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Atomic Emission Spectroscopy: Overview

Atomic Emission Spectroscopy: Overview

Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
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Updated: Jul 9, 2025

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer

Published on: May 29, 2019

Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder.

Lu Liang¹, Jacob Daniels², Michael Biancardi³

¹Department of Geography and the Environment, University of North Texas, Denton, TX, 76203, USA. lu.liang@unt.edu.

Scientific Data

|November 30, 2023

Summary

This summary is machine-generated.

This study created a gapless, long-term Aerosol Optical Depth (AOD) dataset for Texas using satellite data and AI. This improved AOD data aids climate and air quality research.

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Area of Science:

Atmospheric Science
Remote Sensing
Data Science

Background:

Aerosol Optical Depth (AOD) is vital for climate, air quality, and health assessments.
Satellite AOD data offer broad coverage but suffer from data gaps due to various factors.
Existing AOD datasets lack the long-term continuity needed for comprehensive analysis.

Purpose of the Study:

To develop a gapless, long-term satellite-derived AOD dataset for Texas (2010-2022).
To reconstruct missing AOD data using advanced deep learning techniques.
To provide a reliable AOD resource for environmental and public health research.

Main Methods:

Utilized Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products.
Employed a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder for data reconstruction.
Validated the reconstructed data against independent test and ground-based AERONET datasets.

Main Results:

Achieved high accuracy in AOD reconstruction with an RMSE of 0.017 and R² of 0.941 against test data.
Demonstrated satisfactory agreement with ground-based AERONET data (RMSE 0.052-0.067).
Generated a comprehensive dataset available at daily, monthly, quarterly, and yearly resolutions.

Conclusions:

The developed LSTM autoencoder effectively reconstructs missing satellite AOD data.
The gapless Texas AOD dataset provides a valuable resource for atmospheric and health studies.
This work enhances the utility of satellite AOD data for scientific research and decision-making.