Effects of near-bed turbulence on microplastics fate and transport in streams

Quantifying the impact of hyporheic exchange is crucial for understanding the transport and fate of microplastics in streams. In this study, we conducted several Computational Fluid Dynamics (CFD) simulations to investigate near-bed turbulence and analyze vertical hyporheic exchange. Different arranged spheres were used to represent rough and permeable sediment beds in natural rivers. The velocities associated with vertical hyporheic flux and the gravitational force were compared to quantify the susceptibility of microplastics to hyporheic exchange. Four scenario cases representing different channel characteristics were studied and their effects on microplastics movements through hyporheic exchange were quantitatively studied. Results show that hyporheic exchange flow can significantly influence the fate and transport of microplastics of small and light-weighted microplastics. Under certain conditions, hyporheic exchange flow can dominate the behavior of microplastics with sizes up to around 800 μm. This dominance is particularly evident near the sediment-water interface, especially at the top layer of sediments. Higher bed porosity enhances the exchange of microplastics between water and sediment, while increased flow conditions extend the vertical exchange zone into deeper layers of the bed. Changes in the bedform lead to the most pronounced vertical hyporheic exchange, emphasizing the control of morphological features on microplastics transport. Furthermore, it is found that sweep-ejection events are prevailing near the bed surface, serving as a mechanism for microplastics transport in rivers. As moving from the water column to deeper layers in the sediment bed, there's a shift from sweeps dominance to ejections dominance, indicating changes of direction in microplastics movement at different locations.

How mobility restrictions policy and atmospheric conditions impacted air quality in the State of São Paulo during the COVID-19 outbreak

Mobility restrictions are among actions to prevent the spread of the COVID-19 pandemic and have been pointed as reasons for improving air quality, especially in large cities. However, it is crucial to assess the impact of atmospheric conditions on air quality and air pollutant dispersion in the face of the potential variability of all sources. In this study, the impact of mobility restrictions on the air quality was analyzed for the most populous Brazilian State, São Paulo, severely impacted by COVID-19. Ground-based air quality data (PM₁₀, PM_2.5, CO, SO₂, NO_x, NO₂, NO, and O₃) were used from 50 automatic air quality monitoring stations to evaluate the changes in concentrations before (January 01 - March 25) and during the partial quarantine (March 16 - June 30). Rainfall, fires, and daily cell phone mobility data were also used as supplementary information to the analyses. The Mann-Whitney U test was used to assess the heterogeneity of the air quality data during and before mobility restrictions. In general, the results demonstrated no substantial improvements in air quality for most of the pollutants when comparing before and during restrictions periods. Besides, when the analyzed period of 2020 is compared with the year 2019, there is no significant air quality improvement in the São Paulo State. However, special attention should be given to the Metropolitan Area of São Paulo (MASP), due to the vast population residing in this area and exposed to air pollution. The region reached an average decrease of 29% in CO, 28% in NO_x, 40% in NO, 19% in SO₂, 15% in PM_2.5, and 8% in PM₁₀ concentrations during the mobility restrictions period compared to the same period in 2019. The only pollutant that showed an increase in concentration was ozone, with a 20% increase compared to 2019 during the mobility restrictions period. Before the mobility restrictions period, the region reached an average decrease of 30% in CO, 39% in NO_x, 63% in NO, 12% in SO₂, 23% in PM_2.5, 18% in PM₁₀, and 16% in O₃ concentrations when compared to the same period in 2019. On the other hand, Cubatão, a highly industrialized area, showed statistically significant increases above 20% for most monitored pollutants in both periods of 2020 compared to 2019. This study reinforces that the main driving force of pollutant concentration variability is the dynamics of the atmosphere at its various time scales. An abnormal rainy season, with above average rainfall before the restrictions and below average after it, generated a scenario in which the probable significant reductions in emissions did not substantially affect the concentration of pollutants.

Establishing the Suitability of the Model for Prediction Across Scales for Global Retrospective Air Quality Modeling

The U.S. EPA is leveraging recent advances in meteorological modeling to construct an air quality modeling system to allow consistency from global to local scales. The Model for Prediction Across Scales-Atmosphere (MPAS-A or MPAS) has been developed by the National Center for Atmospheric Research (NCAR) as a global complement to the Weather Research and Forecasting model (WRF). Patterned after a regional coupled system with WRF, the Community Multiscale Air Quality (CMAQ) modeling system has been coupled within MPAS to explore global-to-local chemical transport modeling. Several options were implemented into MPAS for retrospective applications. Nudging-based data assimilation was added to support continuous simulations of past weather to minimize error growth that exists with a weather forecast configuration. The Pleim-Xiu land-surface model, the Asymmetric Convective Model 2 boundary layer scheme, and the Pleim surface layer scheme were added as the preferred options for retrospective air quality applications with WRF. Annual simulations were conducted using this EPA-enhanced MPAS configuration on two different mesh structures and compared against WRF. MPAS generally compares well with WRF over the conterminous United States. Errors in MPAS surface meteorology are comparable to WRF throughout the year. Precipitation statistics indicate MPAS performs slightly better than WRF. Solar radiation in MPAS is higher than WRF and measurements, suggesting fewer clouds in MPAS than WRF. Upper-air meteorology is well-simulated by MPAS, but errors are slightly higher than WRF. These comparisons lend confidence to use MPAS for retrospective air quality modeling and suggest ways it can be further improved in the future.

Increasing Freshwater Salinity Impacts Aerosolized Bacteria

Increases in the salt concentration of freshwater result in detrimental impacts on water quality and ecosystem biodiversity. Biodiversity effects include freshwater microbiota, as increasing salinity can induce shifts in the structure of native freshwater bacterial communities, which could disturb their role in mediating basal ecosystem services. Moreover, salinity affects the wave breaking and bubble-bursting mechanisms via which water-to-air dispersal of bacteria occurs. Given this dual effect of freshwater salinity on waterborne bacterial communities and their aerosolization mechanism, further effects on aerosolized bacterial diversity and abundance are anticipated. Cumulative salt additions in the freshwater-euhaline continuum (0-35 g/kg) were administered to a freshwater sample aerosolized inside a breaking wave analogue tank. Waterborne and corresponding airborne bacteria were sampled at each salinity treatment and later analyzed for diversity and abundance. Results demonstrated that the airborne bacterial community was significantly different (PERMANOVA; F_1,22 = 155.1, r² = 0.38, p < 0.001) from the waterborne community. The relative aerosolization factor (r-AF), defined as the air-to-water relative abundance ratio, revealed that different bacterial families exhibited either an enhanced (r-AF ≫ 1), neutral (r-AF ∼ 1), or diminished (r-AF ≪ 1) transfer to the aerosol phase throughout the salinization gradient. Going from freshwater to euhaline conditions, aerosolized bacterial abundance exhibited a nonmonotonic response with a maximum peak at lower oligohaline conditions (0.5-1 g/kg), a decline at higher oligohaline conditions (5 g/kg), and a moderate increase at polyhaline-euhaline conditions (15-35 g/kg). Our results demonstrate that increases in freshwater salinity are likely to influence the abundance and diversity of aerosolized bacteria. These shifts in aerosolized bacterial communities might have broader implications on public health by increasing exposure to airborne pathogens via inhalation. Impacts on regional climate, related to changes in biological ice-nucleating particles (INPs) emission from freshwater, are also expected.

Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science during the LAPSE-RATE Campaign

Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ±2.6∘C and 0.22 ±0.59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.

Influence of bromine and iodine chemistry on annual, seasonal, diurnal, and background ozone: CMAQ simulations over the Northern Hemisphere

Bromine and iodine chemistry has been updated in the Community Multiscale Air Quality (CMAQ) model to better capture the influence of natural emissions from the oceans on ozone concentrations. Annual simulations were performed using the hemispheric CMAQ model without and with bromine and iodine chemistry. Model results over the Northern Hemisphere show that including bromine and iodine chemistry in CMAQ not only reduces ozone concentrations within the marine boundary layer but also aloft and inland. Bromine and iodine chemistry reduces annual mean surface ozone over seawater by 25%, with lesser ozone reductions over land. The bromine and iodine chemistry decreases ozone concentration without changing the diurnal profile and is active throughout the year. However, it does not have a strong seasonal influence on ozone over the Northern Hemisphere. Model performance of CMAQ is improved by the bromine and iodine chemistry when compared to observations, especially at coastal sites and over seawater. Relative to bromine, iodine chemistry is approximately four times more effective in reducing ozone over seawater over the Northern Hemisphere (on an annual basis). Model results suggest that the chemistry modulates intercontinental transport and lowers the background ozone imported to the United States.

Coordinated Unmanned Aircraft System (UAS) and Ground-Based Weather Measurements to Predict Lagrangian Coherent Structures (LCSs)

Concentrations of airborne chemical and biological agents from a hazardous release are not spread uniformly. Instead, there are regions of higher concentration, in part due to local atmospheric flow conditions which can attract agents. We equipped a ground station and two rotary-wing unmanned aircraft systems (UASs) with ultrasonic anemometers. Flights reported here were conducted 10 to 15 m above ground level (AGL) at the Leach Airfield in the San Luis Valley, Colorado as part of the Lower Atmospheric Process Studies at Elevation-a Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) campaign in 2018. The ultrasonic anemometers were used to collect simultaneous measurements of wind speed, wind direction, and temperature in a fixed triangle pattern; each sensor was located at one apex of a triangle with ∼100 to 200 m on each side, depending on the experiment. A WRF-LES model was used to determine the wind field across the sampling domain. Data from the ground-based sensors and the two UASs were used to detect attracting regions (also known as Lagrangian Coherent Structures, or LCSs), which have the potential to transport high concentrations of agents. This unique framework for detection of high concentration regions is based on estimates of the horizontal wind gradient tensor. To our knowledge, our work represents the first direct measurement of an LCS indicator in the atmosphere using a team of sensors. Our ultimate goal is to use environmental data from swarms of sensors to drive transport models of hazardous agents that can lead to real-time proper decisions regarding rapid emergency responses. The integration of real-time data from unmanned assets, advanced mathematical techniques for transport analysis, and predictive models can help assist in emergency response decisions in the future.

Numerical analysis of pollutant dispersion around elongated buildings: an embedded large eddy simulation approach

High fidelity, scale-resolving numerical simulations of flow and pollutant dispersion around several elongated isolated buildings are presented in this paper. The embedded large eddy simulation (ELES) is used to model flow and concentration fields for six test cases with various source-building geometries. Specifically, the influence of building aspect ratio, wind direction, and source location is examined with these cases. Results obtained from the present ELES model are evaluated using available wind tunnel measurements, including those of streamwise and spanwise velocities, turbulent kinetic energy, and streamwise, lateral, and spanwise pollutant concentrations. Comparisons indicate that the ELES provides realistic representations of the flow and concentration fields observed in wind tunnel experiments, and captures several complex phenomena including the lateral shift and enhanced descent of the plume for rotated/elongated buildings. Furthermore, the ELES provides a means to study the advective and turbulent concentration fluxes, plume shapes, and geometry of vortical structures that is used to examine turbulent transport of pollutants around buildings. We investigate the enhancement of vertical and lateral plume spread as the building aspect ratio is increased. In addition, through the study of advective and turbulent concentration fluxes, we shed light on the physics behind higher ground-level concentrations observed for rotated buildings.

Enhancements to AERMOD's Building Downwash Algorithms based on Wind-Tunnel and Embedded-LES Modeling

Knowing the fate of effluent from an industrial stack is important for assessing its impact on human health. AERMOD is one of several Gaussian plume models containing algorithms to evaluate the effect of buildings on the movement of the effluent from a stack. The goal of this study is to improve AERMOD's ability to accurately model important and complex building downwash scenarios by incorporating knowledge gained from a recently completed series of wind tunnel studies and complementary large eddy simulations of flow and dispersion around simple structures for a variety of building dimensions, stack locations, stack heights, and wind angles. This study presents three modifications to the building downwash algorithm in AERMOD that improve the physical basis and internal consistency of the model, and one modification to AERMOD's building pre-processor to better represent elongated buildings in oblique winds. These modifications are demonstrated to improve the ability of AERMOD to model observed ground-level concentrations in the vicinity of a building for the variety of conditions examined in the wind tunnel and numerical studies.

Coupling of organic and inorganic aerosol systems and the effect on gas-particle partitioning in the southeastern US

Several models were used to describe the partitioning of ammonia, water, and organic compounds between the gas and particle phases for conditions in the southeastern US during summer 2013. Existing equilibrium models and frameworks were found to be sufficient, although additional improvements in terms of estimating pure-species vapor pressures are needed. Thermodynamic model predictions were consistent, to first order, with a molar ratio of ammonium to sulfate of approximately 1.6 to 1.8 (ratio of ammonium to 2× sulfate, RN/2S ≈ 0.8 to 0.9) with approximately 70% of total ammonia and ammonium (NH x ) in the particle. Southeastern Aerosol Research and Characterization Network (SEARCH) gas and aerosol and Southern Oxidant and Aerosol Study (SOAS) Monitor for AeRosols and Gases in Ambient air (MARGA) aerosol measurements were consistent with these conditions. CMAQv5.2 regional chemical transport model predictions did not reflect these conditions due to a factor of 3 overestimate of the nonvolatile cations. In addition, gas-phase ammonia was overestimated in the CMAQ model leading to an even lower fraction of total ammonia in the particle. Chemical Speciation Network (CSN) and aerosol mass spectrometer (AMS) measurements indicated less ammonium per sulfate than SEARCH and MARGA measurements and were inconsistent with thermodynamic model predictions. Organic compounds were predicted to be present to some extent in the same phase as inorganic constituents, modifying their activity and resulting in a decrease in [H+]air (H+ in μgm-3 air), increase in ammonia partitioning to the gas phase, and increase in pH compared to complete organic vs. inorganic liquid-liquid phase separation. In addition, accounting for nonideal mixing modified the pH such that a fully interactive inorganic-organic system had a pH roughly 0.7 units higher than predicted using traditional methods (pH = 1.5 vs. 0.7). Particle-phase interactions of organic and inorganic compounds were found to increase partitioning towards the particle phase (vs. gas phase) for highly oxygenated (O : C≥0.6) compounds including several isoprene-derived tracers as well as levoglu-cosan but decrease particle-phase partitioning for low O: C, monoterpene-derived species.

Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales - Atmosphere (version 4.0)

The Model for Prediction Across Scales - Atmosphere (MPAS-A) has been modified to allow four-dimensional data assimilation (FDDA) by the nudging of temperature, humidity, and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of "analysis nudging" developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1°×1° National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92-25km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2m temperature, 2m water vapor mixing ratio, and 10m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.

Improving the simulation of convective dust storms in regional-to-global models

Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional-to-global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies.

Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system

A new windblown dust emission treatment was incorporated in the Community Multiscale Air Quality (CMAQ) modeling system. This new model treatment has been built upon previously developed physics-based parameterization schemes from the literature. A distinct and novel feature of this scheme, however, is the incorporation of a newly developed dynamic relation for the surface roughness length relevant to small-scale dust generation processes. Through this implementation, the effect of nonerodible elements on the local flow acceleration, drag partitioning, and surface coverage protection is modeled in a physically based and consistent manner. Careful attention is paid in integrating the new windblown dust treatment in the CMAQ model to ensure that the required input parameters are correctly configured. To test the performance of the new dust module in CMAQ, the entire year 2011 is simulated for the continental United States, with particular emphasis on the southwestern United States (SWUS) where windblown dust concentrations are relatively large. Overall, the model shows good performance with the daily mean bias of soil concentrations fluctuating in the range of ±1 μg m-3 for the entire year. Springtime soil concentrations are in quite good agreement (normalized mean bias of 8.3%) with observations, while moderate to high underestimation of soil concentration is seen in the summertime. The latter is attributed to the issue of representing the convective dust storms in summertime. Evaluations against observations for seven elevated dust events in the SWUS indicate that the new windblown dust treatment is capable of capturing spatial and temporal characteristics of dust outbreaks.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O₃ mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O₃ mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O₃ mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.

Hepatocyte growth factor/scatter factor induces a variety of tissue-specific morphogenic programs in epithelial cells

Hepatocyte growth factor/scatter factor (HGF/SF) is the mesenchymal ligand of the epithelial tyrosine kinase receptor c-Met. In vitro, HGF/SF has morphogenic properties, e.g., induces kidney epithelial cells to form branching ducts in collagen gels. Mutation of the HGF/SF gene in mice results in embryonic lethality due to severe liver and placenta defects. Here, we have evaluated the morphogenic activity of HGF/SF with a large variety of epithelial cells grown in three-dimensional collagen matrices. We found that HGF/SF induces SW 1222 colon carcinoma cells to form crypt-like structures. In these organoids, cells exhibit apical/basolateral polarity and build a well-developed brush border towards the lumen. Capan 2 pancreas carcinoma cells, upon addition of HGF/SF, develop large hollow spheroids lined with a tight layer of polarized cells. Collagen inside the cysts is digested and the cells show features of pancreatic ducts. HGF/SF induces EpH4 mammary epithelial cells to form long branches with end-buds that resemble developing mammary ducts. pRNS-1-1 prostate epithelial cells in the presence of HGF/SF develop long ducts with distal branching as found in the prostate. Finally, HGF/SF simulates alveolar differentiation in LX-1 lung carcinoma cells. Expression of transfected HGF/SF cDNA in LX-1 lung carcinoma and EpH4 mammary epithelial cells induce morphogenesis in an autocrine manner. In the cell lines tested, HGF/SF activated the Met receptor by phosphorylation of tyrosine residues. These data show that HGF/SF induces intrinsic, tissue-specific morphogenic activities in a wide variety of epithelial cells. Apparently, HGF/SF triggers respective endogenous programs and is thus an inductive, not an instructive, mesenchymal effector for epithelial morphogenesis.