AeroViz 0.2.1

Aerosol data processing and visualization toolkit. Read, QC, and analyze data from SMPS, APS, AE33, TEOM, Nephelometer, XRF, and 15+ atmospheric instruments.

AeroViz

Aerosol Data Processing and Visualization Toolkit for Atmospheric Research

AeroViz is a Python toolkit for reading, processing, and visualizing aerosol measurement data. It supports 18+ atmospheric instruments with built-in quality control, data processing, and publication-ready visualizations.

Installation

pip install AeroViz

Quick Start

from AeroViz import RawDataReader

# Read AE33 Aethalometer data (black carbon)
df = RawDataReader(
    instrument='AE33',
    path='/path/to/data',
    start='2024-01-01',
    end='2024-12-31',
    mean_freq='1h',  # Hourly averages
    qc=True          # Apply quality control
)

# Output: DataFrame with columns like BC1-BC7, abs_370-abs_950, AAE, eBC
print(df[['eBC', 'AAE']].describe())

Supported Instruments

Black Carbon Monitors

Instrument	Description	Output Columns
AE33	Magee Aethalometer (7-wavelength)	BC1-BC7, abs_370-abs_950, AAE, eBC
AE43	Magee Aethalometer (7-wavelength)	BC1-BC7, abs_370-abs_950, AAE, eBC
BC1054	Met One Black Carbon Monitor	BC, abs_880
MA350	AethLabs microAeth (5-wavelength)	BC1-BC5, abs_375-abs_880

# Example: Read black carbon data
bc = RawDataReader('AE33', '/data/AE33', '2024-01-01', '2024-06-30')
print(f"Mean eBC: {bc['eBC'].mean():.2f} ng/m³")
print(f"Mean AAE: {-bc['AAE'].mean():.2f}")  # AAE stored as negative

Particle Sizers

Instrument	Description	Size Range	Output Columns
SMPS	Scanning Mobility Particle Sizer	10-1000 nm	Size bins, total_num, GMD_num, GSD_num
APS	Aerodynamic Particle Sizer	0.5-20 μm	Size bins, total_num, GMD_num, GSD_num
GRIMM	Optical Particle Counter	0.25-32 μm	Size bins, number concentrations

# Example: Read SMPS size distribution
smps = RawDataReader(
    'SMPS', '/data/SMPS', '2024-01-01', '2024-06-30',
    size_range=(10, 500)  # Filter to 10-500 nm
)
print(f"Total number: {smps['total_num'].mean():.0f} #/cm³")
print(f"GMD: {smps['GMD_num'].mean():.1f} nm")

Mass Concentration

Instrument	Description	Output Columns
TEOM	Tapered Element Oscillating Microbalance	PM_NV, PM_Total, Volatile_Fraction
BAM1020	Beta Attenuation Monitor	PM2.5, PM10

# Example: Read TEOM PM mass data
teom = RawDataReader('TEOM', '/data/TEOM', '2024-01-01', '2024-06-30')
print(f"PM2.5 (non-volatile): {teom['PM_NV'].mean():.1f} μg/m³")
print(f"PM2.5 (total): {teom['PM_Total'].mean():.1f} μg/m³")

Optical Instruments

Instrument	Description	Output Columns
NEPH	TSI Nephelometer	scattering_B, scattering_G, scattering_R, SAE
Aurora	Ecotech Aurora 3000	scattering_B, scattering_G, scattering_R, SAE

# Example: Read nephelometer scattering data
neph = RawDataReader('Aurora', '/data/Aurora', '2024-01-01', '2024-06-30')
print(f"Scattering (550nm): {neph['scattering_G'].mean():.1f} Mm⁻¹")

Chemical Composition

Instrument	Description	Output Columns
Xact	Cooper XRF Heavy Metals	Fe, Zn, Pb, Cu, Mn, Cr, Ni, As, Cd, ...
OCEC	Sunset OC/EC Analyzer	OC, EC, TC, OC1-OC4, EC1-EC3
IGAC	Ion Chromatograph	SO4²⁻, NO3⁻, Cl⁻, NH4⁺, Na⁺, K⁺, ...
Q-ACSM	Aerosol Chemical Speciation Monitor	Org, SO4, NO3, NH4, Chl

# Example: Read XRF heavy metals data
xrf = RawDataReader('Xact', '/data/Xact', '2024-01-01', '2024-06-30')
print(f"Fe: {xrf['Fe'].mean():.1f} ng/m³")
print(f"Pb: {xrf['Pb'].mean():.2f} ng/m³")

Other Instruments

Instrument	Description
VOC	Volatile Organic Compounds Analyzer
EPA	Taiwan EPA Air Quality Data
Minion	Low-cost Sensor Network

Key Parameters

Parameter	Type	Description	Default
instrument	str	Instrument name (see tables above)	Required
path	str/Path	Directory containing raw data files	Required
start	str/datetime	Start date ('2024-01-01' or datetime)	Required
end	str/datetime	End date	Required
mean_freq	str	Output frequency: '1h', '30min', '1D'	'1h'
qc	bool/str	Quality control: True, False, or 'MS' for monthly stats	True
reset	bool/str	True to reprocess, 'append' to add new data	False
size_range	tuple	Size range in nm for SMPS/APS: (min, max)	None

Quality Control

AeroViz applies automatic QC based on instrument-specific rules. The QC_Flag column indicates data quality:

Flag	Description
Valid	Data passed all QC checks
Insufficient	Not enough raw data points in period
Status Error	Instrument reported error status
Invalid BC	Black carbon outside valid range
Invalid Number Conc	Particle count outside valid range
Spike	Detected sudden unrealistic change

# Check data quality
df = RawDataReader('AE33', '/data/AE33', '2024-01-01', '2024-06-30')
print(df['QC_Flag'].value_counts())
# Valid          8000
# Insufficient    300
# Status Error     60

Data Processing

Advanced analysis with specialized modules:

from AeroViz import DataProcess
from pathlib import Path

# Optical property calculations
optical = DataProcess(method='Optical', path_out=Path('./results'))

# Available methods:
# - 'Chemistry': Mass reconstruction, volume calculation, kappa
# - 'Optical': Mie theory, IMPROVE extinction, RI retrieval
# - 'SizeDistr': SMPS-APS merge, mode fitting, lung deposition
# - 'VOC': OFP, SOAP, MIR calculations

Visualization

Publication-ready plots:

from AeroViz import plot

# Time series, diurnal patterns, wind rose, polar plots, etc.

File Structure

AeroViz expects data organized by station and instrument:

/data/
├── Station_Instrument/
│   ├── raw_file_001.dat
│   ├── raw_file_002.dat
│   └── instrument_outputs/    # Auto-generated
│       ├── output_instrument.csv
│       ├── _read_instrument_qc.csv
│       └── report.json

Documentation

• Full Documentation
• API Reference
• Changelog

Contributing

Contributions are welcome! Please see our GitHub Issues for bug reports and feature requests.

License

MIT License - see LICENSE for details.

Citation

If you use AeroViz in your research, please cite:

AeroViz: Aerosol Data Processing and Visualization Toolkit
https://github.com/Alex870521/AeroViz

Contributors