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Herein, we present the initial application of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC) in the cloud (“NACC-Cloud”, version 1), which processes NOAA’s operational Global Forecast System version 16 (GFSv16) meteorology on-demand and produces model-ready meteorological files needed to drive U.S. EPA’s Community Multiscale Air Quality (CMAQ) model. NACC is adapted from the U.S. EPA’s Meteorology-Chemistry Interface Processor version 5 (MCIPv5) and is used as the primary model coupler in the current operational NWS/NOAA air quality forecasting model. The development and use of NACC-Cloud in this work are critical to provide the scientific community streamlined access to NOAA’s operational GFSv16 data and user-defined processing and download of model-ready, meteorological input for any regional CMAQ domain worldwide. The NACC-Cloud system was implemented on the Amazon® Web Services High-Performance Computing platform, and results from this work show that the NACC-Cloud system is immediately beneficial to the air quality modeling community worldwide.

The average PM10 daily levels over the urban area of Thessaloniki, Greece, usually exceed the air quality limits and therefore the improved PM chemical composition and air quality modeling results that will facilitate the design of the most appropriate mitigation measures (e.g., limitations in wood combustion for heating purposes) are essential. The air quality modeling system WRF-CAMx was applied over a 2 × 2 km2 horizontal resolution grid covering the greater area of Thessaloniki for the year 2015, when Greece was still confronting the consequences of the financial crisis. The output hourly surface concentrations of twelve PM species at three sites of different environmental type characterization in the city of Thessaloniki were temporally and spatially analyzed. Carbonaceous aerosols (organic and elemental) are the major contributor to total PM10 levels during winter representing a 35–40% share. During summer, mineral aerosols (excluding dust) distribute by up to 48% to total PM10 levels, being the major contributor attributed to road traffic. PM species, during winter, increase in the morning and in the afternoon mainly due to road transport and residential heating, respectively, in addition with the unfavorable meteorological conditions. An underestimation of the primary organic carbon aerosol levels during winter is identified. The application of the modeling system using a different speciation profile for the fine particles emissions from residential heating based on observational data instead of the CAMS emissions profile revealed an improvement in the simulated OC/EC values for which a 50% increase was identified compared to the base run.

Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO2 and total NOx) and particulate matter (PM10 and PM2.5) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO2, 35–67% for NOx and 44–78% for both PM10 and PM2.5. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent.

Malaysia has been facing transboundary haze events repeatedly, in which the air contains extremely high particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to understand the characteristics of PM10 concentration and develop a reliable PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. This study aims to analyze PM10 variation and investigate the performance of quantile regression in predicting the next-day, the next two days, and the next three days of PM10 levels during a high particulate event. Hourly secondary data of trace gases and the weather parameters at Pasir Gudang, Melaka, and Petaling Jaya during historical haze events in 1997, 2005, 2013, and 2015. The Pearson correlation was calculated to find the correlation between PM10 level and other parameters. Moderate correlated parameters (r > 0.3) with PM10 concentration were used to develop a Pearson–QR model with percentiles of 0.25, 0.50, and 0.75 and were compared using quantile regression (QR) and multiple linear regression (MLR). Several performance indicators, namely mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and index of agreement (IA), were calculated to evaluate and compare the performances of the predictive model. The highest daily average of PM10 concentration was monitored in Melaka within the range of 69.7 and 83.3 µg/m3. CO and temperature were the most significant parameters associated with PM10 level during haze conditions. Quantile regression at p = 0.75 shows high efficiency in predicting PM10 level during haze events, especially for the short-term prediction in Melaka and Petaling Jaya, with an R2 value of >0.85. Thus, the QR model has high potential to be developed as an effective method for forecasting air pollutant levels, especially during unusual atmospheric conditions when the overall mean of the air pollutant level is not suitable for use as a model.

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years’ worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models’ performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

This dataset is to support the paper submitted to Environmental Science & Technology, which contains: 1) Population exposure: Annual mean gridded concentrations for PM2.5 and its components, and relevant trace gases including NO2 and O3 in year of 2015 at cubed-sphere resolutions of C48 and C360 by the GEOS-Chem model in its high performance implementation (GCHP) v13.2.1; 2) Population exposure in city centers: annual pollutant concentrations in city centers for year of 2015 simulated at C48 and C360; 2) Sectoral contributions: Monthly mean gridded concentrations for PM2.5 and its components, and relevant trace gases including NO2 and O3 at C48 and C360 from sector sensitivity tests for energy, industry, residential combustion, transportation, and open fires in January 2015.

EnvironmentAsia, 16, 1, 111-125

The biggest problem facing air protection in Poland is the high levels of suspended particular matter concentrations. Air monitoring reports show that air quality standards, related to PM10 and PM2.5 concentrations, are exceeded every year in many Polish cities. The PM2.5 aerosol fraction is particularly dangerous to human and animal health. Therefore, monitoring the level of PM2.5 concentration should be considered particularly important. Unfortunately, most monitoring stations in Poland do not measure this dust fraction. However, almost all stations are equipped with analyzers measuring PM10 concentrations. PM2.5 is a fine fraction of PM10, and there is a strong correlation between the concentrations of these two types of suspended dust. This relationship can be used to determine the concentration of PM2.5. The main purpose of this analysis was to assess the accuracy of PM2.5 concentration prediction using PM10 concentrations. The analysis was carried out on the basis of long-term hourly data recorded at several monitoring stations in Poland. Artificial neural networks in the form of a multilayer perceptron were used to model PM2.5 concentrations.

Benzene, toluene, ethylbenzene, and xylenes (BTEX) are carcinogenic pollutants. However, the average concentration in 1 h of some pollutants belonging to BTEX, such as benzene, in Ho Chi Minh City (HCMC) is higher than the national standard QCVN 06:2009/BTNMT by about ten times. This research is the first to calculate the emission of BTEX for developing countries on a city scale. This paper developed a method to calculate cold emission factors based on hot emission factors for BTEX. Five spreadsheets developed and calculated these cold emission factors for five vehicle categories. A comprehensive emission inventory (EI) for BTEX was conducted in HCMC to determine the cause of BTEX pollution. An innovative methodology with bottom-up and top-down combination was applied to conduct BTEX EI, in which the EMISENS model was utilized to generate the EI for road traffic sources, and the emission factors method was utilized for other emission sources. Among emission reasons, motorcycles contribute the highest to HCMC air pollution, responsible for 93%, 90%, 98.9%, and 91.5% of benzene, toluene, ethylbenzene, and xylene, respectively. Cars contributed 5%, 6%, 0.8%, and 6.5% of benzene, toluene, ethylbenzene, and xylene, respectively. For LDVs, the emission from benzene, toluene, ethylbenzene, and xylene accounted for 1%, 2%, 0.2%, and 1.9%. The major reasons for point sources were metal production, which had 1%, 2%, and 0.1% for benzene, toluene, ethylbenzene, and xylenes emissions. The area source had a minority emission of total BTEX in Ho Chi Minh City. Our findings can be used to invest in the most significant sources to reduce BTEX in HCMC. Our approach can be applied in similar urban areas in BTEX EI. This research also developed nine measures to reduce BTEX in HCMC for human health protection.

In this work, we identify the current atmospheric sulfur dioxide emissions of the Veracruz port, an important Mexican seaport experiencing rapid growth, and its influence on the surrounding areas. Sulfur dioxide emissions based on port activity, as well as meteorology and air quality simulations, are used to assess the impact. It was found that using marine fuel with low sulfur content reduces emissions by 88%. Atmospheric emission estimates based on the bottom-up methodology range from 3 to 7 Mg/year and can negatively impact air quality up to 3 km downwind. After evaluating different characteristics of vessels in CALPUFF, it was found that maximum sulfur dioxide concentrations ranging between 50 and 88 µg/m3 for a 24-h average occurred 500 m from the port. During 2019, five days had unsatisfactory air quality. The combination of a shallow planetary boundary layer, low wind speed, and large atmospheric emissions significantly degraded local air quality.

Ground-level ozone (O3) is a significant source of air pollution, mainly in most urban areas across the globe. Ground-level O3 is not emitted directly into the atmosphere. It results from photo-chemical reactions between precursors and is influenced by weather factors such as temperature. This study investigated the spatial and temporal analysis of ground-level ozone and analyzed the significant anthropogenic precursors and the weather parameters associated with ground-level ozone during daytime and nighttime at three cities in peninsular Malaysia, namely, Kuala Terengganu, Perai, and Seremban from 2016 to 2020. Secondary data were acquired from the Department of Environment (DOE), Malaysia, including hourly data of O3 with trace gases and weather parameters. The secondary data were analyzed using temporal analysis such as descriptive statistics, box plot, and diurnal plot as well as spatial analysis such as contour plot and wind rose diagram. Spearman correlation was used to identify the association of O3 with its precursors and weather parameters. The results show that a higher concentration of O3 during the weekend due to “ozone weekend effects” was pronounced, however, a slightly significant effect was observed in Perai. The two monsoonal seasons in Malaysia had a minimal effect on the study areas except for Kuala Terengganu due to the geographical location. The diurnal pattern of O3 concentration indicates bimodal peaks of O3 precursors during the peak traffic hours in the morning and evening with the highest intensity of O3 precursors detected in Perai. Spearman correlation analysis determined that the variations in O3 concentrations during day and nighttime generally coincide with the influence of nitrogen oxides (NO) and temperature. Lower NO concentration will increase the amount of O3 concentration and an increasing amount of O3 concentration is influenced by the higher temperature of its surroundings. Two predictive models, i.e., linear (multiple linear regression) and nonlinear models (artificial neural network) were developed and evaluated to predict the next day and nighttime O3 levels. ANN resulted in better prediction for all areas with better prediction identified for daytime O3 levels.

RESUMEN La presente investigación tuvo como objetivo evaluar el desempeño del modelo de Redes Neuronales Artificiales (RNA) para pronosticar las concentraciones de PM10 en el aire, para lo cual se hizo un caso estudio para el distrito de Ate, Lima. Para ello se desarrolló distintas arquitecturas de RNA usando como datos de entrada a los registros de contaminantes del aire y variables meteorológicas obtenidas de la Estación de Monitoreo de la Calidad del Aire “ATE” y datos simulados del modelo WRF-CHEM. Las diferentes arquitecturas de RNA pasaron por un proceso de entrenamiento y verificación, y su desempeño se evaluó mediante el Error Cuadrático Medio (ECM), la precisión (BIAS) y el coeficiente de determinación (R2). Se determinó que la arquitectura que tiene un mejor desempeño tuvo 19 neuronas en la capa oculta, con valores de 0,0230 para el ECM, 0,5308 para la BIAS y 0,823 para el R2, asimismo, esta puede brindar pronósticos hasta con 6 horas de antelación. Este estudio puede contribuir a la implementación de Sistemas de Alertas Tempranas (SAT) sobre posibles incrementos en el aire de las concentraciones de PM10. ABSTRACT The aim of this research was to evaluate the performance of the Artificial Neural Network (ANN) model to predict the concentrations of PM10 in the air, for which a case study was made for the district of Ate, Lima. For this, different ANN architectures were developed using as input data the records of air pollutants and meteorological variables obtained from the Air Quality Monitoring Station "ATE" and simulated data from the WRF-CHEM model. The different ANN architectures went through a training and verification process, and their performance was evaluated using the Mean Square Error (MSE), precision (BIAS) and determination coefficient (R2). It was determined that the architecture that has a better performance had 19 neurons in the hidden layer, with values of 0,0230 for the ECM, 0,5308 for the BIAS and 0,823 for the R2, likewise, it can provide forecasts up to 6 hours in advance. This study can contribute to the implementation of Early Warning Systems (SAT) on possible increases in the air of PM10 concentrations.

Ground-level ozone (O3) is one of the most significant forms of air pollution around the world due to its ability to cause adverse effects on human health and environment. Understanding the variation and association of O3 level with its precursors and weather parameters is important for developing precise forecasting models that are needed for mitigation planning and early warning purposes. In this study, hourly air pollution data (O3, CO, NO2, PM10, NmHC, SO2) and weather parameters (relative humidity, temperature, UVB, wind speed and wind direction) covering a ten year period (2003–2012) in the selected urban areas in Malaysia were analyzed. The main aim of this research was to model O3 level in the band of greatest solar radiation with its precursors and meteorology parameters using the proposed predictive models. Six predictive models were developed which are Multiple Linear Regression (MLR), Feed-Forward Neural Network (FFANN), Radial Basis Function (RBFANN), and the three modified models, namely Principal Component Regression (PCR), PCA-FFANN, and PCA-RBFANN. The performances of the models were evaluated using four performance measures, i.e., Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Index of Agreement (IA), and Coefficient of Determination (R2). Surface O3 level was best described using linear regression model (MLR) with the smallest calculated error (MAE = 6.06; RMSE = 7.77) and the highest value of IA and R2 (0.85 and 0.91 respectively). The non-linear models (FFANN and RBFANN) fitted the observed O3 level well, but were slightly less accurate compared to MLR. Nonetheless, all the unmodified models (MLR, ANN, and RBF) outperformed the modified-version models (PCR, PCA-FFANN, and PCA-RBFANN). Verification of the best model (MLR) was done using air pollutant data in 2018. The MLR model fitted the dataset of 2018 very well in predicting the daily O3 level in the specified selected areas with the range of R2 values of 0.85 to 0.95. These indicate that MLR can be used as one of the reliable methods to predict daytime O3 level in Malaysia. Thus, it can be used as a predictive tool by the authority to forecast high ozone concentration in providing early warning to the population.

This study examines the performance of a data assimilation and forecasting system that simultaneously assimilates satellite aerosol optical depth (AOD) and ground-based PM10 and PM2.5 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The data assimilation case for the surface PM10 and PM2.5 concentrations exhibits a higher consistency with the observed data by showing more correlation coefficients than the no-assimilation case. The data assimilation also shows beneficial impacts on the PM10 and PM2.5 forecasts for South Korea for up to 24 h from the updated initial condition. This study also finds deficiencies in data assimilation and forecasts, as the model shows a pronounced seasonal dependence of forecasting accuracy, on which the seasonal changes in regional atmospheric circulation patterns have a significant impact. In spring, the forecast accuracy decreases due to large uncertainties in natural dust transport from the continent by north-westerlies, while the model performs reasonably well in terms of anthropogenic emission and transport in winter. When the south-westerlies prevail in summer, the forecast accuracy increases with the overall reduction in ambient concentration. The forecasts also show significant accuracy degradation as the lead time increases because of systematic model biases. A simple statistical correction that adjusts the mean and variance of the forecast outputs to resemble those in the observed distribution can maintain the forecast skill at a practically useful level for lead times of more than a day. For a categorical forecast, the skill score of the data assimilation run increased by up to 37% compared to that of the case with no assimilation, and the skill score was further improved by 10% through bias correction.

This package is part of the electronic supplement to the article "GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool". The source code of GENOA has been removed in order to make the package compliant with the license CC BY 4.0. This package contains: - the input data required to run the GENOA reduction on the beta-caryophyllene (BCARY) mechanism from the Master Chemical Mechanism. - An example of the reduction result produced with the given input data. Please copy the source code of GENOA from here and place it in the folder GENOA-BCARY-MCM/ in order to use the program. For more information, please refer to the user guide (GENOA/GENOA_v1.0_Manual.pdf).

Kao-Ping Air Basin (KPAB) is a heavy industrial area, and also the first and only air quality total quantity control district in Taiwan. Pollutant emission offsetting is an important tool to reduce pollution source emissions and improve air quality in the total quantity control district. In this study, an air quality model was employed to evaluate the sensitivity of SOX, NOX, and primary PM2.5 emissions from point, mobile, and fugitive sources on PM2.5 concentrations in KPAB. The findings show that the emission offset ratios of mobile PM2.5-to-point PM2.5 and fugitive PM2.5-to-point PM2.5 were both greater than one in urban areas (1.3 and 2.0, respectively) and both less than one in non-urban areas. The offset ratios of point SOX-to-point PM2.5 and point NOX-to-point PM2.5 were significantly greater than one, especially those in urban areas (20 and 60, respectively) were higher than those in non-urban areas by more than 2–4 times. No matter whether in urban or non-urban areas, the offset ratio of mobile NOX-to-point NOX was close to one, and the offset ratios of point NOX-to-point PM2.5 and mobile NOX-to-point PM2.5 were similar. The above findings were closely related to the proximity of point sources to densely populated urban areas in KPAB.

This work proposes a robust and non-Gaussian version of the shrinkage-based knowledge-aided EnKF implementation called Ensemble Time Local H∞ Filter Knowledge-Aided (EnTLHF-KA). The EnTLHF-KA requires a target covariance matrix to integrate previously obtained information and knowledge directly into the data assimilation (DA). The proposed method is based on the robust H∞ filter and on its ensemble time-local version the EnTLHF, using an adaptive inflation factor depending on the shrinkage covariance estimated matrix. This implies a theoretical and solid background to construct robust filters from the well-known covariance inflation technique. The proposed technique is implemented in a synthetic assimilation experiment, and in an air quality application using the LOTOS-EUROS model over the Aburrá Valley to evaluate its potential for non-linear and non-Gaussian large systems. In the spatial distribution of the PM2.5 concentrations along the valley, the method outperforms the well-known Local Ensemble Transform Kalman Filter (LETKF), and the non-robust knowledge-aided Ensemble Kalman filter (EnKF-KA). In contrast to the other simulations, the ability to issue warnings for high concentration events is also increased. Finally, the simulation using EnTLHF-KA has lower error values than using EnKF-KA, indicating the advantages of robust approaches in high uncertainty systems.

gráficos, tablas. The ability of the Weather Research and Forecasting model couple with chemistry (WRF-Chem) to accurately represent the atmospheric processes depends on the accuracy of the input information and the adequate model configuration for the intended application. In this study, WRF-Chem was used to test the sensitivity of CO and O3 predictions to (I) lateral chemical boundary conditions (LCBC), (II) domain configurations, (III) nesting techniques, (IV) chemical mechanisms, (V) local anthropogenic emission inventories and (VI) biogenic emission inventories. For that purpose, a total of eight simulations were conducted over the city of Manizales, characterized by its complex topography. The model outputs were intercompared with each other and against ground measurements, in order to identify the effect of the proposed options in the prediction of CO and O3, and ultimately define an optimal model configuration that could reduce the prediction errors. The results show that CO predictions were not sensitive to LCBC, domain configurations, or chemical mechanisms, but exhibited a strong dependency on the anthropogenic emission inventory and the nesting technique. This is caused because CO is a primary pollutant, and its variations are mainly associated with the local emission patterns, whereas the impact of CO transport from other areas, and the production/consumption of this pollutant in the atmosphere are neglectable for the city of Manizales. Indeed, the main contribution towards improving CO accuracy was the implementation of the anthropogenic emission inventory with IVE emission factors, instead of COPERT, due to a more representative activity factors approach of IVE for the particular conditions in Manizales, characterized by road slopes that exceed inclinations of 22%. On the other hand, using a two-way nesting approach provided the worst performance for CO predictions, due to inaccurate predictions of nighttime CO concentrations. O3 predictions exhibited a strong sensitivity to LCBC. The use of CAM-Chem LCBC reduced considerably the overestimation (MB: 3.1 vs 11.5 ppbv) of O3 predictions with the default static LCBC (NALROM). Likewise, the use of a 3-nested domain with a 1:5 nesting ratio enhanced O3 predictions and reduced the computational time needed to run the simulations compared to using 4-nested domains with a 1:3 nesting ratio. Similar to CO, the prediction of O3 were disbenefit by using a two-way nesting approach, in this case, due to higher overestimations during the daytime compared to using a one-way nesting approach. The CBMZ chemical mechanism enhanced the representation of O3 dynamics due to more efficient O3–NOX titration reactions, in comparison with the RADM2 mechanism. O3 predictions were also sensitive to the anthropogenic emission inventory, with the IVE emission factors leading to the most accurate results. Finally, despite the significant difference in biogenic emission fluxes with the biogenic emission models implemented (MEGAN and BIGA), O3 predictions were almost identical with both options. In summary, the best model performance was obtained by using CAM-Chem LCBC, 3-nested domains with a 1:5 nesting ratio, a one-way nesting approach, the CBMZ chemical mechanism, the anthropogenic emission inventory with IVE emission factors, and BIGA biogenic model. These results can guide WRF-Chem setup for future air quality simulations in the city of Manizales, and other cities with similar topographic conditions. La habilidad del modelo Weather Research and Forecasting coupled with chemistry (WRF-Chem) pare representar de forma precisa los procesos atmosféricos está ligada a la precisión de la información de entrada al igual que a la configuración del modelo en sí mismo. En este estudio, se utilizó el modelo WRF-Chem para estudiar la sensibilidad de las predicciones de O3 y CO a (I) condiciones de borde laterales químicas (LCBC), (II) configuración de dominios, (III) opciones de anidamiento, (IV) mecanismos químicos, (V) inventarios de emisiones antropogénicos locales e (VI) inventarios de emisiones biogénicas. Por lo tanto, se realizaron un total de ocho simulaciones para la ciudad de Manizales, la cual se caracteriza por su topografía compleja. Los datos modelados fueron comparados entre sí y contra medidas en superficie, con el objetivo de verificar el impacto que tienen los cambios propuestos en la predicción del CO y O3, y en última medida, definir la configuración óptima, que permita reducir los errores de predicción del modelo. Los resultados muestran que el CO no fue sensible a LCBC, configuración de dominios ni mecanismos químicos, pero presentó una fuerte dependencia a los inventarios de emisiones antropogénicos y a la técnica de anidamiento empleada. Esto se debe a que el CO es un contaminante primario, y sus variaciones están asociadas a los patrones de emisiones locales, mientras que el transporte de CO de otras regiones, y el consumo/producción de este en la atmósfera no son representativos para la ciudad de Manizales. De hecho, el mejor desempeño en la predicción de CO fue obtenido al usar el inventario de emisión antropogénico con factores de emisión de la metodología IVE en lugar de COPERT, debido a que esta aproximación tiene factores de actividad más representativos para Manizales, ciudad que se caracteriza por tener carreteras con pendientes pronunciadas que exceden el 22% de inclinación. Por el contrario, el peor desempeño para predecir CO fue obtenido al utilizar una técnica de anidamiento two-way, la cual causa imprecisiones en la predicción de los perfiles nocturnos de CO. Las predicciones de O3 exhibieron una fuerte sensibilidad a las LCBC. Usar CAM-Chem para suplir las LCBC redujo considerablemente la sobreestimación (MB: 3.1 vs 11.5) de O3 en comparación con el LCBC estático que posee WRF-Chem por defecto (NALROM). Asi mismo, la aplicación de tres dominios anidados con una relación de anidamiento de 1:5 mejoro las predicciones de O3, al igual que redujo el tiempo computacional requerido para las simulaciones, en comparación con usar 4 dominios anidados con una relación de anidamiento de 1:3. Como paso con el CO, las predicciones de O3 se vieron afectadas al utilizar una técnica de anidamiento two-way, debido a que se incrementó la sobreestimación durante el periodo diurno. El mecanismo químico CBMZ represento de forma más precisa las dinámicas de la concentración de O3, debido a que este mecanismo estima reacciones de titulación O3-NOX más eficientes, en comparación con el mecanismo RADM2. Las predicciones de O3 también fueron sensibles a los inventarios de emisión antropogénicos, siendo el inventario desarrollado con factores de emisión IVE el que ofreció los resultados más precisos. Finalmente, a pesar de que hay una diferencia significativa en el flujo de emisiones biogénicas con los dos modelos empleados (MEGAN y BIGA), las predicciones de O3 fueron casi idénticas al emplear ambos modelos. En conclusión, la configuración del modelo que maximizo el desempeño incluye LCBC derivadas de CAM-Chem, tres dominios anidados con una relación de anidamiento de 1:5, una técnica de anidamiento one-way, el mecanismo químico CBMZ, la utilización de un inventario de emisiones con factores de emisión IVE, y el modelo biogénico BIGA. Estos resultados pueden guiar la configuración de WRF-Chem para ejercicios futuros de simulación atmosférica en la ciudad de Manizales, o en otras regiones con características topográficas similares. Maestría Magíster en Ingeniería - Ingeniería Química Calidad del aire y contaminación atmosférica Química Y Procesos

Cet article présente deux schémas chimiques semi-explicites pour simuler la formation et le vieillissement des aérosols organiques secondaires biogènes (AOS) des bêta-caryophyllène (BCARY) dans des modèles de qualité de l'air à grande échelle. Les mécanismes sont créés à l'aide du GENerator of Reduced Organic Aerosol Mechanisms (GENOA) à partir de la réduction du schéma de dégradation BCARY du Master Chemical Mechanism (MCM v3.3.1) quasi-explicite. Comparé au mécanisme MCM de référence, le mécanisme semi-explicite méc. I (méc. II) réduit le nombre de composés d'aérosol de 356 à 14 (10) avec une erreur moyenne de 2% (4%) sur les concentrations d'aérosols organiques., This paper presents two semi-explicit chemical mechanisms for simulating the formation and aging of secondary organic aerosols (SOA) from Beta-caryophyllene (BCARY) in large-scale air-quality models. The mechanisms are reduced by the GENerator of Reduced Organic Aerosol Mechanisms (GENOA) from the BCARY degradation mechanism of the near-explicit Master Chemical Mechanism (MCM v3.3.1). Compared to the MCM mechanism, the reduced schemes mech. I (mech. II) reduces the number of aerosols from 356 to 14 (10) with an average error of 2% (4%) on organic aerosol concentrations.

The changes in the concentration and composition of biomass-burning organic aerosol (OA) downwind of a major wildfire are simulated using the one-dimensional Lagrangian chemical transport model PMCAMx-Trj. A base case scenario is developed based on realistic fire-plume conditions and a series of sensitivity tests are performed to quantify the effects of different conditions and processes. Temperature, oxidant concentration and dilution rate all affect the evolution of biomass burning OA after its emission. The most important process though is the multi-stage oxidation of both the originally emitted organic vapors (volatile and intermediate volatility organic compounds) and those resulting from the evaporation of the OA as it is getting diluted. The emission rates of the intermediate volatility organic compounds (IVOCs) and their chemical fate have a large impact on the formed secondary OA within the plume. The assumption that these IVOCs undergo only functionalization leads to an overestimation of the produced SOA suggesting that fragmentation is also occurring. Assuming a fragmentation probability of 0.2 resulted in predictions that are more consistent with available observations. Dilution leads to OA evaporation and therefore reduction of the OA levels downwind of the fire. However, the evaporated material can return to the particulate phase later on after it gets oxidized and recondenses. The sensitivity of the OA levels and total mass balance on the dilution rate depends on the modeling assumptions. The high variability of OA mass enhancement observed in past field studies downwind of fires may be partially due to the variability of the dilution rates of the plumes.

The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was 2.5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

International audience; Deposition of the gas fraction of Semi-Volatile Organic Compounds (SVOC) may be an important removal pathway and may strongly influence concentrations of organic aerosols due to the gas-particle partitioning of SVOC. All the studies on this process are based on the classic Wesely resistance approach that uses Henry's law constants to calculate a deposition rate scaled on the deposition rate of SO2. However, even highly hydrophobic SVOC could be efficiently removed by the vegetation and soils as shown by numerous studies on Persistent Organic Pollutant (POP) modeling. Moreover, the re-volatilization of deposited SVOC is possible and could influence organic aerosol concentrations. An atmosphere-soil-vegetation module was developed and implemented in the 3D air quality model CHIMERE 2017 beta to represent the accumulation of compounds in the different compartments of the biosphere and the exchanges between them. The soil compartment was represented with a multi-layer approach (the layers corresponding to different in-soil depths) to simulate the multiphase diffusion of compounds inside the soil. Exchanges of SVOC between the air, soil and vegetation compartments were simulated using bi-directional approaches based on R-g (the gas-phase partitioning in the soil compartment) and K-va the vegetation-air partitioning coefficient. Parameters were estimated based on the physical properties of the compounds and their molecular structure. Simulations performed over Europe show that air-vegetation-soil exchanges may be a more efficient removal pathway than dry deposition of particles for SVOC with a gas-phase fraction above 10%. Considering airvegetation-soil exchanges in the simulations lead to a decrease of organic aerosol concentrations by 15% and primary SVOC (considered as hydrophobic compounds) may be efficiently removed by those pathways (contrary to what is calculated with the Wesely approach). This decrease of concentrations is mainly due to air-vegetation exchanges. During summer, the use of the Wesely approach may lead to a slight overestimation of deposition fluxes (leading to an underestimation of concentration by 1%). Re-volatilization may limit the amount of deposited SVOC. Depending on assumptions, simulations showed that re-emissions (inversion of exchanges toward the emissions) in summer of SVOC accumulated during winter is theoretically possible and may be a minor source of organic aerosol.

The interpolation of fine-grained air quality has significant prospects in the area of air quality monitoring. The solution to this problem can effectively monitor the air quality of the areas by sparse air quality monitoring stations, so as to reduce the monitoring cost. Most of the existing researches are to solve the problem of air quality monitoring in the areas without stations by different interpolation methods. However, most of them are unable to verify the reliability of the proposed interpolation methods and can’t provide a feasible range of interpolation methods at spatial resolutions. Therefore, this paper proposes an effectively unsupervised PM2.5 estimation method based on time distributed convolutional gated recurrent unit (TCGRU) and an interpolation method based on $k$ -nearest neighbor inverse distance weighted (KIDW) to solve these problems. The model is trained by the data obtained by the interpolation method with missing one station in turn to get the monitoring capability of the areas without monitoring stations. Inverse distance weighted is combined with $k$ -nearest neighbor to improve the performance of interpolation. In addition, time distributed convolutional neural network extracts the spatial features of air quality and storage time information for extracting temporal features by the gated recurrent units. A large number of experiments are carried out to evaluate the performance of the method by using the air quality dataset of Hubei Province, China. The experimental results show that the proposed model is effective for the monitoring of PM2.5 in the unsupervised areas and the performance (i.e. MAE=8.23, RMSE=11.27 and MAPE=19.14%) of estimating PM2.5 concentration is better than the comparative methods.

This article presents a dataset comparing emissions of Biogenic Volatile Organic Compounds (BVOC) in a zone of complex topography in the tropical Andes, which presents elevations ranging from 250 to more than 4000 m above sea level in a radius of only 50 km. Two approximations were evaluated, (1) online with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) coupled with the Weather Research and Forecast model with Chemistry (WRF-Chem) and (2) offline applying the Biogenic Altitudinal Gradient Model (BIGA). Modeled concentrations of pollutants (mainly isoprene and tropospheric ozone) were obtained with WRF-Chem employing the biogenic emission models mentioned previously. This information identified areas where BVOC emissions vary significantly, comparing the global emission inventory (MEGAN) and the local inventory (BIGA). Re-evaluation of the emission factors and land cover assigned to those areas in the global online biogenic models should be considered in order to reduce the uncertainty in the values. In addition, the dataset shows the impact of the biogenic emission inventories on the air quality simulations on a tropical high mountain area, where vegetation is diverse, and the altitudinal changes influence meteorological variables., Graphical abstract Image, graphical abstract

Commercial airport activity can adversely impact air quality in the vicinity of airports, and millions of people live close to major airports in the United States. Because of these potential impacts, a systematic literature review was conducted to identify peer reviewed literature on air quality near commercial airports and assess the quality of the studies. The systematic review included reference database searches in PubMed, Web of Science, and Google Scholar, inclusive of years 2000 through 2020. We identified 3,301 articles, and based on the inclusion and exclusion criteria developed, seventy studies were identified for extraction and evaluation using a combination of supervised machine learning and manual screening techniques. These studies consistently showed that ultrafine particulate matter (UFP) is elevated in and around airports. Furthermore, many studies show elevated levels of particulate matter under 2.5 µm in diameter (PM2.5), black carbon, criteria pollutants, and polycyclic aromatic hydrocarbons as well. Finally, the systematic review, while not focused on health effects, identified a limited number of on-topic references reporting adverse health effects impacts, including increased rates of premature death, pre-term births, decreased lung function, oxidative DNA damage and childhood leukemia. More research is needed linking particle size distributions to specific airport activities, and proximity to airports, characterizing relationships between different pollutants, evaluating long-term impacts, and improving our understanding of health effects.

Large industrial plants, power plants, and combined heat and power plants are popularly believed to be the main sources of point emissions, affecting both local and global air quality. This is because these installations emit significant amounts of pollutants at high altitudes every year. In this study, we investigate the impact of two solid fuel (hard coal)-fired CHP plants located within the urban agglomeration on the air quality of the city of Lodz in Poland (Europe). We used an OPA03 computer software to model the spatial distribution of pollutants. The results show that the annual average concentrations of pollutants were highest at an altitude of 25 m above ground level and decreased at lower measurement heights. The concentrations did not exceed permissible levels, reaching only 4% of national and international regulatory limits. We also made field measurements during the winter heating period, using an unmanned aerial vehicle (UAV) equipped with sensors to map the distributions of dust and gas pollutants in the areas with the highest concentrations of emissions from the two heat and power plants. Overall, the field measurements confirmed that it is not high-altitude emissions that have the greatest impact on local air quality.

International audience; This study used AEDT 2.d to estimate the pollutant emissions at Montreal’s Pierre Elliott Trudeau International Airport (YUL) for 2015 while quantifying the impact of the airport’s taxi time and atmospheric conditions on aircraft emissions. Using the more airport-specific parameters available and ICAO standard values otherwise, the yearly emissions of NOx, HC, CO, PM10, SOx, and CO2 at YUL were, respectively, 7.64 102, 1.18 102, 1.33 103, 1.35 101, 6.77 101, and 2.31 105 tonnes/year. The results show that reducing aircraft taxi time by 31 % reduced aircraft emissions by 6 % (NOx) and 27 % (CO). Atmospheric conditions impact aircraft emissions of NOx, CO and HC with a seasonal effect. Summer conditions reduced NOx emissions by 5 % and increased CO emissions by 2 %, while winter conditions reduced HC and CO emissions by approximately 15 % and increased NOx emissions by 1 %. To further investigate the impact of atmospheric conditions, dispersion calculations of NOx and CO emissions were carried out using identical air-traffic and weather data from the warmest and coldest weeks in 2015. The results demonstrate that pollutant concentrations were higher during the winter and that pollutants were also dispersed further during the winter than the summer according to the dominant wind direction. A 1-h NOx and CO concentrations greater than 10 μg/m3 were found up to 24 km and 30 km, respectively, away during the winter compared to 14 km and 20 km during the summer. Yet, local air quality standards were satisfied as pollutant concentrations found outside the airport enclosure were below those standards.

Air pollution has a major impact on human health, especially in cities, and elevated concentrations of PMx are responsible for a large number of premature deaths each year. Therefore, the amount of PM10 in the air is monitored and forecasts are made to predict the air quality. In Poland, mainly deterministic models are used to predict air pollution. Accordingly, research efforts are being made to develop other models to forecast the ambient PM10 levels. The aim of the study was to compare the machine learning models for predicting PM10 levels in the air in the city of Lublin. The following machine learning models were used: Linear regression (LR), K-Nearest Neighbors Regression (KNNR), Support Vector Machine (SVM), Regression Trees (RT), Gaussian Process Regression Models (GPR), Artificial Neural Network (ANN) and Long Short-Term Memory network (LSTM). The collected data for three consecutive years (January 2017 to December 2019) were used to develop the models. In total, 19 parameters, covering meteorological variables and concentrations of several chemical species, were explored as potential predictors of PM10. The data used to build the models did not take into account the seasons. The algorithms achieved the following R2 values: 0.8 for LR, 0.79 for KNNR, 0.82 for SVM, 0.77 for RT, 0.89, 0.90 for ANN and 0.81 for LSTM. Research has shown that the selection of a machine learning model has a large impact on the quality of the results. In this research, the ANN model performed slightly better than other models. Then, an ANN was used to train a network with five output neurons to predict the approximate level of PM10 at different time points (PM level at a given time, after 1 h, after 6 h, after 12 h and after 24 h). The results showed that the developed and tuned ANN model is appropriate (R = 0.89). The model created in this way can be used to determine the risk of exceeding the PM10 alert level and to inform about the air quality in the region.

The habitants of Sofia, the capital city of Bulgaria, are unfortunately exposed to high levels of particulate matter (PM), despite a lot of efforts made by the government and the municipality in recent decades. A high resolution model using the Atmospheric Dispersion Modeling System (ADMS)-Urban was carried out for one year (2014) with elevated levels of PM concentration for the Sofia region measured during the winter. Four main emission sources (point industrial, domestic heating, roads, and unorganized transport) were taken into account for this study. This works intends to evaluate the impacts of different sources on the air pollution and quality of life in Sofia city. Although this type of modeling only accounts for the local sources, and has some deficiency, it can be a very useful tool for estimating the contribution of each source type to the concentration field, and it allows simulating different scenarios for future emission reduction that can help authorities with decision making.

EnvironmentAsia, 14, 2, 51-63

Due to their major role in atmospheric chemistry and secondary pollutant formation such as ozone or secondary organic aerosols, an accurate representation of OH and HO2 (HOX) radicals in air quality models is essential. Air quality models use simplified mechanisms to represent atmospheric chemistry and interactions between HOX and organic compounds. In this work, HOX concentrations during the oxidation of toluene and xylene within the Regional Atmospheric Chemistry Mechanism (RACM2) are improved using a deterministic–near-explicit mechanism based on the Master Chemical Mechanism (MCM) and the generator of explicit chemistry and kinetics of organics in the atmosphere (GECKO-A). Flow tube toluene oxidation experiments are first simulated with RACM2 and MCM/GECKO-A. RACM2, which is a simplified mechanism, is then modified to better reproduce the HOX concentration evolution simulated by MCM/GECKO-A. In total, 12 reactions of the oxidation mechanism of toluene and xylene are updated, making OH simulated by RACM2 up to 70% more comparable to the comprehensive MCM/GECKO-A model for chamber oxidation simulations.

Poland faces two great challenges in the field of environment and atmosphere protection: improving air quality, especially by reducing particulate matter (PM) emissions, and reducing relatively high greenhouse gas emissions. The aim of this research was to investigate how the fuel and technological transformations in the power, road transport, and household and tertiary sectors aimed at reducing carbon dioxide (CO2) emissions in Poland would affect air quality, human health, and the associated external costs. The study was conducted for 2050 while considering 2015 as the base year. Ambient PM2.5 (particles with a diameter of less than 2.5 &micro, m) concentration was used as a proxy air quality indicator. The analysis was based on decarbonization scenarios developed within the REFLEX Project (Analysis of the European energy system under the aspects of flexibility and technological progress). The three scenarios of the REFLEX Project focused on the reduction of CO2 emissions up to 2050 from various sectors, mainly by the means of fuel and technological switches. This also led to the changes in the emission levels of pollutants that directly affect air quality, which were calculated with the use of fuel- and technology-specific emission factors. Next, for each emission scenario, ambient concentrations of PM2.5 and others pollutants were calculated with the use of the Polyphemus&mdash, an Eulerian-type air quality modelling system. Subsequently, the health impact of population exposed to air pollution and associated external costs were calculated using the &pi, ESA (Platform for Integrated Energy System Analysis) platform. The health impacts considered were the number of years of life lost, restricted activity days, and number of chronic bronchitis cases. The results showed that the largest reductions in both greenhouse gas and PM emissions&mdash, and consequently improvements of air quality resulting in a decrease of negative impacts on human health and a decrease of external costs&mdash, can be achieved by the transformation of heat production in the household and tertiary sector. The results also showed that the decrease in PM2.5 emissions envisaged in the analyzed scenarios in 2050 will lead to a reduction in the number of lost years of life by about 35 thousand and an avoidance of external costs by EUR 2.4 billion.

Along with its rapid urban development, Ho Chi Minh City (HCMC) in recent years has suffered a high concentration of air pollutants, especially fine particulate matters or PM2.5. A comprehensive study is required to evaluate the air quality conditions and their health impact in this city. Given the lack of adequate air quality monitoring data over a large area of the size of HCMC, an air quality modeling methodology is adopted to address the requirement. Here, by utilizing a corresponding emission inventory in combination with The Air Pollution Model-Chemical Transport Model (TAPM-CTM), the predicted concentration of air pollutants is first obtained for PM2.5, NOx, and SO2. Then by associating the pollutants exposed with the mortality rate from three causes, namely Ischemic Heart Disease (IHD), cardiopulmonary, and lung cancer, the impact of air pollution on human health is obtained for this purpose. Spatial distribution has shown a high amount of pollutants concentrated in the central city with a high density of combustion vehicles (motorcycles and automobiles). In addition, a significant amount of emissions can be observed from stevedoring and harbor activities, including ferries and cargo handling equipment located along the river. Other sources such as household activities also contribute to an even distribution of emission across the city. The results of air quality modeling showed that the annual average concentrations of NO2 were higher than the standard of Vietnam National Technical Regulation on Ambient Air Quality (QCVN 05: 2013 40 &micro, g/m3) and World Health Organization (WHO) (40 &micro, g/m3). The annual average concentrations of PM2.5 were 23 &micro, g/m3 and were also much higher than the WHO (10 &micro, g/m3) standard by about 2.3 times. In terms of public health impacts, PM2.5 was found to be responsible for about 1136 deaths, while the number of mortalities from exposure to NO2 and SO2 was 172 and 89 deaths, respectively. These figures demand some stringent measures from the authorities to potentially remedy the alarming situation of air pollution in HCM City.

The exhaust gases expelled from the tailpipe during the transit of the buses are composed by different pollutants, each of them dangerous to the human health in different ways. Self-pollution has been detected as a phenomenon related with the passengers exposition to pollutants inside transport buses during their travels. The emissions from the tailpipe make their way to the cabin of the bus, exposing the passengers to toxic gases and particulate matter during the whole time of the trip they are making. The quantitative and detailed modeling of the pollutants in-cabin is important due to the sensitive nature of the children population to respiratory disease related to air pollution. This study assesses self-pollution in school buses using CFD modeling, this is achieved by simulating the dispersion of a tracer gas Sulfur Hexa-Fluoride (SF6) inside and outside the bus. Two previous studies datasets were used to validate the CFD model performance, and accuracy in its capacity to quantitatively describe the phenomenon. The CFD model developed and validated considered the turbulence through the k-epsilon realizable model. The system was considered as a multicomponent single-eulerian-phase flow with the “species to transport” model. Transient formulation and energy were implemented. Mesh was optimized to polyhedral elements, reducing considerably the simulation time by 35%, and mesh size to 25%. The model was able to determine the behavior of the tailpipe emissions and the self-pollution phenomenon into the bus, showing that the highest concentrations (and therefore the maximum exposure) are located in the rear part of the bus. Validation of the CFD results with previous experimental measures and modeling results, reported in two previous studies, determined an error of 12% and 17% for the concentrations outside and inside the bus respectively, correlation coefficient (R2) values between 0.5 and 0.9 were obtained between CFD results for SF6 concentrations and validation dataset.

A large petrochemical complex, dubbed Petro&ndash, complex, situated in a rather rural region of Taiwan, was used as a test bed to detect emissions from the Petro&ndash, complex to its surroundings. Hourly observations of speciated non&ndash, methane hydrocarbons (NMHCs) by the photochemical assessment monitoring stations (PAMSs), as well as the total amounts of NMHCs, SO2, and NOx provided by the air quality stations (AQSs), were utilized to find useful petro&ndash, emission indication methods. The analytical aspect of NMHCs either as a speciated form or as total amounts was demonstrated through field comparison to illustrate data quality. Using ethyne to offset traffic influence, the ratios of ethene to ethyne (acetylene) (E/A) and propene to ethyne (P/A) were proven to be effective indicators of petro&ndash, emissions owing to pronounced emissions of ethene and propene, revealed as tall spikes in PAMS measurements. SO2 and NOx were also explored as petro&ndash, emission indicators mainly for stack sources. By coordinating with three&ndash, dimensional modeling, SO2 from petro&ndash, emissions could be distinguished from other prominent sources, such as coal&ndash, fired power plants. An attempt was also made to use SO2 and NOx as indicators of broader petro&ndash, emissions with stringent criteria to minimize traffic interference and increase specificity. Similar findings were observed with the three indicators, that is, volatile organic compounds (VOCs) ratios, SO2 and NOx, to identify the southwest area of the Petro&ndash, region as the most affected area, as represented by Taisi station (F2). The percent affected time of a year at F2 was 10%&ndash, 14%, owing to the dominant wind field of northeast monsoonal (NEM) in the region, as compared with other sites in the east and north of 1&ndash, 5%. Using VOC ratios as petro&ndash, emission indicators is more effective than using other gases, owing to the compositional advantage to minimize traffic interference.

Fine particulate matter (PM2.5) is dangerous to human health. At midnight on 31 December, in Ecuadorian cities, people burn puppets and fireworks, emitting high amounts of PM2.5. On 1 January 2022, concentrations between 27.3 and 40.6 µg m−3 (maximum mean over 24 h) were measured in Cuenca, an Andean city located in southern Ecuador; these are higher than 15 µg m−3, the current World Health Organization guideline. We estimated the corresponding PM2.5 emissions and used them as an input to the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model to simulate the change in PM2.5 concentrations, assuming these emissions started at 18:00 LT or 21:00 LT on 31 December 2021. On average, PM2.5 concentrations decreased by 51.4% and 33.2%. Similar modeling exercises were completed for 2016 to 2021, providing mean decreases between 21.4% and 61.0% if emissions started at 18:00 LT. Lower mean reductions, between 2.3% and 40.7%, or even local increases, were computed for emissions beginning at 21:00 LT. Reductions occurred through better atmospheric conditions to disperse PM2.5 compared to midnight. Advancing the burning time can help reduce the health effects of PM2.5 emissions on 31 December.

The European Directive (2008/50/EC) encourages the use of models in the assessment and forecasting of air quality, and assigns them a supporting or replacing role with respect to fixed ground-based measurements. A thorough knowledge of performance of the modeling tools over urban areas is therefore required. In this study, we analyze sensitivity tests with the WRF-CHIMERE modeling system in order to investigate the effect of (1) the horizontal model grid size, (2) the resolution of the anthropogenic emission inventory, and (3) the introduction of urban canopy models. The work focuses on L'Aquila and Milan, two Italian cities widely differing for the number of inhabitants, the extension and the geographical location. We found a clear advantage in increasing the model resolution up to ∼4 km, but a further increase at ∼1 km resolution does not seem to be justified. Moreover, we found that the ozone simulation is generally degraded at higher resolution. The introduction of a more detailed treatment of the urban canopy and of the anthropogenic emissions suggests the potential for further improvement, but this requires a fine tuning on the area of application. For example, the Building Environment Parameterization corrects the surface wind speed daily cycle, but it also increases the planetary boundary layer height, resulting in excessive dilution of primary pollutants. The anthropogenic emissions should be refined proportionally to the increase in dynamic model resolution, possibly through new bottom-up inventories, rather than through a downscaling of a coarser inventory. We suggest that future work should primarily focus on intensive campaign periods, where a comprehensive observational characterization of the three dimensional structure and evolution of the planetary boundary layer is available.

A new analytical air pollution modeling system is introduced in this paper to estimate concentrations of primary and secondary air pollutants and using it for further studies in order to improve the knowledge of pollutants emission and dispersion over Tehran, and developing a decision support system. For this purpose, WRF/CAMx modeling system was used to simulate the gas-phase pollutants concentrations including primary and secondary pollutants, over Tehran during a wintertime episode, which is characterized by very high concentrations of pollutants. Pollutants were triggered by meteorological conditions leading to a forced holiday imposed on citywide operations to protect the health of citizens. Based on calculated Values of NMB error, WRF performs acceptable in predicting temperature and wind speed. Generally, time series plots show that WRF performs acceptable in mild selected episode. Also, the daily trends of pollutant concentrations are greatly affected by changes in local meteorological conditions such as planetary boundary layer (PBL) height, temperature, wind, and relative humidity over the Tehran area. An underestimation in prediction of all pollutants concentrations episode at Poonak and Aghdasyeh sites show due to the insufficient emission data at the site position used for the simulation were seen. Results showed that WRF/CAMx modeling system proved to be a useful tool for analyzing urban environmental problems, investigating the impact of air quality control policies, and predicting critical conditions. However, there were weaknesses in input data and modeling system calibration that should be improved before using the system for further studies.

This study examines the concentrations of air pollution in the vicinity of a combined heat and power plant (CHP) and a communication route, using computer modeling of pollutant dispersion and spatial analysis based on real measurements in the city of Łódź, Poland, Europe. The research takes into account the concentrations of particulate matter (PM10, PM2.5, PM1.0) and gaseous pollutants (SO2 and VOC) in winter and summer. The spatial distribution of pollutants is discussed, including the presence of areas with increased accumulations of pollutants. Because atmospheric air has no natural boundaries, when analyzing any location, not only local sources of pollution, but also background pollution, should be analyzed. A clear difference was observed between the concentrations of pollutants in the summer and winter seasons, with significantly higher concentrations in the winter (heating) period. The impacts of road transport, individual heating systems, and combined heat and power plants were also assessed. Computer calculations confirmed that road transport accounted for the largest share of both PM and SO2 emissions. The CHP plant was responsible for the smallest percentage of dust emissions and was the next largest producer of SO2 emissions. The share of the total emissions from the individual sources were compared with the results of detailed field tests. The numerical analysis of selected pollution sources in combination with the field analysis shows that the identified pollution sources included in the analysis represent only a part of the total observed pollutant concentrations (suggesting that other background sources account for the rest).

To cope with computing power limitations, air quality models that are used in integrated assessment applications are generally approximated by simpler expressions referred to as “source-receptor relationships (SRR)”. In addition to speed, it is desirable for the SRR also to be spatially flexible (application over a wide range of situations) and to require a “light setup” (based on a limited number of full Air Quality Models - AQM simulations). But “speed”, “flexibility” and “light setup” do not naturally come together and a good compromise must be ensured that preserves “accuracy”, i.e. a good comparability between SRR results and AQM. In this work we further develop a SRR methodology to better capture spatial flexibility. The updated methodology is based on a cell-to-cell relationship, in which a bell-shape function links emissions to concentrations. Maintaining a cell-to-cell relationship is shown to be the key element needed to ensure spatial flexibility, while at the same time the proposed approach to link emissions and concentrations guarantees a “light set-up” phase. Validation has been repeated on different areas and domain sizes (countries, regions, province throughout Europe) for precursors reduced independently or contemporarily. All runs showed a bias around 10% between the full AQM and the SRR. This methodology allows assessing the impact on air quality of emission scenarios applied over any given area in Europe (regions, set of regions, countries), provided that a limited number of AQM simulations are performed for training., Highlights • Integrated Assessment Modeling applies source receptor relationships (SRR). • SRR need to be fast, flexible, accurate and easy to be set-up. • Existing SRR lack flexibility in terms of spatial emission reductions. • In this work a novel approach to flexible SRR is formalized and validated. • These SRR can simulate emission scenarios applied over any domain in Europe.

This study evaluates the performance of the Weather Research and Forecasting Model with Chemistry (WRF-Chem) for simulating biomass burning aerosol transport at high resolution in the tropics using two different biomass burning emission inventories. Hourly, daily, and monthly average PM10 dry mass concentrations at 5 km resolution&mdash, simulated separately using the Brazilian Biomass Burning Emission Model (WRF-3BEM) and the Fire Inventory from NCAR (WRF-FINN) and their averages (WRF-AVG) for 3 months from February to April&mdash, are evaluated, using measurements from ground stations distributed in northern Thailand for 2014 and 2015. Results show that WRF-3BEM agrees well with observations and performs much better than WRF-FINN and WRF-AVG. WRF-3BEM simulations are almost unbiased, while those of WRF-FINN and WRF-AVG are significantly overestimated due to significant overestimates of FINN emissions. WRF-3BEM and the measured monthly average PM10 concentrations for all stations and both years are 89.22 and 87.20 &mu, g m&minus, 3, respectively. The root mean squared error of WRF-3BEM simulated monthly average PM10 concentrations is 72.00 and 47.01% less than those of WRF-FINN and WRF-AVG, respectively. The correlation coefficient of WRF-3BEM simulated monthly PM10 concentrations and measurements is 0.89. WRF-3BEM can provide useful biomass burning aerosol transport simulations for the northern region of Thailand.

Air pollution is associated to poor urban mobility conditions, as in the megacity of São Paulo. Three scenarios of policies of driving restrictions were simulated using the WRF/Chem model, to assess the impacts on ozone pollution and the consequences for environmental justice: urban toll, sustainable transition, and vehicle free zone. Results show that the urban toll leads to a decrease of 20 µg.m-3 in maximum 8-hour ozone averages and the prevention of six hours of exceedances of the air quality standard. Sustainable transition was the most promising scenario, with a decrease of 50 µg.m-3 and avoiding 22 hours of exceedances. The vehicle free zone did not show any advantages. For the implementation of adequate public transport systems, massive investments are required, in order to achieve better air quality with the greatest environmental justice possible, aiming towards the protection of human health along with a more democratic access to the urban spaces. Resumo A poluição atmosférica pode ser agravada devido à precárias condições de mobilidade urbana, como ocorre em São Paulo. Visando avaliar os impactos na poluição por ozônio e as consequências para a justiça ambiental, 3 cenários de políticas de restrição veicular foram simulados com o modelo WRF/Chem: pedágio urbano, transição sustentável e centro sem veículos. Os resultados indicaram que o pedágio urbano levou a uma redução de 20 µg.m-3 nas médias das máximas de 8 horas, e evitou seis ultrapassagens do padrão de qualidade do ar. A transição sustentável teve máximas até 50 µg.m-3 mais baixas e evitou 22 ultrapassagens. O centro sem veículos não demonstrou vantagens. Para a implementação de um sistema de transporte público adequado, são necessários expressivos investimentos para se atingir melhorias na qualidade do ar com a maior justiça ambiental possível, buscando a proteção da saúde da população juntamente à democratização do acesso aos espaços urbanos. Resumen La contaminación atmosférica puede agravarse debido à la movilidad urbana ineficiente, como en São Paulo. Visando avaliar los impactos em la contaminación por ozono y las consecuencias para la justicia ambiental, se simularan 3 escenarios de restricción vehicular con en el modelo WRF/Chem: peaje urbano, transición sustentable y centro sin vehículos. Los resultados indicaron que el peaje urbano condujo a una reducción de 20 µg.m-3 en las concentraciones medias máximas de 8 horas, y evito 6 excedencias de las normas de calidad del aire. La transición sustentable tuvo un máximo de hasta 50 µg.m-3 más bajo e 22 excedencias evitadas. El centro sin vehículos no presento ventajas. Para la implementación de un transporte público adecuado, se necesitan inversiones significativas para lograr mejoras en la calidad del aire con la mayor justicia ambiental posible, buscando proteger la salud de la población junto con la democratización del acceso a los espacios urbanos.

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 &micro, m diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index&mdash, NDVI, normalized difference soil index&mdash, NDSI, soil-adjusted vegetation index&mdash, SAVI, normalized difference water index&mdash, NDWI, and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

Agricultural burning is still a common practice around the world. It is associated with the high emission of air pollutants, including short-term climate change forcing pollutants such as black carbon and PM2.5. The legal requirements to start any regulatory actions to control them is the identification of its area of influence. However, this task is challenging from the experimental and modeling point of view, since it is a short-term event with a moving area source of pollutants. In this work, we assessed this agricultural burning influence-area using the US Environmental authorities recommended air dispersion model (AERMOD). We considered different sizes and geometries of burning areas located on flat terrains, and several crops burning under the worst-case scenario of meteorological conditions. The influence area was determined as the largest area where the short-term concentrations of pollutants (1 h or one day) exceed the local air quality standards. We found that this area is a band around the burning area whose size increases with the burning rate but not with its size. Finally, we suggested alternatives of public policy to regulate this activity, which is based on limiting the burning-rate in the way that no existing households remain inside the resulting influence-area. However, this policy should be understood as a transition towards a policy that forbids agricultural burning.

Air quality models are currently the best available tool to estimate ozone (O3) concentrations in the Metropolitan Area of Buenos Aires (MABA). While the DAUMOD-GRS has been satisfactorily evaluated against observations in the urban area, a Monte Carlo (MC) analysis showed that it is the region around the MABA, where the lack of observations impedes model testing, that concentrates not only the greatest estimated O3 peak levels but also the largest model uncertainty. In this work, we apply clustering analysis to these MC outcomes in order to study the spatial patterns of conditions leading to peak ozone hourly concentrations. Results show that families of conditions distribute, as emissions, radially around the city. A cluster exhibiting an O3 morning peak dominates in low-emission areas, a behavior that can be explained both from theory and from the few monitoring campaigns carried out in the city. Its distinct dynamics compared with the typical O3 diurnal profile occurring in the urban area suggests the need of new ozone measurements in the surroundings of the MABA which could contribute to improve our understanding of O3 formation drivers in this region. The results illustrate the potential of applying clustering analysis on large ensembles of modeled data to better understand the variability in model solutions. Fil: Pineda Rojas, Andrea Laura. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Leloup, Julie A.. Centre National de la Recherche Scientifique. Institut de Recherche pour le Développement; Francia Fil: Kropff, Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina

Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.

Meteorology plays a crucial role in air quality. The presence of uncertainties of a significant nature in the meteorological profile used during air quality model simulation has the potential to affect negatively the results of the simulations. This paper describes a most recent version of the meteorological model called Weather Research and Forecasting (WRF) model and its importance in air quality. The performance of WRF depends upon the intended application and parameterization scheme of physics options. WRF model is also applied to investigate the simulation results with various land surface models (LSMs) and Planetary Boundary Layer (PBL) parameterizations and various set of microphysics options. It predicts various meteorological spatial parameters like mixing layer height, temperature, humidity, rain fall, cloud cover and wind. The WRF results are integrated with air quality model (AQM) and the AQM depends upon the performance of WRF. It has been applied for evaluation of national pollution control policy, behaviour of plume rise, property of aerosols, prediction of Ozone, SO2, NOx, PM10, PM2.5 etc. using AQM for various sources. The effect of topography and different seasons on the concentration of pollutants in the atmosphere has also been studied using AQM. AQM AERMOD has also been reviewed with various other AQM models such as ADMS-Urban and CALPUFF. AERMOD has been used for different time scales, health risk assessment, evaluation of various control strategies, Environmental Impact Assessment (EIA) studies and emission factor estimation. This paper presents the importance of meteorological model to AQM as well as many applications of AQM to demonstrate various scientific questions and policies.

Regional and local authorities have the obligation to design air quality plans and assess their impacts when concentration levels exceed the limit values. Because these limit values cover both short- (day) and long-term (year) effects, air quality plans also follow these two formats. In this work, we propose a methodology to analyze modeled air quality forecast results, looking at emission reduction for different sectors (residential, transport, agriculture, etc.) with the aim of supporting policy makers in assessing the impact of short-term action plans. Regarding PM10, results highlight the diversity of responses across European cities, in terms of magnitude and type that raises the necessity of designing area-specific air quality plans. Action plans extended from 1 to 3 days (i.e., emissions reductions applied for 24 and 72 h, respectively) point to the added value of trans-city coordinated actions. The largest benefits are seen in central Europe (Vienna, Prague) while major cities (e.g., Paris) already solve a large part of the problem on their own. Eastern Europe would particularly benefit from plans based on emission reduction in the residential sectors; while in northern cities, agriculture seems to be the key sector on which to focus attention. Transport is playing a key role in most cities whereas the impact of industry is limited to a few cities in south-eastern Europe. For NO2, short-term action plans focusing on traffic emission reductions are efficient in all cities. This is due to the local character of this type of pollution. It is important, however, to stress that these results remain dependent on the selected months available for this study.

One of the central problems in large cities is air pollution, mainly caused by vehicular emissions. Tropospheric ozone is an atmospheric oxidizing gas that forms in minimal amounts naturally, affecting peoples’ health. This pollutant is formed by the NO2 photolysis, creating a main peak during the day. Nighttime secondary peaks occur in several parts of the world, but their intensity and frequency depend on the local condition. In this sense, this works aims to study the local characteristics for tropospheric nocturnal ozone levels in the Metropolitan Area of São Paulo, in Brazil, using the Simple Photochemical Module coupled to the Brazilian Developments on the Regional Atmospheric Modeling System. For this, three different situations of nocturnal occurrence were studied. The results show that the nocturnal maximum of ozone concentrations is related to the vertical transport of this pollutant from higher levels of the atmosphere to the surface and is not related to the synoptic condition.