Preprints
https://doi.org/10.5194/ar-2026-15
https://doi.org/10.5194/ar-2026-15
13 Apr 2026
 | 13 Apr 2026
Status: this preprint is currently under review for the journal AR.

Dispersion normalisation method for improved long-term trend evaluation: Heavy Metals in ambient air in the Czech Republic, Central Europe (2010–2021)

Adéla Holubová Šmejkalová, Radek Lhotka, Hana Škáchová, and Jan Pacner

Abstract. Long-term trends in atmospheric concentrations of heavy metals subject to legislative immission limits - Arsenic (6.0 ng m-3), Cadmium (5.0 ng m-3), Lead (500 ng m-3), and Nickel (20 ng m-3) - were evaluated at selected monitoring stations representing different environmental settings across the Czech Republic (in Central Europe) over twelve years. The dispersion normalisation method, which suppresses the influence of meteorological conditions on observed heavy metal concentrations, was employed to assess the effectiveness of legislative emission control measures. The results demonstrate statistically significant decreasing trends (p < 0.001) across all station types and all monitored heavy metals, except for Nickel at industrial stations, where no significant trend was detected. Furthermore, the systematic differences between original and dispersion-normalised concentration data confirm that meteorological variability can, in some cases, mask true emission levels, potentially leading to misinterpretation of air quality trends.

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Adéla Holubová Šmejkalová, Radek Lhotka, Hana Škáchová, and Jan Pacner

Status: open (until 25 May 2026)

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Adéla Holubová Šmejkalová, Radek Lhotka, Hana Škáchová, and Jan Pacner
Adéla Holubová Šmejkalová, Radek Lhotka, Hana Škáchová, and Jan Pacner
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Short summary
This research examined whether legislative measures to reduce heavy metal pollution have been effective in the Czech Republic. We analysed 12 years of data from 16 monitoring stations and used a method that reduces the influence of changing weather. The results showed that this method is suitable for evaluating long-term trends and helps avoid errors in interpreting pollution levels affected by year-to-year weather differences.
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