Search

Chemical transport models (CTM) have wide and profound applications in air quality simulations and managements. However, its applications are often constrained by high computational burdens. In this study, we developed a neural network based CTM model (FastCTM) to efficiently simulate ten air pollutant composition variables, including major PM2.5 species of SO42-, NO3-, NH4+, organic matters and other inorganic components, coarse part of PM10, SO2, NO2, CO and O3. The FastCTM has a principle-informed structure by explicitly encoding atmospheric physical and chemical processes in a basic simulator. Specifically, in the simulator, five neural network modules are proposed to respectively represent five major atmospheric processes of primary emissions, transport, diffusion, chemical reactions and depositions. Given 1-hour initial condition data, the FastCTM is able to simulate future 24-hour concentrations of the ten air pollutants with corresponding meteorology fields and emissions as input. The FastCTM is trained with operational forecast data from a numerical CTM model named Community Multiscale Air Quality (CMAQ) in 2018–2022. The well-trained FastCTM is evaluated comparing to the long-term CMAQ forecast in an independent year 2023, and achieves high agreements with mean RMSE values of 9.1, 11.9, 4.4, 4.0, 48.9 and 10.9 μg/m3 and R2 values of 0.8, 0.81, 0.8, 0.83, 0.9 and 0.7 for PM2.5, PM10, SO2, NO2, CO, and O3. Besides, assessed against hourly site observations of six criteria pollutants, the RMSE values of FastCTM have small relative differences of 4.3 %, 4.2 %, -2.8 %, -1.7 %, -0.3 % and -3.2 % compared to that of CMAQ. The FastCTM model also exhibited reasonable responses of air quality to meteorological variables of air temperature, wind speed and planetary boundary layer height, as well as to input pollutant emissions. Furthermore, due to the principles-oriented structure, internal process analysis could be performed by FastCTM to quantify the specific contribution from each of the five processes for hourly air pollutant concentration changes. In a nutshell, FastCTM has multi-functional advantages in air pollutant concentration simulations, sensitivity analysis and internal process analysis with high computation efficiencies on GPU and accuracy. [ABSTRACT FROM AUTHOR]

We conducted a 2-year study utilizing a network of fixed sites with sampling throughout an extended prescribed burning period to characterize the emissions and evolution of smoke from silvicultural prescribed burning at a military base in the southeastern USA. The measurement approach and an assessment of the instrument performance are described. Smoke sources, including those within and off the base, are identified, and plume ages are determined to quantify emissions and study the evolution of smoke PM2.5 (particulate matter with aerodynamic diameters 2.5 µ m or smaller) mass, black carbon (BC), and brown carbon (BrC). Over the 2021 and 2022 prescribed burning seasons (nominally January to May), we identified 64 smoke events based on high levels of PM2.5 mass, BC, BrC, and carbon monoxide (CO), of which 61 were linked to a specific burning area. Smoke transport times were estimated in two ways: using the mean wind speed and the distance between the fire and the measurement site, and from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectories. PM2.5 emission ratios based on Δ PM2.5 mass / Δ CO for fresh smoke (age ≤ 1 h) ranged between 0.04 and 0.18 µ g m−3 ppb−1 with a mean of 0.117 µ g m−3 ppb−1 (median of 0.121 µ g m−3 ppb−1). Both the mean emission ratio and the variability were similar to findings from other prescribed fire studies but were lower than those from wildfires. The mean emission ratios of BC and BrC were 0.014 µ g m−3 ppb−1 and 0.442 Mm−1 ppb−1, respectively. Ozone enhancements (Δ O3) were always observed in plumes detected in the afternoon. Δ PM2.5 mass / Δ CO was observed to increase with plume age in all of the ozone-enhanced plumes, suggesting photochemical secondary aerosol formation. In contrast, Δ BrC/ Δ CO was not found to vary with plume ages less than 8 h during photochemically active periods. [ABSTRACT FROM AUTHOR]

The clogging induced by particle transport is often mentioned in the practice of vacuum preloading, but the percolation behavior of soft clay under negative pressure is still not clear. To understand this, the percolation behavior was investigated using an artificial paste of silica sand and kaolinite under different hydraulic pressures. The hydraulic conductivity under positive pressure (90 kPa) was about twice as high as that of negative pressure (−90 kPa). However, the transport capacity and leached particle size under negative pressure were more significant than those under positive pressure, despite the same tendency of the porosity under the positive or negative pressure. This is an interesting and paradoxical finding, suggesting complexity accompanying the erosion and clogging under the nominal hydraulic gradient, where a higher concentration of the hydraulic gradient occurs under negative pressure. The percolation under negative pressure––a self-adaptive process––included three elements: (1) the colloidal transport of clay minerals; (2) colloid accumulation and clogging in the seepage channel close to the outlet; and (3) a gradient concentration close to the outlet. [ABSTRACT FROM AUTHOR]

AbstractThis study assesses the performance of cut-off walls in a vacuum preloading project at Lianyungang Port, China, to tackle challenges posed by ultra-soft reclaimed land. It investigates the walls’ impermeability and structural integrity by analyzing changes in hydraulic conductivity, in-situ lateral displacement, particle size distribution, and dry density both before and after vacuum suction. The introduction of a hydroxypropyl starch-based coupling agent (CA) in slurry reduces the hydraulic conductivity in the sandy matrix. Significant lateral displacement near the surface amplifies the likelihood of cracks, which lead to increased hydraulic conductivity. The cut-off wall is characterized by three zones: an erosion zone with large amount of fine particle mobility, a central effective zone with minimal deformation, and an outer tensile zone whose soil loosening leads to impermeability reduction. These findings provide valuable insights into the design and construction of cut-off walls for similar geotechnical applications and aid in forecasting the performance and lifespan of the cut-off wall. [ABSTRACT FROM AUTHOR]

Nanoindentation measurements were conducted to investigate the high-cycle response of 316L stainless steel in bending fatigue. Hardness variation owing to the gradient flexure stress amplitude for different curvatures was plotted along with the thickness and length, respectively. Scanning electron microscopy (SEM) was subsequently conducted to explore the deformation characteristics in multiple layers, which had cyclic gradient stress, on the cross-section of specimens. The nanoindentation results indicated that the cyclic hardening response of 316L stainless steel is correlated with the level of stress amplitude in the high-cycle fatigue (HCF) regime. Furthermore, an analytical model was proposed to clarify the relationship between nanohardness and stress amplitude. Finally, the evolution of damage accumulation due to irreversible plastic deformation is continuous during stress reduction up to the neighboring zone at the neutral surface of the flexure beam in some individual grains. [ABSTRACT FROM AUTHOR]