Transport Model Research Articles

Simulating PFAS transport in effluent-irrigated farmland using PRZM5, LEACHM, and HYDRUS-1D models.

Application of wastewater effluent to agricultural lands can serve as a sustainable approach to meet irrigation and nutrient needs for crop production. While nitrogen and phosphorous loadings can be effectively managed, concerns have been raised regarding the fate of emerging contaminants, including per- and polyfluoroalkyl substances (PFAS), which are widely detected in wastewater effluent. The objective of this paper was to evaluate the ability of three unsaturated flow and transport models, Pesticide Root Zone Model 5 (PRZM5), LEACHM, and HYDRUS-1D, to predict the distribution of PFAS in the soil profile at the Pennsylvania State University Living Filter site, which has received daily wastewater effluent applications for several decades. The models were modified to include adsorption at the air-water interface (AWI), which has been shown to be an important factor governing PFAS transport and phase distribution in the vadose zone. Simulations showed that PRZM5 did not accurately reproduce the observed perfluorooctanesulfonic acid (PFOS) behavior, which was attributed to the "tipping bucket" approach used for water flow that results in the disappearance of AWI during water flow. In contrast, both LEACHM and HYDRUS-1D captured the observed retention of PFOS and perfluorooctanoic acid (PFOA) over a 50-year simulation period. Due to differences in the approach used to calculate the AWI area, LEACHM predicted greater accumulation of PFOS and PFOA at the AWI compared to HYDRUS-1D. These findings indicate that mathematical models that directly account for unsaturated water flow and adsorption at the AWI are able to provide reasonable predictions of long-term PFAS leaching resulting from land application of wastewater effluent.

Transported Entropy of Ions and Peltier Coefficients in 8YSZ and 10Sc1CeSZ Electrolytes for Solid Oxide Cells.

In this study, the transported entropy of ions for 8YSZ and 10Sc1CeSZ electrolytes was experimentally determined to enable precise modeling of heat transport in solid oxide cells (SOCs). The Peltier coefficient, crucial for thermal management, was directly calculated, highlighting reversible heat transport effects in the cell. While data for 8YSZ are available in the literature, providing a basis for comparison, the results for 10Sc1CeSZ show slightly smaller Seebeck coefficients but higher transported ion entropies. Specifically, at 700°C and an oxygen partial pressure of pO2=0.21 bar, values of SO2-*=52±10 J/K·F for 10Sc1CeSZ and SO2-*=48±9 J/K·F for 8YSZ were obtained. The transported entropy was also validated through theoretical calculations and showed minimal deviations when comparing different cell operation modes (O2||O2-||O2 and H2, H2O||O2-||O2). The influence of the transported entropy of the ions on the total heat generation and the partial heat generation at the electrodes is shown. The temperature has the greatest influence on heat generation, whereby the ion entropy also plays a role. Finally, the Peltier coefficients of 8YSZ for all homogeneous phases agree with the literature values.

Assessing field scale spatiotemporal heterogeneity in salinity dynamics using aerial data assimilation

Soil moisture and salinity shifts within the root zone can significantly alter crop yields. Thus, spatiotemporal dynamics of these parameters are essential for precision crop management. Airborne or spaceborne earth observation methods based on vegetation and soil observations have sometimes been used with limited success to indirectly understand parameters like soil salinity. These datasets lack spatiotemporal resolution to discern field-scale heterogeneities, and estimates’ accuracy is poor. A Metropolis-Hasting Markov Chain-Monte Carlo (MCMC) based method was developed to estimate field-scale soil salinity by assimilating estimated evapotranspiration (ET) data obtained from aerial canopy temperature sensing with ET outputs from a one-dimensional soil-water transport model. By aligning the two estimated ET values, we inferred anticipated soil salinity levels in a mature pecan orchard (28,951 m2). Our results aligned closely with in-situ measurements with a spatial cross-correlation more than 0.86 and highlighted the expected heterogeneities and nonlinearities. This research offers an approach to refine the current state-of-the-art crop models by accounting for field scale heterogeneities using remotely sensed data. This assimilation method will pave the way for a more inclusive agricultural system modeling that can infer critical but hard-to-measure soil properties from easier-to-obtain remotely sensed datasets. Though this paper concentrates on aerial observations, we anticipate similar methods can be used for satellite-based imagery, especially those with high spatial, temporal, and spectral resolutions.

Physics-based prediction of moisture-capture properties of hydrogels

Moisture-capturing materials can enable potentially game-changing energy-water technologies such as atmospheric water production, heat storage, and passive cooling. Hydrogel composites recently emerged as outstanding moisture-capturing materials due to their low cost, high affinity for humidity, and design versatility. Despite extensive efforts to experimentally explore the large design space of hydrogels for high-performance moisture capture, there is a critical knowledge gap on our understanding behind the moisture-capture properties of these materials. This missing understanding hinders the fast development of novel hydrogels, material performance enhancements, and device-level optimization. In this work, we combine synthesis and characterization of hydrogel-salt composites to develop and validate a theoretical description that bridges this knowledge gap. Starting from a thermodynamic description of hydrogel-salt composites, we develop models that accurately capture experimentally measured moisture uptakes and sorption enthalpies. We also develop mass transport models that precisely reproduce the dynamic absorption and desorption of moisture into hydrogel-salt composites. Altogether, these results demonstrate the main variables that dominate moisture-capturing properties, showing a negligible role of the polymer in the material performance under all considered cases. Our insights guide the synthesis of next-generation humidity-capturing hydrogels and enable their system-level optimization in ways previously unattainable for critical water-energy applications.

Optimization of a Groundwater Pollution Monitoring Well Network Using a Backpropagation Neural Network-Based Model

Selecting representative groundwater monitoring wells in polluted areas is crucial to comprehensively assess groundwater pollution, thereby ensuring effective groundwater remediation. However, numerous factors can affect the effectiveness of groundwater monitoring well network optimizations. A local sensitivity analysis method was used in this study to analyze the hydrogeological parameters of a simulation groundwater solute transport model. The results showed a strong effect of longitudinal dispersion and transverse dispersion on the output results of the simulation model, and a good fit between the backpropagation neural network (BPNN)-based alternative model’s results and those obtained using the solute transport simulation model, accurately reflecting the input and output relationship of the simulation model. The optimized groundwater monitoring layout scheme consisted of four groundwater monitoring wells, namely no. 7, no. 16, no. 23, and no. 24. These wells resulted in a groundwater fluoride pollution rate of 98.44%, which was substantially higher than that obtained using the random layout scheme. In addition, statistical analysis of the fluoride groundwater pollution results obtained using the Monte Carlo random simulation highlighted continuous and high groundwater fluoride levels in the second and third pollution sources and their downstream groundwater. Therefore, more attention should be devoted to these sources to ensure the effective remediation of groundwater pollution in the study area.

Hard jet substructure in a multistage approach

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the framework. The details of the multistage model and the various parameter choices are described in []. A novel feature of this model is the presence of two stages of jet modification: a high-virtuality phase [modeled using the modular all twist transverse-scattering elastic-drag and radiation model ()], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model ()], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction zg and angular separation rg distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider. Published by the American Physical Society 2024

ACEIC: a comprehensive anthropogenic chlorine emission inventory for China

Abstract. Chlorine species play a crucial role as precursors to Cl radicals, which can significantly impact the atmospheric oxidation capacity and influence the levels of trace gases related to climate and air quality. Several studies have established a chlorine emission inventory in China in recent years, but the emission remains uncertain and requires further investigation. The Anthropogenic Chlorine Emission Inventory for China (ACEIC) was the first chlorine emission inventory for China based on local data developed in our previous study, which only includes the emissions from coal combustion and waste incineration. In this study, we updated this inventory to include data for a more recent year (2019) and expanded the range of species considered (HCl, fine particulate Cl−, Cl2, and hypochlorous acid (HOCl)) and the number of anthropogenic sources (41 specific sources). Compared with previous studies, this updated inventory considered more anthropogenic sources, used more localized emission factors, and adopted more refined estimation methods. The total emissions of HCl, fine particulate Cl−, Cl2, and HOCl in mainland China for the year 2019 were estimated to be 361 (−18 % to 27 %), 174 (−27 % to 59 %), 18 (−10 % to 15 %), and 79 (−12 % to 18 %) Gg, respectively. To facilitate analysis, we aggregated the chlorine emissions from various sources into five economic sectors: power, industry, residential, agriculture, and biomass burning. HCl emissions were primarily derived from the industry (43 %), biomass burning (38 %), and residential (13 %) sectors. The biomass burning and industry sectors accounted for 74 % and 19 % of the fine particulate Cl− emissions, respectively. Residential and industry sectors contributed 61 % and 29 % of the total Cl2 emissions. HOCl emissions were predominantly from the residential sector, constituting 90 % of the total emissions. Notably, the usage of chlorine-containing disinfectants was identified as the most significant source of Cl2 and HOCl emissions in the residential sector. Geographically, regions with high HCl and fine particulate Cl− emissions were found in the North China Plain, northeastern China, central China, and the Yangtze River Delta, whereas the Pearl River Delta, Yangtze River Delta, and Beijing–Tianjin–Hebei regions exhibited elevated levels of Cl2 and HOCl emissions. Regarding monthly variation, emissions of HCl and fine particulate Cl− were higher during early spring (February to April) and winter (December to January) due to intensified agricultural activities, while Cl2 and HOCl emissions were higher in the summer months due to increased demand for water disinfection. We incorporated this emission inventory into the chemical transport model and found the simulated concentrations of chlorine species agreed reasonably well with the observations, which suggested the relatively faithful estimations of their emissions. This updated inventory contributes to a better understanding of anthropogenic sources of chlorine species and can aid in the formulation of emission control strategies to mitigate secondary pollution in China.

Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth

Abstract. Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to help fill these gaps worldwide when augmented with a modeled PM2.5–AOD relationship. This study aims to understand the spatial pattern and driving factors of this relationship by examining η (PM2.5AOD) using both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6870 ground-based PM2.5 measurement sites and satellite-retrieved AOD. The global chemical transport model GEOS-Chem, in its high-performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η value of 96–98 µg m−3, with regional values ranging from 59.8 µg m−3 in North America to more than 190 µg m−3 in Africa. The highest η value is found in arid regions, where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η values are found over regions distant from strong aerosol sources. The spatial correlation of observed η values with meteorological fields, aerosol vertical profiles, and aerosol chemical composition reveals that spatial variation in η is strongly influenced by aerosol composition and aerosol vertical profiles. Sensitivity tests with globally uniform parameters quantify the effects of aerosol composition and aerosol vertical profiles on spatial variability in η, exhibiting a population-weighted mean difference in aerosol composition of 12.3 µg m−3, which reflects the determinant effects of composition on aerosol hygroscopicity and aerosol optical properties, and a population-weighted mean difference in the aerosol vertical profile of 8.4 µg m−3, which reflects spatial variation in the column–surface relationship.

A bio‐reactive transport model for biomethanation in hydrogen underground storage sites

AbstractUnderground biomethanation, which relies on the subsurface microbial activity to convert hydrogen and carbon dioxide into methane, is a promising approach to support carbon capture, utilization, and storage technology. The process involves injecting hydrogen with captured CO2 into depleted oil and gas reservoirs or aquifers colonized by hydrogenotrophic methanogens that can convert these two substrates into methane. Despite the attractiveness of this technology, there are still uncertainties about the efficiency of the conversion process, particularly the impact of microbial parameters. To investigate the efficiency of the hydrogen conversion process, we relied on a bio‐reactive transport model that can mimic microbial growth and decay, consumption of substrates, and transport of reactants and products. It was found that the methane concentration peaks near the injection well when the hydrogen fraction is in the range of 75% to 80% of the injected gas composition. In addition, a noticeable hydrogen sulfide concentration can be produced due to sulfide ions in the brine. Using the Kozeny‐Carman relation, an attempt was made to correlate microbial growth with reduced porosity and permeability. It was then revealed that substrate consumption by microbes leads to a drastic increase in the microbial population in the subsurface, which can reduce the petrophysical properties of the reservoir, especially in the near wellbore area. The results obtained from a series of parametric analyses showed that the hydrogen concentration in the injected gas, pressure, well spacing, and injection rate are some of the most important parameters contributing to the biomethanation process. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Quantifying NO x Emission Sources in Houston, Texas Using Remote Sensing Aircraft Measurements and Source Apportionment Regression Models.

Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NO x emissions to improve ozone and PM2.5 air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and regression models to investigate NO x emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO2 was shown to be close to the "truth" for validating column NO2 in model simulations. Column NO2 from CAMx was substantially low biased compared to Pandora (-20%) and GCAS measurements (-31%), suggesting an underestimate of local NO x emissions. We applied a flux divergence method to the GCAS and CAMx data to distinguish the linear shape of major highways and identify NO2 underestimates at highway locations. Using a multiple linear regression (MLR) model, we isolated on-road, railyard, and "other" NO x emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NO x emissions. Based on the MLR, we modified on-road and shipping NO x emissions in a new CAMx simulation and increased the background NO2, and better agreement was found with GCAS measurements: bias improved from -31% to -10% and r2 improved from 0.78 to 0.80. This study outlines how remote sensing data, including fine spatial information from newer geostationary instruments, can be used in concert with chemical transport models to provide actionable information for air quality managers to identify further opportunities to reduce NO x emissions.

Development of a Virtual Chemistry Reaction Mechanism for H2/CH4 Turbulent Combustion Modelling

Abstract The identification of the combustion model is always guided by the compromise between accuracy and computational cost. While species transport models offer accuracy, their computational cost requirement can become prohibitive, especially when de-aling with higher-order hydrocarbon fuels. To mitigate this, the virtual mechanism definition aims to optimize the predictivity minimizing the amount of information to be transported. Thereby the virtual mechanism leverages fictitious species and a few step reactions whose parameters calibration is performed with a genetic algorithm. This work outlines the procedure for the derivation of a virtual reaction mechanism for the study of lean H2/CH4 fuel mixtures with 60% of H2 content (by vol.). These conditions require an adequate characterization of the virtual species differential diffusion oriented to reconstruct the flame sensitivity toward the aerodynamic stretch. After the mechanism derivation, its predictivity has been validated on a swirl-stabilized perfectly premixed turbulent test case. The artificially thickened flame model has been adopted to allow the flame front discretization on an large eddy simulation (LES) grid and to model the turbulence chemistry interaction. The numerical results show a very good agreement with the experimental optical measurements confirming the effectiveness of this approach for predicting the H2/CH4 blend.

Channel-facilitated transport under resetting dynamics.

The transport of particles through channels holds immense significance in physics, chemistry, and biological sciences. For instance, the motion of solutes through biological membranes is facilitated by specialized proteins that create water-filled channels. Valuable insights can be obtained by studying the transition paths of particles through a channel and gathering information on their lifetimes inside the channel as well as their exit probabilities. In a similar vein, we consider a one-dimensional model of channel-facilitated transport where a diffusive particle is subject to attractive interactions with the walls of the channel. We study the statistics of conditional and unconditional escape times in the presence of resetting-an intermittent dynamics that brings the particle back to its initial coordinate stochastically. We determine analytically the physical conditions under which such a resetting mechanism becomes beneficial for the faster escape of the particles from the channel, thus enhancing transport. Our theory has been verified with the aid of Brownian dynamics simulations for various interaction strengths and extents. The overall results presented herein highlight the scope of resetting-based strategies to be universally promising for complex transport processes of single or long molecules through biological membranes.

National and transboundary contributions to surface ozone concentration across European countries

Tropospheric ozone impacts health, climate, and ecosystems. Effective ozone mitigation policies are challenged by limited quantitative understanding of national versus transboundary contributions to surface ozone. This study uses a chemical transport model with a source apportionment algorithm to analyze ozone contributions across Europe from 2015 to 2017 during peak ozone season. We quantify country-level ozone production and imported ozone, distinguishing contributions from 35 European countries, neighboring countries, seas, and hemispheric influences. Results show substantial contributions from outside the 35 European countries, with hemispheric contributions playing a significant role. European contributions are crucial during high ozone episodes, especially from Germany, France, Italy, the UK, Poland, and Spain. Spain, northern Italy, and northwest France are identified as areas where national precursor reductions would be more effective in improving national air quality. Furthermore, 25 of the 35 European countries studied are net importers of cumulative ozone mass, with the Netherlands, Belgium, and the UK acting as major exporters. These findings highlight the need for comprehensive air quality policies and cross-border cooperation.

Power handling in a highly-radiative negative triangularity pilot plant

Abstract This work explores detailed power handling solutions for a class of high-field, highly-radiative negative triangularity (NT) reactors based around the MANTA concept [Rutherford et al. (2024)]. The divertor design is kept as simple as possible, opting for a standard divertor with standard leg length. FreeGS is used to create an equilibrium for the boundary region, prioritizing a short outer leg length of only ∼50 cm (∼40% of the minor radius). The UEDGE code package is used for the boundary plasma solution, to track plasma temperatures and fluxes to the divertor targets. It is found that for PSOL = 25 MW and nsep = 0.96 × 1020 m−3, conditions consistent with initial core transport modeling, little additional power mitigation is necessary. For a fixed impurity fraction of just 0.13\% Ne in the plasma, the peak heat flux density at the more heavily loaded outer targets falls to 7.8 MW/m2, while the electron temperature, Te, remains just under 5 eV. Scans around the parameter space reveal that even at densities lower than in the primary operating scenario, PSOL can be increased up to 50 MW, so long as a slightly higher fraction of extrinsic radiator is used. With less than 1% neon (Ne) impurity content, the divertor still experiences less than 10 MW/m2 at the outer target. Design of the plasma-facing components includes a close-fitting vacuum vessel with a tungsten inner surface as well as FLiBe-carrying cooling channels fashioned into the VV wall directly behind the divertor targets. For the seeded heat flux profile, Ansys Fluent heat transfer simulations estimate that the outer target temperature remains at just below 1550◦C. Initial scoping of advanced divertor designs shows that for an X-divertor, detachment of the outer target becomes much simpler, and plasma fluxes to the targets drop considerably with only 0.01% Ne content.

On the charge transport models in high intrinsic defect doped transparent and conducting p-type Cu–Cr–O delafossite thin films

Abstract In contemporary optoelectronic devices, the transparent conductive oxides commonly demonstrate n-type conduction characteristics, with indium-doped tin oxide emerging as a prominent example. However, in applications involving fully invisible electronics that necessitate p-type conductive oxides, there exists a demand for a quintessential material possessing properties akin to its n-type counterpart. CuCrO2, a delafossite semiconductor based on copper, presently represents a notable compromise between optical and electrical attributes within the realm of p-type semiconductors. Despite numerous studies focusing on this material, the charge carrier transport regime within the material remains unclear. The commonly reported hole transport mechanism in CuCrO2 is the small polaron model. However, this work evidences several contradictions when this transport mechanism is assumed. Using the same methodology as previous studies, we investigated the holes’ transport mechanism by the means of the measurement of electrical conductivity and the Seebeck coefficient at varying temperatures. Different charge transport models in high intrinsic defect doped CuCrO2 thin films are explored: small polarons, grain boundaries scattering in degenerate semiconductors, and variable range hopping with nearest neighbor hopping. The small polaron model does not provide conclusive results within the temperature range analyzed. Interestingly, no specific hole transport mechanism can be undoubtedly selected. The limitations of the models highlight the influence of peculiar defects within CuCrO2 thin films on the hole transport mechanism, particularly the adoption of well-ordered copper vacancies columns.

Temperature dependence of spin transport behavior in Heusler alloy CPP-GMR

In this study, we investigate the effect of temperature on the performance of a read sensor by utilizing an atomistic model coupled with a spin transport model. Specifically, we study the temperature dependence of spin transport behavior and MR outputs in a Co2FeAl0.5Si0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Co}_2\ ext {FeAl}_{0.5}\ ext {Si}_{0.5}$$\\end{document} (CFAS)(5nm)/Cu(5nm)/CFAS(5nm) trilayer with diffusive interfaces. Initially, the two-channel model of spin-dependent resistivity is used to calculate the temperature dependence of spin transport parameters which serves as essential input for the spin accumulation model. Our findings demonstrate that as the temperature increases, the spin transport parameters and magnetic properties decrease due to the influence of thermal fluctuation. At a critical temperature, where the ferromagnet transitions to a paramagnetic state, we observe zero spin polarization. Furthermore, at elevated temperatures, the spin accumulation deviates from the equilibrium value, leading to a reduction in the magnitude of spin current and spin transport parameters due to thermal effects. As a consequence, the MR ratio decreases from 65% to 20% with increasing temperature from 0 to 400 K. Our results are consistent with previous experimental measurements. This study allows to deeply understand the physical mechanism in the reader stack which can significantly benefit reader design.

In situ optical measurement of particles in sediment plumes generated by a pre-prototype polymetallic nodule collector

This study presents in situ, high-resolution optical measurements of particle size distributions (PSD) within sediment plumes generated by a pre-prototype deep seabed nodule collector vehicle operating in the abyssal Pacific Ocean. These measurements were obtained using a cutting-edge instrument, the LISST-RTSSV sensor. The data collected in situ reveal marked differences compared to previously reported laboratory-based, ex situ measurements. The grain size and other key particle shape characteristics are found to be dependent on multiple factors, including the collector vehicle maneuvers, the time elapsed following sediment discharge, and the complex hydrodynamic processes that generate the sediment in suspension. Significantly, the PSD from a highly complex succession of straight-line maneuvers converges to that of the canonical case of a simple straight-line driving maneuver within a timescale of ten minutes. Our results underscore the importance of parameterizing sediment plume transport models based on well-informed, comprehensive PSDs of detrained suspended sediment measured in situ at adequate timescales and in regions no longer strongly influenced by active and complex hydrodynamic processes.

A mixed layer height parameterization in a 3-D chemical transport model: Implications for gas and aerosol simulations

Vertical mixing within the planetary boundary layer (PBL) is crucial for determining surface-level pollutant concentrations. However, standard PBL schemes in chemical transport models (CTMs) often fail to adequately define the upper bounds of vertical mixing, particularly at night. This limitation frequently results in overestimated nocturnal concentrations of pollutants near the surface. To address this issue, we propose a parameterization of mixed layer height (MLH) derived from the Yonsei University (YSU) PBL scheme and thoroughly evaluate it by comparing simulations with various observations. We utilized the Weather Research and Forecasting model coupled with GEOS-Chem (WRF-GC) to simulate gas and aerosol distributions over South Korea during the Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaign in 2021. The WRF-GC simulations incorporating the MLH parameterization improved the excessive titration of O3 and the overproduction of HNO3 and NO3− in the model. Consequently, the model performances in gaseous and aerosol simulations showed a better agreement with observations, with changes in normalized mean biases (NMBs) of NOX (from 50 % to −27 %), O3 (from −49 % to −28 %), NO3− (from 126 % to 91 %), NH4+ (from 113 % to 85 %), BC (from 322 % to 135 %), and PM2.5 (from 58 % to 28 %).

Numerical Investigation of the Sediment Load Exchange between a Coastal Mud Bank and Its Neighbouring Estuary

Muddy coastlines cover much of the world’s shores, yet studies on the interaction between mud-affected coasts and estuaries are limited. This study focuses on the Mahury River estuary and its interaction with the muddy coast of the Guianas, primarily fed by the Amazon. A coupled wave–current–sediment transport model is developed to analyze the sediment exchange in an environment with strong interactions between the waves and the fluid mud. Simulations explore how seasonal changes in waves, mud availability, and tides affect sediment fluxes. The main processes influencing suspended particulate matter (SPM) and sediment transport are well emulated, notwithstanding the complexity of the ambient muddy environment. The results show that during the rainy season, strong wave damping and wave refraction zones cause high SPM resuspension in shallow waters (<5 m). In contrast, during the dry season, wave influence shifts to the estuary mouth. Erosion and sedimentation patterns indicate that ebb currents associated with neap tides during the rainy season represent the most favourable conditions for the alongshore migration of mud banks. Neaptide ebb currents also contribute to sedimentation during the dry season but only in the estuary mouth and the nearby coastal area. The abundance of mud leads to an extension of the estuary’s intertidal area during the dry season.

Prenatal Exposure to Source-Specific Fine Particulate Matter and Autism Spectrum Disorder.

In this study, associations between prenatal exposure to fine particulate matter (PM2.5) from 9 sources and development of autism spectrum disorder (ASD) were assessed in a population-based retrospective pregnancy cohort in southern California. The cohort included 318,750 mother-child singleton pairs. ASD cases (N = 4559) were identified by ICD codes. Source-specific PM2.5 concentrations were estimated from a chemical transport model with a 4 × 4 km2 resolution and assigned to maternal pregnancy residential addresses. Cox proportional hazard models were used to estimate the hazard ratios (HR) of ASD development for each individual source. We also adjusted for total PM2.5 mass and in a separate model for all other sources simultaneously. Increased ASD risk was observed with on-road gasoline (HR [CI]: 1.18 [1.13, 1.24]), off-road gasoline (1.15 [1.12, 1.19]), off-road diesel (1.08 [1.05, 1.10]), food cooking (1.05 [1.02, 1.08]), aircraft (1.04 [1.01, 1.06]), and natural gas combustion (1.09 [1.06, 1.11]), each scaled to standard deviation increases in concentration. On-road gasoline and off-road gasoline were robust for other pollutant groups. PM2.5 emitted from different sources may have different impacts on ASD. The results also identify PM source mixtures for toxicological investigations that may provide evidence for future public health policies.