Supplement of Simulation of O 3 and NO x in São Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0)

Abstract. We evaluate the performance of the Model of Urban Network
of Intersecting Canyons and Highways (MUNICH) in simulating ozone (O3)
and nitrogen oxides (NOx) concentrations within the urban street
canyons in the São Paulo metropolitan area (SPMA). The MUNICH simulations
are performed inside the Pinheiros neighborhood (a residential area) and
Paulista Avenue (an economic hub), which are representative urban canyons in
the SPMA. Both zones have air quality stations maintained by the São Paulo
Environmental Agency (CETESB), providing data (both pollutant
concentrations and meteorological) for model evaluation. Meteorological
inputs for MUNICH are produced by a simulation with the Weather Research and
Forecasting model (WRF) over triple-nested domains with the innermost domain
centered over the SPMA at a spatial grid resolution of 1 km. Street
coordinates and emission flux rates are retrieved from the Vehicular
Emission Inventory (VEIN) emission model, representing the real fleet of the
region. The VEIN model has an advantage to spatially represent emissions and
present compatibility with MUNICH. Building height is estimated from the
World Urban Database and Access Portal Tools (WUDAPT) local climate zone map
for SPMA. Background concentrations are obtained from the Ibirapuera air
quality station located in an urban park. Finally, volatile organic
compound (VOC) speciation is approximated using information from the São Paulo
air quality forecast emission file and non-methane hydrocarbon
concentration measurements. Results show an overprediction of O3
concentrations in both study cases. NOx concentrations are
underpredicted in Pinheiros but are better simulated in Paulista Avenue.
Compared to O3, NO2 is better simulated in both urban zones. The
O3 prediction is highly dependent on the background concentration,
which is the main cause for the model O3 overprediction. The MUNICH
simulations satisfy the performance criteria when emissions are calibrated.
The results show the great potential of MUNICH to represent the
concentrations of pollutants emitted by the fleet close to the streets. The
street-scale air pollutant predictions make it possible in the future to
evaluate the impacts on public health due to human exposure to primary
exhaust gas pollutants emitted by the vehicles.



Supplementary Material 1 Weighted emission factors
We weighted emission factors, shown in Figure S1. It was calculated as a weighted mean with vehicles in circulation in 2011 and emission factors for 2011, both obtained from CETESB (2015). Figure S1. NO X weighted emission factors for light and heavy vehicles. Figure S2 shows the mean emission from all street links from the Pinheiros neighborhood for NO X and VOCs. For NO X emissions, Sunday total emissions are 25 % lower than Saturday total emissions. For VOCs emissions, the values are almost the same between Saturday and Sunday. According to Ibarra et al. (2020), the difference between NO X emission during the weekday and the weekend is explained by the buses contribution, which is lower during the weekend, and even lower during Sunday.

WRF simulation quality analysis
To assess the quality of WRF simulation we calculate the statistical indicator in Table A1. The results are shown in Table S1. We then compare them with the recommended benchmark of Emery et al. (2001).
To calculate wind direction MB and MAGE we use the following equation based on Reboredo et al. (2015): Further, according to Keyser and Anthes (1977) and Pielke (2013), model skill is estimated if It satisfies these criteria (Table S2): 1. 2.

3.
Where:  (83) to Pinheiros (99) and Cerqueira Cesar (83) air quality stations. We can see that for our study period during daylight, when ozone is formed and present higher concentration, wind still present a South Easterly direction, which justifies the selection of Ibirapuera air quality station to provided background concentration to MUNICH. During nighttime, wind presents a westerly direction, but ozone concentrations are low.

Test with another background concentration
We perform a test by using measurements from a different AQS as MUNICH background information. We select Santos AQS (light blue diamond in Figure 4). This AQS recorded lower O 3 concentration and higher NO concentrations than Ibirapuera AQS. Figure S4 shows a comparison of MUNICH results against background and observation concentrations for O 3 , NO x , NO, and NO 2 and Figure S5 shows the diurnal profile. Table S3 shows the statistical indicator of the tests. a ̅ -Model value mean, ̅ -Observation mean, -model standard deviation, -observation standard deviation, MB -mean bias, NMB -normalized mean bias, NMGE -normalized mean gross error, RMSEroot mean square error, R -correlation coefficient, FB -fractional mean bias, NMSE -normalized meansquare error, FAC2 -fraction of predictions within a factor of two , and NAD -normalized absolute difference. Values in bold satisfied Hanna and Chang (2012) acceptance criteria.

NO X emission increase
We conduct a sensitivity simulation in which NO x emissions are increased by four relative to the calibrated emission case, and maintaining VOCs emission as the original case scenario. Figure S6 shows a comparison of MUNICH results against background and observation concentrations for O 3 , NO x , NO, and NO 2. Figure S7 shows the diurnal profile. Though there was an improvement in O 3 simulation, improbable NO x concentrations are simulated, too. Table S4 shows the statistical performance indicator of this test.   b ̅ -Model value mean, ̅ -Observation mean, -model standard deviation, -observation standard deviation, MB -mean bias, NMB -normalized mean bias, NMGE -normalized mean gross error, RMSEroot mean square error, R -correlation coefficient, FB -fractional mean bias, NMSE -normalized meansquare error, FAC2 -fraction of predictions within a factor of two , and NAD -normalized absolute difference. Values in bold satisfied Hanna and Chang (2012) acceptance criteria.