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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]

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]

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]