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Anammox is an efficient and carbon-saving process for biological nitrogen removal; however, the long start-up period and poor stability of anammox bioreactors hinder their application in wastewater treatment. An innovative technical strategy was developed in this study that used a packed-bed biofilm reactor (PBBR) to grow healthy anammox biofilms for the fast start-up of bioreactors. With a greatly reduced fluid turbulence in the PBBR, the attachment and growth of anammox biofilms were found to be accelerated and the specific anammox biofilm formation rate was 60% higher than that in the conventional moving-bed biofilm reactor (0.032 vs. 0.020/d). In the PBBR operation, maintaining a low free ammonia (<2.7 mg/L) and free nitrous acid (<5.8 μg/L) concentrations in the early stage and stepwise increasing the nitrogen load also assisted the biofilm formation. With the rapid anammox biofilm formation and growth, the nitrogen removal capacity of the PBBR increased from 77.6 to 876.8 mg N/(L·d) in 2 months. The anoxic condition and prolonged sludge retention time in the PBBR also benefited the growth of fermentative bacteria and denitrifiers, which induced the partial denitrification–anammox process and decreased the nitrate accumulation by 50%. [Display omitted] • Low turbulent condition in PBBR improved the biofilm formation rate by 60%. • Low FA (<2.7 mg/L) and FNA (<5.8 μg/L) benefited anammox growth and N removal. • PBBR provides a reliable technique for rapid start-up of anammox biofilm reactors. [ABSTRACT FROM AUTHOR]

Surface mining has been subject to increased criticism and pressure due to its potential to cause environmental damage. China accounts for more than half of world coal consumption, and this continued demand for coal, combined with low costs, has accelerated surface coal mining worldwide. However, it isn't easy to understand the impact of surface mining in China. Therefore, combined with POI, the Exposed Coal Frequency Index (ECFI) was constructed without any mining information in China. A LandTrendr algorithm was used to identify past mining disturbances and reclamation events (1986–2021). While examining these events, surface destruction due to coal mining in China was revealed. Based on the results, (1) the detection accuracy of statistical surface mining sites by province reaches 91.8%, while the detection accuracy of disturbance and reclamation events exceeds 72.59 %. Also, by comparing the mine data, our results provide more accurate information regarding both time and spatial consistency. (2) 253.61 km2 of surface mining sites were identified in 14 provinces or autonomous regions during 1986–2021. Of these, Inner Mongolia, Ningxia, Shanxi and Xinjiang ranked in the top four occupying 89.0% of the total mining site. (3) China has cumulatively disturbed 545.84 km2 and reclaimed 169.41 km2 including more comprehensive human activities. In the last 12 years, large-scale mining and reclamation have accounted for 51.63% and 75.17% of the cumulative area, respectively. (4) Generally, reclamation in China can be completed within four years. In contrast, reclamation time intervals and the proportion of reclamation varied widely between mining areas. Clearly, this study provides the most accurate data on surface coal mining disturbances in China, which will be beneficial for future studies in spatial geography related to mining. It also includes more policies and management creation for mines. [Display omitted] [ABSTRACT FROM AUTHOR]

The eastern plain of China is one of the most important grain production areas in China. Meanwhile, the plain is also an important coal production area with a large number of "coal-grain composite regions". Coal mining causes land subsidence and waterlogging, which destroys a significant amount of cultivated land. However, there is no dataset for the spatio-temporal distribution of subsidence water to assist related decision-making on a regional scale. Accurate monitoring of subsidence water is still a challenge, especially distinguishing among subsidence water, natural water and artificially excavated water by only using remote sensing data. Here, A new method to generate a dynamic map for the subsidence ponding year and the restoration year using the Google Earth Engine platform with 33-year-old Landsat imagery was created. The time segmentation method was used to first extract the change water pixels corresponding to subsidence water and artificially excavated water with similar trajectory features. Then, the morphological characteristics of the two types of change water at the beginning year of water accumulation are used to construct 13 landscape indexes. This approach utilizes the Random Forest algorithm to distinguish between subsidence water and artificially excavated water. The Huang-Huai-Hai plain area in eastern China was selected as the study area and extracted the subsidence water areas from 1986 to 2018. The identification accuracies for subsidence ponding year and restoration year are 88% and 85%. 79% of the subsidence water areas are located in cultivated land, which shows significant impacts to agricultural activities. The method proposed could be applied to other similar areas, the results could provide reference and data for decision-making and related land reclamation planning. • A novel methodology was developed for mapping mining subsidence water. • Map of the subsidence ponding year and restoration year. • Random forest algorithm can effectively extract the patch of subsidence water. • Contribute to monitor annual subsidence water at a regional scale. • This paper belongs to change detection using Landsat time series. [ABSTRACT FROM AUTHOR]

Coal resources play an important role in socioeconomic development around the world. Yet the development driven by coal mining is accompanied by ecological damage, social instability, and economic vulnerability, which seriously hinder the sustainable development of coal mining cities. It is, thus, urgent to realize the sustainable development of coal mining cities, premised on comprehensive and scientific analysis of these cities' vulnerability characteristics. In this study, based on the analysis of the social-economic-natural compound ecosystem (SENCE), we constructed a vulnerability evaluation indicator system (28 indicators) for coal mining cities. Took cities producing more than ten million ton of coal in China in 2015 as examples, the weight of every indicator was obtained using entropy weight method. In addition, cities were ranked and classified based on vulnerability characteristics of three subsystems. The reasons for subsystem vulnerability in different regions were analyzed. The results showed that industrial smoke and dust emissions, elevation, proportion of the area containing coal are the main reason for natural vulnerability. Unemployment rate of urban employees, and mining workers as a proportion of the secondary industry workforce are the main cause to social vulnerability. Self-sufficiency rate of local finance, and value of secondary industries as a proportion of GDP are the main indicators leading to economic vulnerability. Our findings suggest that preventing soil erosion, accelerating the resolution of historical problems, promoting green mining, and encouraging renewable energy should be adopted to reduce natural vulnerability; resettling the unemployed and adjusting employment structure are effective in enhancing the sustainability of social subsystems; changing industrial structure, encouraging self-employment, as well as implementing scientific and technological innovation should be considered to realize economic sustainability. Image 1 • A vulnerability evaluation indicator system for coal mining cities was constructed based on SENCE. • The vulnerability score of each subsystem was obtained by Entropy weight method. • Reasons for vulnerability of each subsystem are analyzed to provide reference for decision-making. [ABSTRACT FROM AUTHOR]

High-intensity coal mining has resulted in numerous ecological and environmental problems, and seriously threatens ecological security in China. The identification and optimization of the ecological security pattern (ESP) can facilitate sustainable development while preserving the integrity and stability of ecosystems, which is important for improving ecological security in fragile ecosystems and areas of intensive coal mining. This paper presents an integrated framework for identifying, analyzing, and optimizing ESP in consideration of the core area extracted based on morphological spatial pattern analysis (MSPA), ecosystem services importance, and ecological risk index. The innovative method combines the model of linking different ecological source grades to multi-process minimum cumulative resistance (multi-process MCRE model) and kernel density analysis and includes the following steps: (1) ecological patches are delimited by assessing ecosystem services importance based on the contributions of water resources supply, soil conservation, gas regulation, and habitat maintenance; (2) ecological sources are identified by considering landscape connectivity along with the number and area of important ecological patches; (3) ecological resistance coefficient is determined based on the ecological risk index, which is constructed by coupling the surface drought index, landscape ecological risk index, and mine disaster risk index; (4) ecological corridors are extracted using the multi-process MCRE model, which is based on minimum cumulative resistance (MCR) theory, and the ecological potentials of ecological sources; and (5) the ESP is optimized via kernel density analysis, and regulation zones are defined based on the optimized ESP. Results demonstrate that the optimized ESP in study area included 3326 km2 of ecological sources that primarily dominated by grassland, 58 ecological nodes, 827.92 km of group corridors and 308.21 km of landscape corridors; which distribution pattern was "three horizontal, two vertical, and two circles" to ensure continuity of ecological process within study area. The restrictive, protective and key development areas accounted for 17.36%, 59.81%, and 22.83%, respectively; and corresponding planning recommendations were raised. This study not only provides an effective framework to identify, construct, and optimize ESPs in ecologically fragile coal-mining areas, but also purposes the multi-process MCRE model and ecological sources quantification that suitable for global region to achieve the sustainability between resource exploitation, urban development, and ecological protection. [ABSTRACT FROM AUTHOR]

The armed conflict in Ukraine has been ongoing, with firefights concentrated in Kherson, Zaporizhia, Dnepropetrovsk, Donetsk, Luhansk, and Kharkiv oblasts of eastern Ukraine. Armed conflicts have caused damage to valuable black land and agricultural infrastructure, resulting in reduced food production and greatly affecting the world food supply. Therefore, it is urgent and important to quantify the extent of the impact of armed conflicts on cropland. We applied the Sentinel dataset based on the GEE platform and used the time-weighted dynamic time warping (TWDTW) method to map abandonment in six eastern oblasts and propose a cumulative NDVI (cumNDVI) method to characterize food production loss. The accuracy of the 2022 cropland abandonment in Krain was excellent. The overall accuracy was 92.5%, with a kappa coefficient of 0.85. The area of abandoned cropland in southeastern Ukraine was 11,892.49 km2, which was 7.17% of the cropland. The most abandoned cropland in Kherson was 26,692.22 km2, with 3,661.12 km2 of abandoned and a 13.72% abandonment rate. Because of abandonment, the loss of grain production in the six oblasts was from 3.67% to 15.18% respectively. And the other loss of grain production of cultivated cropland was found to be 33.02%, with a general loss characteristic in all regions except Donetsk slightly increased. In 2022, the grain production loss was a totally 8.19 million tons. That means the grain production loss of the six eastern oblasts in 2022 was up to 31.5% compared to the average yield level in 2016–2020 and accounted for 6.76% compared to the national grain production. The study not only mapped the distribution of abandonment with high precision at a 10 m resolution in 2022 but also quantified the food production loss due to armed conflict. It reveals the severity of the food crisis and proposes measures for post-war replanting and restoration of agricultural economic activities. [Display omitted] • Time weighted dynamic time warping (TWDTW) method is used to map abandoned cropland in eastern Ukraine. • The spatial distribution, area, ratio, and food production loss of abandoned cropland were profiled. • The overall abandonment ratio of cropland in eastern Ukrain was 7.17% in 2022. • Food production loss of abandoned cropland and cultivated cropland was 7.58% and 33.02% respectively. [ABSTRACT FROM AUTHOR]

The process of urbanization and infrastructure construction has resulted in the occupation and loss of high-quality cropland. Complemented cropland with higher altitudes and slopes from either development or reclamation has become an important component of cropland resources and plays a vital role in ensuring food security. However, existing research mainly focuses on the dynamic balance of quantity, quality, productivity, and impact on the environment, and not enough attention is paid to whether the newly added cropland is efficiently used in the long term. Based on a cropping intensity dataset from 2001–2019, this study applied the sliding window and change detection methods to assess the use continuity and efficiency of complemented cropland from the perspective of cropland abandonment and explored the impact of the abandonment on food production. The results showed the following: (1) The total area of complemented cropland in 2002–2019 reached 279,416.4 km2, accounting for 13.1% of total cropland in the base year (2001). (2) 45.1% of the complemented cropland was abandoned at least once during the past 20 years, making only 65.6% of that land efficiently used, and this percentage was higher in the middle plains and downstream of the Yangtze River. (3) Abandonment had significant impact on the food production of complemented cropland—with a trend of increasing and then decreasing—leading to a food production reduction of 24.3% per year on average, which was more severe in the middle plains and downstream of the Yangtze River. Higher slope and a migrated population promoted the abandonment of complemented cropland. Therefore, the government should strengthen the monitoring of long-term utilization of complemented cropland and strategic reserves to maintain stable utilization. [Display omitted] • The continuity and efficiency of complemented cropland use need to receive additional attention. • Sliding window and change detection were used to extract abandoned complemented cropland. • 45.07% of the complemented cropland was abandoned at least once making that efficiently used only 65.64%. • The abandonment reduced the food production of complemented cropland by 24.27% per year on average. • It is significant for food security to promote effective use of complemented cropland by policy reinforcement. [ABSTRACT FROM AUTHOR]

Grasping cropland abandonment patterns in China can help protect croplands, thereby ensuring the perpetuation of nationwide food security. However, the pattern of long-term cropland abandonment at the national scale remains limited. Here, we investigate the spatiotemporal patterns and drivers of cropland abandonment across China during 1990–2019 by integrating continuous long-term series of land cover, natural factors, and socioeconomic data. We find that the mean abandoned cropland area is 2.34 × 104 km2 per year. The spatial pattern of cropland and natural factors are prime drivers of cropland abandonment, but there is a substantial variation across agricultural regions. Further analysis suggests that annual food loss due to cropland abandonment is about 7.94 billion kilograms, which could feed 19.85 million people. These findings allow for a better understanding of the spatial heterogeneity and driving forces of cropland abandonment patterns, and inspire to govern global cropland abandonment and formulate relevant policies. [Display omitted] • The average annual cropland abandonment in China is 2.34 × 104 km2 (1.21%). • Cropland abandonment is mostly concentrated near the Hu line. • Driving forces vary significantly across agricultural regions. • Food loss is approximately 7.94 billion kilograms per year. • About 7.92 billion kilograms of food will still be lost by 2030. [ABSTRACT FROM AUTHOR]

Improving industrial land use efficiency is of great significance for sustainable industrial development. This paper measures the industrial land use efficiency (ILUE) in China, and explores its spatial-temporal characteristics and influencing factors by using a stochastic frontier analysis (SFA) method. A translog SFA model that characterizes the relationship between industrial land productivity and land input level is established to estimate the efficiency. The estimation results of the SFA model and related tests both show that the stochastic frontier method for ILUE measurement is suitable, and the contribution rate of technical inefficiency term to the mixed error reaches 66.9%. ILUE levels in China show an increasing trend from 2004 to 2017. The average efficiency in the east is the highest. ILUE in the west gradually surpassed that in the northeast in recent years. Reduction of the inter-group difference greatly contributed to the decrease in overall difference, but it is still the main source of overall difference. Results of the inefficiency function show that enterprise ownership structure produces negative impact on ILUE. The specialization of high-tech manufacturing industry, urbanization level and transportation condition have positive influence on ILUE, while the specialization of the chemical and rubber industry and the local financial pressure exert negative effects. Based on these, some policy recommendations for ILUE improvement are further proposed. • A stochastic frontier method is adopted to measure industrial land use efficiency (ILUE). • Theil index is used to reveal the regional disparities. • Technical inefficiency accounts for 66.9% of the mixed error. • There is significant heterogeneity of ILUE among different regions. • Policy suggestions that help to improve ILUE are further proposed. [ABSTRACT FROM AUTHOR]