Phase Transformations in Binary Colloidal Monolayers

Phase transformations can be difficult to characterize at the microscopic level due to the inability to directly observe individual atomic motions. Model colloidal systems, by contrast, permit the direct observation of individual particle dynamics and of collective rearrangements, which allows for real-space characterization of phase transitions. Here, we study a quasi-two-dimensional, binary colloidal alloy that exhibits liquid-solid and solid-solid phase transitions, focusing on the kinetics of a diffusionless transformation between two crystal phases. Experiments are conducted on a monolayer of magnetic and nonmagnetic spheres suspended in a thin layer of ferrofluid and exposed to a tunable magnetic field. A theoretical model of hard spheres with point dipoles at their centers is used to guide the choice of experimental parameters and characterize the underlying materials physics. When the applied field is normal to the fluid layer, a checkerboard crystal forms; when the angle between the field and the normal is sufficiently large, a striped crystal assembles. As the field is slowly tilted away from the normal, we find that the transformation pathway between the two phases depends strongly on crystal orientation, field strength, and degree of confinement of the monolayer. In some cases, the pathway occurs by smooth magnetostrictive shear, while in others it involves the sudden formation of martensitic plates.Comment: 13 pages, 7 figures. Soft Matter Latex template was used. Published online in Soft Matter, 201

Ammonia emission abatement does not fully control reduced forms of nitrogen deposition

Human activities and population growth have increased the natural burden of reactive nitrogen (N) in the environment. Excessive N deposition on Earth’s surface leads to adverse feedbacks on ecosystems and humans. Similar to that of air pollution, emission control is recognized as an efficient means to control acid deposition. Control of nitrogen oxides (NO_x = NO + NO₂) emissions has led to reduction in deposition of oxidized nitrogen (NO_y, the sum of all oxidized nitrogen species, except nitrous oxide [N₂O]). Reduced forms of nitrogen (NH_x = ammonia [NH₃] + ammonium [NH₄⁺]) deposition have, otherwise, increased, offsetting the benefit of reduction in NO_y deposition. Stringent control of NH₃ emissions is being considered. In this study, we assess the response of N deposition to N emission control on continental regions. We show that significant reduction of NHx deposition is unlikely to be achieved at the early stages of implementing NH₃ emission abatement. Per-unit NH₃ emission abatement is shown to result in only 60–80% reduction in NH_x deposition, which is significantly lower than the demonstrated 80–120% benefit of controlling NO_x emissions on NO_y deposition. This 60–80% effectiveness of NH_x deposition reduction per unit NH₃ emission abatement reflects, in part, the effects of simultaneous reductions in NO_x and SO₂ emissions

Retrieving Ground-Level PM2.5 Concentrations in China (2013–2021) with a Numerical Model-Informed Testbed to Mitigate Sample Imbalance-Induced Biases

Ground-level PM2.5 data derived from satellites with machine learning are crucial for health and climate assessments, however, uncertainties persist due to the absence of spatially covered observations. To address this, we propose a novel testbed using untraditional numerical simulations to evaluate PM2.5 estimation across the entire spatial domain. The testbed emulates the general machine-learning approach, by training the model with grids corresponding to ground monitor sites and subsequently testing its predictive accuracy for other locations. Our approach enables comprehensive evaluation of various machine-learning methods’ performance in estimating PM2.5 across the spatial domain for the first time. Unexpected results are shown in the application in China, with larger PM2.5 biases found in densely populated regions with abundant ground observations across all benchmark models, challenging conventional expectations and are not explored in the recent literature. The imbalance in training samples, mostly from urban areas with high emissions, is the main reason, leading to significant overestimation due to the lack of monitors in downwind areas where PM2.5 is transported from urban areas with varying vertical profiles. Our proposed testbed also provides an efficient strategy for optimizing model structure or training samples to enhance satellite-retrieval model performance. Integration of spatiotemporal features, especially with CNN-based deep-learning approaches like the ResNet model, successfully mitigates PM2.5 overestimation (by 5–30 µg m-3) and corresponding exposure (by 3 million people • µg m-3) in the downwind area over the past nine years (2013–2021) compared to the traditional approach. Furthermore, the incorporation of 600 strategically positioned ground-measurement sites identified through the testbed is essential to achieve a more balanced distribution of training samples, thereby ensuring precise PM2.5 estimation and facilitating the assessment of associated impacts in China. In addition to presenting the retrieved surface PM2.5 concentrations in China from 2013 to 2021, this study provides a testbed dataset derived from physical modeling simulations which can serve to evaluate the performance of data-driven methodologies, such as machine learning, in estimating spatial PM2.5 concentrations for the community

Risk-based Prioritization among Air Pollution Control Strategies in the Yangtze River Delta, China

Background: The Yangtze River Delta (YRD) in China is a densely populated region with recent dramatic increases in energy consumption and atmospheric emissions. Objectives: We studied how different emission sectors influence population exposures and the corresponding health risks, to inform air pollution control strategy design. Methods: We applied the Community Multiscale Air Quality (CMAQ) Modeling System to model the marginal contribution to baseline concentrations from different sectors. We focused on nitrogen oxide (NOx) control while considering other pollutants that affect fine particulate matter [aerodynamic diameter $\leq 2.5 \mu m (PM_{2.5})$] and ozone concentrations. We developed concentration–response (C-R) functions for $PM_{2.5}$ and ozone mortality for China to evaluate the anticipated health benefits. Results: In the YRD, health benefits per ton of emission reductions varied significantly across pollutants, with reductions of primary $PM_{2.5}$ from the industry sector and mobile sources showing the greatest benefits of 0.1 fewer deaths per year per ton of emission reduction. Combining estimates of health benefits per ton with potential emission reductions, the greatest mortality reduction of 12,000 fewer deaths per year [95% confidence interval (CI), 1,200–24,000] was associated with controlling primary $PM_{2.5}$ emissions from the industry sector and reducing sulfur dioxide $(SO_2)$ from the power sector, respectively. Benefits were lower for reducing $NO_x$ emissions given lower consequent reductions in the formation of secondary $PM_{2.5}$ (compared with $SO_2$) and increases in ozone concentrations that would result in the YRD. Conclusions: Although uncertainties related to C-R functions are significant, the estimated health benefits of emission reductions in the YRD are substantial, especially for sectors and pollutants with both higher health benefits per unit emission reductions and large potential for emission reductions

Model development of dust emission and heterogeneous chemistry within the Community Multiscale Air Quality modeling system and its application over East Asia

The Community Multiscale Air Quality (CMAQ) model has been further developed in terms of simulating natural wind-blown dust in this study, with a series of modifications aimed at improving the model\u27s capability to predict the emission, transport, and chemical reactions of dust. The default parameterization of initial threshold friction velocity constants are revised to correct the double counting of the impact of soil moisture in CMAQ by the reanalysis of field experiment data; source-dependent speciation profiles for dust emission are derived based on local measurements for the Gobi and Taklamakan deserts in East Asia; and dust heterogeneous chemistry is also implemented. The improved dust module in the CMAQ is applied over East Asia for March and April from 2006 to 2010. The model evaluation result shows that the simulation bias of PM10 and aerosol optical depth (AOD) is reduced, respectively, from −55.42 and −31.97 % by the original CMAQ to −16.05 and −22.1 % by the revised CMAQ. Comparison with observations at the nearby Gobi stations of Duolun and Yulin indicates that applying a source-dependent profile helps reduce simulation bias for trace metals. Implementing heterogeneous chemistry also results in better agreement with observations for sulfur dioxide (SO2), sulfate (SO42−), nitric acid (HNO3), nitrous oxides (NOx), and nitrate (NO3−). The investigation of a severe dust storm episode from 19 to 21 March 2010 suggests that the revised CMAQ is capable of capturing the spatial distribution and temporal variation of dust. The model evaluation also indicates potential uncertainty within the excessive soil moisture used by meteorological simulation. The mass contribution of fine-mode particles in dust emission may be underestimated by 50 %. The revised CMAQ model provides a useful tool for future studies to investigate the emission, transport, and impact of wind-blown dust over East Asia and elsewhere

Impact of anthropogenic emission on air quality over a megacity – revealed from an intensive atmospheric campaign during the Chinese Spring Festival

The Chinese Spring Festival is one of the most important traditional festivals in China. The peak transport in the Spring Festival season (spring travel rush) provides a unique opportunity for investigating the impact of human activity on air quality in the Chinese megacities. Emission sources are varied and fluctuate greatly before, during and after the Festival. Increased vehicular emissions during the spring travel rush before the 2009 Festival resulted in high level pollutants of NOx (270 μg m−3), CO (2572 μg m−3), black carbon (BC) (8.5 μg m−3) and extremely low single scattering albedo of 0.76 in Shanghai, indicating strong, fresh combustion. Organics contributed most to PM2.5, followed by NO3−, NH4+, and SO42−. During the Chinese Lunar New Year\u27s Eve and Day, widespread usage of fireworks caused heavy pollution of extremely high aerosol concentration, scattering coefficient, SO2, and NOx. Due to the spring travel rush after the festival, anthropogenic emissions gradually climbed and mirrored corresponding increases in the aerosol components and gaseous pollutants. Secondary inorganic aerosol (SO42−, NO3−, and NH4+) accounted for a dominant fraction of 74% in PM2.5 due to an increase in human activity. There was a greater demand for energy as vast numbers of people using public transportation or driving their own vehicles returned home after the Festival. Factories and constructions sites were operating again. The potential source contribution function (PSCF) analysis illustrated the possible source areas for air pollutants of Shanghai. The effects of regional and long-range transport were both revealed. Five major sources, i.e. natural sources, vehicular emissions, burning of fireworks, industrial and metallurgical emissions, and coal burning were identified using the principle component analysis. The average visibility during the whole study period was less than 6 km. It had been estimated that 50% of the total light extinction was due to the high water vapor in the atmosphere. This study demonstrates that organic aerosol was the largest contributor to aerosol extinction at 47%, followed by sulfate ammonium, nitrate ammonium, and EC at 22%, 14%, and 12%, respectively. Our results indicated the dominant role of traffic-related aerosol species (i.e. organic aerosol, nitrate and EC) on the formation of air pollution, and suggested the importance of controlling vehicle numbers and emissions in mega-cities of China as its population and economy continue to grow

Improvement of the Prediction of Surface Ozone Concentration Over Conterminous U.S. by a Computationally Efficient Second-Order Rosenbrock Solver in CAM4-Chem

The global chemistry-climate model (CAM4-Chem) overestimates the surface ozone concentration over the conterminous U.S. (CONUS). Reasons for this positive bias include emission, meteorology, chemical mechanism, and solver. In this study, we explore the last possibility by examining the sensitivity to the numerical methods for solving the chemistry equations. A second-order Rosenbrock (ROS-2) solver is implemented in CAM4-Chem to examine its influence on the surface ozone concentration and the computational performance of the chemistry program. Results show that under the same time step size (1800 s), statistically significant reduction of positive bias is achieved by the ROS-2 solver. The improvement is as large as 5.2 ppb in Eastern U.S. during summer season. The ROS-2 solver is shown to reduce the positive bias in Europe and Asia as well, indicating the lower surface ozone concentration over the CONUS predicted by the ROS-2 solver is not a trade-off consequence with increasing the ozone concentration at other global regions. In addition, by refining the time step size to 180 s, the first-order implicit solver does not provide statistically significant improvement of surface ozone concentration. It reveals that the better prediction from the ROS-2 solver is not only due to its accuracy but also due to its suitability for stiff chemistry equations. As an added benefit, the computation cost of the ROS-2 solver is almost half of first-order implicit solver. The improved computational efficiency of the ROS-2 solver is due to the reuse of the Jacobian matrix and lower upper (LU) factorization during its multistage calculation

Dedicated Energy Crop Supply Chain and Associated Feedstock Transportation Emissions: A Case Study of Tennessee

and Bradly Wilson This study minimizes total cost for single-feedstock supply chains of two dedicated energy crops, perennial switchgrass and biomass sorghum, in Tennessee using a spatial optimization model. Greenhouse gas emissions from the transport of feedstock to the conversion facility were estimated for respective feedstock supply chains. Results show that different demand for land types from two feedstocks and the geographically diverse landscape across the state affect the economics of bioenergy crops supply chains and feedstock transportation emissions. Switchgrass is more suitable than biomass sorghum for biofuel production in Tennessee based on the supply chains cost and feedstock hauling emissions

A regional chemical transport modeling to identify the influences of biomass burning during 2006 BASE-ASIA

To evaluate the impact of biomass burning from Southeast Asia to East Asia, this study conducted numerical simulations during NASA\u27s 2006 Biomass-burning Aerosols in South-East Asia: Smoke Impact Assessment (BASE-ASIA). Two typical episode periods (27–28 March and 13–14 April) were examined. Two emission inventories, FLAMBE and GFED, were used in the simulations. The influences during two episodes in the source region (Southeast Asia) contributed to CO, O3 and PM2.5 concentrations as high as 400 ppbv, 20 ppbv and 80 μg/m3, respectively. The perturbations with and without biomass burning of the above three species were in the range of 10 to 60%, 10 to 20% and 30 to 70%, respectively. The impact due to long-range transport could spread over the southeastern parts of East Asia and could reach about 160 to 360 ppbv, 8 to 18 ppbv and 8 to 64 μg/m3 on CO, O3 and PM2.5, respectively; the percentage impact could reach 20 to 50% on CO, 10 to 30% on O3, and as high as 70% on PM2.5. An impact pattern can be found in April, while the impact becomes slightly broader and goes up to Yangtze River Delta. Two cross-sections at 15° N and 20° N were used to compare the vertical flux of biomass burning. In the source region (Southeast Asia), CO, O3 and PM2.5 concentrations had a strong upward tendency from surface to high altitudes. The eastward transport becomes strong from 2 to 8 km in the free troposphere. The subsidence contributed 60 to 70%, 20 to 50%, and 80% on CO, O3 and PM2.5, respectively to surface in the downwind area. The study reveals the significant impact of Southeastern Asia biomass burning on the air quality in both local and downwind areas, particularly during biomass burning episodes. This modeling study might provide constraints of lower limit. An additional study is underway for an active biomass burning year to obtain an upper limit and climate effects. doi:10.5194/acpd-11-3071-201