Air Quality Modelling of Natural and Man-made events in New South Wales Using WRF-Chem and WRF-CMAQ

Hiep

Duc Nguyen (1)(*), Merched Azzi (1), Matthew Riley (1), Khalia Monk (1)

(1) Department of Planning and Environment, NSW, PO Box 29, Lidcombe 2141, NSW, Australia (*) Correspondence: hiep.duc@environment.nsw.gov.au

Abstract

This paper presents two recent simulation studies conducted at NSW Department of Planning and Environment on the impact on air quality across NSW and the Greater Metropolitan Region (GMR) urban areas due to a dust storm in February 2019 and Covid 19 lock down in NSW in 2021. These studies highlight the importance of using suitable air quality models to simulate the emissions and transport of air pollutants over a region to assess the impact of events on air quality and population exposure. The WRF-Chem model is used for the large-scale dust storm event and the WRF-CMAQ is used for assessing emission changes in the GMR due to the Covid-19 lockdown on the urban air quality.

Both WRF-Chem and WRF-CMAQ are regional chemical transport models and require high-end Linux-based computing resources. The advantage and disadvantage of applying each of the two models to conduct a particular air quality study will be discussed.

Keywords: Air quality models, WRF-Chem, WRF-CMAQ, chemical mechanism, dust schemes, February 2019 dust storm, Covid19 lockdown.

1. Introduction

The two most widely chemical transport models WRF-CMAQ and WRF-Chem, are usually used to estimate the atmospheric dispersion, transport, and deposition of air pollutants to surface levels from dust storms or wildfires events. For the meteorological component, both systems use the Weather Research Forecast, (WRF) model (Skmarocl et. al, 2019). For the chemical transport model known as Community Multiscale Air Quality Model, CMAQ, the Carbon Bond 6 CB6, is currently used for simulations. The chemical transport component used in WRF-Chem (Grell et al. 2005 and Fast et al. 2006) has access to many available chemical mechanisms or even can make a new chemical mechanism using the KPP (Kinetic Pre-Processor) option.

WRF-CMAQ air quality model was used by many authors to study the deposition of pollutants. Qiao et al. 2015 has used WRF-CMAQ to study dry and wet deposition of sulfate, nitrate, and ammonium ions in Jiuzhaigou National Nature Reserve (China). In this selected study we present the application of WRF-CMAQ for the first time in NSW to assess the change in air quality in the NSW Greater Metropolitan Region (GMR) during the Covid-19 lockdown period.

WRF-Chem v.4 was also used to study the effect of the 2019/2020 wildfires on air quality and health in east coast of Australia (Nguyen et al. 2021) and the transport of dust from the February 2019 dust storm to the Tasman Sea and Antarctica (Nguyen et al. 2019). Van der Velde et al. 2021 has used WRFChem v.4 to estimate the emission and concentration of CO and CO 2 over eastern Australia during the 2019-2020 wildfires based on 5 different fire emission inventories constraint with CO satellite measurements. Dust storms also affect the marine environment as dust deposition with iron contents can stimulate phytoplankton growth in the Tasman Sea and Southern Ocean (Gabric et al., 2010).

As stated previously, WRF-Chem has many available chemical mechanisms of gas-phase and aerosol chemical mechanism such as MADE/SORGAM (The Modal Aerosol Dynamics Model for Europe (MADE) that can be used to assess air quality. Secondary organic aerosols (SOA) have been incorporated into MADE by means of the Secondary Organic Aerosol Model (SORGAM). Other mechanisms include RADM2 (Regional Acid Deposition Model, 2nd generation), GOCART (Goddard Chemistry Aerosol Radiation and Transport), RACM (Regional Atmospheric Chemistry Mechanism), MOZART (Model of Ozone and Related Chemical Tracers), MOZCART (MOZART/GOCART), CBMZ (Carbon-Bond Mechanism version Z, improved CB4), MOSAIC (Model for Simulating Aerosol

Interactions and Chemistry), Carbon Bond 5 (CB5), SAPRC99 developed by California Statewide Air Pollution Research Center (SAPRC) 1999).

These choices make WRF-Chem a very useful tool to conduct scientific investigations on air quality. In addition to the chemical mechanism option, it includes several dust emission schemes as described in the user guide. The choice of different chemical mechanism or schemes are listed in the namelist.input file as options. WRF-Chem with different dust emission schemes have been used to account for the dust emission and deposition in air quality dispersion model over a simulation domain (Foroushani et al. 2020, Zhang et al. 2018, Zhang et al. 2019, Zeng et al. 2019). The emission of dust generated by wind erosion of land surface is described as a function of wind speed threshold, the soil characteristics such as its type or texture and moisture content and the dust source function (DSF) which describes the spatial erodibility of land surface.

In this study, the dust storms of February 2019, events and their effects on atmospheric environment are studied by using WRF-Chem 4.2. The WRF-CMAQ 5.1.3 model is used to simulate the effect of Covid-19 lockdown in 2010 on air quality

2. Methods

In this study, we use the WRF-Chem V4.2 to simulate the dust events of February 2019 and WRFCMAQ to examine the air quality change during Covid-19 lockdown in 2021. Monitoring data from the NSW Department of Planning and Environment DPE, air quality network (Figure 1) across NSW are used to compare with predicted values from the models.

2.1 WRF-Chem

The domain configuration and the physics/chemistry options in WRF-Chem model for this study are given in Figure 1 and Table 1. This single domain has 15km x 15km resolution (lat from −45.5384° to −17.914°, and longitude from 116.025° to 184.175°) grid size and 32 vertical levels with pressure at the top level as 5000 hPa. The National Center for Environmental Prediction (NCEP) Final Analysis (FNL) Reanalysis data provides the boundary and initial meteorological conditions, and the default chemical profiles in WRF-Chem for the simulation.

The WRF-Chem dust emission scheme used is the AFWA (Air Force Weather Agency of the US) version of GOCART (Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport) aerosol scheme. The basic equation, called Ginoux equation (Ginoux et al. 2001), describes the dust emission flux Fp for different particle size as a function of wind speed and soil moisture. Improvement of the dust emission scheme is on going in many studies of dust emission around the world (Ukhov et al. 2021). In AFWA-GOCART emission scheme (dust_opt=3), the emission fluxes for the 5 bins are stored in 4-dim DUST1, DUST2, DUST3, DUST4 and DUST5 variables with unit in µg/kgdry air.

The sources of dust during a dust storm in eastern Australia are mainly from the six desert regions in central Australia (between the states of Queensland, NSW, South Australia) as shown in Figure 2. In February 2019, most of western NSW and northern Victoria were in drought and were prone to increase the dust emission from dry land with favourable prevailing meteorological conditions. The simulation period of the dust storm using WRF-Chem with the above configuration as outlined in Table 1 is from 11 to 15 February 2019.

Figure 2 – (a) Six dust source regions as of O’Loingsigh et al., 2017: (1) Northern Simpson desert (2) Queensland Channel Country (3) Lake Eyre and South Simpson desert ephemeral lakes region (4) South Strzelecki desert and Lake Frome sub basin (5) Lake Torrens and Gairdner region (6) Mallee and Western Riverina regions. (b) The topographic sand erodibility (0–1) contour map (coloured) within the topography of the Australian continent is used by the WRF-Chem dust module to scale the dust flux.

The anthropogenic emission data used in this study covering the modelling domain (Figure 1) is based on the global 2005 anthropogenic EDGAR (Emission Database for Global Atmospheric Research) version 4 compiled for Task Force on Hemispheric Transport of Air Pollution (TF-HTAP). As there were large wildfires in the New England region of northern NSW from 10 February 2019 to 15 February 2019, the Fire Inventory from NCAR (FINN) emission data derived from MODIS Rapid Response fire count (FIRMS) hotspots was also used.

2.2 WRF-CMAQ model

The WRF-CMAQ model (version 5.3.1, US EPA, 2019) with the chemical mechanism CB6 (Carbon Bond 6 version 3 with an aerosol treatment of Secondary Organic Aerosol, cb6r3_ae7_aq, Lueken et al. 2019, Fahey et al. 2017 and Xu et al. 2018) is used in this study. The WRF model is run first and produces the meteorological data in a format used by CMAQ via MCIP (Meteorology Chemistry Interface Processor). The WRF configuration (namelist.input) is similar to the above configuration in the previous WRF-Chem simulation but using Morrison 2–moment scheme as the microphysics option, Mellor–Yamada–Janjic scheme (MYJ) as the boundary layer option and Eta Similarity Scheme as the surface layer physics option. The National Center for Environmental Prediction (US NCEP) Global Reanalysis data is used as meteorological driver for WRF. The NSW EPA GMR emission inventory data (NSW DPE 2013 are used as anthropogenic emission input. Other emissions to the WRF-CMAQ include the global emission database EDGAR for emission outside the GMR, the biogenic emission based on the MEGAN biogenic model, the marine aerosol (sea salt) and soil dust emission as provided with the CMAQ model.

The WRF-CMAQ simulation period is from 1 to 15 of April 2020 which is the first 2 weeks of the start of the Covid-19 lockdown in Sydney and NSW. To simulate the effect of COVID-19 lockdown, the motor vehicle emission is reduced by 30% on average as observed from the traffic count pattern at a number of traffic counter sites in the GMR. To process the emission data from a variety of source and format to produce CMAQ-format emission data input, a set of Python scripts were written in collaboration with University of Melbourne.

The simulation domain configuration for WRF-CMAQ run is a 3-nested domain with the outer domain (d01) covers much of Eastern Australia at the resolution of 12km x 12km. The inner domain (d02) is at 4km x 4km resolution and cover most of NSW while the innermost domain (d03) is at 1km x 1km resolution and covers the GMR of Sydney. Figure 3 shows the three nested domains used for the WRFCMAQ covid-19 study simulation. The initial and boundary conditions chemical species is from the global model CAM_Chem (Community Atmosphere Model—Chemistry) is available for download from NCAR (https://www.acom.ucar.edu/cam-chem/cam-chem.shtml).

It is important to know that CMAQ uses Linux environment variables to pass the information not the configuration data in the configuration file. There are a number of post-processing tools to analyse the output of WRF-CMAQ. There are programs such as combine, sitecmp, writesite bundled with CMAQ. The Fortran program sitecmp requires observation data to be in AQS of US EPA standard format. Observation data from NSW air quality network can be converted to this format by using some custom scripts such as R or Python. Others include the evaluation tool called AMET or the MONET (Model and Observation Evaluation Tool). Model and Observation Evaluation Tool (MONET) v1.0. MONET was developed to evaluate the Community Multiscale Air Quality Model (CMAQ) for the NOAA National Air Quality Forecast Capability (NAQFC) modelling system. As it was developed by NOAA (similarly HYSPLIT), MONET is comprehensive and can be used to validate CMAQ output with observation data. In this study, we use the combine, sitecmp, writesite programs to post-process and validate the output.

3. Results and Discussion

3.1 Dust storm of 11-15 February 2019 effect on air quality over NSW using WRF-Chem

During the dust storm period from 11 to 15 February 2019, the wind direction was mostly westerly on the 11th and 12thFebruary 2019 then the winds changed to south-westerly and southerly in the following days.

The predictions of PM10 from WRF-Chem are compared with observations at DPE air quality monitoring stations in the Sydney region. As shown in Figure 4, there is correspondence between peak observed PM10 concentrations at the stations and the WRF-Chem predicted PM10. At Prospect and Chullora, the peak concentrations of 104 µg/m3 and 90 µg/m3, respectively, occurred at the same time while at Bringelly, peak concentration of 160 µg/m 3 occurred one hour later (12 February 2019 20:00 AEST). As these sites are urban monitoring sites, the observed PM10 include both dust and other local sources while WRF-Chem prediction mainly consists of transported dust as the local emission inventory is not taken into account in our large WRF-Chem modelling domain having grid resolution at 15 km by 15 km. Our focus is on the dust transport and the close timing of the predicted peaks and observed peaks of PM10 at the monitoring sites in the Sydney region. The results indicate that these observed peaks are above the average daily PM10 during these simulation days and are due to the contribution of transported dust (Aragnou et al. 2021).

Figure

3.2 Covid-19 lockdown and effect on air quality in GMR using WF-CMAQ

Australia closed the border on 20 of March 2020 to all non-citizens and a week later most states in the country were locked down, restricting movement and congregation of the community. NSW was locked down for most of April and May 2020 with less restriction in June.

In many cities around the world, including Sydney, the COVID-19 lockdown implemented in the first half of 2020 dramatically reduced the anthropogenic emission due to restricted population mobility and decline in industrial energy production and consumption. This provided an opportunity to study the source contribution of mobile sources on air pollution in Sydney. Air quality modelling tool such as WRFCMAQ can be used to simulate the ozone and PM2.5 levels in the GMR using emission data with and with reduced the mobile source emission. A significant reduction in traffic volume and hence, congestion on the Sydney Road since the lockdown began in late March 2020 is shown in Figure 6.

Figure 6 - Relative difference of average congestion levels in 2020 from standard congestion levels in 2019 (difference greater than the standard weekly congestion level in 2019) (a) (source: Tomtom traffic flow https://www.tomtom.com/en_gb/traffic-index/sydney-traffic/. Accessed on 19 March 2021).

The congestion level is defined as the percentage of extra time that it takes to travel if there is no congestion. The congestion level since then until the end of 2020 is less than that in the corresponding period in 2019. The congestion level in Sydney dropped to 40% compared to that in 2019 at the end of March 2020 with a maximum drop at about 50% in April then back to about 20% less than the 2019 level from May.

To simulate the effect of COVID-19 lockdown using WRF-CMAQ, the motor vehicle emission is reduced by 30% on average as observed from the traffic count pattern at a number of traffic counter sites in the GMR. Similar reduction can be estimated from the congestion level difference between 2019 and 2020 as shown in Figure 6. This emission reduction scenario is then compared with “normal” motor vehicle emissions in the 2013 NSW emission inventory.

Using sitecmp tool to extract WRF-CMAQ NetCDF output hourly data in daily files and observation files, the time series of the predicted concentration and observation can be compared. Figure 7 shows the predicted concentration of NO2 and O3 compared with observation for the 2 scenarios (normal emission and Covid-19 lockdown with 30% vehicle emission reduction) at Liverpool. Figure 8 shows the predicted concentration of NO2, O3, CO and PM2.5 for the 2 scenarios at Wollongong. Similar to Liverpool site, there are small decrease of NO2, CO and PM2.5 concentration and higher peak O3 concentration. The results at many sites show that the covid-19 lockdown has reduced the concentration of NO 2, CO and PM2.5 but increased the O3 concentration. Other studies have shown similar results as those in our study (Brimblecombe et al. 2021, Habibi et al. 2020).

In this paper, WRF-Chem was used for dust emission modelling as it has various dust emission schemes and was studied previously by many authors. It is flexible and can be calibrated the model parameters to match with observation. The calibrated model is then used to predict at any grid point in the modelling domain. WRF-CMAQ is less used in dust emission studies and WRF-CMAQ is not an open-source system as compared to WRF-Chem. However, compared to WRF-CMAQ, WRF-Chem is much slower to run due to the many chemical modules and their options together with the Kinetic PreProcessor (KPP) provided in the model. WRF-Chem provided a comprehensive tool to conduct scientific studies with many modules and options contributed by many researchers. WRF-CMAQ is more regulatory, optimised and supported by the US EPA. One of the most useful and important tools in WRF-CMAQ is the Integrated Source Apportionment Method (ISAM, Shu et al 2023). This tool used to be a separate package, but since CMAQ v.5.3 onward, ISAM was part of CMAQ distribution.

ISAM was developed from the ozone transport problem in the east coast of US where several states must achieve the air quality goals set by the federal US EPA. To account for a states own contribution to air pollution and any sources from outs, ISAM was created. The ISAM version of WRF-CMAQ requires a recompilation to integrate WRF-CMAQ with ISAM. As emission from different sources are tagged and accounted for in the resulting concentration, WRF-CMAQ-ISAM is a tool of choice for policy work. The WRF-CMAQ-ISAM model is currently implemented in NSW DPE to study the contribution of regions of the GMR (such as Sydney Central, Illawarra, Central Coast, Upper Hunter) to the air quality (PM2.5, Ozone, NO2) at a receptor point or over an area.

It should be noted that even though air quality model is a useful tool in estimating the dispersion, transport and deposition of pollutants from source emission, there are still some uncertainties in the modelling results. This is mainly due to different natural emission mechanism such as dust and sea salt, initial and boundary conditions, gas-particle conversion phase and different amounts of deposition. Validation of predictions with observation data should be conducted in each study.

4. Conclusion

In this paper, NSW DPE has used two regional air quality models to study two recent events that impacted NSW. WRF-Chem was used to study the effect of the 2019 February 11-15 dust storm on air quality in the coastal areas of NSW and in particular in the (GMR) of Sydney. The WRF-CMAQ air quality model was used to study the effect of Covid-19 lockdown in NSW during 2020 on air quality due to reduced anthropogenic activities and traffic emission. The modelling system showed a reduction in ground level concentrations of NO2, O3, CO and PM2.5. Both models are useful in predicting the effect of these events on air quality in the areas of interest. The suitability of each of model depends on the research question or problem to be investigate. Both models are useful tools for study of regional air quality in Australia.

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