Data from: Mapping the spatial distribution of sub-10 nm particles in Raleigh, NC
Data files
Mar 02, 2026 version files 18.36 MB
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AERMOD_Model_Output.zip
118.31 KB
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Beltline_shapefiles.zip
49.64 KB
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data_csv.zip
15.22 MB
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GIS_Traffic_Map_Project.zip
1.74 MB
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Jupyter_Notebooks.zip
784.96 KB
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MATLAB_scripts.zip
997 B
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README.md
10.53 KB
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Traffic_xlsx.zip
441.30 KB
Abstract
Sub-10 nm particles represent a critical yet understudied component of urban air pollution, with significant implications for air quality and public health. This study introduces a mobile platform designed to quantify particle number concentrations in the 2.5–10 nm size range and total particle concentrations from a standard vehicle. The platform integrated multiple condensation particle counters with video monitoring to capture both concentration data and visual source identification during systematic sampling campaigns. The system was deployed for a series of drives in Raleigh, North Carolina, USA. Data show two dominant sources of 2.5–10 nm particles in the urban environment: diesel-powered vehicles and aircraft operations at Raleigh-Durham International Airport (RDU). Highway measurements showed particle concentrations averaging 10,000–15,000 cm−3 (approximately tenfold above background levels), with intermittent concentration spikes reaching 70,000 cm−3 where 2.5–10 nm particles constituted over 70 % of the total particle count. These peaks were predominantly associated with "super-emitter" diesel trucks, identified through synchronized video analysis. Concentration gradients demonstrated rapid decay to background levels within approximately 120 m from roadways, highlighting the importance of high-resolution spatial sampling. At RDU Airport, our integrated approach combining field measurements with dispersion modeling indicated that aircraft taxiing and takeoff operations have the potential to function as important contributing sources of 2.5–10 nm particles to the area surrounding the airport, with meteorological conditions influencing their dispersal patterns beyond the immediate airport perimeter. This research provides valuable information for developing targeted strategies to mitigate air pollution in urban environments.
Dataset DOI: 10.5061/dryad.7pvmcvf6r
Publication DOI: https://doi.org/10.1016/j.apr.2025.10274
Description of the data and file structure
This dataset contains mobile measurements of ultrafine particle (UFP) concentrations collected in Raleigh, North Carolina, USA, during June 2023. The study aimed to characterize the spatial distribution of particles smaller than 10 nm (sub-10 nm) and evaluate the influence of traffic emissions on UFP concentrations in both urban and suburban environments. Data were collected using a mobile laboratory equipped with three Condensation Particle Counters (CPCs) with different lower detection limits (2.5 nm, 7 nm, and 10 nm) and a GPS unit to enable spatially-resolved measurements.
The campaign focused on major roadways, including the I-440 Beltline (a major highway encircling Raleigh), urban secondary roads, and suburban areas. Measurements were conducted while the vehicle was in motion to capture spatial variability in UFP concentrations and identify pollution hotspots near high-traffic roadways.
Data and codes within are from a mobile measurement campaign evaluating sub-10 nm particles in Raleigh, North Carolina. The collected data is stored as CSV data and analyzed using MATLAB, Python, AERMOD, and GIS. Video data is also within and was used to pinpoint potential sources.
This dataset includes:
- Raw time-series data from mobile measurements (particle concentrations and GPS coordinates)
- Traffic volume data from the North Carolina Department of Transportation (NCDOT)
- Geospatial shapefiles for the study area
- Dispersion modeling outputs from AERMOD
- Processing scripts and visualization code (MATLAB and Python/Jupyter)
Files and variables
File: AERMOD_Model_Output.zip
Description: Output files from the AERMOD air dispersion model (American Meteorological Society/Environmental Protection Agency Regulatory Model). AERMOD is a steady-state Gaussian plume model used to simulate the dispersion of pollutants from roadway emission sources. These simulations estimate the contribution of traffic emissions to measured UFP concentrations.
Contents:
- .out files: Primary AERMOD output files containing detailed model results,s including:
- Predicted concentrations at receptor locations
- Source contribution estimates
- Meteorological conditions used in modeling
- Model configuration parameters
- .sum files: Summary files containing model run information:
- Model version and execution date
- Input parameter summary
- Warning and error messages
- Overall statistics
- .PLT files: Plot files formatted for visualization software (e.g., AERPLOT, AERMAP)
- Contains formatted concentration data for contour plotting
- Grid-based concentration predictions
Model Configuration:
- Emission sources: Line sources representing roadways with traffic-derived emission rates
- Receptors: Grid points and discrete receptors matching measurement locations
- Meteorological data: Local meteorological conditions (wind speed, direction, temperature, etc.)
- Terrain: Flat/urban terrain assumptions for the Raleigh area
Usage: Used to generate Figure 9, which compares observed particle concentrations with AERMOD-predicted concentrations and illustrates the spatial extent of traffic impacts.
File: Beltline_shapefiles.zip
Description: Geographic Information System (GIS) shapefiles delineating the I-440 Beltline highway in Raleigh, NC. The Beltline is a major limited-access highway that encircles the city and was a primary focus of the mobile measurement campaign due to its high traffic volumes and potential impact on UFP concentrations.
Contents:
- Beltline.shp: Main shapefile containing line geometry
- Beltline.shx: Shape index file
- Beltline.dbf: Attribute database file
- Beltline.prj: Projection/coordinate system file (NAD83)
Coordinate System: Likely NAD83 State Plane North Carolina or WGS84 (check .prj file)
Usage: Used by Python/Jupyter scripts to isolate and analyze particle concentration data, specifically from measurements taken on or near the Beltline. Enables spatial filtering and proximity analysis.
File: MATLAB_scripts.zip
Description: MATLAB code for processing traffic data and generating visualizations of the relationship between particle concentrations and traffic volumes.
Primary Script(s):
- Script(s) for loading and processing AADT traffic data
- Data merging functions to link traffic data with particle measurements
- Statistical analysis code (correlation, regression)
- Plotting functions for scatter plots and trendlines
Required Inputs:
- Traffic_xlsx.zip (AADT data)
- Processed particle concentration data (from data_csv.zip)
Outputs:
- Figure 7: Relationship between particle concentrations and traffic volume
File: Jupyter_Notebooks.zip
Description: Python Jupyter Notebooks containing data processing pipelines, geospatial analysis workflows, and visualization code. These notebooks perform the majority of the data analysis and figure generation for the study.
Notebooks Include:
- Data loading and preprocessing: Reading CSV files, GPS coordinate validation, and temporal averaging
- Geospatial analysis: Spatial binning, proximity calculations, map-based visualizations
- Size distribution analysis: Calculating size-segregated concentrations
- Statistical analysis: Descriptive statistics, spatial variability metrics
- Figure generation: Code to produce Figures 3, 4, 5, 6, and 8
Generated Figures:
- Figure 3: Spatial distribution maps of total UFP concentrations
- Figure 4: Size-resolved particle concentration maps
- Figure 5: Temporal variability in particle concentrations during measurement campaign
- Figure 6: Hotspot identification and characterization
- Figure 8: Statistical summaries and concentration distributions
Python Version Required: Python 3.9 or higher
Required Libraries:
- Core: os, datetime, re, math
- Data manipulation: pandas, numpy
- Geospatial: geopandas, shapely, pyproj, geopy
- Visualization: matplotlib, tilemapbase
- Scientific computing: scipy, netCDF4
Note: Video files referenced in figures listed above are available upon request, but are not included here due to size constraints
File: GIS_Traffic_Map_Project.zip
Description: Complete ArcGIS project file containing all spatial layers, styling, and configurations used to create the study area map. This project integrates traffic data, measurement routes, and geographic features.
Project Components:
- Base layers: Roads, city boundaries, terrain/elevation
- Traffic data layers: AADT data symbolized by traffic volume
- Measurement routes: GPS tracks from mobile measurements
- Study area boundaries: Extent of measurement campaign
- Reference features: Landmarks, labels, scale bars
Map Projection: NAD83
Usage: Used to create Figure 1, which shows the study area, major roadways with traffic volumes, and the spatial extent of mobile measurements.
File: Traffic_xlsx.zip
Description: Annual Average Daily Traffic (AADT) data obtained from the North Carolina Department of Transportation (NCDOT). AADT represents the total volume of vehicle traffic on a highway or road for a year divided by 365 days. These data were used to quantify traffic intensity at different locations along measurement routes and to assess the relationship between traffic volume and UFP concentrations.
Variables:
Route_ID: Road identification number from the NCDOT system
Route_Name: Name or designation of the roadway (e.g., "I-440", "US-1")
County: County in which the road segment is located
Begin_Milepost: Starting milepost for the road segment
End_Milepost: Ending milepost for the road segment
AADT: Annual Average Daily Traffic (vehicles per day)
AADT_Year: Year of the AADT measurement
Usage: Used to create Figure 7, which shows the relationship between particle concentrations and traffic volume. Also used in AERMOD dispersion modeling as input for traffic emission rates.
File: data_csv.zip
Description: Raw comma-separated value (CSV) files containing time-series data from all instruments during the mobile measurement campaign. Each day's measurements are stored in a separate CSV file. Files include data from multiple instruments, though only CPC and GPS data were used in the published analysis.
Variables:
- timestamp/datetime: Date and time of measurement in ISO format (YYYY-MM-DD HH:MM: SS)
- latitude: GPS latitude coordinate in decimal degrees (WGS84)
- longitude: GPS longitude coordinate in decimal degrees (WGS84)
- N_TSI3776: Particle number concentration for particles >2.5 nm in particles per cubic centimeter (particles/cm³)
- N_TSI3025: Particle number concentration for particles >7 nm in particles/cm³
- N_TSI3771: Particle number concentration for particles >10 nm in particles/cm³
- Data from other instruments mounted on the mobile platform (not used in this study): SMPS voltage and state variables, sonic anemometer wind direction/speed components.
Data Quality Notes:
- Instrument response times and flow rates were accounted for in data processing
- Filtering of erroneous GPS points was done inthe processing codes
- Data were collected at 1 Hz (one measurement per second)
Code/software
MATLAB
- Version: R2023b or later recommended
- Toolboxes: Statistics and Machine Learning Toolbox (if used)
Python/Jupyter
- Python version: 3.9 or higher
- Package manager: pip or conda
Required Python packages:
os (built-in)
datetime (built-in)
re (built-in)
math (built-in)
pandas >= 1.3.0
numpy >= 1.20.0
matplotlib >= 3.4.0
geopandas >= 0.9.0
shapely >= 1.7.0
pyproj >= 3.0.0
geopy >= 2.2.0
scipy >= 1.7.0
netCDF4 >= 1.5.7
tilemapbase >= 0.4.5
GIS Software (for GIS_Traffic_Map_Project.zip)
- ArcGIS: Version 10.x or ArcGIS Pro 2.x+
- QGIS: Version 3.x (open-source alternative)
AERMOD Visualization
- AERPLOT or compatible software for viewing PLT files (Google Earth is also usable)
