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Sintered ore is the most important incoming iron material for blast furnaces in China. The foundation characteristics of sintering are an important basis for the sintering process of iron ore powder and a key indicator for evaluating the quality of iron ore powder. The chemical composition of iron ore powder directly affects the foundation characteristics of sintering. Based on the chemical composition and sintering characteristics of iron ore powder, the article adopts the FOA-Catboost algorithm to establish a prediction model for the foundation characteristics of single iron ore powder and mixed iron ore powder. Among the foundation characteristics prediction indicators of a single type of iron ore powder, the accuracy of assimilation temperature prediction is 95.65 %, the accuracy of fluidity index prediction is 95.23 %, and the accuracy of bonding phase strength prediction is 97.46. Among the foundation characteristics prediction indicators of mixed iron ore powder, the accuracy of assimilation prediction is 95.82 %, the accuracy of fluidity index prediction is 91.66 %, and the accuracy of bonding phase strength prediction is 93.7 %. On the basis of predicting the foundation characteristics of a single type of iron ore powder, the model further predicts the foundation characteristics of mixed iron ore powder, which can reduce the workload of laboratory experiments on the foundation characteristics of iron ore powder and reduce the amount of experiments in the process of finding the optimal proportion of mixed ore. Based on the foundation characteristics model of mixed ore, determine the optimal ratio of sintered ore powder to improve the quality of sintered ore. The establishment of the model has a significant contribution to improving experimental and production efficiency, reducing costs, improving sinter quality, and reducing environmental impact. [ABSTRACT FROM AUTHOR]

As is known, the financial market prediction and high investing value is receiving more increasing attentions nowadays. But affected by many complex factors, it is difficult to perform the financial market forecast accurately. Among the solving methods, the time-series prediction has caused the focus for its great predictive effect in many fields. However, most of the existing works focus on single-time-series analysis and cannot obtain good learning results because it trains tasks independently and ignores the cross-correlation among multiple time series. Motivated by the multitask learning, a novel online multitask learning based on the least squares support vector regression (OMTL-LS-SVR) algorithm is proposed for multi-step-ahead financial time-series prediction. OMTL-LS-SVR regards multiple related time series as different learning tasks, which are trained in parallel to obtain the prediction model and shorten the training time. Under this scheme, the knowledge from one certain task can benefit others, allowing it to exploit the relatedness among multiple subtasks. The OMTL-LS-SVR is applied to perform the time-series tendency prediction in four branches of China's financial market, and the experimental results demonstrate the effectiveness of the proposed multitask learning algorithm. • An online multitask learning model is proposed for the financial market forecasting. • The cross-correlations among financial time series is well considered in OMTL-LS-SVR. • OMTL-LS-SVR adopts a fixed memory scheme to capture the latest dynamic features. [ABSTRACT FROM AUTHOR]

In facing the challenge of PM2.5 pollution across China, an overall macro-evaluation of the spatiotemporal pollution distribution and health risk of PM2.5-bound heavy metals at a national level is urgently needed. This study involved a bibliometric analysis of 8 PM2.5-bound heavy metals (Cd, Cr, Hg, As, Pb, Cu, Zn, Ni) in the 27 major cities across China from 2013 to 2019 that was combined with valid data from the published scientific literature. Based on time weight vector, the spatiotemporal metal distributions and their integrated enrichment factors were analyzed, and then a fuzzy health risk assessment model was established to synthetically screen regional priority control regions/pollutants, and explore the pollution trend. The integrated concentrations of As (19.44 ng/m3) and Cd (4.12 ng/m3) in most cities exceeded the limits of the Chinese Ambient Air Quality Standards, while Hg (0.81 ng/m3) and Pb (137.10 ng/m3) did not. Spatially, PM2.5-bound heavy metal pollution generally increased from southern to northern China, with North and Northwest of China being relatively hot regions. For integrated health risks, children (3.31) faced higher non-carcinogenic risks than adults (1.69), with As, Cd, and Cr as the main contributors. The total carcinogenic risk varied from 4.86 × 10−6 to 1.3 × 10−3. The total carcinogenic risk of 77.78% of major cities has reached Grade V (moderate-high risk) or above, and the risk contributions of Cr and As accounted for the largest proportion. The temporal variations showed that the pollution had a recent declining trend in most cities. Finally, 10 major cities and their corresponding metals were determined as the priority control cities/metals. The targeted risk management policies were developed for the identified hierarchical hot cities and their priority pollutants to control were based on the regional pollution trend and source characteristics. Image 1 • Spatiotemporal heavy metals in PM2.5 was investigated in the Chinese major cities. • Fuzzy exposure assessment model was established to identify hot pollutants/areas. • Integrated heavy metals in PM2.5 increased gradually from south to north China. • Priority control metals were As, Cr, Cd, especially in north and northwest China. • Targeted policies for priority control regions were formulated on source analysis. [ABSTRACT FROM AUTHOR]

Environmental Kuznets Curve (EKC) has become the predominant approach between environmentalists and sociologists to modeling environment-economy nexus during the past quarter century, also with regard to China, the largest industrial pollutant emitter in the world, where unprecedented change offers a unique approach to the aggregate emissions. In the case of China the question is whether a tipping point can be reached without de-industrialization and at a lower income phase than in developed economies. It is also the worldwide intrinsic concern of environment-economy nexus in the prospective industrialization process of developing countries. However, China relative researches involve some existing challenges, such as cross-regional time series, various socioeconomic drivers, and turning thresholds. Taking the new normal China economy formulation into consideration, a rethink and reexamination of the environment-economy relationship was performed, using the longest possible environmental and economic data from 30 provincial economies for the period of 1985–2017. Different from the existing mostly time-series based China EKC literature, not only the LLC, IPS, Fisher unit root tests, Pedroni and Kao panel cointegration tests and the panel OLS are used, but, the problem of cross-sectional dependence is handled in panel unit root tests across various cross-economies, and the error-correction based cointegration test is employed, allowing for various forms of heterogeneity, robust against the cross-sectional dependence, and the DOLS estimators are applied to resolve the standard OLS for serial correlation and for endogeneity of regressors, especially in only small possible China samples. In particular, the finding contrasts with the overly optimistic research of certain scholars, who state that China could be on a clearly different path from the routes of some developed economies in the world, realizing the environmental turnaround at low levels of economy and industrialization. China economy is not just having a certain environmental "dividend effect", but mainly faces a more significant environmental "pressure effect". Environmental input and its efficiency is still insufficient to perform the intended pollution reduction effects. Fundamentally, the environmental investment as a proportion of GDP should reach the past levels of some developed economies in the similar pollution-economy phase. [ABSTRACT FROM AUTHOR]

Determination of the pulmonary bioaccessible fraction and its induced health risk with respect to the toxic metals present in tobacco smoke is a significant issue, in terms of scientific and proactive management of clean tobacco production and smoker health. China, the world's largest producer and consumer of tobacco, has about 350 million smokers. The four typical toxic metals in the 20 top selling cigarette brands in China were investigated. The mean levels of As, Cd, Ni, and Pb in tobacco were 0.3891 μg/g, 3.8431 μg/g, 2.4356 μg/g, and 2.7718 μg/g, respectively. Pulmonary bioaccessibility was analyzed by extraction using simulated lung fluid (SLF), where the average rates of As, Cd, Ni, and Pb transferring from tobacco to SLF by mainstream smoke (RSLF) were 7.85%, 2.85%, 0.86%, and 3.45%, respectively. Considering the inhalation pathway of mainstream smoke, an improved health risk assessment (HRA) was conducted using local exposure parameters and fuzzification of sensitive variables. With consideration of the exposure differences of smokers, Cd and As were identified as the priority control metals, and B05, B11, B12, and B19 among the 20 brands (named B01–B20) were found to be the priority control brands. Furthermore, it is worth noting that the membership degrees of a small portion of brands which approached 50% could warn decision-makers of potentially misleading uncertainties. The daily consumption limit (DCL) for each studied brand was calculated, under the risk standard value of Level II (1.00E-05), generally ranging from 1 to 4 cigarette(s) per day. Spatially, DCLs in Southern, Southwestern, and Eastern China were relatively lower than those in other regions. At present, it is of significance to establish a national life-cycle supervisory system for Cd and As. Findings about the health risks posed by toxic metals through the tobacco–smoke–lung exposure pathway can provide scientific risk management reference for smokers, related managers, and producers. Image 1 • Toxic metals in the selected 20 Chinese best-selling cigarettes were investigated. • Pulmonary bioaccessibility of toxic metals was analyzed using simulated lung fluid. • Transfer rates from mainstream smoke to simulated lung fluids (RSLF) were detected. • Health risk posed by tobacco metals was assessed with fuzzy theory and RSLF. • Cd and As need priority control and cigarette consumption limits of brands are 1–4/d. [ABSTRACT FROM AUTHOR]

The refractive index of ambient aerosols is one of the most important parameters indicating the scattering and absorption properties of aerosols. We proposed a new method for retrieving the refractive index (RI) of ambient particles. The main advantage of our method is that it assimilates the single particle mixing states measured by a single-particle soot photometer, when compared to the traditional optical method of retrieving the ambient aerosol RI. This method was validated by good consistency between the determined RI with this method and retrieved RI with the method of Zhao et al. (2019c) using datasets from field measurements conducted in East China in June of 2018. The results show that the real part of the refractive index of the black carbon (BC)-free particles ranged between 1.37 and 1.51 and this value changed little across different tested wavelengths. The mean complex refractive index for the refractory BC was 1.67 ± 0.67 i at 525 nm. The mean imaginary parts of the other non-BC components were 0.019 and 0.023 at 450 nm and 370 nm respectively. Brown carbon contributed to 5%, 13% and 29% of the ambient aerosol light absorption at 525 nm, 450 nm and 370 nm respectively in East China. This study provides the ability to determine the ambient aerosol complex refractive index and these data can be used in models to reduce the uncertainties in estimating aerosol radiative forcing. • A new method for determining the refractive index with assimilating the single-particle mixing states is proposed. •The refractive index of black carbon-free aerosols at the wavelength of 525 nm varied over a wide range between 1.37 and 1.51. •The mean complex refractive index of black carbon was 1.67 ± 0.67i at 525 nm. [ABSTRACT FROM AUTHOR]

Recently frequent hazy weather in China caused by PM2.5 is mainly attributed to irrational energy consumption structure, so the policies cutting coal output and promoting electric power substitution were initiated. Facing some resistance in the policy implementation, what the proper target of cutting coal output is, or whether there is a need to cut electricity prices for electric power substitution, are still unknown or debatable. To exploring this knowledge gap, this paper set the eight action plan policy scenarios, in which the coal output cut is set from −3% to −10% respectively. Further, in order to observe whether the policy of cutting electric price will reduce the negative effects of action plan policy scenarios, the eight enhanced action plan policy scenarios were also set, in which the coal output cut is set from −3% to −10% respectively with the hypothetical electric price cut −3%. By using the developed computable general equilibrium (CGE) model, the policy scenarios are simulated, and the economic and environmental effects are tested. The results showed that: First, in most policy scenarios, the environmental and economic effects were changing synchronously in inverted U shapes, but the policy to cut coal output would lead to positive environmental effects and negative economic effects. Second, cutting electricity price could contribute to promoting electricity substitution, though it couldn't be conducive to improving the electricity structure. Third, the reduction of electricity price could also help to reduce the resistance in policy implementation. Consequently, for optimal environmental effects and economic resistance, it is recommended to set the policy target of cutting coal output as −6%, while the moderate reduction of electricity price is needed and set as −3%. Image 1 • Effects of environment-economic policy to cut coal output are assessed using CGE. • Cutting coal output will contribute to air pollution abatement to certain extent. • The policy intensities and their effect are not in linear sharps. • The proper policy intensity is selected by comprehensively comparing the effects. • Moderately and simultaneously reducing electricity price is beneficial and needed. [ABSTRACT FROM AUTHOR]

China is home to the world's largest cigarette maker, and with more than 320 million smokers, accounts for a fifth of world annual deaths from smoking-related diseases. Basic tobacco characteristics and its lead (Pb) and cadmium (Cd) contents of the 20 best-selling Chinese cigarette brands were detected using wet digestion-inductively coupled plasma mass spectrometry. Pb and Cd contents of the selected brands of cigarettes ranged from 1.3861 μg/g to 7.7124 μg/g and 2.9137 μg/g to 5.4841 μg/g, respectively, showing a significant correlation with tobacco weight per cigarette. Fuzzy health risk assessment via inhaling pathway of mainstream smoke was carried out for Pb and Cd based on the regional exposure parameters and fuzzification of the key variables. Results indicated the indexes of (non-)carcinogenic risk of tobacco Pb in all studied brands were below the corresponding general acceptable risk criteria, while that of tobacco Cd exceeded. Cd was identified as the priority control metal in the studied cigarettes. Moreover, the maximum membership probabilities of E08, E09, S11, S18 and S19 were in marginal range of 50%–60%, showing existed uncertainties and possibly misleading decision-makers. Spatially, the carcinogenic risks posed by regional cigarette Cd showed a relatively high level in the Southern, Southwestern and Northern China. Based on Level II for carcinogenic risk (1.00E-05), the limit daily consumption and limit concentration of Cd were obtained for each brand, and the result ranges were generally 2–4 cigarettes per day and 0.5–1.7 μg/g. Presently, it is of significance for the related international/local decision-makers to establish a comprehensive supervisory framework and system for Cd (and other typical toxic metals) in cigarettes from its life cycle perspective. Image 1 • 20 best-selling cigarette brands across the Chinese six regions were investigated. • Smoking health risk posed by tobacco Pb and Cd was assessed with fuzzy theory. • Cd in the selected cigarettes was found to pose certain health threats to smokers. • Regional limit concentrations for Cd were established on health risk guidelines. • Daily consumption limits were recommended according to the different brands. [ABSTRACT FROM AUTHOR]

Facing the challengeable PM2.5 pollution management across China, it is of significance to identify key pollution driving factors with a nation-region-city perspective and establish targeted joint management strategies on the trans-provincial scale. To identity hot study regions, the spatiotemporal pollution variation of PM2.5 in 2016 was explored using multi-scale spatial autocorrelation analysis based on the ground monitoring data for Chinese 366 cities. In the identified study regions, the relationship of natural factors and PM2.5 was analyzed on annual and daily scales via the correlation analyses, and the multiple socioeconomic driving forces of PM2.5 were identified utilizing geographic weighted regression (GWR) and principal component analysis. Consequently, 111 cities were identified as the hot regions, where their annual means of PM2.5 (AM) were 60.40 ± 14.23 μg·m−3 exceeding the Chinese level II standard and WHO Air Quality Guideline IT-1 (35 μg·m−3). The elevation and annual accumulative rainfall were found to be negatively correlated with the AM (p < 0.01). The increase of temperature and wind speed mainly helped daily PM2.5 reduce. Furthermore, the sensitive socioeconomic factors, mainly including the added value of the industry, the per capita gross domestic product and the per capital annual disposable income, were identified due to GWR. Then, based on the found spatial heterogeneity of sensitive factors, the selected 111 cities were identified into four types (Q 1 , Q 2 , Q 3 , Q 4) and two sub-regions, Q 1 +Q 2 (13+49 cities) and Q 3 +Q 4 (27+22 cities). Finally, for multi-scale environmental sustainability and equity, the joint management strategies were formulated for each cities pattern based on their feature of sensitive pollution driving forces and city role positioning. Image 1 • Spatiotemporal patterns of PM2.5 in 366 Chinese cities were explored on multi-scale. • Increase of elevation, rainfall/wind, temperature help annual/daily PM2.5 reduce. • PM2.5 was sensitive to social factors like Added value of industry, Per Capita GDP. • 111 hot cities were identified into four types on spatially heterogeneous features. • Joint management strategies were formulated for each kind based on driving factors. [ABSTRACT FROM AUTHOR]

Accurate and effective short-term wind power forecasting is vital for the large-scale integration of wind power generation into the power grid. However, due to the intermittence and volatility of wind resources, short-term wind power forecasting is challenging. To address the issue that the existing decomposition forecasting methods ignore the coupling relationship between wind power series and multiple meteorological series, this study proposes a short-term wind power forecasting method based on multivariate signal decomposition and variable selection. First, multivariate variational mode decomposition (MVMD) is used to perform time-frequency synchronous analysis on wind power and multidimensional meteorological series, thereby decomposing them into the same predefined number of frequency-aligned intrinsic mode functions (IMFs). Secondly, elastic net (EN) is used for supervised variable selection on all IMFs to provide a high-quality training set for the forecasting model, thereby enhancing precision and interpretability. Next, a hybrid deep neural network combining convolutional neural network (CNN), bidirectional long-short term memory (BiLSTM) neural network, and multi-head attention (MHA) mechanism is employed to model the output curve of a group of wind turbines in a wind farm. Finally, the proposed method is comprehensively evaluated through four sets of comparative experiments and multiple evaluation metrics on data gathered from the Mahuangshan first wind farm in China with four forecasting horizons: 15-min ahead, 30-min ahead, 45-min ahead, and 1-h ahead. The experimental results show that the proposed method significantly outperforms fifteen existing deep learning methods in terms of precision and stability. • A novel multivariate signal decomposition strategy is proposed for wind power forecasting. • Elastic net is firstly applied to provide a high-quality training set for the forecasting model. • Utilizing the multi-head attention mechanism to capture the long-term dependencies in wind power sequence. [ABSTRACT FROM AUTHOR]

China is the biggest tea producer in the world, and soils in the tea plantations play important roles in the carbon cycle, and regional and national carbon neutrality. The knowledge regarding the variation and the key drivers of soil organic carbon (SOC) stock in tea plantations could provide a scientific basis for assessing the potential of regional and national carbon sequestration and neutrality. This is also helpful for the sustainable management of tea plantations. However, little information is available on soil organic carbon (SOC) stock and influential factors in tea plantations. Thereby, SOC density (SOCD) and stock were estimated based on 1723 datasets collected from 322 published studies. Subsequent estimations reveal that the total SOC stock in China's tea plantations is 207.13 Tg with 124.58 Tg SOC stored in top soil layer (0–20 cm), and the average SOCD is 59.17 Mg ha−1 ranging from 0.9 Mg ha−1 to 589.54 Mg ha−1. The SOCD showed a significant three-dimension zonal pattern and was significantly influenced by managerial practices. Tea plantations managed in ecological ways and/or with long stand age even showed higher SOCD than that in neighbored forests. The lower average but wide range of SOCD in tea plantations indicates their great potential in carbon sequestration. This makes tea plantations critically important in regional and national carbon accounting and carbon neutrality, and cannot be ignored in future research and policymaking initiatives. • Total soil organic carbon (SOC) stock in China's tea plantations is 207.13 Tg. • The average SOC density in China's tea plantations is 59.17 Mg ha−1. • SOC density showed a significant three-dimension zonal pattern. • The SOC density was significantly influenced by managerial practices. • China's tea plantations have great potential in carbon sequestration. [ABSTRACT FROM AUTHOR]

Chinese children are exposed to broad environmental risks ranging from well-known hazards, such as pesticides and heavy metals, to emerging threats including many new man-made chemicals. Although anecdotal evidence suggests that the exposure levels in Chinese children are substantially higher than those of children in developed countries, a systematic assessment is lacking. Further, while these exposures have been linked to a variety of childhood diseases, such as respiratory, endocrine, neurological, behavioral, and malignant disorders, the magnitude of the associations is often unclear. This review provides a current epidemiologic overview of commonly reported environmental contaminants and their potential impact on children's health in China. We found that despite a large volume of studies on various topics, there is a need for more high-quality research and better-coordinated regional and national data collection. Moreover, prevention of such diseases will depend not only on training of environmental health professionals and enhanced research programs, but also on public education, legislation, and networking. [ABSTRACT FROM AUTHOR]

Both EU-DEMO and CFETR need a workable replacement scheme for their tritium breeding blanket (BB). The radioactive environment dictates that all associated operations must be carried out through remotely controlled tools. Accessing and extracting the large BB segments requires their feeding pipes to be removed first from the upper vessel port and later re-installed. In the current design, each of the 80 BB segments is connected with four feeding pipes, two for cooling and two for tritium extraction. Thus, in the process of BB maintenance operation, 320 pipes must be cut, removed and rewelded. The developed concept for this task is presented here. It aims at reducing the associated plant downtime, thereby increasing the overall plant availability. Features underpinning this integrated BB pipe service concept are: (i) parallel pipe service operations, (ii) pipes in each upper port are grouped in a pipe forest and handled as a single component, (iii) the configuration of the individual pipes is standardized such that cutting and joining locations are aligned and with good accessibility from the top, (iv) the number of pipe sizes is limited to two, reducing the number of required tool sets, and (v) the same pipe configuration is adopted in each of the 16 upper ports. The paper will present design solutions and the progress on the manufacturing of prototypes developed for the challenging cutting and welding tasks from both within and outside, as well as the leak detection methodology and pipe stub handling. Prototypes will be used to perform design validation and verification on dedicated test benches currently being implemented in close collaboration between European laboratories and the Comprehensive Research fAcility for Fusion Technology (CRAFT) at ASIPP in China. [ABSTRACT FROM AUTHOR]

Ecological agriculture (eco-agriculture) is an approach to agriculture that seeks a balance between ecological and economic benefits to promote the sustainable development of both. This paper proposes a scientific method for analyzing the environmental and economic effects of eco-agriculture and simulating their long-term trend. Here, we focus on the eco-agriculture system of Kongtong District, Pingliang City, Gansu Province, China, and we build a system dynamics model named “AEP-SD” to evaluate the integrated effects of the system from 2009 to 2050. Under business as usual conditions, simulation results show rapid improvement until a peak is reached in 2027, after which the system will decline gradually. The model identifies some defects and disadvantages of the current agriculture system, such as the excessive increase of cattle slaughter, unstable production of methane, slow development of organic agriculture, and unsustainable energy structure. System improvement policies are offered and then proven by the model that they can indeed reduce the negative effects and eliminate the potential risks of system decline. [Copyright &y& Elsevier]

The accumulation of extracted titanium tailing (ETT) has seriously affected the local environment and human healthcare in the Panxi area of southwestern China. This research provides a practical way to convert ETT (up to high amount of 50 wt%) to high performance glass-ceramic by introducing waste glass (WG). The effects of sintering temperature and heat-treatment duration on the micro-structures and physicochemical properties of the produced glass-ceramic have been systematically investigated. It is found that the crystalline phase of the glass-ceramic has a great influence on the linear shrinkage rate and bulk density, further affecting its water absorption rate and hardness. The optimal product shows enhanced physical properties: linear shrinkage rate of 17.40 ± 0.04 %, bulk density of 2.75 ± 0.02 g cm-3, Vickers hardness of 21.3 ± 0.2 GPa and water absorption rate of 0.060 ± 0.002 % at the optimized sintering conditions. The formation mechanism of recycling two industrial wastes provides a guidance function to synthesize glass-ceramic using similar industrial waste residue and tailings. The obtained glass-ceramic with high performance has potential to be applied extensively in construction and building field. [ABSTRACT FROM AUTHOR]

Iatrogenic biliary injury (IBI) following laparoscopic cholecystectomy (LC) is the most serious iatrogenic complications. Little is known whether LC-IBI would lead to surgeon's severe mental distress (SMD). A cross-sectional survey in the form of electronic questionnaire was conducted among Chinese general surgeons who have caused LC-IBI. The six collected clinical features relating to mental distress included: 1) feeling burnout, anxiety, or depression, 2) avoiding performing LC, 3) having physical reactions when recalling the incidence, 4) having the urge to quit surgery, 5) taking psychiatric medications, and 6) seeking professional psychological counseling. Univariable and multivariable analyses were performed to identify risk factors of SMD, which was defined as meeting ≥3 of the above-mentioned clinical features. Among 1466 surveyed surgeons, 1236 (84.3%) experienced mental distress following LC-IBI, and nearly half (49.7%, 614/1236) had SMD. Multivariable analyses demonstrated that surgeons from non-university affiliated hospitals (OR:1.873), patients who required multiple repair operations (OR:4.075), patients who required hepaticojejunostomy/partial hepatectomy (OR:1.859), existing lawsuit litigation (OR:10.491), existing violent doctor–patient conflicts (OR:4.995), needing surgeons' personal compensation (OR:2.531), and additional administrative punishment by hospitals (OR:2.324) were independent risk factors of surgeon's SMD. Four out of five surgeons experienced mental distress following LC-IBI, and nearly half had SMD. Several independent risk factors of SMD were identified, which could help to make strategies to improve surgeons' mental well-being. [ABSTRACT FROM AUTHOR]

The intensive energy consumption in urban sectors is aggravating global warming, which triggers an in-depth thinking about energy-carbon nexus and low-carbon city actions. Hence, China has launched a pilot low-carbon city program to explore low-carbon pathways since 2010. This study employed a Long-range Energy Alternatives Planning System model (LEAP) to simulate six energy sectors-related GHG emissions in a pilot low-carbon Ningbo city, China. The LEAP-Ningbo model comprises three basic modules, i.e. energy supply, energy transformation and end-use energy demand in six urban sectors (i.e., household, service, agriculture, transport, industry, and transformation sectors), and resulting environmental impacts (CO 2 equivalents). The results identified by the business as usual (BAU) scenario indicate that total energy consumption is expected to reach 449.72 Mtce and results in emissions of 651.83 Mt CO 2e by 2050. In contrast, more aggressive policies and strategies involved in the integrated scenario (INT), which combines the energy structure optimization (ESO) scenario with the policy-oriented energy saving (PES) scenario, can lower energy demand by 14% and CO 2e emissions by 27%. A comparison among global cities and low-carbon plans helps identify the carbon emission level and define the actionable low-carbon policies. The high correlation between sectoral energy use and resulting GHG emissions implies energy-carbon reduction efforts, e.g., low-carbon energy substitution, intensive energy-saving policies, the improvement of energy efficiency, and industrial transformation. Achieving low-carbon city targets requires timeline-restricted actions and backgrounds-based measures in plans. The results of this study shed light on if and how cities can shape energy-carbon reduction trajectories and develop low-carbon pathways in China and beyond. [ABSTRACT FROM AUTHOR]

The process-based Biome-BGC (BioGeochemical Cycles) model is widely used to simulate the storage and flux of water, carbon, and nitrogen within an ecosystem. However, the Biome-BGC model neglects the existence of underground persistent portion of perennial grasses as well as functional differences between the fast-cycling and persistent roots, which are assumed to negatively impact the accurate estimation of net primary productivity (NPP) of alpine meadow on the Qinghai-Tibet Plateau. This study presents the Perennial Deciduous Grasses (PDG) NPP model developed under the framework of Biome-BGC version 4.2. In the PDG NPP model, underground roots of perennial grasses were partitioned into two portions, the fast-cycling portion and the persistent portion. These two portions were represented by different turnover fractions, carbon to nitrogen ratios, and allocation ratios in the PDG NPP model. Processes primarily involving allocation, maintenance respiration, growth respiration, litterfall and mortality, and transfer growth were modified to account for the differences between fast-cycling and persistent roots. Measured NPP including both aboveground NPP (ANPP) and belowground NPP (BNPP) at Zhenqin Station in Qinghai Province, China, was used to validate estimates of the PDG NPP model. The results showed that the PDG NPP model has a higher estimation accuracy of NPP than the Biome-BGC model. The root mean square error (RMSE) and mean absolute percent error (MAPE) of total NPP estimated from the PDG NPP model were 15.1 gC/m 2 /yr and 12.1%, respectively, whereas those from the Biome-BGC model were 49.8 gC/m 2 /yr and 39.9%, respectively. Both RMSE and MAPE of ANPP and BNPP estimates based on the Biome-BGC model were approximately 3 times those based on the PDG NPP model. Improvements of NPP estimation were mainly attributed to the decreasing estimates of gross primary productivity which was caused by the increase of water stress and decrease of soil N availability simulated during growing seasons. Therefore, the PDG NPP model is more reflective of the limitations of water and nutrients to vegetation growth and productivity when applied to the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

• Reveal the complex mechanism in coupling developing CCUS and renewable energy. • Assess the economic effects of CCUS and renewable energy penetration by a CGE model. • Identify the optimal policy intensity to deploy CCUS and renewable energy in power sector. • Explore the impact of CCUS application on carbon peak in China's new power system. • Building a green energy dominated power system is harder than the carbon peak target. Carbon capture, utilization, and storage (CCUS) is identified as a critical decarbonization technology that is expected to be applied globally on a large scale, especially in a thermal power-dominated power system. To achieve a carbon peak in the power sector, assessing the environmental and economic effects, including the costs of CCUS application and the decarbonization efficiency, is essential for making a reasonable CCUS policy. However, there is still a research gap in identifying the proper CCUS application intensity considering the dynamic changing renewable energy percentage and power quantity. Therefore, this study constructs a "dynamic system- computable general equilibrium" (SD-CGE) model to reveal the industrial and macro impacts of CCUS application policies by comprehensively evaluating the social, economic, and environmental effects of CCUS application scenarios where different policy intensities are set. Using China as a typical case, the results show that, first, the policy of applying CCUS combined with renewable energy penetration will contribute to reaching the power sector's carbon peak, however, building a new power system dominated by green power is a more challenging goal. Second, regarding the CCUS application intensity combined with renewable power penetration rate, it is proper to suggest setting the policy intensity as "medium-medium" for the optimal environmental and economic effects. Consequently, the power sector's carbon peak year will be 2025, while green power will dominate the power supply in 2030. Third, through the model's validation check, the models and results in this study are shown to be accurate and robust. This study provides policy support for constructing China's new power system, is conducive to achieving the national carbon peak goal, and provides other countries with theoretical tools to study similar problems. [ABSTRACT FROM AUTHOR]

Recent studies show that the rapid increases in urbanization and human activities in the PRD region have important impacts on regional air quality. In addition to local anthropogenic emissions which are major driving forces for poor air quality in this region, biomass burning in Southeast Asia has also important contribution on aerosol and ozone concentrations in the PRD region. In this paper, this effect is analyzed by using satellite data, ground measurements and models. MODIS aerosol optical depth (AOD) distribution in March 2006 shows a clear enhancement in AOD between Southeast Asia and the PRD region. With detail wind analysis, two distinguished conditions are classified, i.e., Condition-1 (PRD is under influence of the biomass burning from Southeast Asia) and Condition-2 (PRD is not under influence of the biomass burning from Southeast Asia). The characterizations of aerosol, UV, and ozone in Guangzhou city (located in the PRD region) under these two conditions are analyzed. The analyses suggest that aerosols and CO concentrations are higher in Condition-1 than in Condition-2; while the UV intensity and O3 concentrations are lower in Condition-1 than in Condition-2. This result indicates that in Condition-1, the enhanced aerosol concentrations from the Southeast Asia biomass burning produce reduction of UV intensity, and thus decreases the formation of ozone in Guangzhou. [Copyright &y& Elsevier]

This paper suggests that the rapid increase in urbanization and human activities has important impacts on visibility in the Pearl River Delta (PRD) region. A long-term trend of visibility in Guangzhou (one of the largest cities in PRD) shows that between 1954 and 2006, there is a rapid change in visibility. Between 1954 and 1972, low visibility has the smallest occurrence (less than a few days per year). Between 1972 and 1980, the visibility condition is within a transition period, and the occurrence of low visibility increases rapidly. Between 1980 and 2006, the occurrence of low visibility remains very high (150daysyear−1), suggesting that the city of Guangzhou is often under low-visibility conditions since the 1980s. In order to understand the causes of the low visibility in Guangzhou, we analyze physical and chemical characterizations of a low-visibility event occurring between 15 and 29 November 2005. The analysis suggests that high concentration of aerosol particles is a major cause for the low visibility. The threshold aerosol concentration that is corresponding to the low visibility (<10km) is about 120μgm−3. Further analysis indicates that the relationship between aerosol concentrations and visibility appears in a non-linearity correlation. When aerosol concentrations are very high (above 120μgm−3), the change in visibility is not sensitive to aerosol concentration. By contrast, when aerosol concentrations are <120μgm−3, the change in visibility is very sensitive to aerosol concentrations. This study also shows that absorbing aerosol particles (such as element carbon (EC)) play important roles in the reduction of visibility. In most cases, the extremely low visibility (<2km) is often resulted from high EC concentrations (above 15μgm−3). In averaged condition, small scattering aerosol particles (<1μm in radius) have the largest contribution to the reduction of visibility (about 70%). By contrast, large scattering particles have the smallest contribution to the reduction of visibility. This analysis implies that the persistent low visibility is mainly resulted from small aerosol particles, including both scattering and absorbing particles. In order to improve the visibility in the Guangzhou region, the reduction of small particle emissions is urgently needed. This study suggests that there are two important steps to improve the visibility in Guangzhou. First, the aerosol concentration should be reduced to less than the threshold value (120μgm−3). Second, a further decrease in aerosols from its threshold value will lead to a significant improvement in visibility due to the non-linearity relationship between aerosol and visibility. [Copyright &y& Elsevier]

The setting of air quality standards is highly significant in controlling widespread pollutants and mitigating environmental risks. The selection of influential drafting units is a major step for the improvement of the systematicness, harmony, and compatibility of standard systems, which further leads to high air quality. To this end, this paper presents a novel social network-based methodology to assess the influence of drafting units based on a dynamic two-layer standard network model with the publicized data of the national air quality standards of China. Firstly, network theory metrics are aggregated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate their importance dynamically; Secondly, three measurements of participation degree, cooperation degree, and contribution degree are deliberately designed to assess the influence of drafting units. The results show that most of the key standards vary over time driven by policy reform, while the volatility of critical drafting units is relatively stable. It is also interesting to find that most of the influential drafting units are research institutes rather than enterprises. In view of the results, we assert that government agencies should strategically support influential drafting units and encourage them to make great efforts in drafting important standards. [ABSTRACT FROM AUTHOR]