The spatiotemporal features of Greenhouse Gases Emissions from Biomass Burning in China from 2000-2012

Greenhouse gases emissions from biomass burning have been given a little attention, especially the spatiotemporal features of biomass burning sources and greenhouse gases emissions have not been comprehensively uncovered. This research undertook IPCC bottom-up inventory guideline to estimate Chinese greenhouse gases emissions from biomass burning and applied geographical information system to reveal biomass burning emissions spatiotemporal features. The purposes were to quantify greenhouse gases emissions from various biomass burning sources and to uncover the spatial and temporal emissions features so to deliver future policy implications in China. The results showed that the average annual biomass burning emissions in China from 2000-2012 were 880.66 Mt for CO2, 96.59 Mt CO2-eq for CH4, and 16.81 Mt CO2-eq for N2O. The spatial pattern of biomass greenhouse gases emissions showed about 50 % of national emission were in the east and south-central regions. The majority of biomass burning emissions were from firewood and crop residues, which accounted for more than 90 % of national biomass burning emissions. All types of biomass burning emissions exhibited similar temporal trends from 2000-2012, with strong inter-annual variability and fluctuant increase. The large grassland and forest fires induced the significant greenhouse gases emissions peaks in the years of 2001, 2003 and 2006. We found that biofuel burning, with low combustion efficiency, is the major emission source. Open burning of biomass was widespread in China, and east and south-central regions were the major distribution of biomass burning greenhouse gases emission. Optimized design for improving the efficiency of biomass utilization and making emission control policy combination with its spatiotemporal features will be the effective way to reduce the biomass burning emissions

Performance Evaluation of the SLEUTH Model in the Shenyang Metropolitan Area of Northeastern China

Abstract Performance evaluation is crucial for the development and improvement of an urban cellular automata model, such as SLEUTH. In this paper, we employed multiple methods for map comparison and model validation to evaluate the simulation performance of the SLEUTH urban growth model in the Shenyang metropolitan area of China. These multiple methods included the relative operating characteristic (ROC) curve statistic, multiple-resolutions error budget, and landscape metrics. They were used to quantitatively examine model performance in terms of the amount and spatial location of urban development, urban spatial pattern and prediction ability. The assessment results showed that SLEUTH performed well in the way of the quantitative simulation of urban growth for this case study. Similar to other urban growth models, however, the simulation accuracy for spatial location of new development at the pixel scale and urban spatial pattern still needs to be improved greatly. These inaccuracies might be attributed to the structure and nature of SLEUTH, local urban development characteristics, and the temporal and spatial scale of its application. Finally, many valuable suggestions had been put forward to improve simulation performance of SLEUTH model for spatial location of urban development in the Shenyang metropolitan area

Electroacupuncture treatment ameliorates metabolic disorders in obese ZDF rats by regulating liver energy metabolism and gut microbiota

Metabolic disorders represent a major therapeutic challenge to public health worldwide due to their dramatically increasing prevalence. Acupuncture is widely used as adjuvant therapy for multiple metabolic diseases. However, detailed biological interpretation of the acupuncture stimulations is still limited. The gut and the liver are intrinsically connected and related to metabolic function. Microbial metabolites might affect the gut-liver axis through multiple mechanisms. Liver metabolomics and 16S rRNA sequencing were used to explore the specific mechanism of electroacupuncture in treating ZDF rats in this study. Electroacupuncture effectively improved glycolipid metabolism disorders of the ZDF rats. Histopathology confirmed that electroacupuncture improved diffuse hepatic steatosis and hepatocyte vacuolation, and promoted glycogen accumulation in the liver. The treatment significantly improved microbial diversity and richness and upregulated beneficial bacteria that maintain intestinal epithelial homeostasis and decreased bacteria with detrimental metabolic features on host metabolism. Liver metabolomics showed that the main effects of electroacupuncture include reducing the carbon flow and intermediate products in the TCA cycle, regulating the metabolism of various amino acids, and inhibiting hepatic glucose output and de novo lipogenesis. The gut-liver axis correlation analysis showed a strong correlation between the liver metabolites and the gut microbiota, especially allantoin and Adlercreutzia. Electroacupuncture treatment can improve abnormal energy metabolism by reducing oxidative stress, ectopic fat deposition, and altering metabolic fluxes. Our results will help us to further understand the specific mechanism of electroacupuncture in the treatment of metabolic diseases

COVID-19 causes record decline in global CO2 emissions

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures

Near-real-time monitoring of global CO₂ emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO₂) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO₂ emissions (−1551 Mt CO₂) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially

Reduced carbon emission estimates from fossil fuel combustion and cement production in China.

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon).This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/nature1467

Optimization of carton recycling site selection using particle swarm optimization algorithm considering residents’ recycling willingness

With the development of the express delivery industry, how to increase the recycling rate of waste cartons has become a problem that needs to be solved. Recycling enterprises began to provide the new recycling mode, door-to-door recycling services, to residents with waste cartons. In this article, we constructed a site selection model for a carton recycling site with the aim of maximizing total profits. Considering the residents’ recycling willingness and the government subsidy earned through the contribution to carbon emission reduction, this model achieves the task of site selection and unit price fixation for carton recycling. We used the particle swarm optimization (PSO) algorithm to solve the model and compared it with the genetic algorithm (GA) for validity testing. PSO algorithm was also used to carry out sensitivity analysis in this model. The proposed model and the results of the sensitivity analysis can be used for decision-making in recycling enterprises as well as for further research on waste recycling and reverse logistics