Developed as energy-efficient and integrated method for soluble sugars and furfurals with high yields and high carbon efficiency from polysaccharides and lignocellulosic biomass.
Amines are widely used in the manufacture of pharmaceuticals, agricultural chemicals, polymers, and surfactants. However, amines are mostly produced via petrochemical means, which motivates amine production from renewable resources, such as biomass. However, biomass compounds present added challenges involving poor carbon balances. We show that furfural reacts homogeneously with ammonia to produce reactive primary imines, which form large side products and leads to significant carbon losses. The carbon balance is improved by mixing furfural with furfurylamine prior to reaction to form a secondary imine for use as the reaction substrate. While controlling the primary to secondary amine selectivity is a common challenge in reductive amination, supported metal catalysts, including Ni/SiO2, Co/SiO2, and Ru/SiO2 optimize the primary amine yield to 90 to 94 % by using the secondary imine as the reaction substrate. A qualitative correlation between the primary to secondary amine selectivity with the nitrogen binding energy of metals is identified.
Abstract. Significant reductions in emissions of SO2, NOx, volatile organic compounds (VOCs), and primary particulate matter (PM) took place in the US from 1990 to 2010. We evaluate here our understanding of the links between these emissions changes and corresponding changes in concentrations and health outcomes using a chemical transport model, the Particulate Matter Comprehensive Air Quality Model with Extensions (PMCAMx), for 1990, 2001, and 2010. The use of the Particle Source Apportionment Algorithm (PSAT) allows us to link the concentration reductions to the sources of the corresponding primary and secondary PM. The reductions in SO2 emissions (64 %, mainly from electric-generating units) during these 20 years have dominated the reductions in PM2.5, leading to a 45 % reduction in sulfate levels. The predicted sulfate reductions are in excellent agreement with the available measurements. Also, the reductions in elemental carbon (EC) emissions (mainly from transportation) have led to a 30 % reduction in EC concentrations. The most important source of organic aerosol (OA) through the years according to PMCAMx is biomass burning, followed by biogenic secondary organic aerosol (SOA). OA from on-road transport has been reduced by more than a factor of 3. On the other hand, changes in biomass burning OA and biogenic SOA have been modest. In 1990, about half of the US population was exposed to annual average PM2.5 concentrations above 20 µg m−3, but by 2010 this fraction had dropped to practically zero. The predicted changes in concentrations are evaluated against the observed changes for 1990, 2001, and 2010 in order to understand whether the model represents reasonably well the corresponding processes caused by the changes in emissions.
Abstract. Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx (Particulate Matter Comprehensive Air quality Model with Extensions) was used to assess the impact of increasing model resolution on the ability to predict the source-resolved variability and population exposure to PM2.5 at 36×36, 12×12, 4×4, and 1×1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences can be observed, while increasing the resolution to 12×12 km resolves the urban–rural gradient. Increasing resolution to 4×4 km resolves large stationary sources such as power plants, and the 1×1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Regional pollutants that exhibit low spatial variability such as PM2.5 nitrate show modest changes when increasing the resolution beyond 12×12 km. Predominantly local pollutants such as elemental carbon and primary organic aerosol have gradients that can only be resolved at the 1×1 km scale. Contributions from some local sources are enhanced by weighting the average contribution from each source by the population in each grid cell. The average population-weighted PM2.5 concentration does not change significantly with resolution, suggesting that extremely high resolution PM2.5 predictions may not be necessary for effective urban epidemiological analysis at the county level.
<p>Quantification of the spatial and temporal variations in the sources of air pollutants, especially PM<sub>2.5</sub>, can inform control strategies and, potentially, the understanding of PM<sub>2.5 </sub>health effects. Three-dimensional chemical transport models (CTMs) are well suited to help address this problem, since they simulate all the major processes that impact PM<sub>2.5 </sub>concentrations and transport. In this study we quantify the changes in the concentration, exposure, composition, and sources of PM<sub>2.5 </sub>in the US from the early 1990s to the early 2010s. Significant reductions of emissions of SO<sub>2</sub>, NO<sub>x</sub>, VOCs and primary PM have taken place in the US during the last 20 years. We evaluate our understanding of the links between these emissions and concentration changes combining a chemical transport model (PMCAMx) with the Particle Source Apportionment Algorithm (PSAT) (Skyllakou et al., 2017). Results for 1990, 2001 and 2010 are presented. The reductions in SO<sub>2</sub> emissions (64% mainly from electrical generation units) during these 20 years have dominated the reductions in PM<sub>2.5</sub> leading to a 45% reduction of the sulfate levels. The predicted sulfate reductions were in excellent agreement with the available measurements. Also, the reductions in elemental carbon (EC) emissions (mainly from transportation) have led to a 30% reduction of EC concentrations. The most important source of OA through the years according to PMCAMx is biomass burning followed by biogenic SOA. OA from on-road transport has been reduced by more than a factor of 3, on the other hand changes in biomass burning OA and biogenic SOA have been modest. In 1990 90% of the US population was exposed to PM<sub>2.5 </sub>concentrations to equal and higher than the suggested annual mean by the WHO (10 &#956;g m<sup>-3</sup>), but this reduced to 70% in 2010. Also, the predicted changes in concentrations were evaluated against the observed changes for 1990, 2001 and 2010, in order to understand if the model represents well the changes through the years.</p><p>&#160;</p><p>Skyllakou, K., Fountoukis, C., Charalampidis, P., and Pandis, S.N. (2017). Volatility-resolved source apportionment of primary and secondary organic aerosol over Europe, Atmos. Environ., 167, 1&#8211;10.</p><p>&#160;</p>
Abstract. Significant reductions of emissions of SO2, NOx, volatile organic compounds (VOCs) and primary particulate matter (PM) took place in the US from 1990 to 2010. We evaluate here our understanding of the links between these emissions changes and corresponding changes in concentrations and health outcomes using a chemical transport model, the Particulate Matter Comprehensive Air Quality Model with Extensions (PMCAMx) with the Particle Source Apportionment Algorithm (PSAT). Results for 1990, 2001 and 2010 are presented. The reductions in SO2 emissions (64 %, mainly from electric generating units) during these 20 years have dominated the reductions in PM2.5 leading to a 45 % reduction in sulfate levels. The predicted sulfate reductions are in excellent agreement with the available measurements. Also, the reductions in elemental carbon (EC) emissions (mainly from transportation) have led to a 30 % reduction of EC concentrations. The most important source of organic aerosol (OA) through the years according to PMCAMx is biomass burning, followed by biogenic secondary organic aerosol (SOA). OA from on-road transport has been reduced by more than a factor of three. On the other hand, changes in biomass burning OA and biogenic SOA have been modest. In 1990, about half of the US population was exposed to annual-average PM2.5 concentrations above 20 μg m−3, but by 2010 this fraction had dropped to practically zero. The predicted changes in concentrations are evaluated against the observed changes for 1990, 2001, and 2010, in order to understand if the model represents reasonably well the corresponding processes caused by the changes in emissions.
The ability to provide speciated and source-resolved PM2.5 estimates make chemical transport models a potentially valuable tool for exposure assessments. However, epidemiological studies often require unbiased estimates, which can be challenging for chemical transport models. We use geographically weighted regression to predict and correct the bias in source-resolved PM2.5 species (elemental carbon, organic aerosol, ammonium, nitrate, and sulfate) across the continental U.S. for 2001 and 2010. The regression models are trained using speciated ground-level monitors from the CSN and IMPROVE networks. A 10-fold cross-validation shows minimal bias across all simulated PM2.5 species (0 -3%) and improved agreement with ground-level monitors (R 2 = 0.53 -0.97). Corrections also improve the agreement between simulated and observed species mixtures on a fractional basis. The sourceresolved exposure estimates developed in this study are suitable for use in health analyses of PM2.5 toxicity.
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