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meteorological data. Next, CALMET generates hourly, three-dimensional diagnostic fields of wind and
temperature, which characterize the local geographic and meteorological conditions. Then, the CALPUFF
module simulates the atmospheric dispersion of pollutants by generating air pollutant puffs and modeling
their physical transport and chemical transformation in the atmosphere. Finally, the resulting concentration
data are input into the CALPOST module, which produces geographic information output files suitable for
statistical analysis and ArcGIS mapping.

The CALPUFF model utilizes a three-dimensional grid system, where the X, Y, and Z axes represent the
east-west, north-south, and vertical directions, respectively. In this study, the Universal Transverse
Mercator (UTM) coordinate system is adopted as the projection system, with the World Geodetic System
1984 (WGS-84) serving as the geodetic datum.

The study area encompasses a 25 × 27 km region centered on Baoshan District. The UTM coordinates of the
origin are (337.160, 3459.74) km, and the horizontal grid resolution is set to 0.5 km. The domain consists of
50 grid points along the X-axis (east-west) and 54 grid points along the Y-axis (north-south). Vertically, the
Z-axis includes 11 levels with top heights defined as 0, 20, 40, 80, 160, 320, 640, 1,200, 2,000, 3,000, and
4,000 m.

Terrain elevation data are sourced from the USGS SRTM1 dataset. In the terrain preprocessing module,
these data are converted into the “terrel.dat” file to represent the elevation of each grid cell. Land use data
are derived from the USGS Global Land Cover Characteristics (GLCC) database, specifically the Asian
region (USGS Global (Lambert Azimuthal) for Eurasia-Asia), with a spatial resolution of 1 km. These data
are processed into the “lu.dat” file to describe land use across the grid.

Ground-level meteorological data are obtained from 11 observation stations across Shanghai, including
Minhang, Baoshan, Jiading, Chongming, Xujiahui, Nanhui, Pudong, Jinshan, Qingpu, Songjiang, and
Fengxian [Table 1]. The dataset spans from 00:00 on November 11 to 23:00 on November 30, 2019, and
includes hourly records of wind speed (m/s), wind direction (degrees), temperature (Kelvin), cloud cover
(tenths), cloud base height (hundreds of feet), surface pressure (hPa), and relative humidity (%). To
maintain data continuity for model input, missing values in variables such as wind speed, temperature, or
cloud cover were filled using linear interpolation based on the average of adjacent time points.

Upper-air meteorological data must include at least two observations per day, covering geopotential height,
temperature, wind direction, and wind speed at the 500, 700, 850, 925, and 1000 hPa pressure levels. In this
study, the data were sourced from publicly accessible datasets provided by the University of Wyoming
(UWyo) and the National Oceanic and Atmospheric Administration (NOAA).

The road traffic emission data for Baoshan District [Table 2] were obtained from the Shanghai
Environmental Monitoring Center. These data encompass all 4,511 major roads in the district and span the
period from 00:00 on November 11 to 23:00 on November 30, 2019. The dataset includes information such
as road identification number, date, time, vehicle type, road length, average vehicle speed, traffic flow,
vehicle mileage, and NO emissions.
x

Conversion rate of NO /NO
2 x
Nitrogen oxide (NO ) emissions are commonly used as a statistical metric for pollution source data. When
x
air quality models are employed to simulate the spatial and temporal distribution of NO concentrations,
2
and photochemical models are unable to predict NO and NO separately, NO concentrations are typically
2
2