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Atomic Emission Spectroscopy: Overview

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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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Enhancing ATMO-Street model accuracy through emission source analysis using a dense sensor network: a Warsaw case

Anahita Sattari¹, Hans Hooyberghs², Stijn Janssen²

¹Institute of Environmental Protection-National research Institute, Slowica 32, Warsaw, 02-170, Poland. anahita.sattari@ios.edu.pl.

Environmental Monitoring and Assessment

|September 19, 2025

Summary

Improving urban air quality models with high-resolution emissions data and calibrated low-cost sensors significantly reduces particulate matter (PM) prediction bias. This approach enhances PM10 and PM2.5 modelling accuracy for better pollution management.

Keywords:

Particulate matters Residential emissions Resuspension Sensors Street-canyon modelling

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Area of Science:

Environmental Science
Atmospheric Chemistry
Urban Planning

Background:

Urban air quality models are crucial for managing particulate matter (PM) pollution.
Model accuracy is often limited by sparse monitoring and outdated emission inventories.

Purpose of the Study:

To present a scalable framework for enhancing PM10 and PM2.5 modelling.
To improve model accuracy using high-resolution emissions inventories and calibrated low-cost sensor networks.

Main Methods:

Developed a framework integrating high-resolution emissions data (CEEB) and calibrated low-cost sensors.
Applied the framework to Warsaw, Poland, focusing on residential heating and road dust resuspension.
Validated model improvements against calibrated sensor data.

Main Results:

Incorporating detailed emissions data reduced PM concentrations by up to 20% in hotspots.
Prediction bias for PM2.5 was reduced by 57% at key locations.
Calibrated low-cost sensors improved spatial coverage and model validation, despite underestimating extreme events.

Conclusions:

The integrated approach significantly improves urban PM modelling accuracy.
Methodology is broadly applicable to cities worldwide facing similar air quality challenges.
Enhanced modelling supports targeted mitigation and achievement of air quality standards.