From 877f84355312f64c76ccd0a6ad12d964dfa95125 Mon Sep 17 00:00:00 2001 From: KirstyPringle Date: Thu, 22 Aug 2024 14:34:54 +0100 Subject: [PATCH] Update index.html --- about/index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/about/index.html b/about/index.html index 1786aef..88646a2 100644 --- a/about/index.html +++ b/about/index.html @@ -127,7 +127,7 @@

About the data

This website shows the concentration of particulate matter air pollution (PM2.5) in cities around the world. Very few historical observations of PM2.5 exist before the year 2000 so instead we use data produced from a mix of computer model simulations and satellite observations.

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For the most recent years (2000-2021) we use a dataset that combines ground-level and satellite observations of PM2.5 concentrations, from Van Donkelaar et at (2021), this dataset can be found here (V5 0.1 degree resolution).

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For the most recent years (2000-2021) we use a dataset that combines ground-level and satellite observations of PM2.5 concentrations, from Van Donkelaar et at (2021, V5 0.1 degree resolution), this dataset can be found here..

Satellite observations of PM2.5 aren’t available for the years before 1998, so instead we take the historical trend in air pollution from computer models (Turnock 2020); publicly available model data was taken from the Coupled Model Intercomparison Project (CMIP6), these are the climate models used for the IPCC assessment report. We used data from the UKESM submission to CMIP (data is here). The historical concentrations for the UKESM model are calculated using the emissions inventory developed through the Community Emissions Data System (CEDS) by Hoesly et al, 2018

Modelling global concentrations of pollutants is very challenging, and models are continuously evaluated and improved. Previous research has shown that the CMIP6 multi-model simulations tend to underestimate PM2.5 concentrations when compared to global observations (Turnock et al., 2020). To address this issue and to ensure a smooth time series between the model and satellite data, we take the following steps:

For each city, we first calculate a three-year (2000-2002) mean of the satellite data for that city. Next, we calculate the three-year (2000-2002) mean of model concentrations for the same city. The ratio between these values represents the model's bias compared to observations. We then adjust (or "weight") the model values using this ratio. This is a similar approach to that taken by Turnock et al. (2023) and Reddington et al. (2023).