FROM AUTHOR]

Soil moisture is an important physical quantity that reflects the surface conditions. Soil moisture retrieval from remote sensing satellite monitoring data is a common method at present and the crucial issue is how to eliminate the influence of other surface and reflect soil parameters like roughness and soil bulk density, and the interference of vegetated areas to electromagnetic wave. In this paper, the Sentinel-1 SAR data, optical data and other auxiliary data such as the Land Surface Data Assimilation System (CLDAS) of the China Meteorological Administration are used as data sources. By coupling the Advanced Integral equation (AIEM) model, a method suitable for the inversion of soil moisture content in the Duolun area is established, and the inverted soil moisture map is analyzed. [ABSTRACT FROM AUTHOR]

The unavoidable wind power prediction error poses a challenge for wind power to participate in day-ahead scheduling plan. This paper proposes a two-stage correction method considering NWP wind speed and power correction. Firstly, this paper fully considers the historical similarity of NWP wind speed, and adopts the improved clustering distance for segment matching and wind speed correction based on the extracted most relevant trend component, which enhances the utilization of NWP information and avoids the randomness and uncertainty of the intelligent correction method, and gram angular field (GAF) and stacked autoencoder (SAE) are used for feature extraction and conditional variational autoencoder (CVA) is used to generate specific samples to ensure the adequacy of the clustering process. Secondly, considering the historical similarity of error of predicted power, the power is further corrected based on the proposed weighted double-constraint to realize the secondary calibration. The Proposed method is applied to several wind farms in China to verify its effectiveness. The results show that the proposed method reduces the N RMSE by 3.82 % and N MAE by 3.40 % compared with direct prediction in the wind farms in western Inner Mongolia, which is important for promoting wind power consumption and maintaining the safe and stable operation of the power system. • Propose a method to extract the most relevant trend component form wind speed. • Propose a novel method for wind speed correction and power calibration. • Propose a sample condition generation method based on GAF and CVA. • Propose an improved clustering distance into the wind speed correction process. [ABSTRACT FROM AUTHOR]

• This paper analyzes whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. • There is a nonlinear relationship between financial support for ecological restoration and grassland vegetation coverage. • Different fiscal expenditure structure has a heterogeneous response. • The degree of fiscal decentralization has a significant positive regulatory effect on the ecological benefits. Grassland ecological restoration is an important cornerstone to maintain the sustainable development of grassland ecosystem. One of the goals of financial support for grassland ecological protection and restoration is whether the ecological benefits of grassland are fully realized, and grassland vegetation coverage is the main index to measure the ecological benefits. This paper aims to analyze whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. and then verify whether there is room for improvement in the fiscal effect of grassland ecological protection and restoration? Based on the panel data of 54 animal husbandry and semi-agricultural and semi-animal husbandry counties in Inner Mongolia Autonomous Region of China, this paper adopts a bidirectional fixed effect model and cross-fixed effect model to introduce natural factors affecting grassland vegetation coverage into the model and systematically examines the financial effects of ecological benefits of financial support for grassland ecological protection and restoration. The main findings are as follows: (1) there is a nonlinear relationship between financial support for grassland ecological protection and restoration and grassland vegetation coverage when natural factors are taken into account in the model. (2) different fiscal expenditure structure has a heterogeneous response to the impact of grassland ecological protection and restoration on ecological benefits. (3) the degree of fiscal decentralization and the relative performance of the government have a significant positive moderating effect on the ecological benefits of fiscal support for grassland ecological protection and restoration. [ABSTRACT FROM AUTHOR]

• The urban development stage in Inner Mongolia was evaluated. • A comprehensive coupling analysis framework was constructed. • The coupling and coordination commonly shows a growing "basic coordination" state. • Five subsystems of ecological carrying capacity has different effects on the degree of coupling and coordination. With the rapid economic development, the imbalance between the economy and the environment is evident for Chinese cities. The coupling coordination degree (CCD) between urban land use efficiency (ULUE) and ecological carrying capacity (ECC) represents resource-intensive societies and is a significant tool for assessing sustainable development. Therefore, scientific evaluation of the coordinating relationship between ULUE and ECC is essential for optimizing the quality of urbanization and improving the state of the ecological environment. However, current studies on the CCD of ECC and ULUE lack quantitative assessment of the effect of ECC subsystems on overall CCD and have not yet evaluated the urban development stages. In this paper, taking 12 cities in Inner Mongolia as an example, a framework for CCD evaluation that includes comprehensive index analysis, coupling coordination quantification, coupling type identification, and coordination influence evaluation was created. This paper attempts to clarify the distinctions throughout cities and to propose distinctive development suggestions. The results indicate that the ULUE of Inner Mongolian cities has risen significantly, whereas their ECC levels fluctuate, growing at a slower rate than ULUE. As the level of coordination between the two systems increases, it changes from "basic disorders" to "basic coordination". In terms of spatial distribution, the CCD in Inner Mongolia has a pattern of "high in the northeast and low in the southwest". The pressure subsystem primarily performs a reverse blocking role in the CCD between ULUE and ECC, while the rest of the ECC subsystems exhibit different patterns over time. Additionally, the stages of urban development were investigated based on the temporal variations in ECC and ULUE, and several focused suggestions were provided. [ABSTRACT FROM AUTHOR]

• This paper aims at quantifying the factors affecting vegetation growth in the western of Inner Mongolia, and judging the relative contribution of natural factors and man-made factors. • It is obtained through geodetectors (GD) that Rainfall is the most important driving factor, with the q-value of 0.77 and GDP also having a huge impact on vegetation growth, q-value of 0.64. • Different from most studies, human activity also has a positive effect on vegetation growth. • The relative contribution of human activities reached 51.75%, slightly exceeding the influence of natural factors. Vegetation is an essential component of terrestrial ecosystems, and understanding the drivers of vegetation change is of great importance for ecological management. In recent years, vegetation growth has increased under the combined effect of global warming and human activities in Inner Mongolia. The net primary productivity (NPP) was used as an indicator to study the spatial and temporal changes in vegetation in the Qimeng Region (QR). The residual trend analysis method was used to analyze the relative contributions of climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers were explored using a geographical detector approach to quantify the driving forces of NPP. The results show that (1) NPP exhibited a fluctuating growth trend from 2003 to 2020, with an overall growth rate of 2.91%/year. (2) Precipitation, GDP and population density were the dominant driving factors for the spatial distribution of NPP. The combined explanatory power of any two dominant factors exceeded the power of any dominant individual factor, and the interaction between climate and human factors had a significant effect on NPP. (3) The change in NPP was influenced by the combined effect of HA and CV, accounting for 37.69% of the total area, with the relative contribution of HA being 51.75%. Finally, the relative contribution of human activities was slightly higher than that of climate change, confirming the initial success of the Grain to Green Program as well as ecological conservation projects. This paper provides a scientific basis for the local government to carry out the conversion of cropland to forest. [ABSTRACT FROM AUTHOR]

[Display omitted] • The concept of industrial ecology (IE) was defined based on symbiosis theory. • IE was evaluated from the functional and structural dimensions of the IE system. • Evaluation subjects of IES include symbiotic units, patterns, and environment. • The overall IE level in the Area has improved with Inner Mongolia standing out. • The industrial and ecological development remained isolated or mutual restriction. This paper defined regional industrial ecology (IE) to include four connotations: sustainable social-economic-ecological development, the high-quality transformation of the industrial system (IS) and the ecological system (ES), and harmonious symbiosis between IS and ES. Regarding the composition of the symbiotic system, this paper proposed that the IE evaluation be carried out from three aspects: the sustainability of the symbiotic environment, the high-quality development of the symbiont, and the symbiotic pattern between the symbiotic units. Then, the maturity index determined by the symbiont and symbiotic mode and the orderliness index determined by the symbiont and symbiotic environment were measured to determine the IE level from the perspectives of the system's function and structure. Besides, this paper adopted Geodetector and Oprobit model to identify the influencing factors of IE. Taking the π-shaped Curve Area of the Yellow River as the study area, this paper found that the IE systems' structural orderliness of capital cities and resource-based cities was low, while the orderliness level of non-resource-based cities was high. The IE systems' functional maturity level in the northwest Area was better than that of the southeast. The IE level was high in Inner Mongolia while low in Shanxi and Ningxia. Scientific innovation, economic development, openness degree, ecological pressure, industrial structure, urbanization level, and government influence have different influences on the IE systems' orderliness and maturity levels. The findings of this paper provided a basis for realizing IE and promoting the high-quality transformation of the whole basin. [ABSTRACT FROM AUTHOR]

In this paper, the effects of Eu, Nd doping, and Eu–Nd co-doping on the stability, magnetic and optical properties of monolayer MoS 2 are investigated by the density functional theory based on the first principle's calculations. It was found that the Eu–Nd co-doped monolayer MoS 2 is more stable than other considered doping structures. After the introduction of Eu and Nd atoms, the band gap are decreased to different degrees, indicating that the doping is favorable to the electronic jump, which might enhances the conductivity of the system. All doped systems exhibit magnetic properties. In addition, the absorption spectra of all doped systems show significant red-shift compared with those of pure monolayer MoS 2 , indicating that the doping improves the absorption capacity in visible light. These results might provide theoretical guidance for the design of novel monolayer MoS 2 semiconductor materials. • First Inner Mongolia has rich rare earth mineral resources, and rare earth elements can change the electronic structure and magnetic properties of the material, thus improving the conductivity, magnetic properties, optical properties and so on. • Secondly, MoS 2 is a popular material at the forefront, and with its special layered structure and unique properties, it is widely used in electronic devices, catalysts, biomedicine and other fields. • Finally, the band gap of monolayer MoS 2 is significantly changed after doping with rare earth elements, and there are multiple impurity energy levels (ILS) within the band gap, which is helpful for electron excited leaps. • Moreover, all doped systems showed magnetic properties. [ABSTRACT FROM AUTHOR]

• Two-dimensional space theory is applied in soil salt content inversion. • Sentinel-1 and Sentinel-2 data are fused by two-dimensional space theory. • Sentinel-1 after H/a polarization is applied in soil salt content inversion. Multi-spectral satellite remote sensing is widely used in soil salinity monitoring by virtue of its wide coverage, fast update and low cost, but it is susceptible to cloudy weather, limiting the accuracy and application conditions of the inversion, while SAR (synthetic aperture radar) is hardly affected by cloud with strong penetration ability and enables almost round-the-clock, climate-wide monitoring of soil salinity. Two-dimensional feature space model, being easily interpretive and widely used in soil salinity monitoring, requires only some easily extracted parameters and can reduce other influences on soil salinity inversion. Therefore, in order to take full advantages of the SAR and multi-spectral satellite, achieve monitoring of soil salinity in more climatic conditions and longer time, and further improve soil salinity monitoring accuracy, this paper combine SAR-Spectral data with two-dimensional space theory to construct two-dimensional models for monitoring soil salinity. In this study, two-dimensional space models are constructed using Sentinel-1 and Sentinel-2 feature parameters, and models are evaluated with R2 with measured soil salinity data of the Shahaoqu irrigation area in Hetao irrigation district, Inner Mongolia. The results show that it is feasible to use the parameters extracted from Sentinel-1 after H/a polarization for constructing two-dimensional feature space models for the soil salinity inversion(R2 parameters of Sentinel-1 = 0.467,0.587,0.517), and the two-dimensional space models combining Sentinel-1/2 parameters are more accurate and stable in soil salinity monitoring than those using Sentinel-1 or Sentinel-2 alone((R parameters of Sentinel-2 = 0.320,0.517,0.341),(R2 parameters of Sentinel-1/2 = 0.507,0.545,0.562,0.512,0.557,0.568)). [ABSTRACT FROM AUTHOR]

Uranium mainly comes from ISL of sandstone-type uranium deposits in China. The change of porosity and permeability caused by blockage of ore-bearing strata is one of the most serious problems in acid ISL of uranium. In this paper, the groundwater tracer test was carried out before and 1 year after ISL to explore the pore and permeability evolution characteristics of the ore-bearing layer during ISL. The test results showed that the leaching solution migrated along two seepage channels and the water-bearing medium was isotropic. After 1 year of ISL, the flow rate of the leaching solution decreased obviously. However, the flow rate of the leaching solution in slower channel decreased more than that in the faster channel in all directions, which was caused by the more adequate chemical reactions between the leaching solution and the minerals of the ore-bearing layer and the more corresponding precipitation in the slower channel. In addition, the flow rate along the direction of groundwater flow decreased less than that in the direction of vertical groundwater flow. This was closely related to the transformation of aquifer medium by hydrodynamic field. Initial stage of ISL, the occurrence of plugging is closely related to the precipitation-dissolution process of iron and aluminum minerals under the change of pH, which is accompanied by the continuous precipitation of gypsum. • Groundwater tracing tests before and after in-situ leaching were carried out. • The pore and permeability evolution characteristics of the ore-bearing layer was analyzed. • The plugging of pores in different directions was clarified. • The relationship between dissolution-precipitation process and permeability was discussed. [ABSTRACT FROM AUTHOR]

• The training time of Long Short-Term Memory is reduced by changing network structure. • A new hybrid model is proposed for wind speed forecasting. • Four experiments are designed to verify the performance and reliability of the model. Obtaining accurate wind speed forecast result plays a decisive role in ensuring the reliable operation of the power system integrated with large-scale wind power. Deep learning methods are increasingly being used to predict wind speed, which have relatively high prediction accuracy but more time-consuming training processes. The purposes of this study are further to improve the prediction accuracy of the wind speed and reduce the training time of the deep learning method. In this paper, a novel hybrid model, New Cell Update Long Short-Term Memory combined with Empirical Wavelet Transform, is proposed to increase the prediction accuracy in shorter training time. At first, the original wind speed sequence is preprocessed into a series of sub-sequence by the empirical wavelet decomposition. Then each sub-sequence is trained by New Cell Update Long Short-Term Memory which is proposed by this paper respectively and the sum of each sub-sequence is treated as the final prediction results. In order to verify the performance of the proposed model, different decomposition methods and different prediction methods are compared on the four actual wind speed prediction cases in the Inner Mongolia, China from prediction accuracy and training time. The results demonstrate that: (1) New Cell Update Long Short-Term Memory network has slightly higher prediction accuracy and shorter training time than Long Short-Term Memory network. (2) The prediction accuracy of the model is significantly improved after empirical wavelet decomposition. Therefore, New Cell Update Long Short-Term Memory network combined with empirical wavelet decomposition is a competitive wind speed prediction method compared to the existing state-of-the-art approach. [ABSTRACT FROM AUTHOR]

Automatic identification of breeds of sheep can be valuable to the sheep industry. Due to the lack of open source datasets of multiple breeds in some researches, which leads to poor robustness of models for automatic sheep breed recognition. This paper proposes a sheep face image dataset of 10 breeds, which consists of 5200 original images of 823 sheep from seven farms in Inner Mongolia. The dataset is expanded to 44,747 images after data enhancement. A sheep breed recognition model DT-YOLOv5s based on knowledge distillation is proposed for the dataset. The model transfers the knowledge of sheep face features learned by the teacher network YOLOv5x, which has a large number of parameters but high recognition accuracy, to the lightweight student network YOLOv5s through a 15-dimensional high-dimensional semantic feature vector, so as to improve the student network's PANet feature extraction capability and recognition accuracy, achieving the purpose of lightweight and accurate recognition of the overall network model. The experimental results show that the accuracy of our proposed model is 92.75%, which is 11.19%, 13.1% and 13.65% higher than that of SSD, Faster R-CNN and YOLOv5s models, respectively. The mean average precision (mAP @[0.5:0.95]) is 94.67%, which is 7.85%, 2.35% and 4.83% higher than that of SSD, Faster R-CNN and YOLOv5s models, respectively. Meanwhile, the inference time of the model is 12.63 ms, which is 33.9 ms, 53.13 ms and 74.61 ms less than SSD, Faster R-CNN and YOLOv5x, respectively. The proposed DT-YOLOv5s model in this paper has good robustness and generalization ability, which can provide technical support for the real-time accurate identification of the sheep breed for farmers. [ABSTRACT FROM AUTHOR]

Forecasting wind power is crucial to ensuring the electricity system runs steadily. However, wind power forecasting is hampered by the randomness and volatility of wind power data. In this paper, a deterministic and probabilistic decomposition ensemble model based on improved feature screening and optimal Gaussian mixed kernel function is proposed, which mainly includes feature screening, feature learning, intelligent weighted integration and interval prediction modules. First, an improved feature screening (IFS) is proposed to capture the optimal features of the original wind power data, eliminating the interference of redundant features. Then an optimized deep neural network is suggested to learn the outcomes of IFS and further explore the temporal correlation of these features. Then, considering the difference of the contribution of each feature component, the final result is obtained by integrating the preliminary forecast results using intelligent weighted integration (IWI). Finally, an optimal mixed kernel-based Gaussian process regression model is developed for interval prediction, which improves the adaptability of the model. The data sets of two wind farms in Inner Mongolia were used for empirical testing. On the two datasets, the model's root mean square error (RMSE) is 1.1845 and 1.7354, respectively, both of which are lower than the comparison model. The model has the lowest coverage width criterion (CWC) of 0.1499 and 0.1460 on the two datasets, respectively. [ABSTRACT FROM AUTHOR]

Grain size is commonly used as a proxy to reconstruct past lake hydrology; however, published research rarely links grain size with modern processes, and uncertainties exist for interpretation. In this paper, we decompose the grain size frequency distributions (GSFD) for 196 lake surface sediments from 61 lakes and 9 reservoirs across the Inner Mongolia Plateau and adjacent areas and divide them into five GSFD components. We statistically analyze the relationship between the GSFD components and influencing variables to determine the taphonomic dynamics and their environmental implications. Within individual lakes, we found that GSFD component 4 (74.00–209.30 μm) and GSFD component 5 (209.30–592.00 μm) dominate near the lake margin and reduce toward the lake center in proportions. By contrast, GSFD component 2 (2.75–15.56 μm) increases toward the lake center. Therefore, the proportions of GSFD component 2, 4, and 5 reflect the relative distance to the lakeshore and allow inferences about lake area changes. We apply this large modern dataset to a sediment core obtained from Lake Wulanhushao (WLHS) on the Inner Mongolia Plateau and link the lake surface sediment-based GSFD components to BEMMA end-members of grain size records to define their environmental implications over the last 18.59 cal. kyrs BP. We found that the Lake WLHS has responded to the orbital and suborbital climate changes since the last deglaciation. Specifically, maximum lake transgression prevailed during the early to middle Holocene (11.70–7.10 cal. kyr BP), medium lake transgression dominated during 17.05–15.80 cal. kyr BP, and weak transgression developed during 14.60–12.90 cal. kyr BP. Maximum lake regressions prevailed before 17.05 cal. kyr BP, during 15.80–14.60 cal. kyr BP and 12.90–11.70 cal. kyr BP. Our reconstructed early Holocene maximum lake area is consistent with regional abiotic proxy-based moisture records, but contrasts with the regional biotic proxy-based moisture records, which reconstruct increasing moisture conditions. Our new method to determine lake area changes will provide valuable data to understand the climate dynamics of northern China. • We numerically revealed the environmental implications of grain size components of lake surface sediment. • Linking lake surface sediment grain size component to core end members provide basis for interpretations. • Maximum lake transgression prevailed during the early Holocene in northern China. • Abiotic proxies-based early Holocene lake transgression is distinct with biotic proxies' reconstructions. [ABSTRACT FROM AUTHOR]

Coal is an important energy source worldwide. Objectively and accurately predicting coal consumption is conducive to healthy coal industry development, because such predictions can provide references and warnings that are useful in formulating energy strategies and implementing environmental policies. Population size and area economic development are the main factors that affect coal consumption. Considering the above influences, this paper first establishes a differential equation and proposes a novel multivariable Verhulst grey model (MVGM(1,N)) based on grey information differences. MVGM(1,N) extends classical model from single-variable to multivariate and diminishes the characteristics of Verhulst's reliance on saturated S-shaped and single-peak data, making classical model more applicable to real situations. To prove the effectiveness of MVGM(1,N) simulation experiments are carried out in areas with high coal consumption. The result of this proposed model is more precise than that of NLARX, ARIMA and five classical grey models Finally, this novel multivariable model predicates coal consumption of Inner Mongolia and Gansu Provinces in China, the results show that MVGM(1,N) is preferable to other models, indicating that this model can effectively predict coal consumption. • Based on the impact of GDP and population on coal consumption, a differential equation is established. • A new multivariable Verhulst grey prediction model (MVGM(1,n)) is established. • The comparative study shows that the new model is superior to the other seven benchmark models. • Coal consumption have been successfully predicted. [ABSTRACT FROM AUTHOR]

Wind power is recognized as one of the most promising renewable and clean energy sources under the context of the increasing depletion of fossil fuels. The exact wind speed forecasting has great significance for the large-scale connection of wind farms with the power grid. In light of this, this paper contributes to establishing a novel hybrid model that can predict the future wind speed accurately. Firstly, the original wind speed time series is decomposed by the fast ensemble empirical mode decomposition into several sub-series that are further integrated by the runs test. The phase space reconstruction is used to dynamically choose each integrated sub-series' input and output vectors for the prediction model. Additionally, an improved whale optimization algorithm is exploited to optimize the weights and bias of the extreme learning machine. Finally, prediction results are obtained from the aggregation of each integrated sub-series prediction. To verify the accuracy and applicability of the proposed hybrid model, we apply several comparative models to conducted two case studies using different wind speed time series from Inner Mongolia that is Asia's largest gathering area of wind power farms. According to the experimental results, it can be concluded that the decomposition reduces the volatility and randomness of wind speed, and the runs test lowers the forecasting complexity. The phase space reconstruction can capture the chaotic property of wind speed series. The optimization for the whale optimization algorithm enhances its global and local optimization ability to further improve the performance of extreme learning machine. Overall, the proposed hybrid model can effectively capture the non-linear characteristics of wind speed series. • A novel hybrid short-term wind speed prediction model is proposed. •The model of FEEMD-RT-PSR is used for the data preprocessing. •Whale optimization algorithm is optimized. •The performance of extreme learning machine is improved. [ABSTRACT FROM AUTHOR]

• Taking the high-quality development of the Yellow River basin as the background. • Proposing principles for the initial water rights allocation in Yellow River basin. • Analyzing the priority of Industry water use in the Yellow River basin. • Constructing a model for initial water rights allocation with water-use priorities. • Calculated the initial water rights of Hetao Irrigation District. In September 2019, the Chinese government elevated ecological protection and high-quality development of the Yellow River basin to a major national strategy. However, the scarcity and inequitable distribution of water resources in the basin remain a tough and urgent problem. Therefore, this study probed into the initial water rights allocation (IWRA) of Industry in the Yellow River basin driven by high-quality development. First, based on the status quo of water resources in the basin, this study proposed principles of IWRA of Industry were total quantity control, safeguard, fairness, and efficiency. Next, the paper set the Industry water priority based on the configuration rules, constructed a goal programming model for IWRA of Industry, anticipated the ideal value via the gray model(1,1)(GM (1,1)), and suggested IWRA for end-users through both quota method and water-demand proportion. Last, this study calculated that the initial water rights (IWR) for Hetao Irrigation District in Bayannur city of Inner Mongolia should be 4.38 billion m3. Theories and methods put forward in this paper helps enhance water resources management and improve the high-quality development of the Yellow River basin. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we used transmission electron microscopy (TEM) to study metal-bearing particles in the Gobi Desert soil of the concealed Xiaohulishan deposit in north-western Inner Mongolia. By analysing the category, size, shape, and chemical composition of metal particles in soil samples, as well as their co-occurrence with soil mineral particles in the surface overburden, we determined the characteristics of the metal particles that are related to mineralization. Along with the geological setting, this information can provide insight into prospecting the concealed ore body based on the surface overburden particles. In addition, studying the association between metal particles and soil mineral particles allows us to summarize the adsorption properties of soil minerals for metal particles, and analysing the metal particles carried by ascending gas helps us determine the mechanism by which metal particles are transported by ascending gas flow in the surface overburden. This paper discusses both the natural metal particles and the soil mineral particles that contain metals. We propose that it is possible to prospect a concealed ore body using the characteristics of surface overburden particles, and we summarize the preferences and behaviour of metal particles (mainly Fe-, Mo-, Cu-, Pb- and Zn-bearing particles) adsorbed by soil minerals such as Ca sulphates, silicates, carbonate minerals, and clay minerals. This knowledge is significant because it can help explain the migration mechanisms of metal particles in the soil-covered areas of the Gobi Desert. These findings will also help to explore concealed deposits by facilitating the use of elemental concentrations, as well as the characteristics of metal particles carried by ascending gas flow. [ABSTRACT FROM AUTHOR]

Green ammonia production as an important application for propelling the upcoming hydrogen economy has not been paid much attention by China, the world's largest ammonia producer. As a result, related studies are limited. This paper explores potential supply chain design and planning strategies of green ammonia production in the next decade of China with a case study in Inner Mongolia. A hybrid optimization-based simulation approach is applied, considering traditional optimization approaches are insufficient to address uncertainties and dynamics in a long-term energy transition. Results show that the production cost of green ammonia will be at least twice that of the current level due to higher costs of hydrogen supply. Production accounts for the largest share of the total expense of green hydrogen (~80 %). The decline of electricity and electrolyser prices are key in driving down the overall costs. In addition, by-product oxygen is also considered in the model to assess its economic benefits. We found that by-product oxygen sales could partly reduce the total expense of green hydrogen (~12 % at a price of USD 85/t), but it also should be noted that the volatile price of oxygen may pose uncertainties and risks to the effectiveness of the offset. Since the case study may represent the favourable conditions in China due to the abundant renewable energy resources and large-scale ammonia industry in this region, we propose to take a moderate step towards green ammonia production, and policies should be focused on reducing the electricity price and capital investments in green hydrogen production. We assume the findings and implications are informative to planning future green ammonia production in China. • The H 2 -based energy transition in China's ammonia industry is explored. • A hybrid approach for green ammonia supply chain design and planning is proposed. • The cost of green ammonia will be at least twice that of the current level. • Electricity price and expenses in electrolysers are key impacts to the total cost. • Storing and selling by-product oxygen can partly reduce the total cost. [ABSTRACT FROM AUTHOR]

The northern edge of the East Asian monsoon region is also a transition zone of temperate continental and monsoon climate, which is a sensitive zone of global climate and ecological environment change. It is an urgent problem to find out the long-term climate change rules of this region. Using 101 tree core samples obtained from 54 trees, a 343-year tree-ring width chronology of the Platycladus orientalis (L.) Franco (1673–2016) in the Wula Mountains, Inner Mongolia, China was established. The analysis shows that the radial growth of P. orientalis is closely related to hydrothermal conditions, the average minimum temperature and relative humidity are the main factors that determine the growth of P. orientalis. Based on the strong correlation between the tree-ring indices and the annual average minimum temperature (r = −0.870, p < 0.001), we reconstructed an average annual minimum temperature series from 1673 to 2016 using a linear regression equation, and the reconstructed equation can explain 75.724% of the variance, respectively. According to the data from the past 343 years, the changes in the annual average minimum temperature series had the cycles of 58.6-, 7.7-, 3.4-, 3.1-, 2.2-, and 2.1-years; the series showed three warm periods (1824–1830, 1909–1922, and 1986–2011) and 4 cold periods (1703–1712, 1731–1738, 1754–1763, and 1937–1951). The low temperature periods recorded in the reconstructed series for the 17th and 18th centuries are consistent with the Little Ice Age that occurred in the northern hemisphere, and the heating rate significantly increased at the end of the 20th century. In addition, the cold and warm periods recorded by the reconstruction result is basically consistent with the extreme climate events recorded in the historical documents of the region and the reconstruction results in other regions. In this paper, the world's first chronology of P. orientalis tree ring width was established. A historical sequence of annual average minimum temperature of 343 years was reconstructed in a typical region of the East Asian monsoon climate. It fills in the blank of reconstruction sequence and variation characteristics of mean minimum temperature in the northern edge of East Asian monsoon and transition zone of temperate continental and monsoon climate in recent four centuries. [ABSTRACT FROM AUTHOR]

[Display omitted] • Predict the evolution of land use and landscape ecological risk. • Couple landscape ecological risk and ecological security network. • Construct an ecological security network for multiple future development scenarios. • Explore sustainable development scenario for Northeast China. • Make recommendations for ecological security network optimization. The expansion of human activities has significantly increased ecological risks, which threaten the security of ecosystems. This paper takes Northeast China, a national typical ecological barrier region, as a case study to explore the evolution of the ecological environment under various future development scenarios. Considering the complexity of ecological assessments, a comprehensive approach integrating various methods is employed. This includes the Patch-based Land Use Simulation (PLUS) model, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, a landscape ecological risk assessment index system, the Space Minimum Cumulative Resistance (MCR) model, among others. These methods are used to dynamically assess changes in land use, landscape ecological risk (LER), and the layout of ecological networks. An integrated framework is proposed for identifying, constructing, and optimizing the ecological security network (ESN). The results show that the LER in Northeast China presents a "high in the middle, low around" distribution characteristic. High risk areas are mainly distributed in water bodies and construction lands with high landscape fragmentation and isolation, while low risk areas are primarily in the Northeast forest belt and Inner Mongolia grassland areas with lower landscape vulnerability and separation. From 2000 to 2020, the LER in the Northeast China has increased, primarily manifested in the Greater and Lesser Khingan Range areas in the northern part of the study region. Forecast results indicate significant differences in the evolution of LERs and the layout of the ESN under different socio-economic development scenarios. Under the ecological protection (EP) scenario, the average LER in Northeast China is 0.2215. The area of ecological source lands is 302,405.56 km2, the length of ecological corridors is 179,024.71 km, with 339 ecological nodes and 410 ecological breakpoints identified. Compared with the natural development (ND) and urban construction (UC) scenarios, the EP scenario in Northeast China shows the lowest LER and the most ideal layout of the ESN. This study provides a reference for future land resource optimization and ecological restoration in Northeast China. [ABSTRACT FROM AUTHOR]

Determining the rural domestic water consumption (RDWS, L/person-day) and rural sewage production (RSP, L/person-day) of rural residents at the county scale can provide decision support for rural domestic sewage treatment. However, methods for predicting county-scale RDWS and RSP based on geographical, social, and governance factors are rarely reported. This paper proposes an RDWS and RSP prediction method based on Automated Machine Learning (AutoML) for the Inner Mongolia Autonomous Region. The RDWS and RSP in the Inner Mongolia Autonomous Region were successfully predicted from multi-source data. The AutoML model accurately predicted RDWS (mean absolute error = 1.7072, R2 = 0.7759) and RSP (mean absolute error = 0.6580, R2 = 0.65107). Average years of education (AYOS), proportion of resident population, and proportion of elderly and children (POEAC) were key driving factors related to RDWS prediction. The most important factors associated with RSP prediction were AYOS, POEAC, and annual average precipitation (AAP). An explanatory analysis further revealed dependence between the input variables, RDWS, and RSP. The framework was demonstrated to be a practical tool for qualitative and quantitative analyses of rural domestic water consumption and domestic sewage production in the Inner Mongolia Autonomous Region. Overall, this study provides a solution for predicting county-level RDWS and RSP, providing a reference for the design of rural domestic sewage treatment. [Display omitted] • Less than 24% of rural domestic sewage is currently treated in Inner Mongolia. • Planning needs county-scale domestic water consumption and sewage production data. • Best of many models based on data from multiple sources were selected automatically. • Nonlinear relationships between input output variables were quickly revealed. [ABSTRACT FROM AUTHOR]

Water rights trading volume (WRTV) is an integral part of an effective transaction. At present, in China, WRTV is mostly based on the water demand of the transferee, not the transferor. Some transactions last for a long time, yet lack proper measures to adjust trading volume, making theoretical and quantitative studies on WRTV necessary and essential. Therefore, based on the theory of property rights, this study first analyzed the concept of WRTV, and proposed that WRTV must consider the tradable water volume (TWV) of the transaction parties. For that, this paper divided water rights trading into two stages: initial stage and operation stage, to analyze how to measure WRTV in different stages. Second, currently, industry or sector is a major player of water rights trading. TWV of industry is, hence, the difference between the initial water rights volume (IWRV) and water demand of industry, significant for calculating WRTV in different stages. For that, this study employed the ratio method to calculate the IWRV of industry, and the Grey Model—GM (1,1) to predict the water demand of industry. Last, this paper carried out a case study on the inter-league and inter-city water rights trading between Bayannur city and Ordos city in Inner Mongolia, China. Results show that the TWV of industry of Bayannur and Ordos in 2030 are – 904.966 million m3 and − 87.135 million m3 respectively, implying insufficient TWV for the transferee, which may lead to transaction breakdown. The theory and calculation method of WRTV proposed in this study lay a theoretical foundation for WRTV measurement, and can help facilitate the orderly development of water rights trading in China. [Display omitted] • The volume of water rights should be considered the tradable water volume of the transaction subjects at the same time. • The impact of the change of the tradable water volume on the water rights transaction volume is analyzed. • The change of water rights trading volume has a great impact on trading, and even leads to the transaction rupture. • Forecasting the trading volume of water rights between Bayannur city and Ordos city in Inner Mongolia of China in 2030. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Topp-Leone Lindley (TLL) distribution by using Topp-Leone (TL) family. Mathematical properties of the TLL distribution are studied. Estimation of the unknown parameters is derived by the methods of maximum likelihood, least squares and maximum product spacings. The performance of the estimation methods is evaluated by means of Monte-Carlo simulation. In the application part of the research, we have used a long term measured wind speed data from ten stations in Inner Mongolia of China. The suitability of the TLL distribution and other six distributions (inverse Gaussian, Birnbaum-Saunders, power Lindley, weighted Lindley, Weibull, inverse Weibull) used to fit for wind speed data is evaluated based on root mean square error, coefficient of determination, the log-likelihood, Akaike information criterion (AIC), Bayesian information criterion (BIC), Kolmogorov-Smirnov (K–S) statistic and power density error. The results substantiate that TLL distribution is widely applicable at all selected stations. TLL distribution outperforms the others at five stations, ranks the second at three stations and classifies as the third in remaining two stations. Alternatively, Weighted Lindley distribution is the next best distribution for analyzing wind speed data in selected stations. It is stronger than others at three stations while ranking the second at five stations and fourth in two stations. Although this finding questions the accuracy of Weibull distribution in modeling wind speed data, it provides superior estimation of wind power density for the ten stations. • The study solved a knowledge gap that existing wind speed distributions perform poorly in wind energy application. • A novel distribution, called the Topp-Leone Lindley (TLL), is proposed to fit the Inner Mongolia wind speed data. • The performance of the TLL distribution is compared with six commonly-used distributions for the considered wind data. • Wind characteristics are analyzed for wind energy potentials at ten sites in Inner Mongolia Autonomous Region, China. [ABSTRACT FROM AUTHOR]

With the increasing urbanization but growing resource scarcities, the securing provision of fundamental resources as food, energy and water (FEW) has become a unique challenge for urban sustainability. This is not only because of continuous demand of resource imports from different regions for urban areas, but also due to the complex interrelationships among FEW systems. In such context, exploring the interactions between FEW resources and economic activities when investigating FEW provisions to meet urban demand through trade is very essential to find effective policy intervention points and priority areas for actions. This paper investigates external binding FEW resource flows with internal certain interlinkages driven by final demand of Beijing city at different nodes along their supply chains, by combing structural path analysis and multi-regional input-output model of China 2010. The results show that the key source regions present overall neighborhood pattern that Hebei, Inner Mongolia, Anhui, Jiangsu, and Shandong near Beijing are the five leading contributors of tran-regional FEW provisions. The top 20 nexus paths are identified and the most important nexus pathways start with the other services in Beijing. Besides this, the critical supply chains appear divergent directions for FEW flows, driven by food, construction and agriculture industries respectively. Moreover, the key nodes mainly concentrate on less developed regions and energy-related sectors. For example, non-metal products manufacturing in Hebei, petroleum refining and coking in Heilongjiang, and coal mining and washing in Inner Mongolia have larger impacts on all of FEW flows across the supply chains. These results are very informative to targeting our efforts to address the urban FEW nexus issue both from the perspective of supply side and demand side. • Tele-coupling mechanism of urban FEW nexus is explored using MRIO and SPA methods. • Key source regions for urban FEW provisions present neighboring characteristics. • Service sector is the most important final consumption driver for top critical pathways. • Key nodes mainly concentrate on less developed regions and energy-related sectors. [ABSTRACT FROM AUTHOR]

• Dynamics of net primary productivity in Inner Mongolia show spatial heterogeneity. • Spatial statistics and ADMA are combined to study NPP clustering patterns. • NPP correlates negatively with temperature, but positively with precipitation. • Negative spatial association in NPP is likely associated with human activities. Vegetation Net Primary Productivity (NPP) is an important indicator for agriculture production. Understanding spatio-temporal dynamics of NPP and their driving factors have attracted much attention from both academic field and practical applications. In this paper, we coupled spatial statistics and a new approach called accumulated density map analysis (ADMA) to explore spatio-temporal variations in NPP distribution and possible contributing factors to the variations in Inner Mongolia Autonomous Region (IMAR), China. Density of Spatio-temporally Aggregated Clustering (D-STAC) index of NPP distribution, as output from ADMA, was proposed to indicate the impact of local factors on NPP. The study showed that spatially averaged NPP did not exhibit significant changes over time, but inter-annual variability of NPP presented critical spatial heterogeneity. Local spatial association analysis, as a preliminary step for ADMA analysis, detected two positive autocorrelation patterns, namely H-H (high NPP enclosed by high NPP) and L-L (low NPP enclosed by low NPP), and two negative autocorrelation patterns, including H-L (high NPP surrounded by low NPP) and L-H (low NPP surrounded by high NPP), for localized places in the study area. While positive autocorrelation patterns were found to dominate most parts of the study area, D-STAC for negative autocorrelation patterns was closely associated with Neighborhood to Cities (NC), an index for urbanization level. To evaluate the relationship between the NPP variation and possible driving factors, local regression analysis using geographically weighted regression (GWR) revealed that NPP largely had positive correlation with precipitation, but showed substantial spatial variations in the relationships with temperature and NC. We concluded that, through LISA, ADMA and GWR, the associations between the spatio-temporal NPP variations and their driving factors could be examined under localized context. [ABSTRACT FROM AUTHOR]

• Proposing a solution for tree height estimation based on trunk curve. • Estimating the trunk volume combining segmentation measurement and cylinder fitting. • Achieving automatic measurement of the individual tree parameters using CRP data. The forest plots survey's primary measurement factors are the diameter at breast height (DBH), tree height, and the individual tree volume. The reconstruction of a 3D forest scene based on dense images can contribute to the automatic extraction of individual tree parameters. Close-range photogrammetry (CRP) can achieve the reconstruction of a 3D scene from the sequence images at a highly efficient and low cost. However, the reconstructed point clouds often miss the trees' upper part, resulting in the tree height and volume not being measured directly. This paper proposes an integrating extraction framework and methods of the DBH, tree height, and individual tree volume for achieving automatic measurement of the individual tree parameters with high accuracy based on CRP. Four plantation plots in Wangyedian Forest Farm, Inner Mongolia, and Gaofeng Forest Farm, Guangxi Zhuang, are selected to obtain sequence close-up photographs and construct 3D scenes. The DBH, tree height, and trunk volume of the individual tree are extracted using circle fitting, stem curve fitting, growth equation, and segmentation measurement. The extracted individual tree parameters from Terrestrial laser scanning (TLS) are used as validation data to evaluate the accuracy. It shows a good performance in two Larix gmelinii plots, one Pinus tabulaeformis plot, and one Eucalyptus grandis plot with the accuracy of extracted DBH above 90%, which is 90.18%, 96.54%, 90.17%, and 95.00%, respectively. The accuracy of extracted tree height of the four plots is all above 85%, which is 93.34%, 91.71%, 90.32%, and 85.96%, respectively, and the accuracy of trunk volume is 91.55%, 91.94%, 89.86%, and 89.6%, respectively. The results show that our proposed integrated parameter extraction frame and methods can achieve high accuracy economically, efficiently, and conveniently, and be used for automatic measurements of individual tree factors in forest plot surveys. [ABSTRACT FROM AUTHOR]

The terrestrial Yanliao Biota in northeastern China represents a Jurassic biodiversity hotspot. Abundant and diverse fossils include the oldest known representatives of several groups of plants, invertebrates, and vertebrates that thrived in a forest ecosystem under a humid and warm climate. Since the early 21st century, discoveries associated with the Yanliao Biota have significantly improved our understanding of Jurassic terrestrial ecosystems. However, a robust chronostratigraphic framework is required to calibrate the biota with key evolutionary events and to improve understanding of possible links between evolution and tectonism. In this paper, we report novel 40Ar/39Ar ages for two tuff layers (161.4 ± 0.8 Ma and 161.6 ± 0.6 Ma) positioned directly above the less-studied but salamander fossil-rich beds in Reshuitang, near Lingyuan, Liaoning Province, China. These new age data improve knowledge of the timing of diversification of early salamanders. We also review all reported age data and summarize significant fossils from classic Yanliao outcrops. Our study indicates that the Reshuitang fossil beds were deposited slightly later than the well-known Daohugou fossil beds situated ∼15 km away in Inner Mongolia and somewhat earlier than the Linglongta fossil beds in western Liaoning Province. These findings advance understanding of the Jurassic world during an episode of pronounced climatic warming. • We provide detailed age calibration for the Jurassic Yanliao Biota. • Our study allows us to understand Jurassic terrestrial ecosystems. • A robust chrono-stratigraphy reconstructs the link between life and climate. [ABSTRACT FROM AUTHOR]

Integrating renewable electricity and green hydrogen with CO 2 utilization to produce chemicals has drawn much attention due to its low carbon emission characteristics. The goal of this paper is to investigate the environmental sustainability of producing a steady hourly output of methanol by utilizing electricity and hydrogen produced via renewable energy resources, and CO 2 captured from a coal-fired power plant located in Inner Mongolia, China. Six renewable energy-based electricity and hydrogen systems are considered based on photovoltaic, wind turbines and combinations thereof, including battery and grid technologies. The environmental impact indicators of these system are compared comprehensively through life cycle assessment approach, including not only global warming potential but also fossil fuel depletion, water & soil & air pollution indices and human health index. The results indicate that the most environmentally friendly system is Case B2 which uses photovoltaic to produce hydrogen and electricity, with battery for energy storage and excess electricity sold back to grid. It shows apparently the best sustainability performance in global warming potential, which is 0.105 kg CO 2 -eq per kg methanol. In general, the proposed six systems outperform conventional methanol production systems in terms of global warming potential and abiotic depletion potential, with reductions ranging from 74% to 92% and 51.1%–73.8% respectively, but they have high levels in other environmental sustainability indicators such as human toxicity, eutrophication potential, and ozone layer depletion potential due to the photovoltaic electricity generation unit. The findings highlight that it is essential to consider a comprehensive range of environmental sustainability indicators when developing renewable energy-based methanol systems on a large scale. [Display omitted] • Component capacity of the integrated system is optimized. • Comprehensive life cycle environmental impact assessment is conducted. • GWP of proposed route is 74 %–92% lower than fossil-based route. • PV manufacturing is critical in most environmental impact indicators. • HTP, ODP and EP indicators of proposed route are much high. [ABSTRACT FROM AUTHOR]

The Jehol Biota comprises significant fossils that help to elucidate the evolution of Early Cretaceous terrestrial ecosystems. In this paper, we provide three new zircon U Pb age constraints (124.3 ± 1.5 Ma, 124.9 ± 1.6 Ma, and 121.5 ± 1.4 Ma) from the base to the top of the Jiufengshan Formation (Xinganling Group) at the Naketa section in the north Great Xing'an Range of eastern Inner Mongolia, China, within a succession that contains the typical Eosestheria - Ephemeropsis trisetalis - Lycoptera davidi (EEL) assemblage of the Jehol Biota. Combined biostratigraphic and radio-isotopic dating suggests that the Jiufengshan Formation in the Naketa section corresponds to the upper part of the Yixian Formation and the lower part of the Jiufotang Formation, making it Barremian to earliest Aptian in age. Our findings allow us to test a widely accepted hypothesis about the migration and radiation of the Jehol Biota, and show that these organisms preserved within the lower part of the Jiufengshan Formation reached the Naketa area during the second and third evolutionary stages. The preservation of Jehol elements in the Naketa section provides valuable clues regarding the evolutionary history of the Jehol Biota and its spatiotemporal development. Furthermore, our study allows us to explore possible links between evolutionary processes and concurrent tectonism in the North China Craton, as the timing of this Jehol migration occurred when the tectonic setting changed from a compressional to an extensional regime. • We provide detailed fossil records and robust age for the far north Jehol outcrop. • This study refutes the widely accepted model for the Jehol migration. • Our results test the link between biological radiation and tectonic activity. [ABSTRACT FROM AUTHOR]

• A meso-microscale coupling model is applied to a real onshore wind farm. • The atmospheric stability is introduced and refined in the multi-scale coupling process. • The wind flow data is simulated throughout the year with a resolution of 1 h. • Downscaling simulation is performed using mesoscale data from three different heights. The abundance level of wind resources is the first and most important factor to consider in the wind farm design. The accuracy of wind energy resource assessment directly determines the operating income of a wind farm after its construction is completed. Therefore, aiming to meet the requirements of accurate wind resource assessment in areas with a complex terrain or those lacking the observation information, this paper proposes a multi-scale coupling numerical simulation method based on atmospheric stability. First, the atmospheric flow field simulation results at the heights of 200 m, 300 m, and 400 m are extracted from the mesoscale Weather Research and Forecasting (WRF) model and used as inlet boundary conditions of a microscale Computational fluid dynamics (CFD) model. Then, to obtain more accurate coupling data at the height of a wind turbine hub, the atmospheric stability parameter is introduced into the multi-scale coupling process, data correction is conducted at the three heights, and a comparison with the observation data of a wind tower is made. Finally, a numerical analysis is conducted using real data of a wind farm in eastern Inner Mongolia and Jilin. The analysis results show that the proposed method can improve the assessing accuracy of wind resources of wind farms using mesoscale data, which is valuable to a certain extent in real-world applications. [ABSTRACT FROM AUTHOR]

• A gas migration-diffusion model was constructed. • The optimization scheme of gas extraction parameters were discussed. • The effects of parameters on the extraction effect were investigated. Gas drainage is the main technical measure to prevent and control coal and gas disasters. Reasonable extraction parameters can improve extraction efficiency and reduce the risk of gas accidents. In order to further study the influence of extraction parameters on gas drainage effect, this paper combines COMSOL Multiphysics numerical simulation and field experiment to optimize the two parameters of intubation spacing and extraction negative pressure in goaf of 42,201 working face in Buertai Coal Mine, Inner Mongolia. The three-dimensional gas distribution model in goaf was established, and the COMSOL numerical simulation software was used for calculation. Combined with the field experiment, the influence of intubation spacing and extraction negative pressure on the extraction effect was obtained. The results show that in the range of 50 m-70 m, the drainage effect is ideal when the negative pressure is 12–18 kPa. [ABSTRACT FROM AUTHOR]

• Designing an integrated regional water environment modeling framework • Concurrent analysis of seasonal water and its driving forces across space and time • Differentiating how different blocks of driving factors interact with water scarcity • Driving factor impacts on water scarcity increase substantially over time • Local resource endowment and utilization critically impact spatial heterogeneity Increased grazing and agricultural production, industrialization, population growth, and consequent land use land cover (LULC) changes considerably increase water consumption. Global climate change exaggerates the uncertainty of water sources and supplies. Unfortunately, most current examinations are either confined within disciplinary silos or not integrated for considering wide-ranging socioenvironmental, management, and policy factors. The paper develops an integrated regional water environment modeling framework, examining how climate, LULC, socioenvironmental, and policy factors interact with the water environment. It also adopts a block-based econometric panel data analysis to quantify this framework. The paper extracts seasonal water area and LULC data through image processing from 2000 to 2014 in the Hulun-Buir watershed, Inner Mongolia of China. The paper quantitatively analyzed the interactions between seasonal water changes and major driving factors, such as climatic, land-use, socioeconomic, policy, space, and time. Many of these driving factors were interacting with the seasonal water environment and showing long-term causal relationships. The socioeconomic variables explained 71% of the variance of seasonal water change, the environmental and climatic factors about 9%, the regional disparities around 13%, and the yearly differences about 4%. The findings confirm that it is critical to carry out a time-series examination of causal relationships between seasonal water change and its manifold driving factors at the scale of regional watershed studies. This integrated watershed modeling framework is suitable for adaptation in other geographic areas or for integrated studies of other socio-environmental systems. Graphical Abstract [Display omitted]. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this paper is to study pathways to a low-carbon economy for Inner Mongolia whose economy highly depends on fossil energy. The paper analyzed the industrial and energy structure of the economy and further demonstrated the necessity of developing the low-carbon economy in Inner Mongolia, a typical province in the western China. In addition, the LMDI Decomposition Method is used to analyze the factors that affect the growth in carbon emission and the Grey Relational Analysis (GRA) is applied to examine the relation between the carbon emission and GDP growth. At last, the paper further studied the advantages of developing the low carbon economy in Inner Mongolia and suggested different modes and pathways for Inner Mongolia to achieve the low carbon economy. [Copyright &y& Elsevier]

• Model-based polarimetric decomposition method for soil moisture estimation (SME). • Dihedral scattering component is neglected in bare soil and sparse vegetation areas. • SME results of C- and L-band full polarimetric data are analyzed. • SME accuracies of bare soil, sparse, and dense grassland are evaluated. Polarimetric synthetic aperture radar (PolSAR), as an advanced active sensor, can penetrate vegetation to detect ground information. The multimode observation data can provide more information to interpret different scattering mechanisms and have great application potential in soil moisture estimation (SME). In this paper, a modified method of soil moisture inversion from PolSAR data based on a two-component polarimetric decomposition model is introduced. The proposed method neglects the dihedral scattering component in the case of sparse vegetation cover and no-building areas, compensates the orientation angle by considering the influence of surface roughness and terrain slope, and calculates the dielectric constant with the hybrid X-Bragg/Fresnel model. Gaofen-3 (C-band) data from Ordos, Inner Mongolia, and Soil Moisture Experiment 2003 (SMEX03) airborne synthetic aperture radar (AIRSAR) (C- and L-band) data from Southern Oklahoma were applied in the experimental research. Furthermore, the performance and validity limits were assessed by comparing the retrieval results with in situ measurements and Soil Moisture Active Passive (SMAP) soil moisture products. The results reveal a root-mean-square error (RMSE) of 8.66% with an inversion rate of up to 61% when using the Gaofen-3 data from Ordos. For the Southern Oklahoma study area, the inversion RMSE of the AIRSAR C- and L-band data is 8.57% and 8.95%, respectively. The tendency of the inversion results is also consistent with the change trend of the site observations. In the bare soil and sparse vegetation covered areas, the retrieval error is relatively small, while in dense vegetation covered areas, soil moisture is underestimated in both the C- and L-band data, and the former is more underestimated than the latter. [ABSTRACT FROM AUTHOR]

Grassland Ecological Compensation Policy (GECP) is a large-scale program in which China has invested since 2011 to alleviate grassland degradation and increase herders' income. Although the amount invested in the program has increased each year, little research has been conducted to evaluate the impacts of the program; as such, this study examines such impacts on livestock production, grazing intensity, and structure using panel data from a herders' field survey conducted in 2015 in Inner Mongolia, China. Results from the econometric models show that the forage-livestock balance, which is one sub-policy of the GECP, had incentivized large farms to reduce their total number of sheep, but the implementation of the GECP could not greatly influence the reduction in the number of cattle present on any farm size. This study also reveals that herders made their livestock production and grazing decisions in response to market prices and that herder households that also had off-farm jobs raised fewer livestock and grazed lighter. Several policy implications are discussed in this paper. • We evaluate the impacts of grassland conservation subsidy on livestock. • Herders' survey and econometric models are used. • Subsidies were found to be more effective for larger farms. • Herders make livestock production decisions in response to livestock market prices. • Herder households with off-farm jobs raised fewer livestock and grazed lighter. [ABSTRACT FROM AUTHOR]

Abstract Inland salt lakes and saline wetlands are intricate systems in which moisture and salinity are continuously changing and especially sensitive to climate change and human pressure. Remote sensing helps to monitor the dynamic changes in the moisture and salinity of salt lakes and saline wetlands. Based on the Landsat 8 satellite data, this paper developed a multivariable linear regression model (MLRM) to predict the spatial distribution of salt on the saline playa at the Qehan Lake in Inner Mongolia, China. The developed MLRM used the remote sensing spectral indices and the Tasseled Cap Transformation wetness band (TCTW) as a variable. By using the TCTW and the surface temperature inversion data, the response of salt to surface moisture and temperature in the dry lake bed was explored. The results showed: The MLRM with brightness index (BI) and the TCTW as the auxiliary variables has the nice applicability (R adj 2 = 0.598, RMSE = 1.640). The MLRM established using the vegetation index NDVI may be more applicable to environments where vegetation and salts were doped-together. Salt distribution in the Qehan Lake may be controlled by flat lake bed and miniature piedmonts. There was a significant correlation between measured soil electrical conductivity (EC) and TCTW, but no correlation with surface temperature. The influence of surface temperature on salt may be biased towards the time scale. Highlights • A new auxiliary variable was used for surface salt inversion modeling. • The application range of vegetation indices and salinity indices was clarified. • The study shows a large variability in EC across various surface coverage type. • Soil moisture is very important in salt prediction. [ABSTRACT FROM AUTHOR]

Abstract There are ten ephemeral tributaries of the Yellow River in the Inner Mongolia of China. As their catchments are covered by loose materials and are dominated by an intense interaction between wind and water erosion, these tributaries have had a high sediment yield and alluvial fans have been formed at the confluences of the tributaries with the Yellow River. However, there is a lack of understanding in the deposition model of the alluvial fans and the roles of fluvial and aeolian processes in sediment accumulation. In this paper, we used Ground Penetrating Radar (100 MHz) and sediment cores to investigate the sediments in channels and alluvial fans of four out of the ten tributaries. Radar reflection profiles of sedimentary strata are dominated by horizontal or sub-horizontal layers with a large scale, showing that the fluvial deposition on the alluvial fans has been dominated by a vertical accretion pattern. The reflection features like reflection free, inclined reflections, discontinuous, undulate, chaotic, sinuous reflections and channel fill exist to a lesser extent. A few GPR profiles send signs of intense erosion, and the one near the apex of an alluvial fan reveals that erosion had been active likely during the early Holocene and the channel position should be relatively fixed. The information extracted from this profile and the widely exposed late Pleistocene strata in the upper parts of the alluvial fans according to the geological map of the study area indicates that sediment accumulation has been slow in the upper alluvial fans since the early Holocene. The sediment cores indicate that sedimentary strata under the alluvial fans are mainly composed by sand and silt deposited in a fluvial environment and rarely reworked by wind actions. Also according to the core data, the sediment deposits in the west fans were coarser than those in the east fans, likely suggesting that the hydrodynamic force of the western tributaries was stronger than that of the east ones. The hydrodynamic environment of the tributaries was usually unstable historically and hyperconcentrated flows might occur with a higher frequency in one of the tributaries in history. [ABSTRACT FROM AUTHOR]

Biochar is proposed as a soil amendment to improve soil physical-chemical properties and crop productivity. However, the effect of biochar on crop yield is not consistent and the mechanisms affecting plant growth are still not well-understood. In this paper, field experiments were carried out to investigate the impact of biochar addition on soil properties, water and fertilizer productivity of tomato in semi-arid area of Inner Mongolia, China. The tested biochar was made from maize straw by slow pyrolysis. Four biochar treatments were conducted including addition of 10, 20, 40 and 60 t ha −1 and no biochar as a control. Addition of biochar reduced the bulk density and increased the porosity of soil. Electrical conductivity of soil increased with increasing biochar application rate. Soil pH was not affected by addition of biochar in one growing season. Yield, crop water productivity and partial factor productivity for fertilizer were nonlinearly (quadratic) proportional to application rate of biochar. Highest water and fertilizer productivity obtained from the 40 t ha −1 biochar treatment. Comprehensive analysis of biochar impact on yield, water and nutrient productivity, and cost-benefit for the grower indicate that addition of approximate 30 t ha −1 is an appropriate rate for tomato production in the study area. This suggestion is based on the relatively short term effect and local market conditions. A prolonged experiment is desirable to understand the long term response of soil and crop to biochar addition. [ABSTRACT FROM AUTHOR]

Due to the intermittency and uncertainty natures of wind power, electrical energy storages (EESs) are often equipped in the power systems to reduce the side-effect of wind power fluctuations, and adiabatic compressed air energy storage (A-CAES) is one of EES technologies to smooth the power fluctuation of wind farms (WFs). This paper proposes a coordinated control framework of WF and A-CAES station to achieve frequency response, and discusses the active power distribution scheme among wind turbines (WTs) and A-CAES units during frequency regulation. Firstly, the models of WT and A-CAES used in frequency regulation are presented. Then, considering that the power distribution might go through a long iteration process when the number of WTs in WF is quite large, these WTs are clustered into several groups using a comprehensive multi-view grouping indicator. On the basis of the WTs grouping result and with a defined generalized energy increment (GEI), this paper proposes a discrete consensus based tri-level coordinated frequency control method, which divides the control into three levels, i.e., group level, wind farm level and coordinated level. Through the three levels' control, the method can reasonably and rapidly distribute the frequency regulation powers among WTs and A-CAES units without being limited by the scale of WF, and the coordination of WF and A-CAES station during frequency regulation is achieved. To demonstrate the effectiveness of the proposed method, a modified WF in Inner Mongolia of China is utilized for case study. Simulation results show that the proposed method is valid in various frequency events and can reach consensus within 4 s in the studied cases, and it is well-performed with different capacities of wind powers and A-CAESs in the power systems. The common communication failures have few influences on the methods, and the frequency nadirs fluctuate lower than 0.1 Hz with time delays in the communications. Compared with centralized and multi-machine equivalent methods, the proposed distributed method can balance the computational speed and the solution accuracy, and thus is beneficial to improve the system frequency nadirs when frequency drops. [ABSTRACT FROM AUTHOR]

• A sustainable livelihoods indicator across a geographical gradient of aridity was developed. • Sustainable livelihoods vary greatly across dryland zones. • Semi-arid zones have the most vulnerable livelihood rather than the arid zones. • The lack of physical capital and social capital is an important obstacle to sustainable livelihoods development in drylands. • Call for integrated land management and planning approach in drylands. Drylands cover about 41% of the Earth's land surface and are inhabited by more than two billion people, who rely on the diversified ecosystem services provided by drylands for their livelihoods. Achieving sustainable livelihoods (SLs) is a key component of achieving the sustainable development goals set by the agenda in 2030. Although it has aroused extensive interest, research on SLs in drylands at a regional scale is still limited. This paper aims to address this research gap by evaluating SLs through a geographic gradient of aridity in Inner Mongolia. A sustainable livelihood index (SLI) was developed using a wide range of indicators in a sustainable livelihood framework (SLF). The weight of the indicators was determined by the entropy weight method, and the characteristics of the spatial distribution of the SLI were analyzed. The results showed that the SLI varies greatly across aridity zones. In terms of livelihood assets, the SLI in the dry sub-humid zone was 15% higher than in the arid zones, while, surprisingly, semi-arid zones were found to have the most vulnerable livelihoods (rather than the arid zones). The reason for this is that land management and planning approaches are necessary in drylands. In further detail, Moran's I index illustrated that the overall performance of the SLI of each league or city has a positive spatial correlation, while through local spatial correlation it was found that Hinggan and Chifeng are hot spot areas and Hohhot is a cold spot area. The lack of physical and social capital is an important obstacle for SLs. Based on the analysis of SLs in Inner Mongolia, the characteristics of the sustainable development of local residents were revealed. In this paper, we call for an integrated (i.e., focusing on natural and human capital) land management and planning approach for drylands to reflect the nature of the tightly coupled socio-ecological systems. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

Understanding the modes of occurrence of elements in coal is important, not only to help properly evaluate the impacts of potentially toxic elements on the environment and human health but also to provide technical guidance for recovering critical elements from coal ash. Statistical and multivariate data analysis methods have widely been used, together with physical and chemical methods, to determine the modes of occurrence of elements in coal. However, some of the statistical methods, e.g., average linkage hierarchical clustering algorithm have some disadvantages (e.g., statistical errors and poor visualization). A self-organizing map is an unsupervised artificial neural network, and it is known for its high data mining capability and excellent data visualization. In contrast to the average linkage hierarchical clustering algorithm that is commonly used for analyzing the modes of occurrence of elements in coal, the self-organizing map algorithm can provide a topological relationship among elements instead of merely providing the groups to which the elements belong. This paper focuses on the application of self-organizing map to coal elemental data for analyzing the modes of occurrence of elements in coal. Samples used in this study are from the Adaohai, Hailiushu, and Datanhao mines, all located in the Daqingshan Coalfield, Inner Mongolia, China. The results obtained from the self-organizing map algorithm are compared with those produced by average linkage hierarchical clustering algorithm. Based on the previous investigations (mainly direct methods) and further analysis, it can be concluded that the results from the self-organizing map algorithm in this investigation are more consistent with the geochemical nature and previous investigations by direct methods than those from average linkage hierarchical clustering algorithm. Consequently, the self-organizing map algorithm is a new reliable and intuitive method for analyzing the modes of occurrence of elements in coal. [ABSTRACT FROM AUTHOR]

Water, energy and food (WEF) are three basic elements that safeguard human life and socioeconomic development. As the demand for water increases in food and energy production, water scarcity is more prominent globally. WEF Nexus plays an important role in evaluating and alleviating the contradiction between food and energy for water. Based on the water footprint of food and energy in China from 1997 to 2016, this paper used the "Structure-Conduct-Performance" analysis paradigm to evaluate the water competition mechanism. The newly constructed competition mechanism model includes type, conduct and performance. The results showed that the blue water footprint of energy increased from 3.25 Gm³ in 1997 to 12.54 Gm³ in 2016. The blue water footprint of food remained above 200 Gm³ for many years, and competition between food and energy production for water was intensifying. The gray water footprint of energy was increasing yearly, and food reached the maximum value of 181.01 Gm³ in 2014. Through model analysis, the South-to-North Water Diversion has not effectively alleviated water scarcity, and a few provinces were still classified in the water pressure-dominated area. The northern channel of West-to-East Power Transmission only transferred electric energy from water pressure-dominated area, causing the unsustainability coefficients of Inner Mongolia and Yunnan to increase from 0.09 and 0.03 in 1997 to 0.23 and 0.05 in 2016. This paper provides effective policy insights for global challenges facing WEF Nexus from the source, process and end of food and energy production. [Display omitted] • The WEF Nexus internal competition mechanism is evaluated by constructing a model. • The model analysed dominant factor, driving mechanism and feedback effect of competition. • The increasing energy water footprint yearly makes water security more prominent. • This study finds that water scarcity has not been effectively alleviated in China. • Many measures provide policy insights into global challenges facing WEF Nexus. [ABSTRACT FROM AUTHOR]

In China, cross-regional electricity trade is an important measure to alleviate the unbalanced spatial distribution of electricity consumption and generation. This paper analyses the greenhouse gasses (GHG) and air pollutant emissions embodied in the cross-province trade of electricity in China, by using a Quasi-Input-Output model. The results of the analysis suggest that the trade-embodied emissions in China amounted to 607 Mt, 294 kt, 318 kt, and 61 kt, for GHG, SO 2 , NO x and dust, respectively, in 2017. Much of these emissions was transferred from the eastern coastal city-clusters (Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta) to the resource-rich western provinces, especially Inner Mongolia and Xinjiang, where coal has continued to occupy a central place in their generation technology-fuel mix. The amount of emissions transferred, however, reduced in relative term in 2017, for the Yangtze River and Pearl River regions, when compared with that in 2013, as indicated by emissions factors decreasing faster at the consumption side than that at the production side in these regions over the period 2013–2017. In contrast, in Beijing and Tianjin, the relative emissions transferred became more pronounced in 2017, as compared with that in 2013, due probably to the fact that much of their imported electricity was from Shanxi, Inner Mongolia, and Xinjiang with large share of coal generation. Moreover, in addition to direct emissions transfer, indirect emissions transfer caused by electricity exporters purchasing electricity from other grids are also significant, and in some cases (such as, Hunan), these emissions account for over half of the total emissions transferred. This paper also discusses some key policy insights on issues related to the design of the national emissions trading scheme, network infrastructure investment, and consumer education aimed at encouraging the emissions-reduction behavior. [Display omitted] [ABSTRACT FROM AUTHOR]

The Middle Jurassic Daohugou Biota is named for a fossil assemblage after a village in Ningcheng County, Inner Mongolia, China. Over the last twenty-five years, abundant, exceptionally well-preserved fossils have been reported from various localities in the Daohugou area. While the fauna of the Daohugou Biota has been well-studied, little research has been done on the flora. In this paper, we establish the Daohugou Flora for the first time and document its high diversity of plants which are mainly composed of Filicopsida, Cycadopsida, Ginkgopsida, Czekanowskiales and Coniferopsida. We recognize 49 species belonging to 32 genera, including a new species Sphenobaiera eximia sp. nov. Na, Sun et Wang. An emended diagnosis of Anomozamites villosus Pott, McLoughlin, Wu et Friis is presented. The floristic composition indicates that the Daohugou Flora belongs to the North Floristic Province and it reflects a warm-temperate and humid climate with seasonal temperature and precipitation variations. This report, representing the first systematic treatment of fossil plants from the Daohugou area, is of great significance for understanding the Daohugou Biota. It also provides important information for reconstructing the paleoclimate and paleoenvironment in this area, and lays a solid foundation for studying co-evolution of plants and animals of the Daohugou Biota. [ABSTRACT FROM AUTHOR]

• Radar image and spectral images were fused for monitoring soil salinity. • Soil salinity inversion models were built based on Sentinel-1/2 fusion and machine learning. • GS was used to fuse images of Sentinel-1 and Sentinel-2. In the application of satellite remote sensing data to salinity inversion in bare soil, multispectral satellites enjoy richer wavelength bands compared with synthetic aperture radar (SAR), but their vulnerability to cloudy weather limits the inversion accuracy. In contrast, SAR has stronger penetration, is less affected by cloudy weather, and can work around the clock. Therefore, in order to monitor soil salinity over a longer period of time and under various climatic conditions, and to further improve the accuracy of salinity inversion, we fused Sentinel-1 and Sentinel-2 images and investigated the feasibility of SAR and multispectral satellite image fusion in salinity inversion in this paper. And we conducted experiments in Hetao Irrigation Area, Inner Mongolia, China. Gram-Schmidt(GS) was used to fuse Sentinel-1 and Sentinel-2 images at the same time, and maximum-normalization was carried out on the DN value of the fused image. Then some remote indices conducted with the normalized DN, together with high relevant fused single bands were input into Back Propagation(BP), Support Vector Machine(SVM), Random Forest(RF) as independent variables after two-tailed significance test and Variable importance in projection(VIP) selection, for soil salinity inversion model construction. Finally, the inversion results of the study area were evaluated with R2, RMSE, RPD, RSS, and RPIQ. The results of this study showed that the bare soil salinity inversion based on the fusion of VV(Sentinel-1) and Sentinel-2 had a higher accuracy than that on the fusion of VH (Sentinel-1) and Sentinel-2 image and that without the fusion. Among the three machine learning models, random forest (RF) achieved the most satisfying results (R2 = 0.801, RMSE = 0.686), followed by SVM(R2 = 0.624, RMSE = 0.875) and BP(R2 = 0.613, RMSE = 0.918). This study demonstrated the feasibility of fusing Sentinel-1 radar images with Sentinel-2 multispectral images to improve the accuracy of soil salinity inversion, and constructed the soil salinity inversion model based on machine learning and GS fusion. [ABSTRACT FROM AUTHOR]

With the gradual maturity of wind power technology, China's wind power generation has grown rapidly over the recent years. However, due to the on-site inconsumable electricity, the phenomenon of large-scale "wind curtailment" occurs in some areas. In this paper, a novel hybrid hydrogen/electricity refueling station is built near a wind farm, and a part of the surplus wind power is used to charge electric trucks, and the other part of the surplus power is used to produce "green hydrogen". According to real-time load changes, different amounts of liquid hydrogen and gas hydrogen can be properly coordinated to provide timely energy supply for hydrogen trucks. For a 400 MW wind farm in the western Inner Mongolia, China, the feasibility of the proposed system has been carried out based on the sensitivity and reliability analysis, the static and dynamic economic modeling, with an entire life cycle analysis. Compared to the conventional technology, the initial investment of the proposed scheme (700.07 M$) decreases by 13.97%, and the dynamic payback period (10.93 years) decreases by 25.87%. During the life cycle of the proposed system, the accumulative NPV reaches 184.63 M$, which increases by 3.14 times compared to the case by conventional wind technology. • A novel hybrid hydrogen/electricity refueling system to charge electric vehicles, and produce "green hydrogen". • A realistic multi-energy study based on a 400 MW wind farm in western Inner Mongolia, China. • Feasibility study with sensitivity and reliability analysis for the proposed system. • Static and dynamic economic modeling on a hybrid hydrogen/electricity refueling station. • The proposed system has technical and economic benefits, but much less carbon emissions. [ABSTRACT FROM AUTHOR]

• Flue gas emission characteristics of NH 3 /pulverized coal co-firing process with a wide range of NH 3 blending ratios were studied in a drop tube furnace. • Effects of coal type, NH 3 injection mode, temperature, excess air ratio (α) and NH 3 co-firing ratio (E NH3) were compared to achieve efficient combustion and low pollutants emission. • Fuel-N transformation analysis was conducted to explore the nitrogen migration characteristics in high temperature NH 3 /coal co-firing process which have not yet been investigated before. • Through staged combustion strategy, E NH3 = 40 % is the optimal NH 3 co-firing ratio which not only can reduce half of CO 2 but also achieve the minimum NO x conversion rate and low residual NH 3 level. As a feasible way to decarbonize coal combustion under the background of carbon neutrality in recent years, ammonia co-firing with pulverized coal is receiving considerable critical attention from the academic and industrial communities. This paper investigates the effects of coal type, NH 3 injection mode, temperature, excess air ratio (α) and NH 3 co-firing ratio (E NH3) on combustion characteristics and fuel-N transformation of ammonia/pulverized coal in a 6 kW drop tube furnace. Emission of O 2, CO 2 , CO, NO, NO 2 and NH 3 at the furnace exit was monitored online. The unburned carbon (UBC) content and elementary composition in fly ash were also tested. Results show that NO x generation behaviors of premixed and staged modes are significantly different. Typically, NO x emission peaks at E NH3 = 20 % under high temperature and staged mode. As for NH 3 /coal cases at 1000 °C, NO x level is even lower than pure coal combustion, but NH 3 residue is higher. The water gas shift reaction (WGSR) under fuel-rich condition generates more CO, and the conversion ratio from fuel-N to NO x decreases when α diminishes. Ammonia co-firing can achieve lower CO emission with anthracitic coal from Gongyi (GY-coal) while lower NO x emission with bituminous coal from Inner Mongolia (IM-coal). Ammonia co-firing can considerably improve the transformation from fuel-N to N 2. From the perspective of emission control and decarbonization, E NH3 = 40 % is an optimal ammonia co-firing ratio which not only can reduce half of CO 2 but also brings the conversion ratio from fuel-N to NO x and residual NH 3 to the minimum level. The micromorphology of ash samples is greatly affected by coal/ammonia co-firing. In premixed mode, the surface of ash sample is porous and adhered to each other; while in staged mode, obvious fractures and cuts exist between the ash particles due to the sharp decrease of local gas temperature. [ABSTRACT FROM AUTHOR]