Quality Model Research Articles

Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses

Abstract. Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; https://doi.org/10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement.

Hybrid modeling of an ultracentrifugation process for separation of full and empty adeno-associated virus particles

Ultracentrifugation is an attractive method for separating full and empty capsids, exploiting their density difference. Changes of the serotype/capsid, density of loading material, or the genetic information contained in the adeno-associated viruses (AAVs) require the adaptation of the harvesting parameters and the density gradient loaded onto the centrifuge. To streamline these adaptations, a mathematical model could support the design and testing of operating conditions.Here, hybrid models, which combine empirical functions with artificial neural networks, are proposed to describe the separation of full and empty capsids as a function of material and operational parameters, i.e., the harvest model. In addition, critical quality attributes are estimated by a quality model which is operating on top of the harvest model. The performance of these models was evaluated using test data and two additional blind runs. Also, a “what-if” analysis was conducted to investigate whether the models’ predictions align with expectations.It is concluded that the models are sufficiently accurate to support the design of operating conditions, though the accuracy and applicability of the models can further be increased by training them on more specific data with higher variability.

Systematic Review of Air Pollution in Morocco: Status, Impacts, and Future Directions

AbstractDespite the recognition of the importance of air pollution in Morocco, current scientific studies are predominantly descriptive and limited. This systematic review aims to provide a comprehensive overview of air pollution in Morocco, delineating the research status and identifying gaps. Searches conducted across PubMed, Google Scholar, and Scopus from 2016 to May 2023 yielded over 3200 articles. From these, 64 original studies are deemed eligible and selected, focusing on air pollution in Moroccan regions or cities. Review articles and those dealing with indoor air quality are excluded, with no restriction on specific pollutant. Research highlights road traffic as the primary contributor to air pollution in Morocco, followed by industrial activities. Exposure to air pollutants is associated with adverse health effects, including mortality, lung disease, respiratory disorders, asthma attacks, and cardiovascular disease. Pollution levels vary among cities, with Casablanca, Kenitra, and Essaouira consistently facing higher pollution levels due to transportation and industrial emissions. Altitude also influences air quality, with higher‐altitude areas generally experiencing lower pollutant concentrations. The review highlights the need for extensive research on air pollution's health and economic effects, improved air quality modeling, and broader pollutant focus beyond just regulated ones.

Analyses and Simulations of PM2.5 Pollution Characteristics under the Influence of the New Year’s Day Effects in China

Regional haze often occurs after the New Year holiday. To explore the characteristics of PM2.5 pollutions under the influence of the New Year’s Day effect, this study analyzed the spatiotemporal changes relating to PM2.5 during and around the New Year’s Day holiday in China from 2015 to 2022, and used the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to study the effects of human activities and meteorological factors on PM2.5 pollutions, as well as the differences in the contributions of different industries to PM2.5 pollutions. The results show that for the entire study period (i.e., before, during, and after the New Year’s Day holiday) from 2015 to 2022, the average concentrations of PM2.5 in China decreased by 41.9% overall. In 2019~2022, the New Year’s Day effect was significant, meaning that the average concentrations of PM2.5 increased by 18.9~46.8 μg/m3 from before to after the New Year’s Day holiday, with its peak occurring (64.3~74.9 μg/m3) after the holiday. In terms of spatial differences, the average concentrations of PM2.5 were higher in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and central China. Moreover, the Beijing–Tianjin–Hebei region and its surrounding areas, the Chengdu–Chongqing region, the Fenwei Plain, and the middle reaches of the Yangtze River region were greatly affected by the New Year’s Day effect. Human activities led to higher increases in PM2.5 in Henan, Hubei, Hebei, and Anhui on 3 and 4 January 2022. If the haze was accompanied by cloudy days or weak precipitation, the accumulation of surface water vapor and atmospheric aerosols further increased the possibility of heavy pollution. It was found that, for the entire study period, PM2.5 generated by residential sources contributed the vast majority (60~100 μg/m3) of PM2.5 concentrations, and that the main industry sources that caused changes in time distributions were industrial and transportation sources.

Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec

Abstract. Air pollution is a major health hazard, and while air quality overall has been improving in industrialized nations, pollution is still a major economic and public health issue, with some species, such as ozone (O3), still exceeding the standards set by governing agencies. Chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution both at local and regional scales. In this study, the Polair3D v1.11 CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model's capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales. The simulation by the model included three nested domains, at horizontal resolutions of 9 km by 9 km and 3 km by 3 km, as well as two 1 km by 1 km domains covering the cities of Montréal and Québec. We find that the model captures the spatial variability and seasonal effects and, to a lesser extent, the hour-by-hour or day-to-day temporal variability for a fixed location. The model at both the 3 km and the 1 km resolution struggled to capture high-frequency temporal variability and showed large variabilities in correlation and bias from site to site. When comparing the biases and correlation at a site-wide scale, the 3 km domain showed slightly higher correlation for carbon monoxide (CO), nitrogen dioxide (NO2), and nitric oxide (NO), while ozone (O3), sulfur dioxide (SO2), and PM2.5 showed slight increases in correlation at the 1 km domain. The performance of the Polair3D model was in line with other models over Canada and comparable to Polair3D's performance over Europe.

Nitrogen dioxide exposure, health outcomes, and associated demographic disparities due to gas and propane combustion by U.S. stoves.

Gas and propane stoves emit nitrogen dioxide (NO2) pollution indoors, but the exposures of different U.S. demographic groups are unknown. We estimate NO2 exposure and health consequences using emissions and concentration measurements from >100 homes, a room-specific indoor air quality model, epidemiological risk parameters, and statistical sampling of housing characteristics and occupant behavior. Gas and propane stoves increase long-term NO2 exposure 4.0 parts per billion volume on average across the United States, 75% of the World Health Organization's exposure guideline. This increased exposure likely causes ~50,000 cases of current pediatric asthma from long-term NO2 exposure alone. Short-term NO2 exposure from typical gas stove use frequently exceeds both World Health Organization and U.S. Environmental Protection Agency benchmarks. People living in residences <800 ft2 in size incur four times more long-term NO2 exposure than people in residences >3000 ft2 in size; American Indian/Alaska Native and Black and Hispanic/Latino households incur 60 and 20% more NO2 exposure, respectively, than the national average.

Quality of services in health education nurse-led clinics: an Iranian service providers and service recipients experience

BackgroundPatient education is a vital role of nurses in nurse-led clinics(NLCs). Since 2011, independent NLCs entitled health education Nurse-led clinics(HENLCs) have been established in Iran. In order for this newly developed service to be able to perform perfectly in implementation and evaluation, it should be explained based on one of the quality evaluation models. The objective of the study was to determine the dimension of service quality in HENLCs based on service providers’ and service recipients’ experience.MethodsThis research is a qualitative study of directed content analysis type conducted between May and November 2020. Twenty-nine participants who had rich experiences in the patient education in HENLCs were interviewed in this study. Asarroodi et al.’s (2018) qualitative content analysis method was used for data analysis, and MaxQDA software was used for data management. We used credibility, dependability, and Confirmability to confirm the trustworthiness of the study’s findings.ResultsIn this study service providers including managers, policymakers, decision-makers, nurses, physicians, and service recipients including patients and families participated. Seven generic categories, including (1) a competent and self-motivated nurse educator, (2) an easily accessible and comfortable environment, (3) informational-educational materials and health education equipment, (4) motivational facilities, (5) access to the health education support team, (6) organizational communication supporting the education process, and (7) receiving the patient education fee, constituted the main category of structure. Five generic categories, including (1) assessment and determination of the educational needs of the target group, (2) description of the nurse’s duties, (3) teaching-learning methods, (4) patient referral, and (5) the process of preparing and publishing educational content, constituted the main category of process. One generic category called evaluation constituted the main category of outcome.ConclusionBased on the results of this study, it is suggested to managers to pay attention to the dimensions of the quality model of Donabedian (SPO) in setting up and developing the performance of HENLCs, it is recommended that future quantitative studies based on the categories formed in this study evaluate the observance of the dimensions of structure, process and outcome.

NOMA Assisted Two-Tier VR Content Transmission: A Tile-Based Approach for QoE Optimization

Virtual reality (VR) provides users with an immersive and interactive experience through head-mounted devices, which has attracted increasing attention in recent years. Specifically, tile-based VR content transmission provides a promising approach to alleviate the conflict between limited bandwidth and high-performance requirements (e.g., high-resolution and low-delay). However, the tiling pattern affects the encoding efficiency and visual distortion of the VR content. Accounting for this issue, in this paper, a quality of experience (QoE)-aware cost minimization problem is investigated for a tile-based VR content transmission scenario. In particular, an edge server (ES) co-located at a cellular base station (BS) separates its generated VR content into several tiles according to the tiling pattern selection, and a weighted-to-spherically-uniform quality model is used to evaluate the effect of different tiling patterns on QoE. Moreover, to improve the transmission performance between the edge server and VR users (VRUs), unmanned aerial vehicles (UAVs) are leveraged as relay points to provide line of sight channels. Then, we formulate an optimization problem to minimize the sum of weighted total energy consumption and VR content distortion (i.e., QoE-aware cost) by jointly optimizing the tiling pattern selections, the VRUs-UAV grouping, partial computing decisions, and resource allocation. The formulated problem is a mixed integer non-linear programming problem, which is challenging to solve. To address this difficulty, we equivalently decompose the formulated problem into three subproblems and propose corresponding algorithms to solve them, respectively. Numerical results demonstrate that our proposed solution can effectively reduce the QoE-aware cost for VR content transmission in comparison with other baseline algorithms.

Deep learning models for air quality forecasting based on spatiotemporal characteristics of data

The distribution of air-borne pollutants is governed by complex fluid dynamics processes involving convection and diffusion. The process is further affected by the characteristics of emission sources, meteorological parameters, socioeconomic factors, and land use patterns. Compared to deterministic and probabilistic air quality forecasting methods, data driven modeling of air quality parameters can address the large degree of freedom in air quality influencing parameters as well as offer interpretability and understanding of air pollutants' distribution at an increased spatial and temporal resolutions. This study focuses on the citywide prediction of air quality index (AQI) based on observations of pollutant concentrations, meteorological parameters, and spatiotemporal data. The study area includes Ansan city in South Korea, which has been observed as a hotspot for high concentrations of particulate matter. The air quality and meteorological were collected from 16 monitoring stations located in Ansan city. A detailed spatiotemporal analysis was performed to investigate the correlation between AQI records at the air quality monitoring stations. Based on strong spatiotemporal correlations observed between stations, several deep learning (DL) models were proposed, and their performance was investigated for different scenarios. It was observed that the selection of appropriate DL models should be based on (1) understanding of the underlying fluid dynamics process that control pollutant distribution and (2) spatiotemporal characteristics of data. Additionally, the complexity of DL models does not always guarantee the accuracy of the forecasts, and simple models can give good performance if the predictors are selected carefully to reflect the underlying physical process.

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

ObjectivesClimate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air...

Modeling the molecular composition of secondary organic aerosol under highly polluted conditions: A case study in the Yangtze River Delta Region in China

A near-explicit mechanism, the master chemical mechanism (MCMv3.3.1), coupled with the Community Multiscale Air Quality (CMAQ) model (CMAQ-MCM-SOA), was applied to investigate the characteristics of secondary organic aerosol (SOA) during a pollution event in the Yangtze River Delta (YRD) region in summer 2018. Model performances in predicting explicit volatile organic compounds (VOCs), organic aerosol (OA), secondary organic carbon (SOC), and other related pollutants in Taizhou, as well as ozone (O3) and fine particulate matter (PM2.5) in multiple cities in this region, were evaluated against observations and model predictions by the CMAQ model coupled with a lumped photochemical mechanism (SAPRC07tic, S07). MCM and S07 exhibited similar performances in predicting gaseous species, while MCM better captured the observed PM2.5 and inorganic aerosols. Both models underpredicted OA concentrations. When excluding data during biomass burning events, SOC concentrations were underpredicted by the CMAQ-MCM-SOA model (−28.4 %) and overpredicted by the CMAQ-S07 model (134.4 %), with better agreement with observations in the trend captured by the CMAQ-MCM-SOA model. Dicarbonyl SOA accounted for a significant fraction of total SOA in the YRD, while organic nitrates originating from aromatics were the most abundant species contributing to the SOA formation from gas-particle partitioning. The oxygen-to‑carbon ratio (O/C) for SOA and OA were 0.68–0.75 and 0.20–0.65, respectively, indicating a higher oxidation state in the areas influenced by biogenic emissions. Finally, the phase state of SOA was examined by calculating the glass transition temperature (Tg) based on its molecular composition. It was found that semi-solid state characterized SOA in the YRD, which could potentially impact their chemical transformation and lifetimes along with those of their precursors.

Impacts assessment of nitrification inhibitors on U.S. agricultural emissions of reactive nitrogen gases

Fertilizer-intensive agriculture leads to emissions of reactive nitrogen (Nr), posing threats to climate via nitrous oxide (N2O) and to air quality and human health via nitric oxide (NO) and ammonia (NH3) that form ozone and particulate matter (PM) downwind. Adding nitrification inhibitors (NIs) to fertilizers can mitigate N2O and NO emissions but may stimulate NH3 emissions. Quantifying the net effects of these trade-offs requires spatially resolving changes in emissions and associated impacts. We introduce an assessment framework to quantify such trade-off effects. It deploys an agroecosystem model with enhanced capabilities to predict emissions of Nr with or without the use of NIs, and a social cost of greenhouse gas to monetize the impacts of N2O on climate. The framework also incorporates reduced-complexity air quality and health models to monetize associated impacts of NO and NH3 emissions on human health downwind via ozone and PM. Evaluation of our model against available field measurements showed that it captured the direction of emission changes but underestimated reductions in N2O and overestimated increases in NH3 emissions. The model estimated that, averaged over applicable U.S. agricultural soils, NIs could reduce N2O and NO emissions by an average of 11% and 16%, respectively, while stimulating NH3 emissions by 87%. Impacts are largest in regions with moderate soil temperatures and occur mostly within two to three months of N fertilizer and NI application. An alternative estimate of NI-induced emission changes was obtained by multiplying the baseline emissions from the agroecosystem model by the reported relative changes in Nr emissions suggested from a global meta-analysis: −44% for N2O, −24% for NO and +20% for NH3. Monetized assessments indicate that on an annual scale, NI-induced harms from increased NH3 emissions outweigh (8.5–33.8 times) the benefits of reducing NO and N2O emissions in all agricultural regions, according to model-based estimates. Even under meta-analysis-based estimates, NI-induced damages exceed benefits by a factor of 1.1–4. Our study highlights the importance of considering multiple pollutants when assessing NIs, and underscores the need to mitigate NH3 emissions. Further field studies are needed to evaluate the robustness of multi-pollutant assessments.

Uncertainty and sensitivity analysis of a travel‐time distribution‐based stream water electrical conductivity model

AbstractIn this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.

Development and application of water quality modeling with GESCAL-AQUATOOL in the Lurín river-Lima-Peru

La cuenca baja del río Lurín es una de las últimas grandes extensiones de áreas verdes en Lima, capital de Perú, sin embargo es afectada por diversas actividades antrópicas. Ante esta situación, y con el objetivo de mejorar el manejo de los recursos hídricos, se implementó un modelo de calidad del agua con el módulo GESCAL del sistema de soporte a la decisión AQUATOOL. La construcción de la red hidrográfica del río se hizo a partir de los datos recogidos en salidas de reconocimiento, mientras que para modelar la calidad del agua del tramo bajo del río se desarrolló un programa de monitoreo que tuvo como principales parámetros al oxígeno disuelto, DBO5 y Escherichia coli. Con la información obtenida se determinaron primero los coeficientes de degradación de materia orgánica, desoxigenación y reaireación de las secciones del río, y finalmente se parametrizó el modelo. La calibración del modelo en oxígeno disuelto obtuvo un rendimiento de “bueno” a “muy bueno”, con valores de 0.77, 0.23 y 0.8058 para E, RSR y r2, respectivamente; para el caso de la DBO5, se obtuvieron valores de 0.99, 0.01 y 0.9995 para E, RSR y r2, respectivamente; y para la Escherichia coli, valores de 0.99, 0.01 y 0.9993 para E, RSR y r2, respectivamente. Finalmente, se simularon cuatro escenarios, a partir de los cuales se determinó que para alcanzar los objetivos ambientales es necesario mejorar los procesos de las plantas de tratamiento de aguas residuales (PTAR) Manchay y San Bartolo; clausurar el canal de regadío proveniente de la capilla de la zona, e implementar una PTAR en el distrito de Pachacámac. El estudio demuestra la utilidad del sistema AQUATOOL/GESCAL en la toma de decisiones informadas para la gestión de los recursos hídricos a nivel de cuenca.

Assessing the water potential and wastewater load capacity of the La Tinh River, Binh Dinh province by MIKE 11AD and Ecolab model combined with GIS method

The study used the NAM model of flow simulation with a time series of 14 years (2008 - 2021), statistical analysis method, and MIKE 11 AD water quality model, and Ecolab combined with GIS method to calculate wastewater reception capacity and load capacity of the basin. The model validation is highly reliable with a model efficiency factor of R2 of 0.777 and BIAS = 5%. The results of the application of the NAM model have determined the daily flow series of 14 years (2008 - 2021) and the average flow characteristics of many years of the La Tinh basin such as Qo flow is about 5.9; 6.0 m3/s, total amount of Wo = 292.1; 188.8 million m3 flow modulus Mo = 19.3 l/s-km2 flow class Yo = 611 mm and flow coefficient α= 0.3 for all sub-basins. The results of the study show that the capacity of receiving wastewater, the capacity of most river sections in the La Tinh basin is still received with five parameters at a relatively high level, the section of the Binh Tri river upstream alone has exceeded its capacity to receive. This is one of the very useful tools in integrated management and effective use management of river basin water resources.

The value of instream stable water isotope and nitrate concentration data for calibrating a travel time‐based water quality model

AbstractTransit time‐based water quality models using StorAge Selection (SAS) functions are crucial for nitrate (NO3−) management. However, relying solely on instream NO3− concentration for model calibration can result in poor parameter identifiability. This is due to the interaction, or correlation, between transport parameters, such as SAS function parameters, and denitrification rate, which challenges accurate parameters identification and description of catchment‐scale hydrological processes. To tackle this issue, we conducted three Monte‐Carlo experiments for a German mesoscale catchment by calibrating a SAS‐based model with daily instream NO3− concentrations (Experiment 1), monthly instream stable water isotopes (e.g. δ18O) (Experiment 2) and both datasets (Experiment 3). Our findings revealed comparable ranges of SAS transport parameters and median water transit times (TT50) across the experiments. This suggests that, despite their distinct reactive or conservative nature, and sampling strategies, the NO3− and δ18O time series offer similar information for calibration. However, the absolute values of transport parameters and TT50 time series, as well as the degree of parameter interaction differed. Experiment 1 showed greater interaction between certain transport parameters and denitrification rate, leading to greater equifinality. Conversely, Experiment 3 yielded reduced parameters interaction, which enhanced transport parameters identifiability and decreased uncertainty in TT50 time series. Hence, even a modest effort to incorporate only monthly δ18O values in model calibration for highly frequent NO3−, improved the description of hydrological transport. This study showcased the value of combining NO3− and δ18O model results to improve transport parameter identifiability and model robustness, which ultimately enhances NO3− management strategies.

A hybrid model for enhanced forecasting of PM2.5 spatiotemporal concentrations with high resolution and accuracy

Forecasting concentrations of PM2.5 is important due to its known impacts on public health and environment. However, PM2.5 concentrations can vary significantly over short distances and time, which can be influenced by local emissions and short-term weather patterns. This spatiotemporal variability makes accurate PM2.5 forecasting an inherently complex and challenging task. This study presented novel methodologies for short-term PM2.5 concentration forecast by combining the atmospheric chemistry transport model Community Multiscale Air Quality Modeling System (CMAQ) with data-driven machine learning methods, namely long short-term memory (LSTM) and random forest (RF) models. The combined model system forecast PM2.5 with 1 h, 1km × 1 km spatiotemporal resolution. The LSTM system forecast time-dependent PM2.5 concentrations at observation sites with a maximum root mean square error (RMSE) of 3.66 μg/m3 for 1-hr forecast and 23.75 μg/m3 for 72-hr forecast, leveraging results obtained from the atmospheric transport model with RMSE of 45.81 μg/m3. Wavelet transform in the LSTM system allowed learning and prediction of PM2.5 concentrations at different frequencies, capturing temporal variability of PM2.5 at various time scales. The RF model predicted distributions of PM2.5 concentrations by learning LSTM results and integrating crucial features such as CMAQ results, meteorological and topographical information. The feature significance of CMAQ results was the highest among the input features in RF models. Overall, the hybrid model could help with managing and mitigating the adverse effects of air pollution by enabling informed decision-making at the individual, community and policy levels.

Adaptation of Wind Drag Coefficient Parameterization: Improvement of Hydrodynamic Modeling by a Wave‐Dependent Cd in Large Shallow Lakes

AbstractWind is a critical driving force in hydrodynamic and water quality modeling of large shallow lakes, and is characterized by the wind drag coefficient Cd, representing the momentum transfer at the air‐water interface. Contemporary empirical formulae for Cd estimation were derived over oceans and some of which are solely wind velocity U10 dependent. These formulae were previously found to be inadequate in inland lake models often resulting in the water velocity underestimation. To address this problem, a physical scale experiment was designed, in which Cd was measured using a wind profile and eddy covariance methodology. A new wind‐induced wave‐dependent Cd parameterization was also established and validated in two lake studies. The driving force was modified by the wave‐dependent Cd formula in a hydrodynamic model of the shallow Upper Klamath Lake (UKL), OR, USA. The experimental Cd was negatively correlated to the wind velocity up until the critical U10 = 1.6 m s−1 which was 1.0~3.1 times previous empirical extrapolations at light winds. The variation partitioning results showed that wave parameters contributed to more than 30% of Cd variation combined with wind parameters. The modified wind stress field was spatially heterogeneous and the modeled water velocity was closer to the observations at two sites. Significant main circulation and outer bank circulation were modeled accompanied by higher surface vorticity, compared to the original UKL model. Overall, the wave‐dependent Cd formula provided an improvement of the surface flow field in the UKL model and will improve the management of the lake ecosystems.

Spatiotemporal Interpolation of Water Quality Index and Nitrates using ArcGIS Pro for Surface Water Quality Modeling in the Oum Er-Rabia Watershed

Spatiotemporal Interpolation of Water Quality Index and Nitrates using ArcGIS Pro for Surface Water Quality Modeling in the Oum Er-Rabia Watershed

Street-scale air quality modelling over the West Midlands, United Kingdom: Effect of idealised traffic reduction scenarios

Air pollution is the major environmental risk to human health. Road transport is one of the major sources for air pollution, particularly nitrogen dioxide, in urban areas, and hence traffic control is an important measure in air quality management. A street-scale air quality model, ADMS-Urban, was configured for a case study of the West Midlands, UK to represent a baseline year (2019). Model outputs were evaluated using hourly air pollutant measurement data, and the model demonstrates good performance overall. This modelling tool was then used to explore the effect of five hypothetical traffic reduction scenarios, ranging from 10% to 90% reduction in traffic activity; scenario impacts were analysed over a range of spatial resolutions. The impacts of traffic reduction are highly dependent on spatial resolution (i.e. street scale, electoral ward level and local authority level), which has to be taken into account when formulating policies for managing air quality on local and city-wide scales. There was an almost linear relationship between the predicted annual concentration and traffic reduction for both NO2 and PM2.5. Traffic reduction would principally reduce NO2 concentrations, with even very substantial changes in traffic having more limited effects on reducing PM2.5 concentrations reflecting the importance of regional and non-traffic PM2.5 sources.