An Evaluation of the CHIMERE Chemistry Transport Model to Simulate Dust Outbreaks across the Northern Hemisphere in March 2014

A new application of multi-criteria decision making in identifying critical dust sources and comparing three common receptor-based models

Dust storms are a common phenomenon in arid and semi-arid regions in West Asia, which has led to high levels of PM₁₀ in local and remote area. The Yazd city in Iran with a high PM₁₀ level located downstream of dust sources in the Middle East and Central Asia. In this study, based on meteorological and PM₁₀ monitoring data, backward trajectory modeling of air parcels related to dust events at Yazd station was performed using the HYSPLIT model in 2012-2019. The trajectory cluster analysis was used to identify the main dust transport pathways and wind systems. Three methods of Cross-referencing Backward Trajectory (CBT), Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) were used to identify the most critical dust sources. Multi-Criteria Decision Making (MCDM) methods were also used to integrate the results. Nine dust sources affecting central Iran were determined, and six criteria from different aspects were considered. To prioritize the dust sources affecting central Iran from four new MCDM methods, including WASPAS, EDAS, ARAS and TOPSIS were used. The results showed that the Levar wind system (51%), the Shamal wind system (32%) and the Prefrontal wind system (18%) were the most important wind systems to cause dust events in central Iran. The MCDM approach to identify dust sources also showed that Dasht-e-Kavir in central Iran was the most critical dust source. The results also showed that in hot seasons (spring and summer), local and Central Asia dust sources and cold seasons (autumn and winter), Middle East dust sources have the greatest impact on dust events in central Iran. Also, a comparison of common receptor-based methods for identifying dust sources showed that CBT, CWT and PSCF were the most appropriate methods for identifying dust sources, respectively.

Silver linings in the dark clouds of COVID-19: Improvement of air quality over India and Delhi metropolitan area from measurements and WRF-CHIMERE model simulations

The current study examines the impact of the COVID-19 lockdown (25th March until May 17, 2020) period in particulate matter (PM) concentrations and air pollutants (NO_x, SO₂, CO, NH₃, and O₃) at 63 stations located at Delhi, Uttar Pradesh and Haryana states within the Delhi-NCR, India. Large average reductions are recorded between the stations in each state such as PM₁₀ (-46 to -58%), PM_2.5 (-49 to -55%), NO₂ (-27 to -58%), NO (-54% to -59%), CO (-4 to -44%), NH₃ (-2 to -38%), while a slight increase is observed for O₃ (+4 to +6%) during the lockdown period compared to same periods in previous years. Furthermore, PM and air pollutants are significantly reduced during lockdown compared to the respective period in previous years, while a significant increase in pollution levels is observed after the re-opening of economy. The meteorological changes were rather marginal between the examined periods in order to justify such large reductions in pollution levels, which are mostly attributed to traffic-related pollutants (NO_x, CO and road-dust PM). The WRF-CHIMERE model simulations reveal a remarkable reduction in PM_2.5, NO₂ and SO₂ levels over whole Indian subcontinent and mostly over urban areas, due to limitation in emissions from the traffic and industrial sectors. A PM_2.5 reduction of -48% was simulated in Delhi in great consistency with measurements, rendering the model as a powerful tool for simulations of lower pollution levels during lockdown period.

Impact of Physics Parameterizations on High-Resolution Air Quality Simulations over the Paris Region

The accurate simulation of meteorological conditions, especially within the planetary boundary layer (PBL), is of major importance for air quality modeling. In the present work, we have used the Weather Research and Forecast (WRF) model coupled with the chemistry transport model (CTM) CHIMERE to understand the impact of physics parameterizations on air quality simulation during a short-term pollution episode on the Paris region. A lower first model layer with a 4 m surface layer could better reproduce the transport and diffusion of pollutants in a real urban environment. Three canopy models could better reproduce a 2 m temperature (T2) in the daytime but present a positive bias from 1 to 5 °C during the nighttime; the multi-urban canopy scheme “building effect parameterization” (BEP) underestimates the 10 m windspeed (W10) around 1.2 m s−1 for the whole episode, indicating the city cluster plays an important role in the diffusion rate in urban areas. For the simulation of pollutant concentrations, large differences were found between three canopy schemes, but with an overall overestimation during the pollution episode, especially for NO2 simulation, the average mean biases of NO2 prediction during the pollution episode were 40.9, 62.2, and 29.7 µg m−3 for the Bulk, urban canopy model (UCM), and BEP schemes, respectively. Meanwhile, the vertical profile of the diffusion coefficients and pollutants indicated an important impact of the canopy model on the vertical diffusion. The PBL scheme sensitivity tests displayed an underestimation of the height of the PBL when compared with observations issued from the Lidar. The YonSei University scheme YSU and Boulac PBL schemes improved the PBL prediction compared with the Mellor–Yamada–Janjic (MYJ) scheme. All the sensitivity tests, except the Boulac–BEP, could not fairly reproduce the PBL height during the pollution episode. The Boulac–BEP scheme had significantly better performances than the other schemes for the simulation of both the PBL height and pollutants, especially for the NO2 and PM2.5 (particulate matter 2.5 micrometers or less in diameter) simulations. The mean bias of the NO2, PM2.5, and PM10 (particulate matter 10 micrometers or less in diameter) prediction were −5.1, 1.2, and −8.6 µg m−3, respectively, indicating that both the canopy schemes and PBL schemes have a critical effect on air quality prediction in the urban region.

Simulating Performance of CHIMERE on a Late Autumnal Dust Storm over Northern China

The accurate forecasting of dust emission and transport is a societal demand worldwide as dust pollution is part of many health, economic, and environment issues, which significantly impact sustainable development. The dust forecasting ability of present air quality forecast systems is mainly focused on spring dust events in East Asia, but further improvement may be needed as there is still difficulty in forecasting autumn dust activities, such as failing to predict the serious dust storm that occurred on 25 to 26 November 2018. In this study, a state-of-the-art air quality model, CHIMERE, with three coupled dust schemes was introduced for the first time to simulate the dust emissions during this event to qualitatively and quantitatively validate its dust simulating performance over Northern China. The model results reported that two of the three dust schemes were able to capture the dust emission source located in Gansu Province and reproduce the easterly dust transport path, showing moderately close agreement in the horizontal and vertical distribution patterns with the ground-based and satellite observations. The simulated PM10 concentration had a better relationship with the observed values with a correlation coefficient up to 0.96, while it was lower in the transported areas. Meanwhile, the simulations also presented incorrect dust emission positions such as in areas between the Hulun Buir sandy land and Horqin sandy land. Our results indicate that CHIMERE exhibits reasonably good performance regarding its dust simulation and forecast ability over this area, and its application would help to improve the dust analysis and forecast abilities in Northern China.