Computational fluid dynamics simulation of reactive fine particulate matter in a street canyon

Atmospheric Environment 209 (2019) 54–66

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Computational fluid dynamics simulation of reactive fine particulate matter in a street canyon

T

Minjoong J. Kima,∗, Rokjin J. Parkb, Jae-Jin Kimc, Sung Hoon Parkd, Lim-Seok Change, Dae-Gyun Leee, Jin-Young Choie a

Department of Environmental Engineering and Energy, Myongji University, Yongin, Gyunggi, Republic of Korea School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea c Department of Environmental Atmospheric Sciences, Pukyong National University, Busan, Republic of Korea d Department of Environmental Engineering, Sunchon National University, Suncheon, Jeonnam, Republic of Korea e Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon, Republic of Korea b

ARTICLE INFO

ABSTRACT

Keywords: Street canyon Fine particulate matter Urban pollution CFD

We developed a coupled computational fluid dynamics–chemistry model to examine the transport and chemical transformation of reactive aerosols on an urban street. The model was evaluated by comparing the results of simulations with those of observational campaigns in a street canyon in Elche, Spain. The model generally captured the composition of fine particulate matter (PM1) in the street canyon in summer and winter. However, compared with the observed concentration of PM1 in summer, the simulated concentration of PM1 was overestimated by 40%, indicating that the model predicted a weaker canyon vortex. Although the model has some bias, it reasonably reproduced the observed aerosol concentration. We also investigated the diurnal variations and spatial distribution of PM1 and its composition in the street canyon. The simulated sulfate concentrations were mostly affected by boundary transport, showing weak diurnal variations. The nitrate aerosol concentrations exhibited clear sinusoidal diurnal variations following the precursor gas, HNO3, which is mainly formed by photochemical reactions. We also found that nitrate aerosol formation was suppressed by low O3 concentrations under extreme volatile organic compound-limited conditions. The concentrations of PM1, organic carbon, and black carbon followed traffic volume curves, indicating the dominant effect of vehicular emissions on aerosols. Our sensitivity model simulation showed that considering chemical reactions significantly affects the diurnal variations of secondarily produced aerosol concentrations. These results clearly demonstrate that considering chemical production and loss is essential to investigate the diurnal variations of PM1 in street canyons, especially in winter.

1. Introduction The effects of fine particulate matter (PM1) on air quality in urban street canyons are important for assessing living conditions and public health (Pope and Dockery, 2006; Pöschl, 2005). Previous studies have indicated that PM1 accumulates in street canyons owing to poor ventilation in a complex geometry (Kumar et al., 2008). In an urban street canyon, people may be directly exposed to high concentrations of PM1, which affects the respiratory system (Buonanno et al., 2011; Habilomatis and Chaloulakou, 2015; Rakowska et al., 2014). Thus, the dispersion and distribution of PM1 in urban street canyons is an essential topic of research on the health effects of PM. Numerical modeling is widely used in studies of PM1 in street canyons because field measurements are limited by low spatial and ∗

temporal resolutions (Vardoulakis et al., 2003). Computational fluid dynamics (CFD) models, which can reproduce complex dynamics, have been constructed to diagnose the dispersion and distribution of aerosols in street canyons. Previous studies involving PM modeling have focused on the dynamic effects of aerosols, including their transport, coagulation, condensation, and deposition (Kumar et al., 2008; Nikolova et al., 2011; Scungio et al., 2013; Zhang et al., 2011). However, PM1 is mostly produced by the chemical oxidation of gas-phase precursors (Zhang et al., 2007). The primary sources of sulfate and nitrate aerosols are the subsequent gas-to-particle conversion of SO2 and nitrogen oxides (NOx ≡ NO + NO2), respectively (Bassett and Seinfeld, 1983). Most organic aerosols are produced by the oxidation of volatile organic compounds (VOCs) (Odum et al., 1996; Pandis et al., 1992). The major sources of aerosols indicate the importance of the chemical transformation of

Corresponding author. E-mail address: [email protected] (M.J. Kim).

https://doi.org/10.1016/j.atmosenv.2019.04.013 Received 1 January 2019; Received in revised form 30 March 2019; Accepted 3 April 2019 Available online 10 April 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.

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aerosols; however, only one study addressed the chemical reactions of aerosols and their precursors using a CFD model. Kim et al. (2012) investigated the effects of PM on air quality in street canyons using a coupled CFD–chemistry model. The authors found that the NOx, NH3, and SO2 gases emitted by automobiles produced secondary inorganic aerosols, especially in winter. However, the only aerosol concentrations considered in their study were those of sulfate, nitrate, and ammonium aerosols. Carbonaceous aerosols constitute a large portion of PM1 (Zhang et al., 2007) and are emitted in vehicle exhaust (Gentner et al., 2012; Mohr et al., 2009), but Kim et al. (2012) did not address the high concentration of PM1 due to carbonaceous aerosols. Moreover, the sulfate, nitrate, and ammonium concentrations were estimated by performing offline thermodynamics calculations using the concentrations of precursor gases in the coupled CFD–chemistry model in their study. This approach cannot account for the exchange between aerosols and precursor gases (Metzger et al., 2002) but does account for the effects of transport from the boundary (Wehner et al., 2002). Coupled simulations of aerosols and their precursor gases should be performed to calculate the production and loss of aerosols accurately. Here, we address the dispersion and distribution of aerosols in a street canyon using the CFD model with fully coupled gas–aerosol chemistry. We also demonstrate the importance of secondary aerosol production in the street canyon using the CFD model.

Km is given by Km =

k k + Uj = t xj

t

0

R=

=

as

2U i

P + xi

2C C C + Uj = D t xj xj x j

xj x j

xj

(ui uj )

xj

(cuj ) + Sc .

C xj

Uj xi

2 3

ij k

Km k xj k

(7)

Km xj

xj

C2

2

k

R,

(8)

Cµ

k

3 (1

/ 0)

(1 +

3) k

0

2

(9)

Uj Ui + xj xi

Here, Cµ, C 1, C 2, k,

1/2

Ui xj

,

. k,

0)

, and

(10) 0

are empirical constants specified

= (0.0845, 1.42, 1.68, 0.7179, 0.7179, 4.377, (11)

The Schmidt number Sc (=Km/Kc) was set to 0.9. The governing equations were numerically solved using a finite volume method (Patankar, 1980). The surface or wall heating due to solar radiation was not included in this simulation. This model has been previously used to examine the flow and dispersion not only of scalar pollutants (Kim and Baik, 2004), but also of reactive gas pollutants (Kim et al., 2012; Park et al., 2016, 2015). The gas chemistry simulation was based on the work conducted by Kim et al. (2012) but with the extensive modifications described below. We used the chemical mechanism of the model, including a full tropospheric NOx-Ox-VOC chemical scheme from a global 3-D chemical transport model (GEOS-Chem V11-1) developed by the Harvard University modeling group (Bey et al., 2001). The chemical scheme consisted of 140 species and 393 reactions, among which 61 reactions were photochemical reactions. The chemical computations were performed using a RODAS-3 (4-stage, order 3, stiffly accurate) solver with a selfadjusting internal time step (Hairer and Wanner, 1996) as part of the Kinetic Preprocessor (KPP) (Eller et al., 2009; Sandu and Sander, 2006) instead of a GEAR-type solver, which was used by Kim et al. (2012). The RODAS-3 solver with the KPP is accurate and efficient and is applicable to a wide variety of multidimensional atmospheric problems, including photochemical issues (Eller et al., 2009; Long et al., 2015). Among the 140 species simulated in the chemistry module, the CFD model transported 65 chemical tracers. Radical species with very short chemical lifetimes were not transported. The tracers that were transported by the CFD model are listed in Table 1. The photolysis rate coefficients were calculated using the Fast-JX radiative transfer model (Neu et al., 2007; Wild et al., 2000). The FAST-JX algorithm was designed to be sufficiently computationally efficient to permit its use in 3D chemical transport models without significant loss of accuracy over time. The Fast-JX algorithm calculates photolysis rates every minute considering the location of the grid box and diurnal variation of the solar zenith angle. We developed a new online aerosol module in the CFD model to examine the impacts of aerosols in the street canyon. The module calculates the concentrations of sulfate, nitrate, ammonium, black carbon (BC), and organic carbon (OC). Sulfate formation generally occurs via two pathways: gas-phase oxidation of SO2 by OH and aqueous-phase oxidation of SO2 by ozone and hydrogen peroxide. The CFD model only accounts for gas-phase oxidation of SO2 by OH because it does not have

(1)

(3)

Here, x i and Ui indicate the ith Cartesian coordinate and mean velocity component, respectively; C is the mean concentration of any passive scalar; 0 is the air density; P* is the deviation of the pressure from the reference value; ui and c are the fluctuations from the means of Ui and C, respectively; ν is the kinematic viscosity of air; D is the molecular diffusivity of the pollutant; and Sc is the source term of the pollutant. 0 and ν vary with temperature following the ideal gas law. The Reynolds stress and turbulent flux in Equations (1) and (3) can be respectively parameterized as

cuj = K c

xj

0.012)

(2)

Ui + xj

xj

Ui + xj

U C 1 u i uj i + k xj

=

(Cµ, C 1, C 2,

Ui =0 xj

ui uj = K m

+ Uj

ui uj

where R is a strain rate term given by

The CFD model used in this study is based on that employed by Kang et al. (2017). This model is a Reynolds-averaged Navier–Stokes equation model and assumes a three-dimensional (3-D), nonrotating, nonhydrostatic, and incompressible airflow system (Kim and Baik, 2004). For the turbulence parameterization, the k−ε turbulence closure scheme based on the renormalization group (RNG) theory suggested by Yakhot et al. (1992) was employed. This scheme differs from the standard k–ε turbulence scheme in that it includes an additional sink term in the turbulence dissipation equation to account for nonequilibrium strain rates and employs different values for the model coefficients (Tutar and Oguz, 2002). The momentum equation, mass continuity equation, and transport equation for a passive scalar can be respectively written as

1

(6)

where Cµ is an empirical constant. The prognostic equations of the TKE and its dissipation rate can be respectively written as

2. Model description

Ui U + Uj i = t xj

Cµ k2

(4) (5)

where Km and K c are the turbulent viscosities of the momentum and pollutant concentration, respectively; ij is the Kronecker delta; and k is the turbulent kinetic energy (TKE). In the RNG k–ε turbulence scheme, 55

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Table 1 Tracers transported by the CFD model. Tracer name

Formula

Tracer name

Formula

Tracer name

Formula

NO O3 PAN CO ALK4 ISOP HNO3 H2O2 ACET MEK ALD2 RCHO MVK MACR PMN PPN R4N2 PRPE C3H8 CH2O C2H6 N2O5 HNO4

NO O3 CH3C(O)OONO2 CO ≥C4 alkanes CH2=C(CH3)CH=CH2 HNO3 H2O2 CH3C(O)CH3 RC(O)R CH3CHO CH3CH2CHO CH2=CHC(=O)CH3 CH2=C(CH3)CHO CH2=C(CH3)C(O)OONO2 CH3CH2C(O)OONO2 RO2NO C3H6 C3H8 HCHO C2H6 N2O5 HNO4

MP DMS SO2 SO4 SO4s MSA NH3 NH4 NIT NITs BCPI OCPI BCPO OCPO DST1 DST2 DST3 DST4 SALA SALC Br2 Br BrO

CH3OOH (CH3)2S SO2 Sulfate Sulfate (sea salt) CH4SO3 NH3 Ammonium Inorganic nitrates Inorganic nitrates (sea salt) Black carbon aerosol (hydrophilic) Organic carbon aerosol (hydrophilic) Black carbon aerosol (hydrophobic) Organic carbon aerosol (hydrophobic) Dust aerosol (< 0.7 μm) Dust aerosol (< 1.4 μm) Dust aerosol (< 2.4 μm) Dust aerosol (< 4.5 μm) Sea salt aerosol (accumulation mode) Sea salt aerosol (coarse mode) Br2 Br BrO

HOBr HBr BrNO2 BrNO3 CHBr3 CH2Br2 CH3Br MPN ISOPND ISOPNB MOBA PROPNN HAC GLYC MVKN MACRN RIP IEPOX MAP NO2 NO3 HNO2 –

HOBr HBr BrNO2 BrNO3 CHBr3 CH2Br2 CH3Br CH3O2NO2 C5H9NO4 C5H9NO4 HOC(=O)C(CH3) = CHCHO CH3C(=O)CH2ONO2 HOCH2C(O)CH3 HOCH2CHO HOCH2CH(ONO2)C(=O)CH3 HOCH2C(ONO2)(CH3)CHO C5H10O3 (Peroxide from RIO2) C5H10O3 (Isoprene epoxide) CH3C(O)OOH NO2 NO3 HONO –

an atmospheric physics module that simulates hydrometeors such as clouds and rain. The gas-phase oxidation reaction of SO2 by OH can be written as SO2 + OH + H2O + O2 + M → HOSO2 + HO2 +M.

species not only for gases, but also for aerosols (Sehmel, 1980). We updated the dry deposition processes of the model to treat aerosols and a variety of gas species. Dry deposition of gases and aerosols was simulated using a standard big-leaf resistance-in-series model (Wesely, 1989) identical to that employed by Zhang et al. (2012). The model accounts for the dry deposition of 46 species. The average dry deposition velocities of NO2, NH3, and HNO3 in this work were 0.3, 0.5, and 1.9 m s−1, respectively, which are comparable to those reported by Zhang et al. (2012). The CFD model does not have an atmospheric physics module that simulates hydrometeors, as mentioned above; thus, we did not calculate wet deposition, similarly to Kim et al. (2012). We set up an observed case for reactive tracers and aerosols with aerosol chemistry to evaluate the model. Several simulations were conducted for two cases: a summer case and a winter case. We chose a field campaign in Elche, Spain, which was used by Yubero et al. (2015), who conducted extensive measurements of aerosol species, including the concentrations of sulfate, nitrate, ammonium, BC, OC, and PM1. The sampling period was from March 2011 to September 2012. The observed chemical species samples were collected three days a week using low-volume samplers equipped with PM1 cutoff inlets and were analyzed by ion chromatography. The street is approximately 7 m wide and is surrounded by buildings approximately 25 m in height. The sampling site was 3 m above ground level on the east side of the street canyon. The sampler was on the leeward side in summer and the windward side in winter. Wind and temperature measurements were performed at a station belonging to the regional network and located in a semirural area 3.5 km from the sampling site to represent background conditions. We compared our model data to the observed data. Following the geometry of the street, we set the simulation domain as shown in Fig. 1. The domain size was 20 m × 40 m × 50 m, and the number of grid points was 42 × 82 × 52. The grid intervals in the xand y-directions were uniform at 0.5 m, while the grid size in the zdirection was uniform at 1 m. For the meteorological conditions, we used the seasonal mean values during the campaign period in each season. The observed seasonal mean temperatures were 26.7 °C and 12.8 °C in summer and winter, respectively. We applied artificial diurnal temperature variations because the observed campaign only provided seasonal average temperatures to simulate the diurnal changes in buoyancy and their effects on the transport and chemical reaction rates in the model. These values

(R1)

Gas-phase sulfur chemistry also includes oxidation of dimethyl sulfide (DMS) by OH to form SO2 and methanesulfonic acid and oxidation of DMS by NO3 to form SO2 (Park et al., 2004); however, DMS emissions were not considered because the modeling domain did not include any oceans. Nitrate and ammonium aerosol were calculated by partitioning of total ammonia and nitric acid between the gas and aerosol phases. We used the ISORROPIA-II model as a thermodynamic equilibrium model for aerosol partitioning (Fountoukis and Nenes, 2007; Nenes et al., 1998) and employed it to calculate the thermodynamic equilibrium of an K+-Ca2+-Mg2+eNH4+- Na+eSO42--NO3--Cl--H2O aerosol system based on the NH3, HNO3, and SO42− concentrations. The model also includes the production of HNO3 via heterogeneous chemistry between aerosols and gases following Jacob (2000). The reactions contributing to HNO3 production by heterogenous chemistry can be written as 2 NO2 → HNO3 + HONO (R2) NO3 → HNO3 (R3) N2O5 → 2 HNO3. (R4) The uptake coefficients γ of R2 and R3 were 10−4 and 0.1, respectively, following Jacob (2000). For R4, γ was set equal to 0.01, as suggested by Zhang et al. (2012) and Walker et al. (2012). The carbonaceous aerosol simulation was performed following the GEOS-Chem model (Park et al., 2003). The primary carbonaceous aerosol followed a passive tracer without any chemical reactions. However, the model resolves primary BC and OC, with hydrophobic and hydrophilic fractions for each (i.e., four aerosol types) for the deposition processes. All sources emit hydrophobic aerosols that then become hydrophilic with an e-folding time of 1.2 days, following Cooke et al. (1999) and Chin et al. (2002). Secondary organic aerosol (SOA) chemistry is not considered in the model; however, we treated the boundary inflow and transport of SOAs. Deposition processes are essential to determine the loss of chemical 56

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(=0.05 m), and von Karman constant (=0.4), respectively; Cμ is an empirical constant (=0.0845); and θ is the wind direction in each season. The surface and top boundary pressures in the model were assumed to be 1013.15 and 993.72 hPa, respectively. We also used the observed humidity, which was measured during another campaign in Elche (Galindo and Yubero, 2017). We estimated the pollutant emissions based on the traffic volumes obtained from the Elche Traffic Office (Yubero et al., 2015). Fig. 2b displays the traffic volumes used in this simulation. The traffic volumes increase in the morning, peak at 1 p.m., slightly decrease in the afternoon, and show another late peak at 7 p.m. They then rapidly decrease during the night. The daily average traffic flow was assumed to be 6710 and 7420 vehicles day−1 in summer and winter, respectively. The daily average traffic flow was 10% lower in summer than in winter to reflect the lower traffic volume during the vacation season (Yubero et al., 2015). The maximum values were 450 and 495 vehicles h−1 in summer and winter, respectively. The vehicular emissions were computed using the emission rates in Spain reported by the European Monitoring and Evaluation Programme/European Environment Agency (Amon et al., 2016), which were calculated by multiplying the mean ratios of the vehicle sizes (large, medium, and light vehicles and motorcycles) in Spain. The emission rates and ratios of vehicles according to size and fuel type that were assumed in the simulation are summarized in Table 2. The calculated NOx, CO, VOC, NH3, and PM emissions per vehicle were 0.19, 0.26, 0.034, 0.0056, and 0.0010 g km−1, respectively. We then separated the NOx emissions into NO and NO2 emissions using a 10:1 ratio by volume (Buckingham et al., 1997). The total VOC emissions were speciated further using the method developed by the Amon et al. (2016). The VOC with the highest emission was found to be ALK4 (lumped C4 alkanes), followed by PRPE (lumped C3 alkenes). The estimated ALK4 and PRPE emissions were 192.5 and 138.6 kg day−1, respectively, in summer, while the emissions of RCHO (lumped C3 aldehydes), formaldehyde, acetaldehyde, and acetone were 102.1, 96.8, 52.2, and 23.5 g day−1, respectively, in summer. The PM emissions were separated into BC and OC emissions, following the Amon et al. (2016), with values of 0.00031 and 0.00038 g km−1, respectively. We assumed that 80% of the BC and 50% of the OC emitted from all primary sources were hydrophobic (Chin et al., 2002; Cooke et al., 1999; Park et al., 2003). All secondary OC at the boundary was assumed to be hydrophilic. Table 3 summarizes the species emissions used in our model simulations. Traffic in the street canyon only affected the emission rate in the street canyon, and the dynamic effects of vehicles were not considered. For the rooftop boundary conditions of the species, we used a reanalysis dataset from the Monitoring Atmospheric Composition and Climate in Elche with 6 h diurnal variations (Inness et al., 2013). We conducted 48 h model simulations for the summer and winter case

Fig. 1. Schematic diagram of the coupled CFD–chemistry simulation domain.

had imposed diurnal variations such that the maximum temperatures occurred at 2 p.m. with a diurnal temperature range of 10 °C. The diurnal temperature variations are displayed in Fig. 2a. The wind speeds at the top of the model domain (50 m) were also assumed to be the observed seasonal mean values during the campaign, which were 1.1 and 0.9 m s−1 in summer and winter, respectively. The wind directions were southeast and northwest in summer and winter, respectively, and were perpendicular to the street canyon (Yubero et al., 2015). The ambient wind speed and direction were fixed during a oneday simulation. The vertical profiles of the wind, TKE, and TKE dissipation rate were imposed as follows:

U (z ) =

V (z ) =

u cos

u sin

ln

z z0

(12)

ln

z z0

(13) (14)

W (z ) = 0 k (z ) =

(z ) =

u2 1 Cµ1/2

Cµ3/4 k 3/2 z

z

2

(15) (16)

Here, u∗, z0, and κ are the friction velocity, roughness length

Fig. 2. Diurnal variations of hourly (a) temperature and (b) traffic volume imposed in the simulation in summer (solid lines) and winter (dashed lines). 57

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Table 2 Emission rates and ratios of vehicles according to size (passenger cars (PCs), light-duty vehicles (LDVs), heavy-duty vehicles (HDVs), buses, mopeds, and motorcycles) and fuel type (diesel, gasoline, and gas) obtained from the Amon et al. (2016) and assumed in the simulation.

PCs (diesel) PCs (gasoline) LDVs (diesel) LDVs (gasoline) HDVs Buses Mopeds Motorcycles

CO [g km−1]

NOx [g km−1]

VOC [g km−1]

NH3 [g km−1]

PM [g km−1]

Ratio

0.040 0.620 0.080 1.210 0.070 0.220 2.600 2.600

0.170 0.030 0.520 0.030 1.020 1.560 0.100 0.160

0.010 0.060 0.040 0.100 0.010 0.020 0.400 0.500

0.002 0.012 0.002 0.012 0.011 0.030 0.001 0.002

0.001 0.001 0.001 0.001 0.008 0.025 0.002 0.002

0.486 0.324 0.149 0.015 0.019 0.002 0.001 0.004

Table 3 Estimated vehicular emissions used in the coupled CFD–chemistry model simulations for the campaign by Yubero et al. (2015). The details regarding the species emission estimates are provided in the text. Emission

Summer

Winter

NO [g] NO2 [g] CO [g] OC [g] SO2 [g] NH3 [g] BC [g] OC [g]

5081.8 564.6 5664.2 504.2 2.8 119.8 6.6 8.0

5590.0 621.2 6230.6 554.6 3.0 131.8 7.2 8.8

studies; the first 24 h was for the model spin-up, and the results for the last 24 h were used in our analysis. 3. Model evaluation We evaluated our model results by comparing the observed aerosol concentrations to assess the aerosol simulation performance. Fig. 3 displays the observed and simulated PM1 concentrations and their compositions at the sampler site in summer and winter. Note that the sampler was located 3 m above the ground on the leeward side in summer and on the windward side in winter. The observed concentration of PM1 in summer is 9.0 μg m−3, which is lower than those observed in other studies of populated cities in Spain (Yubero et al., 2015). The aerosol observations performed in summer showed that the major component of PM1 was OC, which accounted for 39% of the mass (3.5 μg m−3). The concentration of BC was also high at 17% of the total PM1 (1.5 μg m−3). The carbonaceous aerosol ratio between BC and OC was 2.4, which is lower than the values obtained in other studies in Spain, indicating the higher impact of vehicular emissions (de la Campa et al., 2009; Plaza et al., 2011; Viana et al., 2006). The second most abundant component was sulfate (2.7 μg m−3). The ammonium and nitrate concentrations were low (1.0 and 0.3 μg m−3, respectively), indicating the low portion of ammonium nitrate. Nitrate represented only 3% of the total PM1 mass because of evaporation into HNO3 under high-temperature conditions. The simulated PM1 concentration in summer (12.5 μg m−3) was 38% higher than the observed value. The higher concentration in the simulation might be because the model predicted a weaker canyon vortex. Previous studies have indicated that a weaker canyon vortex promotes the presence of higher pollutant concentrations, especially at the bottom of a street canyon (Park et al., 2015). The bottom and wall heating induced by solar insolation affect not only the strength, but also the structure of a canyon vortex, especially in summer (Kim and Baik, 2010). The concentration overestimation could be related to these heating processes, which were not considered in the simulation.

Fig. 3. Comparisons of sulfate, nitrate, ammonium, BC, OC and PM1 concentrations (μg m−3) in the (a) summer and (b) winter cases obtained in this study and by Yubero et al. (2015) (Fig. 1 in Yubero et al., 2015). The values are averages over the analysis period at the sampler station. The green bars are the observed concentrations, and the blue bars are the simulated concentrations.

However, the composition of PM1 is generally consistent with the observations. The concentration of OC was the highest in the simulation, similarly to the observed OC concentration, although it was 42% higher than the observed concentration (5.1 μg m−3). The BC concentration was 1.8 μg m−3, accounting for 14% of the total PM concentration, which is similar to the observed PM concentrations. The ratio between BC and OC was 2.8, which is slightly higher than the observed ratio but lower than the values obtained in other studies (de la Campa et al., 2009; Plaza et al., 2011; Viana et al., 2006). For inorganic aerosols, sulfate exhibited the second highest concentration, accounting for 27% of the total aerosols (3.4 μg m−3). The nitrate and ammonium concentrations were 0.8 and 1.5 μg m−3, respectively, showing a low ratio similar to that observed. Nitrate accounted for only 6% of the total aerosols, reproducing the observed thermal evaporation of ammonium nitrate. The observed PM1 concentrations and their composition in winter were similar to those in summer, showing moderate seasonal variations. The BC and OC concentrations in winter were 1.9 and 4.4 μg m−3, respectively, constituting 19% and 46% of the total PM1. The observed OC concentrations in winter were higher than those in summer, 58

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Fig. 4. Simulated hourly (a) SO2, (b) NH3, (c) OH, and (d) HNO3 concentrations (ppbv) averaged in the street canyon in summer (solid lines) and winter (dashed lines).

reflecting a higher impact of secondary formation. Yubero et al. (2015) explained that lower temperatures promote condensation of organic compounds onto preexisting particles. The sulfate concentration in winter was two times lower than that in summer, measuring 1.3 μg m−3 due to the low reaction rate of sulfuric acid in winter. The nitrate concentration in winter was much higher than that in summer due to the prevention of thermal evaporation of ammonium nitrate, as reported in previous literature (1.3 μg m−3) (Kim et al., 2012). The ammonium concentration was 0.7 μg m−3, which is 30% lower than that in summer because of the low ammonium sulfate concentration. The concentration of simulated PM1 in winter was similar in magnitude to the observed PM1 concentration (10.0 μg m−3). The simulated PM1 concentration was lower in winter than in summer owing to the absence of SOA production in the street canyon and was different from the observed concentration. Note that our approach can treat SOA for a boundary condition. The simulated OC concentration was 4.4 μg m−3, which is similar in magnitude to the observed OC concentration.

Compared to the observed BC concentration, the simulated BC concentration of 20% was an underestimate. The simulated BC concentration was 1.5 μg m−3, accounting for 15% of the total PM1 concentration. The simulated sulfate aerosol level in winter was 1.8 μg m−3, which is also two times lower than that in summer. The lower sulfate concentration is due to a decrease in the reaction rate of sulfuric acid, as explained above. The simulated nitrate concentration in winter was 75% higher than that in summer (1.4 μg m−3), indicating a higher proportion of HNO3 in the nitrate concentration at lower temperatures. The simulated ammonium concentration was 40% lower than that in summer (0.9 μg m−3). The model generally captured the observed seasonal variations of sulfate, nitrate, and ammonium, indicating the successful calculation of chemical production and loss. Although the model has some bias compared with the observations, it reasonably reproduced the observed aerosol concentration in magnitude and composition.

59

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Fig. 5. Simulated hourly (a) sulfate, (b) nitrate, (c) ammonium, (d) BC, (e) OC, and (f) PM1 concentrations (μg m−3) averaged in the street canyon in summer (solid lines) and winter (dashed lines).

4. Diurnal variations and distributions of aerosols in the street canyon

NH3 concentration in summer is 20% lower than that in winter owing to higher wind speeds and lower emissions from residents leaving for the holiday period. The OH concentrations exhibit clear sinusoidal diurnal variations, reaching maxima at noon due to the photolysis reaction from solar insolation in both summer and winter. The OH concentrations in summer are 70% higher than those of winter owing to higher solar insolation in summer. The HNO3 concentrations show sinusoidal variations, peaking at 2 p.m. and 1 p.m. in summer and winter, respectively. The diurnal HNO3 variations indicate that the diurnal variations of OH, a precursor of HNO3, significantly affect HNO3 production. However, the times of peak HNO3 concentration are slightly late compared to those of peak OH concentration, demonstrating that the enhanced reaction rate due to high temperature delayed the time of peak HNO3 concentration. These changes imply that HNO3 is mostly produced through photochemical reactions by OH oxidation rather than through nighttime heterogeneous chemical production in the street canyon. The HNO3 concentration reached 6.4 and 3.7 ppbv in summer and winter, respectively. The HNO3 concentration in summer was two times higher than that in winter because the nitrate aerosol in summer evaporated into HNO3 at high temperatures and there was greater production of HNO3 during the day via OH oxidation. Fig. 5 shows the time series of the average PM1 concentration and its composition inside the street canyon for both cases. The sulfate aerosol concentrations have weak diurnal changes in both the summer and

To examine the effects of diurnal changes in traffic and turbulence on aerosol air quality in the street canyon, we investigated the diurnal variations in the PM1 concentration and its composition in both the summer and winter cases. Before investigating the PM1 changes, we checked the diurnal variations of the processor gases of aerosols to understand the aerosol changes in the street canyon. Fig. 4 shows the simulated SO2, NH3, HNO3, and OH concentrations inside the street canyon in summer and winter. The diurnal variation of the SO2 concentration is weak, indicating the low impact of vehicular emissions. Most SO2 originates from the boundary conditions of other sources, such as fossil fuel burning in both summer and winter. The average SO2 concentration in summer and winter is up to 1.01 and 1.09 ppbv, respectively. The lower SO2 concentration in summer reflects higher oxidation to sulfate aerosols at high temperatures. The NH3 concentration in summer traces the traffic curve, indicating the dominant impact of vehicular emissions on the NH3 concentration. The peak concentrations occur at 1 p.m. and 7 p.m., reaching 5.3 ppbv. The NH3 concentration exhibits diurnal variations in winter similar to those in summer; however, the diurnal variations during the day have clearer double peaks due to the chemical oxidation of ammonium nitrate at low temperatures. The NH3 concentration reaches 6.7 ppbv in winter. The 60

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Fig. 6. Distributions of the daily average concentrations of (a) sulfate, (b) nitrate, (c) ammonium, (d) BC, (e) OC, and (f) PM1 (μg m−3) in the street canyon in summer. The vectors in (f) indicate the simulated wind (u; w).

winter cases; however, there is slight production of sulfate aerosol by OH oxidation during the day (Fig. 5a). The sulfate aerosol concentration reaches 3.8 and 1.9 μg m−3 in summer and winter, respectively. The weak diurnal variations of sulfate reflect the dominant impact of the boundary conditions and indicate that the production of sulfate inside the street canyon is relatively low compared to the amount of sulfate from the boundary. However, sulfate aerosol shows clear production during the day via photochemical oxidation of OH. The sulfate concentrations in summer are two times higher than those in winter owing to the higher boundary concentration under the high temperature conditions in summer. The nitrate concentrations exhibit sinusoidal variations following the precursor of nitrate, HNO3, and the concentration reaches 2.0 and 4.9 μg m−3 in summer and winter, respectively (Fig. 5b). The nitrate concentration peaks occur at 2 p.m. and 1 p.m. in summer and winter, respectively, which are same as the times of the HNO3 concentration peaks, indicating the significant effects of the diurnal temperature and solar insolation variations on the nitrate aerosol concentration. The sinusoidal variations of the nitrate concentration indicate that photochemical production inside the street canyon is a major pathway in the model, unlike for the sulfate concentration. The nitrate concentrations in winter are much higher than

those in summer, indicating the temperature dependency of nitrate formation. The diurnal ammonium aerosol concentration variations display a combined pattern between those of sulfate and nitrate, indicating that ammonium aerosol separates into ammonium sulfate and ammonium nitrate. The ammonium concentration shows sinusoidal variations during the day and moderate variations at night, and its concentration peaks occur at noon, reaching 2.0 and 1.9 μg m−3 in summer and winter, respectively. The diurnal ammonium variations in winter show a clearer sinusoidal pattern than those in summer during the day, indicating that a lower temperature is favorable for ammonium nitrate formation. The carbonaceous aerosol variations follow the traffic volume such that peak concentrations occur at 1 p.m. and 7 p.m. in both cases. The OC and BC concentrations in winter reach 6.4 and 2.3 μg m−3, respectively, 30% higher than those in summer (4.8 and 1.6 μg m−3, respectively). The lower concentrations in summer are due to higher wind speeds and lower emissions. Yubero et al. (2015) demonstrated that the lower concentrations in summer were resulted from lower daily traffic due to residents leaving for vacation. Considering that carbonaceous aerosol accounts for up to 65% of the total PM1 in the street canyon in the observed campaign, these results imply that the temporal PM1 61

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Fig. 7. Distributions of the daily average concentrations of (a) sulfate, (b) nitrate, (c) ammonium, (d) BC, (e) OC, and (f) PM1 (μg m−3) in the street canyon in winter. The vectors in (f) indicate the simulated wind (u; w).

variations were significantly affected by the traffic volume in the street canyon (Yubero et al., 2015). The total PM1 concentration increases during the day, showing the highest concentration at 1 p.m. with rapid nitrate production in both cases (14.2 and 16.9 μg m−3 in winter and summer, respectively). We also found that the PM1 concentration changed rapidly in winter, indicating the high impact of nitrate aerosols. After 7 p.m., the simulated PM1 concentrations decreased rapidly owing to the decrease in traffic volume. Except for peak times (1–3 p.m.), the PM1 levels generally followed the variations of the carbonaceous aerosols resulting from vehicular emissions. The total PM1 concentration in summer is 10% lower than that in winter owing to the higher wind speed and lower emission, as explained above. The diurnal PM1 variations show a combined pattern between those of the primary aerosol following traffic emissions and the secondary aerosol affected by solar insolation, indicating the importance of both vehicular emissions and chemical reactions. The effects of vortex changes in the street canyon are small because the buoyancy changes due to the density and kinematic viscosity, which vary 3% over a day, are small. However, the dynamic effects on the PM variations could have been underestimated in this simulation because the model does not account for surface heating

processes from solar insolation, especially in summer. Figs. 6 and 7 show the spatial distribution of PM1 averaged over 24 h and its composition in the street canyon in summer and winter. The sulfate concentration outside the street canyon is 50%–60% higher than that inside in both cases, indicating the high impact of boundary transport (Figs. 6a and 7a). The sulfate concentration near the roof on the windward side is higher than that on the leeward side, indicating aerosol intrusion inside the street canyon. The nitrate aerosol concentration distributions differ substantially from those of sulfate (Figs. 6b and 7b). The spatial distributions of nitrate aerosols show that high concentrations occur in the street canyon and are affected by vehicular emissions, which include precursors of nitrate such as NOx and NH3. However, the nitrate concentrations are not the highest at the surface, unlike the typical pattern of primary pollutants, although precursor gases are emitted by vehicles. In general, typical primary pollutants emitted by vehicles accumulate at the bottom of a street canyon following a vortex according to the canyon geometry (Kim and Baik, 2004). As mentioned above, nitrate production depends on the HNO3 concentration, which is the lowest near the surface on the leeward side (Fig. 8a). We found that low O3 concentrations (Fig. 8b) suppressed the production of OH and HNO3 (Fig. 8c). Previous reports 62

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Fig. 8. Distributions of the daily average concentrations of (a) HNO3, (b) O3, and (c) OH in winter.

times higher than those outside, showing the dominant impact of vehicular emissions. The average PM1 concentrations also exhibit the general dispersion patterns of the primary pollutants emitted from the bottom of the street canyon, showing that the concentrations on the leeward side are higher than those on the windward side. These dispersion patterns are similar to those of carbonaceous aerosols, implying the importance of carbonaceous aerosols in the street canyon. 5. Effects of chemical production and loss on aerosol concentrations To examine the sensitivity of the model with and without the chemical scheme for reactive aerosols, we conducted a sensitivity model simulation of the observed case study in winter without chemical production and loss. All other conditions were identical to the observed case in winter. Fig. 9 shows the simulated daily average PM1 concentration and its composition at the observed site. Compared with the results of the chemistry case (Fig. 3b), the average concentrations of aerosol without the chemical scheme are similar. However, the concentrations of sulfate and ammonium without the chemical scheme differ slightly compared to those with the chemical scheme. The sulfate concentration is 0.3 μg m−3 lower than that in the case with the chemical scheme owing to the chemical production of sulfate by OH oxidation. The ammonium aerosol concentration is 0.1 μg m−3 higher than that in the case with the chemical scheme. These results demonstrate that the chemical scheme not only produces, but also reduces secondary pollutants. These absolute differences between the cases with and without the chemical scheme appear to be small for all species. Fig. 10 shows the diurnal concentration variations in the simulation without the chemical scheme for the winter case. The simulated sulfate, nitrate, and ammonium concentrations in the case without the chemical scheme exhibit diurnal variations different from those in the case with the chemical scheme. The sulfate concentration does not show a production pattern during the day in the case without the chemical scheme. The nitrate concentration also does not show any diurnal variations, similarly to the sulfate concentration, reflecting the importance of secondary production. The nitrate concentration at the peak is three times lower than that in the case with the chemical scheme, whereas the simulated nitrate concentration at night is 20% higher than that in the case with the chemical scheme. This finding implies that nitrate aerosols decompose into nitric acid, where ammonia emission is low in the case with the chemical scheme. This result suggests that this process is important not only for production, but also for reduction of

Fig. 9. Comparisons of sulfate, nitrate, ammonium, BC, OC and PM1 concentrations (μg m−3) in winter obtained in this study without the chemistry scheme and by Yubero et al. (2015) (Fig. 1 in Yubero et al., 2015). The values are averages over the analysis period at the sampler station. The green bars are the observed concentrations, and the blue bars are the simulated concentrations.

have explained that O3 buildup is suppressed by the titration of high NO concentrations due to relatively low concentrations of VOCs near the surface on the leeward side. Note that the VOC to NOx ratio of vehicular emissions is 0.1, indicating extremely VOC-limited conditions. Consequently, nitrate production is inhibited near the surface, implying the importance of the oxidant concentration in the street canyon. The spatial distributions of ammonium aerosols have different patterns depending on the season. In summer, the dispersion of ammonium aerosol follows that of sulfate due to favorable conditions for ammonium sulfate production. In general, temperature increases enhance the ammonium sulfate concentration due to the increased SO2 oxidation rate, whereas the ammonium nitrate concentration decreases as a result of partitioning to the gas phase (Dawson et al., 2007). The spatial patterns of ammonium are similar to those of nitrate, indicating a low concentration of sulfate in winter. OC and BC exhibit typical passive tracer transport emitted from the load side. The concentrations at the leeward side are higher than those at the windward side, showing a vortex feature. The concentrations inside the street canyon are two 63

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Fig. 10. Simulated hourly (a) sulfate, (b) nitrate, (c) ammonium, (d) BC, (e) OC, and (f) PM1 concentrations (μg m−3) without the chemistry scheme averaged in the street canyon in winter. The dashed lines indicate the simulated concentration with the chemistry scheme in the street canyon in winter (same as the dashed lines in Fig. 5).

the total PM1 concentration are not substantial, considering that the contribution of nitrate aerosol to PM1 is low. Ammonium aerosols also have predominantly flat curves for chemical production. The ammonium concentration without the chemical scheme is two times lower than that with the chemical scheme at 2 p.m., implying that a large portion of ammonium is combined into ammonium nitrate when cold temperatures occur. The concentrations of BC and OC aerosol are identical to those obtained with the chemical scheme (Fig. 4d and e), indicating that most of the carbonaceous aerosols were primary aerosols from vehicular emissions or were formed outside the boundary. We calculate the normalized root-mean-square differences of hourly concentrations between the cases with and without a chemical scheme to estimate the chemical contribution of PM1 and its composition in the street canyon (Table 4). The chemical contribution of nitrate aerosols is 0.83 in winter, indicating that most of the nitrate is produced in the street canyon. The chemical contributions of ammonium and sulfate aerosols are 0.19 and 0.03, showing relatively low contributions compared to nitrate. The chemical effect on the total PM1 concentration is 10% in the street canyon, mostly owing to ammonium nitrate formation. These results suggest that the contribution of chemical effects to the aerosol concentration in the street canyon is low because of the dominant effect of traffic emissions; however, chemical production and loss are essential to investigate the diurnal aerosol variations via ammonium nitrate formation, especially in winter. Note that our

Table 4 Normalized root-mean-square differences of hourly concentrations (sulfate, nitrate, ammonium, BC, OC, and PM1) between the cases with and without the chemistry scheme in the winter case (

Xnoch |2 Xnoch

|X ch

Species

Ratio

SO42NO3− NH4+

0.03 0.83 0.19 0.00 0.00 0.10

BC OC PM1

).

secondary pollutants. The average nitrate concentration in the street canyon is 20% lower than that in the case with the chemical scheme, indicating that the production of nitrate is greater than the loss of nitrate in the street canyon. Note that the spatial distribution of the nitrate aerosol concentration was uniform (not shown), which is contrary to that in the case with the chemical scheme (Fig. 7b). Despite the fact that the nitrate concentration in Fig. 9 is identical to that in the case with the chemical scheme, the average concentration in the street canyon is different. However, the effects of the spatial distribution on 64

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simulation did not include the SOA module; therefore, the chemical production of SOA could not be estimated using this model. Previous studies have indicated that secondary aerosols are also produced by exhaust gases, implying that our estimates of the chemical contribution of PM1 in the street canyon might be underestimated. These results demonstrate that modeling studies only focusing on dynamic processes have limitations in terms of simulating aerosol behavior in street canyons, especially reproducing the diurnal aerosol variations.

Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Acknowledgements

6. Conclusions

This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (ex: NIER-2018-01-02-047).

Modeling aerosol dispersion in street canyons is an area of growing interest because of the detrimental effects of aerosols on the human respiratory system. Previous studies involving the modeling of aerosols in urban street canyons have been focused on dynamic processes such as the coagulation and condensation of aerosols. However, most of the aerosols are produced by the chemical reaction of precursor gases, implying the importance of chemical reactions in street canyons. Therefore, we developed a coupled CFD–chemistry model for simulating aerosol air quality in street canyons. To evaluate the model, we applied it to a street canyon in Elche, Spain and compared the model results with the observations made by Yubero et al. (2015). The model generally captured the PM1 composition in the street canyon in summer and winter. However, compared with the observed concentration of PM1 in summer, the simulated concentration of PM1 was overestimated by 40%, suggesting that the model predicted a weaker canyon vortex than actually exists. Although the model has some bias, it reasonably reproduced the observed aerosol concentrations, demonstrating its effectiveness. We also investigated the diurnal variations and spatial distribution of PM1 and its composition in the street canyon. The simulated sulfate aerosol concentrations exhibited weak diurnal changes, reflecting the dominant impact of the boundary conditions. The nitrate aerosol concentrations displayed clear sinusoidal diurnal variations. The diurnal nitrate changes indicate that photochemical production in the street canyon is a major pathway in the model. The spatial distributions of nitrate aerosols showed that high concentrations occur in the street canyon and are affected by vehicular emissions such as NOx and NH3. However, the nitrate concentrations exhibited differences from the typical pattern of primary pollutants. We found that these differences resulted from the suppression of nitrate production owing to low O3 concentrations under extremely VOC-limited conditions. Ammonium also exhibited sinusoidal variations in both summer and winter, implying that chemical production occurs in the street canyon. The spatial distributions of ammonium aerosols displayed different patterns depending on the season. In summer, the dispersion of ammonium aerosol followed that of sulfate due to the favorable conditions for ammonium sulfate production. However, the spatial patterns of ammonium were similar to those of nitrate, indicating low sulfate concentration in winter. The BC and OC variations followed the traffic volume, being directly affected by exhaust gases. The PM1 levels generally followed the variations of carbonaceous aerosols, except during peak times (1–3 p.m.), indicating the dominance of the contributions of vehicular emissions to the aerosol concentrations. To examine the sensitivity of the model to the chemistry of reactive aerosols, we conducted a sensitivity model simulation without the chemistry scheme. The average aerosol concentrations obtained with and without the chemistry scheme were similar. However, the diurnal variations differed from those with the chemistry scheme for sulfate, nitrate, and ammonium aerosols. The nitrate aerosols showed the largest differences compared to those obtained using the standard simulation, i.e., an approximately three-fold magnitude change in winter. The chemical effect of total PM1 was 10% in the street canyon, mostly owing to ammonium nitrate formation in winter. These results clearly demonstrate that accounting for chemical production and loss is essential for investigating the diurnal variations of PM1 in street canyons, especially in winter.

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