Overview
We estimate annual and monthly ground-level fine particulate matter ($\rm{PM_{2.5}}$) for 2000-2019 by combining Aerosol Optical Depth (AOD) retrievals (Dark Target, Deep Blue, MAIAC) that make use of observations from numerous satellite-based NASA instruments (MODIS/Terra, MODIS/Aqua, MISR/Terra, SeaWiFS/SeaStar, VIIRS/SNPP, and VIIRS/NOAA20) with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a residual Convolutional Neural Network (CNN), as detailed in the below reference for V6.GL.01. V6.GL.02.04 follows the methodology of V6.GL.01 but updates the ground-based observations used to calibrate the geophysical $\rm{PM_{2.5}}$ estimates for the entire time series, extends temporal coverage through 1998 – 2023, and includes retrievals from the SNPP VIIRS instrument. Also, previous versions were reported to contain abnormally low values in certain, rare circumstances. This limitation has been addressed in V6.GL.02.04 using a modified padding strategy and stronger geophysical constraints.
View dataset
Annual and monthly mean $\rm{PM_{2.5}}$ [ug/m3] at 0.01° × 0.01°: https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25
Annual and monthly mean $\rm{PM_{2.5}}$ [ug/m3] at 0.1° × 0.1°: https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25c0p10
Sat$\rm{PM_{2.5}}$ data is alternatively available via the Registry of Open Data on AWS: https://registry.opendata.aws/surface-pm2-5-v6gl/
Processed Datasets:
Sat$\rm{PM_{2.5}}$ data V6.GL.02.04 are licensed under CC BY 4.0
Using data with Python
Start Python:
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import numpy as np
import pandas as pd
Open the file:
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file_path = 'data.csv'
with open(file_path, 'r') as file:
Read data:
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lines = file.readlines()
Parse and process data:
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cillum dolore eu fugiat nulla pariatur data.extend.
data = []
for line in lines:
line_data = line.strip().split(',') # Split the line into a list of values
line_data = [float(value) for value in line_data] # Convert values to floats
data.extend(line_data) # Extend the main list with values from the line
Compute summary statistics using NumPy:
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data_array = np.array(data) # Convert the list to a NumPy array
mean = np.mean(data_array)
median = np.median(data_array)
std_dev = np.std(data_array)
min_value = np.min(data_array)
max_value = np.max(data_array)
Display summary statistics:
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tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo
consequat print.
print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Standard Deviation: {std_dev}")
print(f"Minimum Value: {min_value}")
print(f"Maximum Value: {max_value}")
Description of simulation parameters
| Parameter | Value | Language | Time period | Description |
|---|---|---|---|---|
| $\alpha$ | $1/2$ | French | 1930–1954 | Tempor dolor in |
| $\lambda$ | $e/2$ | French | 1930–1954 | Fugiat sint occaecat |
| $\gamma$ | $\ln(3)$ | Spanish | 1833–1954 | Duis officia deserunt |
| $\omega$ | $10^{-4}$ | Italian | 1930–1994 | Excepteur et dolore magna aliqua |
| $\sigma$ | $1.5$ | Portuguese | 1990–2023 | Lorem culpa qui |
| $\chi^2$ | $\pi^2$ | Portuguese | 1990–2023 | Labore et dolore |
Reference and citation
Shen, S. Li, C. van Donkelaar, A. Jacobs, N. Wang, C. Martin, R. V.: Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. (2024) ACS ES&T Air. DOI: 10.1021/acsestair.3c00054. Link