High-resolution air quality modeling: Sensitivity tests to horizontal resolution and urban canopy with WRF-CHIMERE

Falasca, Serena; Curci, Gabriele

doi:10.1016/j.atmosenv.2018.05.048

Cited by 36 publications

(20 citation statements)

References 27 publications

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“…Reduction of ground-level primary pollutant concentrations (NO x and CO) due to enhanced vertical mixing due to urbanization is modeled by Struzewska and Kaminski (2012) too. If mitigation measures are implemented to reduce UHI, the consequent reduction of PBL height and vertical turbulent transport causes an increase of primary pollutants but a decrease of ozone over the surface (Fallmann et al, 2016). Large Chinese agglomerations of the Pearl River Delta and Yangtze River Delta (PRD and YRD) have been subject of numerous studies and all argued that urbanization-induced increase of vertical turbulent transport favors the dispersion of primary pollutants (e.g., NO x ) but this leads to enhancement of, e.g., ozone (Wang et al, 2007(Wang et al, , 2009.…”

Section: Introductionmentioning

confidence: 99%

Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport

et al. 2020

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Abstract. It is well known that the urban canopy (UC) layer, i.e., the layer of air corresponding to the assemblage of the buildings, roads, park, trees and other objects typical to cities, is characterized by specific meteorological conditions at city scales generally differing from those over rural surroundings. We refer to the forcing that acts on the meteorological variables over urbanized areas as the urban canopy meteorological forcing (UCMF). UCMF has multiple aspects, while one of the most studied is the generation of the urban heat island (UHI) as an excess of heat due to increased absorption and trapping of radiation in street canyons. However, enhanced drag plays important role too, reducing mean wind speeds and increasing vertical eddy mixing of pollutants. As air quality is strongly tied to meteorological conditions, the UCMF leads to modifications of air chemistry and transport of pollutants. Although it has been recognized in the last decade that the enhanced vertical mixing has a dominant role in the impact of the UCMF on air quality, very little is known about the uncertainty of vertical eddy diffusion arising from different representation in numerical models and how this uncertainty propagates to the final species concentrations as well as to the changes due to the UCMF. To bridge this knowledge gap, we set up the Regional Climate Model version 4 (RegCM4) coupled to the Comprehensive Air Quality Model with Extensions (CAMx) chemistry transport model over central Europe and designed a series of simulations to study how UC affects the vertical turbulent transport of selected pollutants through modifications of the vertical eddy diffusion coefficient (Kv) using six different methods for Kv calculation. The mean concentrations of ozone and PM2.5 in selected city canopies are analyzed. These are secondary pollutants or having secondary components, upon which turbulence acts in a much more complicated way than in the case of primary pollutants by influencing their concentrations not only directly but indirectly via precursors too. Calculations are performed over cascading domains (of 27, 9, and 3 km horizontal resolutions), which further enables to analyze the sensitivity of the numerical model to grid resolution. A number of model simulations are carried out where either urban canopies are considered or replaced by rural ones in order to isolate the UC meteorological forcing. Apart from the well-pronounced and expected impact on temperature (increases up to 2 ∘C) and wind (decreases by up to 2 ms−1), there is a strong impact on vertical eddy diffusion in all of the six Kv methods. The Kv enhancement ranges from less than 1 up to 30 m2 s−1 at the surface and from 1 to 100 m2 s−1 at higher levels depending on the methods. The largest impact is obtained for the turbulent kinetic energy (TKE)-based methods. The range of impact on the vertical eddy diffusion coefficient propagates to a range of ozone (O3) increase of 0.4 to 4 ppbv in both summer and winter (5 %–10 % relative change). In the case of PM2.5, we obtained decreases of up to 1 µg m−3 in summer and up to 2 µg m−3 in winter (up to 30 %–40 % relative change). Comparing these results to the “total-impact”, i.e., to the impact of all meteorological modifications due to UCMF, we can conclude that much of UCMF is explained by the enhanced vertical eddy diffusion, which counterbalances the opposing effects of other components of this forcing (temperature, humidity and wind). The results further show that this conclusion holds regardless of the resolution chosen and in both the warm and cold parts of the year.

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Section: Introductionmentioning

confidence: 99%

Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport

et al. 2020

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“…Since this phenomenon influences the climate data employed by EnergyPlus for the calculation of the energy performance of buildings, the WRF model was used for the creation of an input climate file taking into account the built environment of the city. Previous works demonstrated the ability of the WRF model to simulate idealized and real urban heat islands [56,57].…”

Section: The Case Studymentioning

confidence: 99%

“…The vertical grid had 33 levels for all domains, with the lowest one at about 23 m and the top at 50 hPa. Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58]. Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58].…”

Section: Analysis Of Climatic Inputs and Evaluation Of The Wrf Modelmentioning

confidence: 99%

“…Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58]. Further details about the method and the physical options can be found in the article by Falasca and Curci [57,58]. Figure 6 shows the comparison between the results of the WRF runs and the observations provided by two urban weather stations in Rome, for a winter month (January) and a summer month (July).…”

Section: Analysis Of Climatic Inputs and Evaluation Of The Wrf Modelmentioning

confidence: 99%

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Influence of Input Climatic Data on Simulations of Annual Energy Needs of a Building: EnergyPlus and WRF Modeling for a Case Study in Rome (Italy)

et al. 2018

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The simulation of the energy consumptions in an hourly regime is necessary in order to perform calculations on residential buildings of particular relevance for volume or for architectural features. In such cases, the simplified methodology provided by the regulations may be inadequate, and the use of software like EnergyPlus is needed. To obtain reliable results, usually, significant time is spent on the meticulous insertion of the geometrical inputs of the building, together with the properties of the envelope materials and systems. Less attention is paid to the climate database. The databases available on the EnergyPlus website refer to airports located in rural areas near major cities. If the building to be simulated is located in a metropolitan area, it may be affected by the local heat island, and the database used as input to the software should take this phenomenon into account. To this end, it is useful to use a meteorological model such as the Weather Research and Forecasting (WRF) model to construct an appropriate input climate file. A case study based on a building located in the city center of Rome (Italy) shows that, if the climatic forcing linked to the heat island is not considered, the estimated consumption due to the cooling is underestimated by 35–50%. In particular, the analysis and the seasonal comparison between the energy needs of the building simulated by EnergyPlus, with the climatic inputs related to two airports in the rural area of Rome and with the inputs provided by the WRF model related to the center of Rome, show discrepancies of about (i) WRF vs. Fiumicino (FCO): Δ = −3.48% for heating, Δ = 49.25% for cooling; (ii) WRF vs. Ciampino (CIA): Δ = −7.38% for heating, Δ = +35.52% for cooling.

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“…However, they solve huge amounts of equations, demanding plenty of computational resources. The need for high-resolution simulations (up to 1 km) comes from the better representation of small scale processes of dispersion processes [6]. It is necessary to define a pragmatic spatial resolution, suitable to compute volcanic ash dispersion, both in forecasting time and with proper accuracy.…”

Section: Introductionmentioning

confidence: 99%

Influence of Spatial Resolution in Modeling the Dispersion of Volcanic Ash in Ecuador

Parra

2019

WIT Transactions on Ecology and the Environment

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Volcanic ash produces air pollution and other impacts. Regions potentially affected require information about the possible ash dispersion trajectories and affected zones by ash fallout. In the last 19 years, five volcanoes in Ecuador have produced moderate to large explosive eruptions. Information about the volcanic ash dispersion in forecasting time is a priority in Ecuador. Eulerian models can provide results with high spatial and temporal resolutions. However, they need to solve huge amounts of equations, demanding plenty of computational resources when using high spatial resolutions. It is necessary to define a pragmatic spatial resolution, suitable to compute volcanic ash dispersion, both in forecasting time and with enough accuracy. For this purpose, we simulated the meteorology over Ecuador, using the Weather Research and Forecasting (WRF3.7.1) model with spatial resolutions of 36 km, 12 km, 4 km, and 1 km. Meteorological outputs were used into the FALL3DV7.1.4 model to simulate ash dispersion from four eruptions (

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High-resolution air quality modeling: Sensitivity tests to horizontal resolution and urban canopy with WRF-CHIMERE

Cited by 36 publications

References 27 publications

Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport

Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport

Influence of Input Climatic Data on Simulations of Annual Energy Needs of a Building: EnergyPlus and WRF Modeling for a Case Study in Rome (Italy)

Influence of Spatial Resolution in Modeling the Dispersion of Volcanic Ash in Ecuador

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High-resolution air quality modeling: Sensitivity tests to horizontal resolution and urban canopy with WRF-CHIMERE

Cited by 36 publications

References 27 publications

Urban canopy meteorological forcing and its impact on ozone and PM&lt;sub&gt;2.5&lt;/sub&gt;: role of vertical turbulent transport

Urban canopy meteorological forcing and its impact on ozone and PM&lt;sub&gt;2.5&lt;/sub&gt;: role of vertical turbulent transport

Influence of Input Climatic Data on Simulations of Annual Energy Needs of a Building: EnergyPlus and WRF Modeling for a Case Study in Rome (Italy)

Influence of Spatial Resolution in Modeling the Dispersion of Volcanic Ash in Ecuador

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Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport

Urban canopy meteorological forcing and its impact on ozone and PM<sub>2.5</sub>: role of vertical turbulent transport