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Quantifying the atmospheric water balance is critical for the study of hydrological processes in significant regions. This study quantified atmospheric water balance closure at 205 stations in mainland China on a monthly timescale from 2009 to 2018 using datasets from ground- and satellite-based observations and reanalysis data. The closure performances were firstly quantified using the mean and root mean square (RMS) of the residuals, and the possible influencing factors were explored, as well as the influence of different water balance components (WBCs) using different datasets. In the closure experiment using ERA5, the mean and residuals were 6.26 and 12.39 mm/month, respectively, on average, which indicated a closure uncertainty of 12.8%. Using ERA5 analysis as a reference, the closure experiment using different combinations revealed average mean residuals of 8.73, 11.50, and 15.89 mm/month, indicating a precipitation closure uncertainty of 22.0, 23.7, and 24.4% for the ground- and satellite-based observations and reanalysis data, respectively. Two possible influencing factors, station latitude and the climatic zone in which the station is located, were shown to be related to closure performance. Finally, the analysis of the impact from different WBCs showed that precipitation tended to have the most significant impact, which may have been due to larger observation uncertainties. Generally, the atmospheric water balance in mainland China can be closed using datasets from different observational techniques. [ABSTRACT FROM AUTHOR]

Integrating both proactive and reactive risk-mitigation mechanisms, this research constructs a bi-level network design problem for hazardous materials (hazmats) to regulate the carriers' use of roads, such that the environmental impact is minimized. In more detail, by embedding the emergency response time into the risk assessment, the locations of hazmat response teams are determined along with toll schemes, road closures, and new road constructions. The uncertainties of demand, including differences in the number of shipments, origin/destination changes, and amount variations, are also taken into account by applying a scenario-based approach. The proposed bi-level model is first solved optimally by a single-level reformulation, and then by a three-stage heuristic method for larger instances. Through numerical experiments on a real-world road network in Nanchang, a city in China, a set of management insights are derived to facilitate policy-making in regulating hazmat transportation. [ABSTRACT FROM AUTHOR]

Several bridges now require widening owing to the continuous development of road transport. However, the standard of traffic load in design specifications has been enhanced. Consequently, old bridges may not meet the safety requirements of current design specifications and, therefore, must be reconstructed or retrofitted. However, the old structures in widened bridges carry fast-lane traffic loads, which are relatively low due to uneven multilane traffic loads. This paper reports a case study conducted on a Chinese highway widening project for comprehensively evaluating the structural safety of multislab widened bridges using code-based deterministic and reliability-based probabilistic approaches. In the deterministic evaluation, previous and current Chinese bridge designs and other national codes were employed. Meanwhile, in the probabilistic assessment, an uneven multilane truckload modeling was implemented based on site-specific traffic data. Subsequently, bridge safety under normal traffic conditions with all lanes open and occasional traffic conditions with lane closure was investigated. The results indicate that most old slabs, especially those closer to medial dividers, are unsafe under the current Chinese design specifications. However, a site-specific assessment under normal traffic conditions indicates that these old slabs possess adequate security reserves, except that the side slab close to the junction requires safety. Further analyses conducted with occasional traffic conditions demonstrated that closing fast lanes does not affect the safety of slabs. Conversely, closing slow lanes significantly affects the safety of inner old slabs, which should be prohibited or addressed with strict weight restrictions. These results can be beneficial for future bridge widening projects. [ABSTRACT FROM AUTHOR]

• A deep leaning network was designed for farmland segmentation in satellite images. • Two-part framework of pseudo label generation and segmentation model fusion. • New block-wise metrics were introduced for parcel-level evaluation. • Experiments show significant performance improvements at the parcel level. The participation of insured smallholders and involvement of related requirements have been emerging and increasing with the popularization and promotion of agricultural insurance. Compared with traditional field surveys, remote sensing and deep learning technologies, with their rapid development in recent years, have provided an automatic and effective means for underwriting control in the agricultural insurance industry. Moreover, "smallholder-wise underwriting" is intensively expected by the local government and insurance companies to ensure precise claim settlements. In this process, farmland segmentation is a crucial and fundamental step for smallholder farmland claim settlements. However, segmentation of smallholder farmland parcels from satellite images remains a challenge, as the ridges and roads between farmland parcels are usually narrow and display confusing characteristics with their neighboring farmlands. This challenge causes undersegmentation on the farmland boundaries, leading to inaccurate and incomplete farmland parcel recognition. In this study, we aim to solve this problem by proposing a novel Deep Edge Enhancement Semantic Segmentation Network to refine parcels' boundary segmentation and improve the closure level of the farmland segmentation. We designed a framework for farmland edge enhancement through pseudo road label generation and model fusion. The proposed network aggregates feature extraction for ridge and road segmentation to improve its performance on farmland parcel recognition. Furthermore, we found that the commonly used image segmentation evaluation metric, such as pixel-wise–based mean Intersection over Union, cannot objectively reflect the effectiveness of the smallholder farmland segmentation. Therefore, we proposed two block-wise farmland evaluation metrics that are consistent with the practical evaluation rules and requirements of farmland segmentation at the parcel level. We implemented experiments in the study area of Jiaxiang County in northern China using the GaoFen-1 4-band multispectral images (red, green, blue, and near-infrared) at a spatial resolution of 2 m. Experimental results demonstrated the effectiveness of our method, which outperformed the baseline DeepLabv3+ network. [ABSTRACT FROM AUTHOR]

• More than one-fifth of China experienced sustainability change over two decades. • The dominant types of change divide China into four zones. • Roads and ecological engineering are the key drivers for the changes. The sustainable development level, coordinated among three sub-systems in the economy, society, and environment, has changed globally or regionally. The key factors and mechanisms driving these changes remain uncharacterized due to the lack of a systematic method. Here, within the production-living-ecological framework, this study quantified China's sustainable development change at a fine scale and identify the key factors as well as explored its driving mechanism by combining a machine learning approach with multi-dimensional datasets. The results indicate that more than one-fifth of China has experienced sustainability change over two decades, and the dominant types of change divide this nation into four zones. The socioeconomic and the man-made environment rather than nature itself have greatly influenced this change. Road density is the key driver of sustainability change except in east China where arresting environmental deterioration via artificial plantation and mountain closure is the dominant factor for the change to take place. The findings provide a novel insight into understanding sustainable development change and its driving mechanism. [ABSTRACT FROM AUTHOR]

• Three sales growth scenarios are determined for new energy vehicles (NEVs) by PEST analysis. • Quantities of end-of-life NEVs is predicted to be 2300 thousand units by weibull distribution under higher growth scenario in 2030. • Recovered nickel from spent nickel-bearing batteries nearly accounts for one third of the total demand in 2030. • Recovered nickel is likely to play vital role for closing nickel loop in the industry of NEVs in China. China has become the global largest country in application of new energy vehicles (NEVs) and installed capacity of lithium-ion batteries (LIBs). However, the contradiction between the demand and supply of nickel used as the typical critical resource for manufacturing LIBs, is ever-increasing aggravated especially under the tendency of cobalt-free nickel-rich. This research, hereby, predicts the nickel recycling potential from end-of-life NEVs under three sales growth scenarios by linear regression and Weibull distribution, and investigates the role of recovered nickel during closing nickel loop quantitatively. It is predicted that China's NEVs sales can achieve approximately 3800 thousand units, and the quantities of end-of-life NEVs can reach 2300 thousand units under the higher growth rate (HGR) in 2030. If the spent nickel-bearing batteries (NBBs) can be fully recycled, 44.5 Mt of nickel can be recovered, which will account for nearly one third of the total demand for manufacturing NBBs. The results indicate that the recovered nickel from spent NBBs can play a vital role during closing nickel loop in the industry of NEVs in China. [Display omitted] [ABSTRACT FROM AUTHOR]