科研成果

High concentrations of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 mu m) in China have caused severe visibility degradation. Accurate simulations of PM2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive datasets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. The model is unable to capture some of the main features in the diurnal pattern of the PM2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA.

Aim Statistical species distribution models (SDMs) are the most common tool to predict the impact of climate change on biodiversity. They can be tuned to fit relationships at various levels of complexity (defined here as parameterization complexity, number of predictors, and multicollinearity) that may co-determine whether projections to novel climatic conditions are useful or misleading. Here, we assessed how model complexity affects the performance of model extrapolations and influences projections of species ranges under future climate change. Location Europe. Taxon 34 European tree species. Methods We sampled three replicates of predictor sets for all combinations of 10 levels (n = 3-12) of environmental variables (climate, terrain, soil) and 10 levels of multicollinearity. We used these sets for each species to fit four SDM algorithms at three levels of parameterization complexity. The >100,000 resulting SDM fits were then evaluated under environmental block cross-validation and projected to environmental conditions for 2061-2080 considering four climate models and two emission scenarios. Finally, we investigated the relationships of model design with model performance and projected distributional changes. Results Model complexity affected both model performance and projections of species distributional change. Fits of intermediate parameterization complexity performed best, and more complex parameterizations were associated with higher projected loss of current ranges. Model performance peaked at 10-11 variables but increasing number of variables had no consistent effect on distributional change projections. Multicollinearity had a low impact on model performance but distinctly increased projected loss of current ranges. Main conclusions SDM-based climate change impact assessments should be based on ensembles of projections, varying SDM algorithms as well as parameterization complexity, besides emission scenarios and climate models. The number of predictor variables should be kept reasonably small and the classical threshold of maximum absolute Pearson correlation of 0.7 restricts collinearity-driven effects in projections of species ranges.

CO2 flooding is an important method in CO2 enhanced oil recovery (EOR) but is usually accompanied by a low efficiency for the fractured low-permeability formation due to CO2 low viscosity and high mobility. In this paper, a comprehensive experimental research effort including flooding and NMR testing is conducted to investigate the oil recovery and mobility control effects of a novel CO2 oil-based foam in fractured low-permeability cores. First, the foaming performance of the compound surfactant SF in crude oil that consists of Span20 and fluorochemical surfactant F-1 is evaluated by the blender stirring method. The surfactant SF exhibits a good foaming performance in crude oil with a foam volume of 290 mL and a half-life of 352 s. The bubble film is notably thickened, which results in a stable oil-based foam. Second, CO2 flooding and CO2 oil-based foam flooding in nonfractured and fractured cores are conducted under reservoir conditions. CO2 oil-based foam flooding can significantly improve the oil recovery and increase the sweep volume of injected CO2. Consequently, the oil recovery in fractured cores increases by 47.8%, and that in nonfractured cores increases by 39.1%. Third, the residual oil saturation in the cores is tested by NMR. The residual oil saturation of fractured and nonfractured cores after CO2 oil-based foam flooding is low and distributed evenly, indicating that CO2 oil-based foam reduces CO2 mobility and yields a relatively uniform displacement throughout the core.

Changing climate is one of the most challenging environment issues worldwide. The objective of this paper is to develop a Multi-Dimensional Hypothetical Fuzzy Risk Simulation Model to facilitate the Greenhouse Gases mitigation policy development and multi-dimensional risk simulation. In detail, the comprehensive performances of various industries are evaluated and analyzed through Hypothetical Extraction Method. The preferences of decision-makers are considered through Analytic Hierarchy Process and Fuzzy Technique for Order Preference by Similarities to Ideal Solution method to develop the optimized Greenhouse Gases mitigation policies. The multi-dimensional risks of optimized Greenhouse Gases mitigation policies are simulated through RAS method. A detailed case study of the Province of Saskatchewan, Canada, is conducted to illustrate the potential benefits of the proposed model and support the Greenhouse Gases mitigation policy development. It is found that Electric power generation, transmission and distribution sector is the key industry in Saskatchewan. The government supports are suggested to be allocated to the Electric power generation, transmission and distribution sector, since it will benefit the province from environmental, economic, and urban metabolic perspectives.

Surface heating membrane distillation overcomes several limitations inherent in conventional membrane distillation technology. Here we report a successful effort to grow in situ a hexagonal boron nitride (hBN) nanocoating on a stainless-steel wire cloth (hBN-SSWC), and its application as a scalable electrothermal heating material in surface heating membrane distillation. The novel hBN-SSWC provides superior vapour permeability, thermal conductivity, electrical insulation and anticorrosion properties, all of which are critical for the long-term surface heating membrane distillation performance, particularly with hypersaline solutions. By simply attaching hBN-SSWC to a commercial membrane and providing power with an a.c. supply at household frequency, we demonstrate that hBN-SSWC is able to support an ultrahigh power intensity (50 kW m−2) to desalinate hypersaline solutions with exceptionally high water flux (and throughput), single-pass water recovery and heat utilization efficiency while maintaining excellent material stability. We also demonstrate the exceptional performance of hBN-SSWC in a scalable and compact spiral-wound electrothermal membrane distillation module.