Impact of WRF model PBL schemes on air quality simulations over Catalonia, Spain

Human health risk assessment of PM10-bound heavy metals and PAHs around the Latin America's Largest opencast coal mine

Air pollution in opencast coal mine areas is a critical issue, resulting in harmful severe effects on human health. Therefore, it is essential to understand the air pollution factors and to assess the risks to humans. This study evaluated the potential risks (carcinogen and non-carcinogen) of inhalation exposure to PM10-bound heavy metals and Polycyclic Aromatic Hydrocarbons (PAHs) in an open pit mine in northern Colombia. During February-May 2022, PM10 samples were collected at eight sites. Heavy metals (i.e., Al, Cr(VI), Mn, Cu, Zn, As, Pb) and PAHs (thirteen priority PAHs, and one non-priority PAH) levels linked to PM10 were analyzed by X-ray fluorescence and gas chromatography-mass spectrometry, respectively. PM10 concentrations were found to range between 4.70 and 59.90 µg m-3. Out of the three different zones of the study area (i.e., North Zone, South Zone, and Populated Zone), the North Zone recorded the highest daily average concentrations of Cr(VI) (104.16 ng m-3), Mn (28.39 ng m-3), Cu (33.75 ng m-3), Zn (57.99 ng m-3), As (44.92 ng m-3), and Pb (27.13 ng m-3). The fraction of the analyzed heavy metals at all monitoring sites was 82%-89% for Al, followed by Cr(VI) with 3%-6%. Cr(VI) was the major contributor to the carcinogenic risk values, while Cu, Cr(VI), and As were the main drivers for the non-carcinogenic risk. The average cancer risk range for heavy metals was 3.30 × 10-04 -5.47 × 10-04. On the other hand, the cancer risk for PAHs exposure was acceptable. The average incremental lifetime cancer risk (ILCR) values varied between 2.87 × 10-07 and 4.21 × 10-07. Benzo[a]pyrene contributed to 54%-56% of the total risk from inhalation of PM10-bound PAHs, while Indeno[1,2,3-cd]pyrene contributed to 16%-19%. Based on the Monte Carlo sensitivity analysis, exposure to Cr(VI) was the main factor affecting cancer risk in the North, South, and Populated Zones. A suitable risk assessment and management plan requires understanding PM10-bound heavy metals and PAHs concentration levels as well as their potential health risks, mainly in open-cast coal mine zones. Our study found that people living near open-pit mines face potential health risks, so it is crucial to establish policies and regulations to control emission sources.

A multi-objective framework to select numerical options in air quality prediction models: A case study on dust storm modeling

Numerical weather prediction models are very important tools in predicting severe weather phenomena such as dust storms. However, the prediction accuracy in these models depends on the options considered in the modeling. In this study, a multi-objective framework is presented to determine the optimal options of the weather research forecasting with chemistry (WRF-Chem) model. For this purpose, a severe dust storm that occurred in the center of Iran is considered and the effect of 10 options including grid (computational domain size, modeling start time, horizontal, vertical and temporal resolution), physical (initial conditions, boundary layer and land surface schemes) and chemical options (dust emission schemes and dust source functions) are investigated. In general, the results showed that the WRF-Chem model has a high ability to model dust storms, but its results depend on the options considered in the modeling. Evaluation of grid options showed that inappropriate selection of domain size and modeling start time can lead to the failure in dust storm forecasting. Also, the land surface scheme has the greatest impact on dust concentration among the physical options. In addition, chemical options have the greatest impact on the dust storm forecasting as well. Based on the proposed multi-objective framework, the optimal options for dust storm modeling were determined. The proposed approach is comprehensive and can be used for other atmospheric/air quality modeling.

The relationship between the intensified heat waves and deteriorated summertime ozone pollution in the Beijing-Tianjin-Hebei region, China, during 2013-2017

Summertime ozone (O₃) pollution has frequently occurred in the Beijing-Tianjin-Hebei (BTH) region, China, since 2013, resulting in detrimental impacts on human health and ecosystems. The contribution of weather shifts to O₃ concentration variability owing to climate change remains elusive. By combining regional air chemistry model simulations with near-surface observations, we found that anthropogenic emission changes contributed to approximately 23% of the increase in maximum daily 8-h average O₃ concentrations in the BTH region in June-July-August (JJA) 2017 (compared with that in 2013). With respect to the weather shift influence, the frequencies, durations, and magnitudes of O₃ exceedance were consistent with those of the heat wave events in the BTH region during JJA in 2013-2017. Intensified heat waves are a significant driver for worsening O₃ pollution. In particular, the prolonged duration of heat waves creates consecutive adverse weather conditions that cause O₃ accumulation and severe O₃ pollution. Our results suggest that the variability in extreme summer heat is closely related to the occurrence of high O₃ concentrations, which is a significant driver of deteriorating O₃ pollution.

Temporal and spatial characteristics of turbulent transfer and diffusion coefficient of PM2.5

The temporal and spatial characteristics of turbulent transfer and diffusion coefficient of PM_2.5 (K_C) were investigated by determining the deviation, turbulent flux and form of universal function of PM_2.5 mass concentrations. Turbulence and sounding observations from December 8-25, 2019, of three sites, Tuonan, Baoding, and Renqiu stations in the North China Plain were selected. Mean PM_2.5 mass fluxes during the intensive observational period of three stations were negative. The spatial distribution of PM_2.5 mass flux of three stations showed no obvious tendency. Then, the fact that PM_2.5 mass concentrations satisfied the Monin-Obukhov similarity were reconfirmed by examining the relationship between the normalized standard deviation of PM_2.5 mass concentrations and stability factor ζ. Thus, the universal functions in the three stations were achieved. The time series and profiles of K_C in the three stations were also shown. There was a good inverse correlation between K_C and PM_2.5 mass concentration which suggested that the influence of turbulent diffusion is remarkably important during observational time even the emission, deposition, secondary transformation can all affect the change of mass concentrations of PM_2.5 in the ABL. Changes in K_C obviously presented diurnal characteristics. The comparisons of K_C and K_M and K_H suggested that the strength of turbulent PM_2.5 mass flux exchange could be weaker or stronger than the strength of turbulent momentum and heat flux exchange at different stations. The magnitude relationship between K_C and K_H could not be completely determined, so there were limitations in using K_H to replace directly K_C in the existing numerical weather or climate models. Finally, the spatial distribution of K_C at the three stations presented almost symmetrical characteristics from east to west (Tuonan to Renqiu); that is, Baoding always had the lowest K_C, and Tuonan and Renqiu stations had higher K_Cs. The spatial distribution of K_C at the three stations corresponded well with that of PM_2.5 mass concentrations, and Baoding always had the largest PM_2.5 mass concentrations.

Impact of urban morphology on the spatial and temporal distribution of PM2.5 concentration: A numerical simulation with WRF/CMAQ model in Wuhan, China

The urban morphology can significantly change the urban microclimate, which in turn affects the diffusion of air pollutants. Urban planning is the most important means of shaping urban morphology. Therefore, this study takes Wuhan as an example and uses the method of WRF/CMAQ coupled UCM model to analyze the spatial and temporal distribution characteristics of PM_2.5 in the Wuhan metropolitan area in winter 2015. The six most important urban morphological indicators in urban planning: the floor area ratio and building height, building density and building width, vegetation coverage ratio, and urban fraction, are selected and classified into three groups. Studying their impact on the spatial and temporal distribution of PM_2.5 concentration provides support for urban planners to improve air quality. The results show that the maximum value of PM_2.5 concentration in Wuhan urban area occurs in the morning rush hour, and PM_2.5 is distributed concentrically in the downtown of the city (within the second ring highway) according to the highways around the city. The PM_2.5 concentration in the downtown area with the most extensive urban morphological index is the highest, and it decreases with increasing distance from the downtown. Among the six indicators, building density and urban fraction have the most significant impact on PM_2.5 concentration because they have the greatest impact on the wind speed at 10 m. The height of the planetary boundary layer is the key factors affect the vertical and horizontal diffusion of air pollutants. Except for the vegetation coverage ratio, the increase of other urban morphological indicators will lead to a decrease of PM_2.5 concentration in Wuhan urban area at night. During the daytime, increasing the floor area ratio and building height will cause an increasing of PM_2.5 concentration, but other indicators have the opposite effects.

Understanding the Major Impact of Planetary Boundary Layer Schemes on Simulation of Vertical Wind Structure

The structure and evolution of the atmospheric planetary boundary layer (PBL) plays an important role in the physical and chemical processes of cloud–radiation interaction, vertical mixing and pollutant transport in the atmosphere. The PBL parameterization scheme describes the vertical transport of atmospheric momentum, heat, water vapor and other physical quantities in the boundary layer. The accuracy of wind field simulation and prediction is one of the most significant parameters in the field of atmospheric science and wind energy. Limited by the observation data, there are few studies on wind energy development. A 3D Doppler wind LiDAR (DWL) providing the high-vertical-resolution wind data over the urban complex underlying surface in February 2018 was employed to systematically evaluate the accuracy of vertical wind field simulation for the first time. 11 PBL schemes of the Weather Research and Forecasting Model (WRF) were employed in simulation. The model results were evaluated in groups separated by weather (sunny days, hazy days and windy days), observation height layers of wind field, and various observation wind speeds. Among these factors, the simulation accuracy is most closely related to the observation height layers of wind field. The simulation is fairly accurate at a height of 1000–2000 m, as most of the relative mean biases for wind speed and wind direction are less than 20% and 6% respectively. Below 1000 m, the wind speed and direction biases are about 30–150% m·s−1 and 6–30%, respectively. Moreover, when the observed wind speed was lower than 5 m·s−1, the biases were usually large, and the wind speed relative mean bias reaches up to 50–300%. In addition, the accuracy of the simulated wind profile is better in the range of 10–15 m·s−1 than other speed ranges, and is better above 1000 m than below 1000 m in the boundary layer. We see that the WRF boundary layer schemes have different applicabilities to different weather conditions. The WRF boundary layer schemes have significant differences in wind field simulations, with larger error under the complex topographies. A PBL scheme is not likely to maintain its advantages in the long term under different conditions including altitude and weather conditions.

Evaluation and improvement study of the Planetary Boundary-Layer schemes during a high PM2.5 episode in a core city of BTH region, China

Accurate depictions of planetary boundary layer (PBL) processes are important for both meteorological and air quality simulations. This study examines the sensitivity of the model performance of the Weather Research Forecasting model coupled with Chemistry (WRF-Chem) to five different PBL schemes and further to different turbulence parameters for the simulation of a winter haze episode in Tianjin, a core city of the Beijing-Tianjin-Hebei (BTH) region in China. To provide a direct and comprehensive evaluation of the PBL schemes, measurements from multiple instruments are employed, including both meteorological and air quality quantities from near-surface observations, vertical sounding measurements and ceilometer data. Moreover, the vertical distribution of the turbulent exchange coefficient is derived from sounding measurements and is utilized to evaluate the PBL schemes. The results suggest that the Mellor-Yamada-Janjic (MYJ) scheme is generally statistically superior to the other schemes when comparing observations. However, considerable model discrepancies still exist during certain stages of this haze episode, which are found to be predominantly due to the deficiency of MYJ in distinguishing the intensity of turbulent mixing between different pollution stages. To improve the model performance, this study further tests the impact of different closure parameters on the simulation of winter haze episode. In the MYJ scheme, the closure parameters play a key role in the turbulent mixing within the PBL and therefore in haze simulations. Sensitivity experiments with different MYJ parameters confirm this diagnosis and suggest that a larger Prandtl number (Pr), rather than the default value in the MYJ formulation, may be more applicable for haze simulations under stable atmospheric conditions.

Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NO_x), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM_2.5) concentration below the apartment building height. Atmospheric NO_x dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NO_x concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.

The impact of aerosol direct radiative effects on PM2.5-related health risk in Northern Hemisphere during 2013-2017

This study estimates the health risk due to PM_2.5 and ADE primarily in 4 regions across Northern Hemisphere during 2013-2017. Mortality in China due to PM_2.5 dipped from 1.15 million (95%CI (Confidence Interval) 0.64-1.6 million) to 1.02 million (95%CI 0.59-1.49 million) during 2013-2017 as a result of reduction in PM_2.5 population weighted concentration (PWC) from 29.26 μg/m³ to 22.05 μg/m³ while India overtook China in terms of death due to PM_2.5 which increased to 1.16 million (95%CI 0.72-1.67 million) from 1.07 million (95%CI 0.62-1.53 million) as a result of increase in PWC from 38.18 to 44.47 μg/m³. The years of life lost per person (YLL/person) due to PM_2.5 was still observed to be high in China with 5.58 YLL/person followed by India (4.13), Europe (2.19) and US (0.46) in 2017. Aerosols such as PM_2.5 have the capability to scatter or absorb solar radiation resulting in perturbation of ground meteorology which further affects dispersion of pollutants and it's resultant health impacts. ADE resulted in 7.27% of total or 77,866 deaths in India during 2013 which increased to 8.05% or 93,575 deaths in 2017 which was highest among all regions while in China ADE deaths reduced from 59,529 (5.15% of total) to 40,470 (3.94% of total) deaths during the same period, other regions too reported reduction in ADE deaths with US reporting 906 (-1.27%) lower deaths while Europe also reported 785 (-0.46%) lower deaths in 2017 as compared to 2013. ADE resulted in increased YLL/person in India from 0.29 to 0.33 during 2013-2017 while it was observed to reduce in all other regions, in China it reduced from 0.37 to 0.22 likewise YLL/person also reduced in US from 0.04 to 0.01 and in Europe from 0.01 to 0.002.

Impact of the ‘13th Five-Year Plan’ Policy on Air Quality in Pearl River Delta, China: A Case Study of Haizhu District in Guangzhou City Using WRF-Chem

Due to increasingly stringent control policy, air quality has generally improved in major cities in China during the past decade. However, the standards of national regulation and the World Health Organization are yet to be fulfilled in certain areas (in some urban districts among the cities) and/or certain periods (during pollution episode event). A further control policy, hence, has been issued in the 13th Five-Year Plan (2016–2020, hereafter 13th FYP). It will be of interest to evaluate the air quality before the 13th FYP (2015) and to estimate the potential air quality by the end of the 13th FYP (2020) with a focus on the area of an urban district and the periods of severe pollution episodes. Based on observation data of major air pollutants, including SO2 (sulphur dioxide), NO2 (nitrogen dioxide), CO (carbon monoxide), PM10 (particulate matter with aerodynamic diameter equal to or less than 10 μm), PM2.5 (particulate matter with aerodynamic diameter equal to or less than 2.5 µm) and O3 (Ozone), the air quality of Haizhu district [an urban district in the Pearl River Delta (PRD), China] in 2015 suggested that typical heavy pollution occurred in winter and the hot season, with NO2 or PM2.5 as the key pollutants in winter and O3 as the key pollutant in the hot season. We also adopted a state-of-the-art chemical transport model, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), to predict the air quality in Haizhu District 2020 under different scenarios. The simulation results suggested that among the emission control scenarios, comprehensive measures taken in the whole of Guangzhou city would improve air quality more significantly than measures taken just in Haizhu, under all conditions. In the urban district, vehicle emission control would account more than half of the influence of all source emission control on air quality. Based on our simulation, by the end of the 13th FYP, it is noticeable that O3 pollution would increase, which indicates that the control ratio of volatile organic compounds (VOCs) and nitrogen oxides (NOx) may be unsuitable and therefore should be adjusted. Our study highlights the significance of evaluating the efficacy of current policy in reducing the air pollutants and recommends possible directions for further air pollution control for urban areas during the 13th FYP.

Preliminary Tests on the Sensitivity of the FORAIR_IT Air Quality Forecasting System to Different Meteorological Drivers

Since 2017, the operational high-resolution air quality forecasting system FORAIR_IT, developed and maintained by the Italian National Agency for New Technologies, Energy and Sustainable Economic Development, has been providing three-day forecasts of concentrations of atmospheric pollutants over Europe and Italy, on a daily basis, with high spatial resolution (20 km on Europe, 4 km on Italy). The system is based on the Atmospheric Modelling System of the National Integrated Assessment Model for Italy (AMS-MINNI), which is a national modelling system evaluated in several studies across Italy and Europe. AMS-MINNI, in its forecasting setup, is presently a candidate model for the Copernicus Atmosphere Monitoring Service’s regional production, dedicated to European-scale ensemble model forecasts of air quality. In order to improve the quality of the meteorological input into the chemical transport model component of FORAIR_IT, several tests were carried out on daily forecasts of NO2 and O3 concentrations for January and August 2019 (representative of the meteorological seasons of winter and summer, respectively). The aim was to evaluate the sensitivity to the meteorological input in NO2 and O3 concentration forecasting. More specifically, the Weather Research and Forecasting model (WRF) was tested to potentially improve the meteorological driver with respect to the Regional Atmospheric Modelling System (RAMS), which is currently embedded in FORAIR_IT. In this work, the WRF chain is run in several setups, changing the parameterization of several micrometeorological variables (snow, mixing height, albedo, roughness length, soil heat flux + friction velocity, Monin–Obukhov length), with the main objective being to take advantage of WRF’s consistent physics in the calculation of both mesoscale variables and micrometeorological parameters for air quality simulations. Daily forecast concentrations produced by the different meteorological model configurations are compared to the available measured concentrations, showing the general good performance of WRF-driven results, even if performance skills are different according to the single meteorological configuration and to the pollutant type. WRF-driven forecasts clearly improve the model reproduction of the temporal variability of concentrations, while the bias of O3 is higher than in the RAMS-driven configuration. The results suggest that we should keep testing WRF configurations, with the objective of obtaining a robust improvement in forecast concentrations with respect to RAMS-driven forecasts.

Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

The Weather Research and Forecasting (WRF) model is one of the regional climate models for dynamically downscaling climate variables at finer spatial and temporal scales. The objective of this study was to evaluate the performance of WRF model for simulating temperature and rainfall over Lake Tana basin in Ethiopia. The WRF model was configured for six experimental setups using three land surface models (LSMs): Noah, RUC and TD; and two land use datasets: USGS and updated New Land Use (NLU). The performances of WRF configurations were assessed by comparing simulated and observed data from March to August 2015. The result showed that temperature and rainfall simulations were sensitive to LSM and land use data choice. The combination of NLU with RUC and TD produced very small cold bias (0.27 °C) and warm bias (0.20 °C) for 2m maximum temperature (Tmax) and 2m minimum temperature (Tmin), respectively. WRF model with RUC and NLU captured well the observed spatial and temporal variability of Tmax, while TD and NLU for Tmin. Moreover, rainfall simulation was better with NLU; especially NLU and Noah configuration produced the smallest mean bias (2.39 mm/day) and root mean square error (6.6 mm/day). All the WRF experiments overestimated light and heavy rainfall events. Overall, findings showed that the application of updated land use data substantially improved the WRF model performance in simulating temperature and rainfall. The study would provide valuable support for identifying suitable LSM and land use data that can accurately predict the climate variables in the Blue Nile basin.

Integrated system for population dose calculation and decision making on protection measures in case of an accident with air emissions in a nuclear power plant

The accidents in Chernobyl and Fukushima remind us that nuclear power plants should continuously invest resources in improving safety and in risk management. This paper presents the methodology for developing a measuring and modelling system with a high degree of automation, which enables predicting the effects of the spreading of radionuclides from the nuclear power plant to the atmosphere. The end result is the calculated population doses in the event of an accidental release, which is an essential piece of information needed by first responders to take proper action. The key challenge addressed by this methodology is how to build a system so that its operation is maximally automated, ongoing and in real time. Moreover, in a way that "fresh", normalized results for the hypothetically most probable types of emissions are always available to operators. The principle that normalized, fresh results are always automatically available to operators is the only real assurance that they will almost surely be available in the event of an accident and panic. This way, we can avoid performing complex model calculations at the operator's request when the accident is already taking place. The methodology divides the building of the system into key modules, which are substantiated and described. The theoretical section is followed by a description of implementation on the example of the Measuring and Modelling System at the Krško Nuclear Power Plant (in Slovenia). The system has been tested in regular nuclear emergency exercises and rated excellent by IAEA inspections; it has been operating automatically, continuously and in real time for many years. The availability of automatic results is counted for the last two years. Measurements and diagnostic modelling results were available for more than 96% and forecasts were available in more than 91% of all half-hour intervals.