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Digital soil mapping has emerged as a new method to describe the spatial distribution of soils economically and efficiently. In this study, a lightweight soil organic matter (SOM) mapping method based on a deep residual network, which we call LSM-ResNet, is proposed to make accurate predictions with background covariates. ResNet not only integrates spatial background information around the observed environmental covariates, but also reduces problems such as information loss, which undermines the integrity of information and reduces prediction uncertainty. To train the model, rectified linear units, mean squared error, and adaptive momentum estimation were used as the activation function, loss/cost function, and optimizer, respectively. The method was tested with Landsat5, the meteorological data from WorldClim, and the 1602 sampling points set from Xinxiang, China. The performance of the proposed LSM-ResNet was compared to a traditional machine learning algorithm, the random forest (RF) algorithm, and a training set (80%) and a test set (20%) were created to test both models. The results showed that the LSM-ResNet (RMSE = 6.40, R2 = 0.51) model outperformed the RF model in both the roots mean square error (RMSE) and coefficient of determination (R2), and the training accuracy was significantly improved compared to RF (RMSE = 6.81, R2 = 0.46). The trained LSM-ResNet model was used for SOM prediction in Xinxiang, a district of plain terrain in China. The prediction maps can be deemed an accurate reflection of the spatial variability of the SOM distribution. [ABSTRACT FROM AUTHOR]

Soil C, N contents and C:N stoichiometry are important indicators of soil quality, the variation characteristics of which have great significance for soil carbon-nitrogen cycle and sustainable utilization. Based on 597 observations along with soil profiles of 0–20cm depth in the 1980s and the 2010s, the temporal and spatial variations of soil C, N contents and C:N stoichiometry in the major grain-producing region of the North China Plain were illustrated. Results showed that there were significant changes in soil C, N contents over time, with increasing rates of 60.47% and 50%, respectively. The changes of C, N contents resulting in a general improvement of C:N stoichiometry. There was a significant decline in nugget effects of soil C, N contents from the 1980s to 2010s, the spatial autocorrelation of soil nutrients showed an increasing trend, and the effect of random variation was reduced. C:N stoichiometry was higher in Huixian City and Weihui City, and lower in Yanjin County, an apparent decline was observed in the spatial difference of soil C:N stoichiometry from the 1980s to 2010s. Soil C, N contents and C:N stoichiometry differed among soil types, agricultural land-use types, and topography in space. The temperature, precipitation, and fertilization structure were considered as the main factors that induce the temporal variations. These findings indicated that the soil nutrient elements in the farmland ecosystems changed in varying degrees in both time and space scales, and the variation was influenced by soil types, land-use types, topography, meteorological factors, and fertilization structure. [ABSTRACT FROM AUTHOR]

Based on the condition of No. 1-2 coal seam of Shendong minging area Shangwan Coal Mine, the caving characteristics, the fracture position and caving zone and fracture zone "two belts" height of the 5.5 m large mining height workface's roof were studied by similar simulation test, with geometric similarity ratio 1 : 50. The results show that the immediate roof eaves stratified to loose blocks, and the goal can not be full of these loose blocks; because of the increasing of mining height at one time, and the original immediate roof is far from full of goal, the lower basic roof can not form hinge structure after it collapse, and the increasing of mining height evolves into the increasing of immidiate roof; before periodic weighting, fracture position of roof usually advance beyond working face 7.65 m; the height of two belts presents step increasing. [ABSTRACT FROM AUTHOR]

The transportation sector has become the leading and most-rapidly growing contributor to greenhouse gas emissions. Promoting low-carbon travel mode is critical to alleviate this issue. As a new travel mode, online ride-hailing (such as Didi Chuxing and Uber), is becoming increasingly popular in cities around the world. However, there is still no comparative analysis on fuel consumption and emissions: does online ride-hailing have a distinct fuel consumption and emissions pattern with traditional taxis? In this study, we use one month global positioning system dataset and orders dataset, averagely covering around 7 thousand taxis with 0.3 million trips and 23 thousand Didi Chuxing Express vehicles with 0.1 million trips per day in Chengdu, China to answer this question. Empirical results show that taxi trips associate with longer idle distance and shorter delivery distance than Didi trips. Didi trips' average idle velocity is apparently smaller than their delivery velocity. Online ride-hailing mode is concluded to contribute to these difference: after dropping off previous passengers, Didi drivers usually park their cars until being dispatched new orders and then drive directly to pick up passengers rather than search circuitously. Fuel consumption and carbon monoxide, nitrogen oxides, hydrocarbon emissions per passenger-on kilometer of taxi trips are found to be about 1.36, 1.45, 1.36 and 1.44 times that of Didi trips, respectively. Additionally, only taxi drivers with good performance have the ability to reduce fuel consumption and emissions; while most Didi drivers can perform well on fuel consumption saving and emissions reduction. Finally, several feasible policies are suggested for improving and upgrading the traditional taxi business. Our study provides convincing evidence for understanding the advantage of online ride-hailing mode in reduction fuel consumption and emissions sourced from empty cruising, so as to support better traffic policy making and the promotion on low-carbon travel mode. [ABSTRACT FROM AUTHOR]

• A discrete-time MOMILP model for pump scheduling of products pipelines is built. • Various factors about pump operation are considered comprehensively in the model. • A two-stage strategy is adopted to deal with the proposed model. • Two cases from a products pipeline system in China illuminate model's practicability. As one of the major means to link refineries to local markets, pipelines are crucial for refined oil supply chains. Pump scheduling is vital to optimally operate the refined products pipelines. Existing studies mostly quantified the number of pump stop/restart in the form of cost, whereas the cost coefficient was often subjectively selected. The present study built a multi-objective mixed-integer linear programming (MOMILP) model for refined products pipelines to minimize the number of pump stop/restart and reduce the pump running cost simultaneously. In this study, the minimum continuous running time of pumps, changing electricity price (i.e., time-changing electricity price and local electricity) and the pressure limits of special points were considered meticulously, e.g. high-elevation points, low-elevation points and pump stations. In the first stage, the improved augmented ε-constraint method (AUGMECON) was adopted to deal with this model, and obtained a seris of pump operating schemes, acting as a Pareto set. Then the results of the AUGMECON were evaluated by an established evaluation model based on neural network. Finally, two cases from a refined products pipeline system in China were employed to verify the practicability and accuracy of the proposed model. The results of this study can effectively guide the pump scheduling of refined products pipelines. [ABSTRACT FROM AUTHOR]

• GHGs and GWP were quantified under different crop type and fertilization treatments. • C sequestration effect mainly depended on external inputs in arid cropland regions. • CO 2 equivalent from GHGs could be potentially offset by SOC increments. • Relatively low GHGs and net field-GWP existed in arid cropping systems. With the intensification of food production pressure, arid areas have increasingly become one of the key areas for cropland reclamation. However, due to the relatively low indigenous soil organic matter (SOM), it is worth pondering whether the introduction of integrated cropland management measures will affect the soil organic carbon (SOC), greenhouse gases (GHGs) and global warming potential (GWP) in the arid areas. Based on these, a long-term fertilization experiment was used for a three-year GHG study to gain insights into the GHGs and field-GWP accounting in the western arid region of China. Five treatments, including no fertilizer (CK), inorganic fertilizer (NPK), inorganic fertilizer and straw (NPKS) and two combinations of inorganic fertilizer and manure (NPKM and NPKM+) were selected. Moreover, three crops of cotton, wheat and maize were grown over the three-year period, respectively. Nitrous oxide (N 2 O) and methane (CH 4) concentrations were measured using the static chamber method from April 2012 to July 2014. The results revealed that the SOC of all fertilizer treatments in topsoil (0−20 cm) increased from approximately 27 Mg C ha−1 in 1990 to 25.5–66 Mg C ha-1 in 2014; furthermore, the SOCSR (SOC sequestration rate) values were from -0.035 to 1.70 Mg C ha−1 yr−1 in the 25 year experiment. The N 2 O and CH 4 emissions across treatments, crops, and years ranged from 0.2 to 3.8 kg N 2 O-N ha−1 yr−1 and −2.3 to 13.0 kg CH 4 -C ha−1 yr−1, respectively, and the emission factor of CH 4 and N 2 O ranged from −0.1 % to 1.3 % and 0.5 % to 1.0 %, respectively, displaying relatively low GHGs emissions compared with other cropping systems in China. Based on the GHGs and SOCSR results, we found relatively low net field-GWP existed in arid and extremely arid cropping system, especially for the manure amendment treatments, implying the sustainability of certain existing managements in the arid area. [ABSTRACT FROM AUTHOR]