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• MCCA model improves the accuracy of land use structure simulation in YRD. • The index of land ecological risk assessment is expanded and optimized based on the regional characteristics of the YRD. • The ecological risk levels in 2020, 2025 and 2035 are evaluated considering ecological and economic benefits. • The spatial and temporal changes and aggregation patterns of ecological risk levels are discussed from both city-scale and patch-scale. • Several suggestions are provided for formulating more scientific and effective land use management strategies. Since land resources are essential to human survival, the over-exploitation and unreasonable utilization of land threatens the sustainability of human society. This paper uses the well-developed Yangtze River Delta (YRD) region as a case study, and adopts an improved Mixed-cell Cellular Automata Model (MCCA) to simulate the potential utilization of land resources in 2025 and 2035. Then, the paper attempts to construct a comprehensive assessment system that integrates multiple indicators, and evaluates regional risk changes at a micro patch-cell level and a macro city. The results suggest that (1) The utilization of land resources varies greatly in the YRD region from 2020 to 2035. While Shanghai will continue to make use of available built up land resources, the majority of land resources in Jiangsu Province will shift away from being agricultural in nature. Since forests make up a majority of the land resources in Zhejiang Province, and forests and agricultural land make up a majority of land resources in Anhui Province, the change in land utilization in these two provinces will be more gradual. (2) Changes in land utilization are also likely to cause ecological risks. The risk level in Shanghai is the highest, it is relatively high in Zhejiang but is likely to decrease, relatively low in Jiangsu but likely to increase, and Anhui has the lowest risk level. (3) The K-means clustering method obtained three ecological risk zones of the YRD, and the overall risk level is the highest in the central-eastern zoning ③, followed by the southern zoning ②, and the lowest in the northern zoning ①. Furthermore, the agglomeration pattern of the three zones transforms from L-L to H-H. (4) This paper puts forward corresponding suggestions and countermeasures for four aspects, including the strengthening of agricultural land protection, the regulation of built-up land use expansion, the coordination of an all-rounded approach of urban development, and the fostering of regional sustainable development, in hopes of optimizing land usage and alleviating ecological risk levels. Scientific estimations of potential ecological risks are critical to avoiding future ecological crises thus contributing to more sustainability. [ABSTRACT FROM AUTHOR]

Effective control of electricity consumption is crucial to achieving China's dual carbon goals, as it can significantly reduce coal consumption and associated carbon emissions. Using the Logarithmic Mean Divisa Index (LMDI) method, this paper constructs a two-stage decomposition model, which can divide total electricity consumption into industry and domestic electricity consumption and further examine the differential driving mechanism of the two types of electricity consumption separately. Taking the Yangtze River Delta as the research object, this paper analyzes the drivers of its temporal differences in total electricity consumption from 2000 to 2019 and fills the research gap in the identification of drivers of spatial differences in electricity consumption. The results showed: (1) Industry electricity consumption is the main driver of the increase in total electricity consumption, with economic growth and domestic electricity intensity being the primary and secondary factors behind this trend. The population scale also contributes to the increase. However, the effects of industrial structure, industry electricity intensity, and urbanization on total electricity consumption are not constant over time. This paper further decomposes the factors influencing changes in total electricity consumption in the provinces of Shanghai, Jiangsu, Zhejiang, and Anhui. (2) The spatial difference of electricity consumption between the other three provinces and Shanghai mainly comes from industry electricity consumption. (3) There are two kinds of conversion relations for the spatial-temporal differences of total electricity consumption. According to the conclusion, the targeted countermeasures and suggestions for total electricity consumption control are obtained. • We use the LMDI model to study the driving factors of total electricity consumption. • Production is the main reason for the increase of total power consumption. • Economic growth is the first factor to promote the total electricity consumption. [ABSTRACT FROM AUTHOR]

Although a large number of reviews have studied different scales of forest ecosystem services, few have concentrated on accounting the spatiotemporal dynamic changes of forest ecosystem services in a region. To make a more accurate accounting, this paper selects intense data sets that assess the physical quantity and value of forest ecosystem services in Anhui provinces from 2009 to 2014. Regulating and supporting services such as water conservation, carbon sequestration and oxygen release, soil conservation and air purification are included in the paper as variables. Optimize analysis was further conducted in order to identify the spatial and temporal heterogeneity during the study period. We establish a dynamic ecosystem services assessment model with the coefficient of spatial heterogeneity and coefficients of scarce resources and social development to analyze the trend of the value in Anhui province. This model is designed for comparisons of forest services in different scales in Anhui province. The results demonstrate that the total value of forest ecosystem services is beyond the previous estimation while using spatiotemporal visualization. The implications are that the evaluation of forest ecosystem services should be measured more accurately with the help of big data in the future. [ABSTRACT FROM AUTHOR]

Accurate assessment of the sustainable development level is crucial to providing reasonable and effective management recommendations. However, analysis with a single scale can only reflect the sustainable status at a specific level. In this study, a multi-scale analysis of sustainability was conducted via the three-dimensional Ecological Footprint (EF) model in the Yangtze River Delta (YRD). Moreover, driving factors of sustainability were analyzed using the Multi-scale Geographically Weighted Regression model (MGWR). The conclusions include: (1) the development of YRD showed an unsustainable trend from 2014 to 2021, with per capita EF decreasing from 5.769 ha/cap to 5.283 ha/cap. The provincial sustainability is ranked as follows, from being the most sustainable to the least: Anhui Province > Jiangsu Province > Zhejiang Province > Shanghai City. (2) Based on dual indicators of EF depth and size, 41 cities were categorized into nine groups. The dominant type is always Medium size-Low depth (ML) and High size-Low depth (HL) type cities from 2014 to 2021. From the perspective of city scale, the sustainability of five cities weakened, while seven cities showed improvements. (3) Output value of primary industry (OVPI) and population (POP) were always the dominant factors affecting EF, but showed positive and negative correlations respectively. OVPI changes from north-south symmetric type to central symmetric type, while POP changes from central symmetric type to north-south symmetric type. This paper aims to comprehensively assess the sustainable development status from agglomeration-province-city perspectives, and its findings can provide a reference for similar regions with unbalanced development status between ecology and production. • The sustainability of the world-class urban agglomeration was assessed via the three-dimensional ecological footprint model. • More comprehensive and accurate analysis results based on multi-scale analysis. • The four patterns of influencing mechanism are revealed based on the MGWR model. • The combination analysis of dual indicators was adopted to classify the sustainability of 41 cities. [ABSTRACT FROM AUTHOR]

Knowledge of vertical distribution of air pollutants is important for understanding the mechanisms underlying haze pollution. In this paper, we characterize the vertical structure of air pollutants based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in the wintertime in Hefei, East China. Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface, suggesting the major emissions are at ground level. SO 2 profiles extend to slightly higher altitudes than NO 2 , which might be due to the SO 2 emissions from elevated point sources (e.g., power plants). Elevated HCHO was observed in the middle layer during noon time, probably indicating that the larger effect of the photochemical formation of atmospheric HCHO rather than direct HCHO emissions. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models were used to evaluate the potential sources of Hefei's air pollution in the lower, middle and upper boundary layer. The results revealed that the major regional source of NO 2 and aerosol were located in the northern and eastern part of Anhui and the transport mainly occured in the lower layer. Transported SO 2 from the northwestern regions in the middle layer made great contribution to SO 2 in Hefei. HCHO was mostly produced locally while the transport of HCHO from potential source areas was negligible. To further explore the potential source contributions affecting haze pollution, PSCF and CWT analysis for heavy haze episodes suggested that the potential source areas for aerosol were almost located in the northern region, the northwestern region, and the YRD region. Our findings provide a more comprehensive understanding for regional transport through the study on potential sources at different altitudes, which are useful for designing collaborative air pollution control strategies on a regional scale. • Vertical distribution of air pollutants is investigated to identify potential sources at different altitudes during winter. • Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface. • SO 2 and HCHO profile typically extended to higher altitudes than NO 2 and aerosol did during winter. • The main potential sources for aerosol were located in the northern region, the northwestern region, and the YRD region. • This study is useful for designing collaborative air pollution control strategies on a regional scale. [ABSTRACT FROM AUTHOR]

• A large-area digital soil mapping method based on environmental-similarity is proposed. • The distance-decay relationship is adaptively determined by spatial distribution of samples. • Adaptively considering spatial distance to samples improves soil prediction accuracy. Digital soil mapping (DSM) based on environmental similarity may be used to predict the soil properties at unvisited locations based on the soil–environment relationship at each sample locations, which is more suitable for large-area (or regional scale) predictive mapping with comparatively limited soil samples than (geo-)statistical DSM methods with high requirements for the quantity and spatial distribution of samples. Recently, the DSM method based on environmental similarity but with inadequate consideration of the First Law of Geography was improved by considering the spatial distance to samples by means of the inverse distance weight (IDW), following the assumption that the more similar the environmental conditions and the shorter the spatial distance between the locations of interest and a sample are, the more similar the soil properties of the two locations. However, the current consideration of spatial distance by a constant distance-decay parameter should be improper across a whole large area under mapping due to the spatial variation of both sample distribution and the environmental conditions over a large area. In this paper, we propose a new large-area DSM method based on environmental similarity with adaptive consideration of spatial distance to samples by using adaptive distance-decay parameter values across a large area. An evaluation experiment to predict the soil organic matter at a depth of 0–20 cm at a 90-m resolution with a total of 659 soil samples in Anhui Province (approximately 134000 km2), China, was carried out. Evaluation results show that the proposed method (named iPSM-AdaIDW) achieved higher accuracy (with a root mean square error, or RMSE, of 8.273 g/kg) than two representative environmental-similarity-based DSM methods, i.e., individual Predictive Soil Mapping (iPSM) (without considering the spatial distance to samples) (RMSE = 8.878 g/kg) and iPSM considering spatial distance with a constant distance-decay parameter across the study area (RMSE = 8.310 g/kg), and a representative of large-area DSM not based on environmental similarity, i.e., the random forest kriging method (RMSE = 9.647 g/kg). For large areas with quantitively limited and unevenly distributed samples, the proposed method can achieve more applicable and accurate predictions. [ABSTRACT FROM AUTHOR]

Improving carbon emission efficiency(CEE) is crucial to reducing CO 2 emissions. Most studies on CO 2 emission are conducted at national and industrial scales, and city-scale studies still need to be included. In order to collect more consistent city- and county-scale CO 2 emission data, the sparrow search neural network is trained to fit the energy consumption CO 2 emissions with nighttime light in this study. Additionally, using the SBM-DEA model and spatial econometric techniques, the CEE values of 27 cities in the Yangtze River Delta region (YRDR) from 2000 to 2020 were examined from the perspective of total factor inputs. The findings demonstrate that CEE's general trend is erratic and uneven. The CEE value of the YRDR decreases from 0.720 in 2000 to 0.628 by 2020, which means that the YRDR has redundant capital and labour inputs and insufficient economic output. The low value carbon efficiency areas are mainly concentrated in the western part of the YRDR, i.e. the Anhui Province region. Shanghai, Wuxi and Suzhou have high carbon efficiency values of 1.21, 2.08 and 1.00 respectively, and are exemplary cities in terms of carbon efficiency, while the rest of the cities have varying degrees of efficiency loss. Taking Chizhou-Jiaxing as the middle line, the CEE pattern in the YRDR presents a state of "low in the middle and high at each end," and center of gravity for CEE generally shifts southward. Additionally, the cold-spot areas of CEE are concentrated in the southern part of Anhui Province, and develop a low-efficiency zone with Chizhou, Anqing, and Xuancheng as clusters and spreading outwards. Overall, this paper significantly narrows the spatial scale of carbon accounting studies and the findings can be applied to the formulation of customized carbon reduction policies. • The neural network was trained based on the sparrow optimization algorithm. • The relationship between nighttime stable light data and carbon emissions was estimated. • Based on the total factor, the carbon emission efficiency of the city was measured. • There are different degrees of efficiency loss in Yangtze River Delta cities. [ABSTRACT FROM AUTHOR]

Green Mining Construction (GMC) is the Chinese national strategy of the environmental regulation of the mining industry to motivate mining enterprises to improve their resource utilization efficiency, protect the environment and harmonize the relationship between enterprises and communities. This paper uses system dynamics (SD) and the Malmquist-Luenberger (ML) Index to model the sustainable effect of the policy instruments of Green Mining Construction, namely, environmental taxes and subsidies. A case study of typical coal mining enterprises in Anhui, China, is provided to demonstrate the application of the proposed model in which the corporate performances of three optional strategies in response to the Green Mining Construction policies are examined. The responsive strategies are categorized as scale expansion, technical innovation and environmental protection. The simulation results show the following: (i) The environmental taxes and subsidies with disparate rates can help enterprises that focus on technical innovation and environmental protection perform better, albeit with a certain degree of lag. (ii) The synergy impact of subsidies and taxes is not significantly more than the separate ones. (iii) Environmental regulations reduce the productivity of mining enterprises, but to varying extents. This negative effect of environmental regulation should be compensated by other measures. This study offers insights to help enterprises select optimal strategies in response to the Green Mining Construction policies and to inform the government of possible sustainable policy designs to promote the Green Mining Construction. The limitations of the model are discussed for further improvements in simulating the effect of environmental regulation. • The hybrid model is used to test the effect of mining environmental policy in China. • Environmental taxes and subsidies are effective, but there are delays. • The effect of policy combination is not better than that of the single policy. • The model negates the Porter hypothesis, but there are differences among enterprises. [ABSTRACT FROM AUTHOR]

Abstract In the late 1990s, China initiated the Conversion of Croplands to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP) based on the Payments for Ecosystem Services (PES) principle. Positive socioeconomic outcomes of the programs are essential for the long-term success of eco-environment conservation. However, there is lack of understanding of their longer-term (over 10 years) impacts on rural livelihoods. In this paper, we examine income distribution and inequality of rural households under CCFP and EWFP in rural Anhui, China after 12 years of program implementation. Results show that CCFP-participating households have higher income inequality than non-participants, while the EWFP does not have a significant effect. Local off-farm work and out-migration with remittances are the two principal income sources and both add to inequality. A regression-based decomposition of inequality shows that the CCFP indirectly alters livelihoods by increasing out-migration with remittances, but it also adds to inequality from shifting livelihoods to non-agricultural activities. Meanwhile, EWFP payments positively affect agricultural incomes and contribute 16% to agricultural income inequality. Finally, human capital, natural capital and physical capital all play important roles in generating income and inequality, but the factors affecting inequality from agricultural and non-agricultural activities are different. Highlights • We examine rural household income distribution under two concurrent PES programs. • CCFP positively affects non-farm income via out-migration followed by remittances. • EWFP positively affects agricultural income and adds slightly to inequality. • Factors affecting agricultural and non-agricultural income distributions differ. [ABSTRACT FROM AUTHOR]

• INLA-SPDE obtained higher mapping accuracies than RF. • The remote sensing based soil moisture indices (NSDSIs) improved SOM mapping accuracies. • Adding NSDSIs and Fourier transforms decomposed variables with natural variables generated the highest accuracies. Generating accurate spatial information on soil organic matter (SOM) is increasingly important in the context of global environmental change. Both prediction models and environmental covariates influence the mapping results and accuracy, making them important factors in SOM mapping. The Bayesian spatial model INLA-SPDE is an emerging model, that has shown potential in digital soil mapping (DSM), but its application is still limited. Soil moisture, which affects soil water status and the decomposition of SOM, can be a potential predictor for mapping SOM. However, the difficulty of obtaining soil moisture measurements over a large area using ground-based methods hinders its application. Recently, high spatial resolution remote sensing (RS) has provided a possible way to generate soil moisture indices over a large area. However, the effectiveness of RS-based soil moisture indices on SOM mapping is unknown. Fourier transforms decomposed (FTD) variables based on vegetation indices have been proven effective in detecting time-series patterns of crop growth, thereby improving the mapping accuracy of farmland. Yet, the effectiveness of FTD variables has not been verified in other vegetation-covered areas. This paper examines the use of INLA-SPDE with three RS-based soil moisture indices (NSDSIs) and six FTD variables for SOM mapping compared to Random Forest (RF), in a study area with diverse vegetation cover in Anhui Province, China. The finding indicates that with the optimal combination of environmental covariates, INLA-SPDE yields a higher prediction accuracy than RF, with an increase of 18% in R2. Either the RS-based soil moisture indices covariates or the FTD variables are effective in mapping SOM. When compared to using only natural environmental covariates, the best combination including RS-based soil moisture indices and FTD variables improved the mapping accuracy by 25% in terms of R2, 21% of LCCC, and 11% of RMSE. Furthermore, quantitative prediction uncertainty maps are derived based on the INLA-SPDE. This study demonstrates the effectiveness of INLA-SPDE model with the RS-based soil moisture indices and Fourier transforms decomposed variables for SOM mapping. [ABSTRACT FROM AUTHOR]

• Assessing spatio-temporal changes in eco-environmental by Gini coefficient and Kernel density estimation method. • Eco-environmental risks are marked by high east and low west and clustered in blocks. • Spatial variability of eco-environmental risks in the study area is narrowing. • High risk and low risk provinces in the Yangtze River Delta region proximity distribution. • Policymakers should govern eco-environmental risks from a synergistic perspective. The Yangtze River Economic Belt is an essential ecological security barrier and demonstration area for ecological civilization construction in China, so it is vital to prevent and resolve eco-environmental risks for its healthy development. However, the state of the development of eco-environmental risks in this region has remained understudied. Based on panel data from 2000 to 2020, this paper analyzes the development status of eco-environmental risks in the Yangtze River Economic Belt using the Dagum Gini coefficient, kernel density estimation, and Moran's I test model. The results show that: (1) the eco-environmental risk index of the Yangtze River Economic Belt ranges from 50.25 to 92.16, which represents a high risk status overall, showing obvious characteristics of a high index in the east and a low index in the west. (2) The overall Gini coefficient of eco-environmental risks in the Yangtze River Economic Belt decreased from 0.059 in 2000 to 0.0502 in 2020, showing an obvious M-type evolution trend. The internal difference in eco-environmental risks of the lower reaches is the largest, followed by those of the upper and middle reaches. The average Gini coefficient in the upper and the lower reaches is 0.0679, while that in the middle and the lower reaches is 0.0645, and that in the upper and the middle reaches is 0.0604. The average contribution rates of inside reaches, different reaches, and hypervariable density in the study cycle are 29.55%, 27.84%, and 42.61%. (3) The kernel density estimation curve of eco-environmental risks in the study area shifts from the lower reaches to the middle and upper reaches, with low-risk areas adjacent to high-risk areas, especially in Zhejiang, Jiangsu, Anhui, and Shanghai, where the high–low/low–high clustering characteristics are more significant. Finally, we put forward some policy implications to provide practical support for the sustainable development of the Yangtze River Economic Belt eco-environment. [ABSTRACT FROM AUTHOR]

Soybeans, as the major global oil seed crops, are among the large-scale agricultural products imported into China. In this way, the accurate identification of their growing areas is the basis for agricultural decision-making and planting structure adjustment, which are of great significance to the national food security. Currently, there is insufficient research on the identification of soybean, using remote sensing, under complex agricultural planting structures. In this paper, Guoyang County, a typical soybean-producing region in the north of Anhui Province, China, was selected as the study area, and then the multi-temporal Sentinel-1/2 (S-1/2) microwave and optical multispectral data were integrated to obtain the spatial distribution of the planting areas, via a stepwise hierarchical extraction strategy. Considering the Jeffries-Matusita (JM) distance, August 18 (i.e., the early pod setting stage) was correspondingly determined as more appropriate for soybean extraction, compared with other time phases. For this purpose, a set of rules were established to eliminate the non-cropland pixels, and the total distribution of the field vegetation was derived accordingly. In total, 30 candidate features (viz. 10 spectral bands, 11 vegetation indices, 4 texture features, and 5 microwave polarization features) were selected, and their importance was evaluated based on the ReliefF algorithm. With the support of the typical cover type samples, the weight assessment method for the ReliefF feature was further bundled with three machine-learning (ML) algorithms, namely, the random forest (RF), the back-propagation neural network (BPNN), and the support vector machine (SVM), to single out the optimum subset of the features for soybean identification. The results finally revealed that the ReliefF-RF model outperformed the other two. With nine optimum features, Kappa coefficient also reached up to 0.77–0.85. Furthermore, the extraction accuracy produced by the optimum subset of the features was significantly greater than the original 10 spectral bands (Kappa coefficient of 0.75–0.84), albeit slightly lower than the effect of the total 30 features (Kappa coefficient of 0.78–0.85). • The strategy eliminated the interference of non-cropland objects on soybean mapping. • We achieved the optimization of soybean identification scheme by machine learning. • We evaluated the performance of Sentinel-1/2 data features in soybean identification. [ABSTRACT FROM AUTHOR]

• A dynamic two-stage SBM model with non-discretionary variables is proposed. • China's CPLR efficiency under four scenarios of extreme temperatures is measured. • The influences of extreme temperatures on the CPLR efficiency are highlighted. • A comparative analysis of CPLR efficiency under four scenarios is provided. With the influence of climate changes resulting in more extremely days, we might expect to face a rise in associated coal productivity losses and land destruction. Numerous studies have focused on measuring the efficiency of coal production system. Yet scant studies have considered their internal structure, reconstructed lands, and especially the impact of extreme temperatures. Then, this paper divided such a system into stages of coal production (CP) and land reconstruction (LR), named the CPLR system. This study proposes a modified two-stage dynamic slacks-based measure model with non-discretionary variables for measuring the CPLR efficiency under four scenarios of extreme temperatures. The results are as follows. (1) The CPLR system for 24 provinces under four scenarios spanning 2012 to 2017 had relatively low efficiency except for Fujian, Inner Mongolia and Sichuan, which is mainly caused by the bad performance of the LR system. (2) Extreme temperatures must be considered in measuring CPLR efficiency and its stage efficiency, or otherwise the underestimation of CPLR efficiency and CP efficiency will become in 18 provinces. The negative impacts of extreme temperatures on the efficiency of reconstructed lands and raw coal production should be noticed, especially for Anhui. [ABSTRACT FROM AUTHOR]

• Presents a data-driven measurement, evaluation, and identification method. • Uses a coupling coordination degree model for three regional agriculture subsystems. • Suggests ways to reduce costs, increase social support, and enhance competitiveness. This study aims to evaluate the relationship between the agriculture economy environment, and society from the perspective of system theory and then put forward targeted suggestions for sustainable development of Anhui's agriculture in China. Based on the collection of relevant data and the objective weighting of indicators, the entropy weight method is used to measure the comprehensive development level of regional agriculture. The coupling coordination degree model is employed to evaluate the coupling coordination level among the three subsystems of regional agriculture. The obstacle degree model is used to identify the key obstacle factors. The case study results show that the main obstacles of the coordination system are the use of pesticides, the number of agricultural scientific and technological achievements, the application of agricultural plastic film, the number of farmers' technical training schools, and the proportion of animal husbandry. Accordingly, the paper posits suggestions, such as reducing the input of pesticides and agricultural plastic films in agricultural production, improving the social support level of agricultural science, technology, and education, and enhancing the competitiveness of animal husbandry in the Anhui Province. This study provides theoretical and methodological support for the coupled and coordinated development of regional agricultural economy, ecology and society(EES). [ABSTRACT FROM AUTHOR]

Under the dual pressures of the socio-economic development and the increase in population density, the ecological environmental quality issues in Anhui Province have become increasingly prominent, which seriously handicap the sustainable development of regional economy, and it is more important to evaluate ecological environmental quality. By using the data of ecological environment, society, economy and population in Anhui Province, this paper measures ecological footprint, ecological environmental carrying capacity and ecological deficit and surplus in Anhui Province based on ecological footprint. The results show that ecological environmental quality in Anhui Province is not in the best state of the coordinated development, and the relationship between the supply and the demand of per ecological footprint almost is not in balance. [ABSTRACT FROM AUTHOR]

The urban population in Anhui province has increased rapidly in recent years. Simultaneously, society has grown more concerned with protecting the ecological environment, including controlling atmospheric pollution. As such, the relationship between urban population and the environment merits consideration. This paper examines five statistical indicators (urban population, three kinds of industrial waste, and urban green area) using the GMM method to quantitatively explore the relationship between urban population and the ecological environment in cities of Anhui province. The results show that waste gas pollution is truly positive to the emigration of population and to each area, the higher the level of average urban income, the higher the possibility of emigration after the area being polluted; effects are smallest on landscape green area. The article concludes with suggestions on how policymakers can better align the needs of a growing urban population with environmental growth. [ABSTRACT FROM AUTHOR]

Biochar was prepared from rice straw and modified with 15% H 2 O 2 and 1:1 HNO 3 /H 2 SO 4 , respectively. The unmodified biochars and HCl treated biochars for carbonate removal were used as control. The biochars were added to the acid paddy soil collected from Langxi, Anhui Province, China at the rate of 30 g/kg. The paddy soil was flooded and then air-dried, and soil pH and Eh were measured in situ with pH electrode and platinum electrode during wet-dry alternation. Soil pH buffering capacity (pHBC) was determined by acid-base titration after the wet-dry treatment. Then, the simulated acidification experiments were carried out to study the changing trends of soil pH, base cations and exchangeable acidity. The results showed that soil pHBC was effectively increased and the resistance of the paddy soil to acidification was apparently enhanced with the incorporation of H 2 O 2 - and HNO 3 /H 2 SO 4 -modified biochars. Surface functional groups on biochars were mainly responsible for enhanced soil resistance to acidification. During soil acidification, the protonation of organic anions generated by dissociation of these functional groups effectively retarded the decline of soil pH. The modification of HNO 3 /H 2 SO 4 led to greater increase in carboxyl functional groups on the biochars than H 2 O 2 modification and thus HNO 3 /H 2 SO 4 -modified biochars showed more enhancement in soil resistance to acidification than H 2 O 2 -modified biochars. After a wet-dry cycle, the pH of the paddy soil incorporated with HNO 3 /H 2 SO 4 -modified biochar increased apparently. Consequently, the addition of HNO 3 /H 2 SO 4 -modified biochar can be regarded as a new method to alleviate soil acidification. In short, the meaning of this paper is to provide a new method for the amelioration of acid paddy soils. Increase the soil pHBC, slow down the decline of soil pH, and improve the resistance to acidification. [Display omitted] • The addition of modified biochars obviously increased pHBC of an acid paddy soil. • HNO 3 /H 2 SO 4 -modified biochar increased soil resistance to acidification more evident. • Modified biochars increased the pH of the paddy soil after a wet-dry cycle. • Protonation of organic anions on biochars was main mechanism for soil pHBC increase. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes 20th century climate and human impacts on terrestrial and fluvial systems in the Dabie Mountains, Anhui Province, China, based on analyses of four types of information. Analyses of particle size, mineral magnetism, organic carbon, nitrogen and phosphorus in a sediment core taken from the Longhekou reservoir, built in 1958 AD in the upper reaches of Hangbu River, provide an ∼45 year record of fluvial responses, while monitored meteorological and hydrological data provide records of climate and river discharge. Census data compiled for the local Shucheng County provide records of population and land use, complemented with analyses of satellite images. The Xiaotian river delivers over 65% of the total water and silt to the reservoir. Analyses indicate that the fluvial regime tracks the monsoon climate over seasonal timescales, but human activities have a strongly mediating effect on sediment supply, sediment delivery and, to a lesser extent, runoff over longer timescales. Notable periods of human impact on erosion include the Great Leap Forward (1958–1960) and Great Cultural Revolution (1966–1976). A rising trend in precipitation and new land use changes at the present time may be leading to an enhanced flood risk. [Copyright &y& Elsevier]

[Display omitted] • Relative energy efficiency of regional wastewater treatment plants was assessed. • Designed scale and quality of wastewater influence energy efficiency significantly. • Area surrounding Taihu Lake is energy efficiency through in the Yangtze River Delta. With priorities to reach a carbon emission peak and integrated development involving ecological demonstration, a systematic evaluation on the energy efficiency and internal discrepancies of wastewater treatment plants in the Yangtze River Delta region is needed. In this study, a slacks-based measure data envelopment analysis model was applied to quantify the relative energy efficiency of 270 regional wastewater treatment plants. Based on the score of relative energy efficiency, the internal discrepancies of the region were identified through a method combining spatial and sensitivity analysis. Although the wastewater treatment plants had met the pollutant limits of discharge standards, the result of data envelopment analysis showed that 253 wastewater treatment plants were inefficient (with efficiency scores less than 1) due to input excess or output shortfall, indicating substantial potential for improving the energy efficiency. Besides, binary logistic regression demonstrated the significant impact from explanatory factors including designed capacity (104 m3/d), loading rate (%), influent ratio of chemical oxygen demand to total nitrogen, and influent concentration (mg/L) of chemical oxygen demand. Moreover, with regards to regional discrepancies, proportion of efficient wastewater treatment plants for Shanghai, Jiangsu, Zhejiang, and Anhui differs significantly. The area surrounding Taihu Lake was recognized as the trough of the Yangtze River Delta region in terms of the relative energy efficiency of wastewater treatment plants. This paper would give a reference for optimization of wastewater treatment plants in the study area and the evaluation framework on internal discrepancies might also be useful for other regions worldwide. [ABSTRACT FROM AUTHOR]

Regional water resources coordination (RWRC) plays a prominent role in long-term sustainability. In order to achieve harmonious utilization of regional water resources, it is suggested that local governments should focus on balancing three goals - security, equity, and efficiency, resulting in both ecological and socio-economic benefits in a region. Therefore, this paper aims to develop a comprehensive evaluation methodology for assessing the degree of RWRC. First, a security-equity-efficiency (SEE) evaluation indicator system is established. A novel multi-criteria decision-making (MCDM) approach that integrates decision-making trial and evaluation laboratory (DEMATEL) method and VlseKriterijumska Optimizacija I Kompromisno Resenje (in Serbian, known as VIKOR) method considering two different hesitant fuzzy linguistic term sets (HFLTSs) is then proposed. With this approach, a final ranking is obtained for six alternatives for the RWRC of the relevant regions (i.e., Shandong Province, Henan Province, Hebei Province, Anhui Province, Tian City, Jiangsu Province) in North China Plain, and the six relevant regions receive coordination performance ranking of II, III, V, I, VI, IV. Moreover, comparison analysis and sensitivity analysis are conducted to verify the effectiveness and reliability of the proposed method and dig deep into the RWRC conditions in the studied regions. Finally, some managerial suggestions are provided for strengthening the coordination levels in these regions. • A SEE indicator system is established for RWRC evaluation. • A hybrid MCDM method integrating different HFLTSs is proposed. • A maximum consensus and minimum hesitant degree model of HFLTSs is constructed. • Interval HFLTSs are introduced into DEMATEL method for indicators' weights. • A case study is presented to verify the effectiveness of the evaluation method. [ABSTRACT FROM AUTHOR]