the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Jan 2025
Visualising historical changes in air pollution with the Air Quality Stripes
Abstract. This paper introduces the Air Quality Stripes, a data visualisation project which presents historical changes in outdoor particulate matter air pollution (PM2.5) concentrations across major cities worldwide. Inspired by the popular Warming Stripes image showing trends in surface temperature, the Air Quality Stripes aim to make complex information about air quality trends understandable and engaging for a broad audience. A historical PM2.5 dataset (1850–2022) was created by integrating satellite observations with model simulations (with a bias correction step to ensure a smooth time series and address known model biases). Images were produced in collaboration with a visual design specialist and revised after informal feedback from potential audiences. The images show that trends in PM2.5 are varied across the globe; recently there have been significant improvements in air quality in much of Europe and North America but worsening air quality in parts of Asia, Africa and South America. By showcasing historical data in easy to interpret images, the project aims to inspire dialogue among individuals, communities, and policymakers about proactive strategies to combat air pollution.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscience Communication.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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RC1: 'Comment on egusphere-2024-3961', Anonymous Referee #1, 12 Mar 2025
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This is a charming paper with very useful messages. The ability for stakeholders to quickly identify the air pollution situation in different cities is beneficial, as is the ability to see the chronology of air pollution in the cities and how this chronology can rapidly change when successful clean air action is implemented. The paper is novel in its approach and should be published once the following relatively small comments have been addressed.
The paper should acknowledge more fully that there are very few measurements of air pollution anywhere in the world before the 1950s and hence the model outputs are very unconstrained, especially before the satellite period.
In lines 33-38, the role of production and loss terms of PM should be tightened up. Like all pollutants the concentration is governed by the relative importance of source and loss terms. The examples given are a little simplistic. The possible sources of PM should be expanded upon. The one example of the importance of meteorology – “while other urban locations are affected by strong onshore winds which can influence concentrations” should also be further expanded. All locations are going to be influenced by meteorology.
Line 36 “…atmospheric lifetime of a few weeks,…” should be changed to “…atmospheric lifetime of approximately a few weeks,…”
Citation: https://doi.org/10.5194/egusphere-2024-3961-RC1
Data sets
Air Quality Stripes Kirsty Pringle and Jim McQuaid https://doi.org/10.5281/zenodo.14360345
Air Quality Stripes: Comparison of Model and Satellite Values Kirsty Pringle and Jim McQuaid https://doi.org/10.5281/zenodo.14392693
Model code and software
Air Quality Stripes: Code used to create images Kirsty Pringle and Richard Rigby https://doi.org/10.5281/zenodo.14393148
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Viewed (geographical distribution)
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| United States of America | 1 | 59 | 27 |
| United Kingdom | 2 | 51 | 24 |
| India | 3 | 13 | 6 |
| Germany | 4 | 10 | 4 |
| China | 5 | 9 | 4 |
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