[Coordinated Control of Carbon Emission Reduction and Air Quality Improvement in the Industrial Sector in Hunan Province].

Climate warming and air pollution are the main environmental problems in China. This study used China's Carbon Accounting Database, energy economic model, and air quality model to analyze the potential carbon emission peaking path and synergistic air quality improvement gain in the industrial sector in Hunan Province. Based on China's Carbon Accounting Database and the local industry/energy statistical yearbooks in Hunan, the total CO2 emissions in Hunan Province in 2019 were 310.6 Mt, of which the industrial sector accounted for over 70% of the emissions, mainly from the production and supply of electricity, steam, and heat; the production of non-metallic minerals; and the smelting and pressing of ferrous metals. Three potential industrial carbon emission peaking scenarios were analyzed using the LEAP energy economic model, including the business-as-usual scenario (peaking by 2030), moderate emission reduction scenario (peaking by 2028), and aggressive emission reduction scenario (peaking by 2025), by employing different economic growth rates, energy technology progress, and energy structures of the industrial sector. Furthermore, by combining the anthropogenic air pollutant emission inventory and the regional air quality model WRF-Chem, we analyzed the air quality improvement associated with various carbon emission peak paths. The results showed that the annual mean concentrations of major air pollutants had decreased in the three scenarios, especially in the Chang-Zhu-Tan Region. The aggressive emission reduction scenario was the most effective scenario, followed by the moderate emission reduction scenario and the business-as-usual scenario. Manufacturing was the sector with the most significant synergistic effect of pollution and carbon reduction. When carbon emission peaks were achieved, the annual average concentrations of PM2.5 and PM10 in Hunan Province could be synergistically reduced by 0.6-1.8 µg·m-3 and 1.8-8.9 µg·m-3, respectively. Our findings offer important insights into carbon emission peaking and can provide useful information for potential mitigation actions.

Metabolomic and mass isotopomer analysis of liver gluconeogenesis and citric acid cycle. I. Interrelation between gluconeogenesis and cataplerosis; formation of methoxamates from aminooxyacetate and ketoacids.

We conducted a study coupling metabolomics and mass isotopomer analysis of liver gluconeogenesis and citric acid cycle. Rat livers were perfused with lactate or pyruvate +/- aminooxyacetate or mercaptopicolinate in the presence of 40% enriched NaH(13)CO(3). Other livers were perfused with dimethyl [1,4-(13)C(2)]succinate +/- mercaptopicolinate. In this first of two companion articles, we show that a substantial fraction of gluconeogenic carbon leaves the liver as citric acid cycle intermediates, mostly alpha-ketoglutarate. The efflux of gluconeogenic carbon ranges from 10 to 200% of the rate of liver gluconeogenesis. This cataplerotic efflux of gluconeogenic carbon may contribute to renal gluconeogenesis in vivo. Multiple crossover analyses of concentrations of gluconeogenic intermediates and redox measurements expand previous reports on the regulation of gluconeogenesis and the effects of inhibitors. We also demonstrate the formation of adducts from the condensation, in the liver, of (i) aminooxyacetate with pyruvate, alpha-ketoglutarate, and oxaloacetate and (ii) mercaptopicolinate and pyruvate. These adducts may exert metabolic effects unrelated to their effect on gluconeogenesis.

Metabolomic and mass isotopomer analysis of liver gluconeogenesis and citric acid cycle: II. Heterogeneity of metabolite labeling pattern.

In this second of two companion articles, we compare the mass isotopomer distribution of metabolites of liver gluconeogenesis and citric acid cycle labeled from NaH(13)CO(3) or dimethyl [1,4-(13)C(2)]succinate. The mass isotopomer distribution of intermediates reveals the reversibility of the isocitrate dehydrogenase + aconitase reactions, even in the absence of a source of alpha-ketoglutarate. In addition, in many cases, a number of labeling incompatibilities were found as follows: (i) glucose versus triose phosphates and phosphoenolpyruvate; (ii) differences in the labeling ratios C-4/C-3 of glucose versus (glyceraldehyde 3-phosphate)/(dihydroxyacetone phosphate); and (iii) labeling of citric acid cycle intermediates in tissue versus effluent perfusate. Overall, our data show that gluconeogenic and citric acid cycle intermediates cannot be considered as sets of homogeneously labeled pools. This probably results from the zonation of hepatic metabolism and, in some cases, from differences in the labeling pattern of mitochondrial versus extramitochondrial metabolites. Our data have implications for the use of labeling patterns for the calculation of metabolic rates or fractional syntheses in liver, as well as for modeling liver intermediary metabolism.