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As one of the essential components in ecosystems, lakes play a major role in the global phosphorus (P) cycle. It is helpful for further understanding of the inside lake P geochemical cycle to research P pollution and storage in lakes, which is of positive significance for lake eutrophication restoration. In this study, we investigated the total phosphorus concentrations (TPC) of water and sediments from 37 lakes in the Eastern Plain Lake Zone (EPL) of China, evaluated the P pollution degree of lakes, and estimated P storage in lake sediments with quantitative data of lake area and number. The results indicate that the total phosphorus concentrations of water (TPCW) and total phosphorus concentrations of the surface sediments (0–1 cm, TPCSS) in EPL were high, the mean values were 0.11 mg·L−1 and 869.85 mg·kg−1 respectively, with obvious differences between urban and rural areas, as well as between different river basins. Over half (56.76% and 70.27% respectively) of the lakes reached severe pollution levels in water and surface sediments. There were 16224 lakes (> 0.01 km2) with a total area of 21662.37 km2 in the EPL, and the P storage in the lake sediments (0–30 cm) was about 4.87 ± 2.08 Tg (1 Tg = 1 × 1012 g), accounting for about 2.74% of the basin soil. TPCW and TPCSS of lakes in the EPL were significantly positively correlated, may suggest a close nutrient cycling relationship between the lake water and the sediment. During periods of high winds and waves, the stored P in the top sediments in the EPL may continue to participate in the internal P geochemical cycle and migrate to the overlying water, posing a potential pollution hazard. Therefore, it is crucial to take into account the sediment P pools when formulating effective lake phosphorus management strategies. [ABSTRACT FROM AUTHOR]

The proportion of the rural population who live within 2 km of an all-season road is an indicator of the United Nations' Sustainable Development Goals (SDGs) 9.1.1. This paper aims to calculate SDG indicator 9.1.1 in the proximity of five Algerian expressways. Three monitoring methods are proposed for different spatial regions based on the five expressways built by China's Belt and Road Initiative Project. These methods are based on remote sensing and WorldPop and The High Resolution Settlement Layer (HRSL) population data. The results indicate that (1) the WorldPop population statistics show that the five expressways built by China's Belt Project have increased the rural population of the 2 km buffer zone by 192,016 between the start of construction and eight years after its completion. By the end of 2019, the population increased by 329,291 accounting for 1.17% of the rural population. (2) Based on populations estimated form built-up index (NDBI) building areas, the rural populations within the 2 km buffer area of the Bejaia-Haniff Expressway in 2011, 2015, and 2019 were 273,118, 306,430, and 375,408, respectively. (3) HRSL population grid statistics indicate that, in 2015, the populations were: East-West Expressway D 911,549, Bejaia Expressway D 127,471, Tipaza Expressway D 71,411, North-South Expressway D 30,583, and Cherchell Ring Expressway D 41,657. (4) A visual interpretation method based on Google Earth imagery was used to count the number of buildings and number of building floors in the town of Tikhramtath. Based on the estimated population of each building and floor, the population of Tikhramtath town in 2011, 2015, 2017, and 2019 was estimated as 1,790, 2,785, 3,365, and 3,870, respectively. (5) Through analysis and accuracy assessment, the appropriate statistical methods for different regions were determined. [ABSTRACT FROM AUTHOR]

As China and other developing countries continue to urbanize over the next decades, construction and demolition waste (CDW) management has been becoming a significant challenge for urban sustainability in terms of the environment, economy, and safety. However, accurate estimations or statistics of CDW generation are absent from the official national report in spite of their importance to devise sensible interventions to tackle CDW-related problems. This paper examines and compares the applications of three prevailing methods for estimating CDW, including the weight-per-construction-area method (WAM), buildings' life span-based method, and weight-per-capita method. Specifically, China has been chosen as the case study. This study implies that the weight-per-construction-area method is more appropriate because of the data availability and accuracy at a city or national level. The results of WAM indicate that a total of 4.1 billion metric tons (Bt) of CDW were generated in China in 2016, mainly from demolition waste (85%). Taking the changes of buildings' life span into account, a projection analysis reveals that the cumulative CDW generation will be 50 Bt between 2017 and 2040 in China (equal to approximately 38 years cumulative generation of global municipal solid waste). Overall, the findings provide some methodological options for scholars, practitioners, and decision-makers to more accurately estimate the amount of the CDW and to develop a more environmentally sound management strategy. [ABSTRACT FROM AUTHOR]

Abstract China has become the world's largest energy consumer, accounting for approximately 30% of global CO 2 emissions. City contributions make up 84% of China's commercial energy consumption. However, energy consumption data for most Chinese cities are not accessible. Many studies have focused on the estimation of carbon emissions at the provincial or national level; city-level carbon emissions are not well studied. In order to solve this problem, this research constructed a top-bottom method for city-scale energy-related carbon emissions estimation in China. Typically, cities are considered the constituent units of a province. Relying on provincial energy balance tables and utilizing the available city-level socioeconomic data as indicators, we scaled down provincial energy consumption to the city level. We compared our estimation results with city-level point-source data, and found that for the 41 Chinese cities to which we applied this method, the difference was within 10%, while for 25 of these cities, the difference was within 5%. Thus, we believe our method is reasonably accurate. We also subdivided the city carbon emissions into three major energy categories (coal-related, oil-related, and gas-related) and found that the difference could be attributed mainly to coal-related energy emissions. The results of the uncertainty analysis indicated that the uncertainty of the coal emission factor was the largest, thus demonstrating that it is critical not only to choose appropriate indicators to characterize coal-based industrial carbon emissions, but also to identify accurate emission factors for coal-related fossil fuels. Both analyses demonstrated China's coal-dominant energy structure. We believe our method is practical and can provide detailed data support for the establishment of city-level carbon emission inventories, furthermore, it will also be helpful for Chinese cities to negotiate carbon reduction responsibilities and allocating carbon reduction tasks. Highlights • A new city-scale CO2 emission estimation method was proposed. • Spatial differences of energy consumption in three different regions were analyzed. • Uncertainties caused by three types of fossil fuels' emission factors were analyzed. • City CO2 emission data was provided for formulating climate policies. [ABSTRACT FROM AUTHOR]

Characteristics of carbon storage and density in different layers of forest ecosystems should be studied comprehensively and in more detail. Using forest inventory data in combination with field survey data, we explored the characteristics of carbon storage and density in different layers of forest ecosystems in Liaoning Province of China. Results showed that total carbon storage was 813.034 Tg C. The carbon storage of soil layer accounted for 81.0% of the total storage with 658.783 Tg C, followed by those of arbor, litter and shrub layers with 128.403 Tg C (15.8%), 22.723 Tg C (2.8%) and 3.125 Tg C (0.4%), respectively. The average carbon density for the forest ecosystems were 183.571 Mg C ha-1, with soil layer (148.744 Mg C ha-1) being the highest one, followed by arbor layer (28.992 Mg C ha-1), litter layer (5.131 Mg C ha-1) and shrub-grass layer (0.706 Mg C ha-1). Carbon storage in different forest ecosystems varied from 1.595 to 319.161 Tg C, while C density ranged from 165.067 to 235.947Mg C ha-1, with the highest and lowest values being observed in soil layer and shrub-grass layers, respectively, implying that soil is the main body of forest carbon storage. Young-aged forests accounted for a greater proportion of forests in the Province than forests in other age classes; and proper management of forests could increase the carbon sequestration in the forest ecosystems. The comparison with previous estimations of carbon storage for forest ecosystem implied that methods and data used for forest carbon storage estimation affected the results of estimates obviously. [ABSTRACT FROM AUTHOR]

As the world's largest coal producer and consumer, China accounts for a relatively high proportion of methane emissions from coal mines. Several estimation methods had been established for the coal mine methane (CMM) emission. However, with large regional differences, various reservoir formation types of coalbed methane (CBM) and due to the complicated geological conditions in China, these methods may be deficient or unsuitable for all the mining areas (e.g. Jiaozuo mining area). By combing the CMM emission characteristics and considering the actual situation of methane emissions from underground coal mine, we found that the methane pre-drainage is a crucial reason creating inaccurate evaluating results for most estimation methods. What makes it so essential is the extensive pre-drainage quantity and its irrelevance with annual coal production. Accordingly, the methane releases were divided into two categories: methane pre-drainage and methane release during mining. On this basis, a pioneering method for estimating CMM emissions was proposed. Taking the Yanma coal mine in the Jiaozuo mining area as a study case, the evaluation method of the pre-drainage methane quantity was established after the correlation analysis between the pre-drainage rate and time. Thereafter, the mining activity influence factor ( MAIF ) was first introduced to reflect the methane release from the coal and rock seams around where affected by mining activity, and the buried depth was adopted as the predictor of the estimation for future methane emissions. It was verified in the six coal mines of Jiaozuo coalfield (2011) that the new estimation method has the minimum errors of 12.11%, 9.23%, 5.77%, −5.20%, −8.75% and 4.92% respectively comparing with other methods. This paper gives a further insight and proposes a more accurate evaluation method for the CMM emissions, especially for the coal seams with low permeability and strong tectonic deformation in methane outburst coal mines. [ABSTRACT FROM AUTHOR]

• Several energy equations based on a rock-machine tunneling system were established. • The conversion mechanism between cutter energy consumption and wear was proposed. • The correlation between energy conversion factor and rock properties was developed. • The cutter wear prediction method was validated and evaluated by a TBM project. The accurate estimation of disc cutter wear has always been a long-term topic affecting the excavation efficiency and construction cost of tunnel boring machines (TBMs), especially in the project of high-strength and high-abrasiveness stratum. In fact, disc cutter wear is one of the comprehensive results of rock-machine interaction during TBM excavation. Therefore, the change of internal energy of rock-machine system describes the wear information of disc cutter. In this study, a new method for estimation disc cutter wear based on energy conversion mechanism is proposed. Firstly, the basic relationship of energy conversion during TBM excavation is analyzed in detail, and an energy equation considering the influence of geological conditions, TBM technical specifications and rock-machine interactions at three levels is established. Based on this context, according to the working characteristics and abrasion mechanism of disc cutter, the conversion mechanism between the disc cutter wear energy and wear capacity is investigated, and the definite relationship between the energy conversion coefficient and the properties of intact rock is obtained. Furthermore, to illustrate the applicability of this method, a case study of the Shenzhen Metro Project in China is introduced and analyzed in detail. The results show that the proposed method can predict 85 % of the average wear of each disc cutter on the cutterhead. Finally, the prediction method provided in this study is compared with Gehring, NTNU and CSM models to validate the rationality and reliability of this method. Evidently, this method can provide an accurate and reliable estimate of the general wear capacity and life of disc cutters on the cutterhead, and it is also a very practical tool for TBM engineers to make decision and optimize the cutter replacement scheme. [ABSTRACT FROM AUTHOR]

Wetland plays an important role in stabilizing climate change and makes a significant contribution to global soil carbon storage due to its huge carbon storage capacity. Based on a summary of estimation methods of carbon storage, this study analyzed carbon storage and its influencing factors of typical wetlands in China, inclusing climate, vegetation, soil property and land use. The results showed that wetlands in Northeast China and the Tibetan Plateau had the highest carbon sto-rage among the eight wetland areas. Peat wetland had the highest carbon storage in inland area due to its higher stability, lower decomposition rate, and the impact of phenol oxidase. The bidirectional interference of single factor and combined effects of multiple factors made the influencing factors and mechanisms more complicated. Our results would contribute to the prediction and evaluation of wetland carbon storage and the value of ecosystem services through laying emphasis on the combined effects of multiple factors and applying the data assimilation technology.