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

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

doi:10.5194/ar-2026-15

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https://doi.org/10.5194/ar-2026-15

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13 Apr 2026

| 13 Apr 2026

Status: a revised version of this preprint was accepted for the journal AR and is expected to appear here in due course.

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.

Received: 01 Apr 2026 – Discussion started: 13 Apr 2026

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

Status: closed

RC1:
'Comment on ar-2026-15', Anonymous Referee #1, 13 May 2026
This study measures the long-term trends of atmospheric heavy metals (HMs) at various station types in the Czech Republic and applies the dispersion normalization method to demonstrate that, in some cases, meteorological variability can mask the true level of emissions. The study was conducted on a robust temporal (12-year) and spatial series, with statistically significant data for measured heavy metal concentrations and correlation analyses between stations confirming the representativeness of the groups by environmental type.
Except for Ni at the industrial site, the results demonstrate statistically significant (p < 0.001) decreasing trends at all station types and for all monitored heavy metals, confirming that dispersion normalization is a robust method capable of providing clearer results on the effectiveness of legislative emission control measures.

The article presents good data quality obtained using an appropriate methodology, therefore requiring minor revisions, as suggested below:
Improve the clarity of the text in the introduction from lines 36-44.

Check and correct some errors in station abbreviations. For example, the OPR station in the text is listed as Industrial, while in Table S2 it is BS (background/suburban); in Table S2, the Industrial station is listed as Ostrava-Přívoz, which is designated POP; in all other Tables S2-S10, check the abbreviation OPO

It is recommended to improve the clarity of Figure S3 (in the Supplementary Information), for example by dividing it into multiple figures by station type or, at least, increasing the font size for easier reading
Citation: https://doi.org/10.5194/ar-2026-15-RC1
- CC1: 'Reply on RC1', Radek Lhotka, 13 May 2026
  
  We sincerely thank the reviewer for his/her thorough and constructive assessment of our manuscript. We are pleased that the reviewer found the study to be of high quality in terms of data, conducted on a robust temporal and spatial basis, and requiring only minor revisions. We will carefully consider all comments made in the comments and will revise our study accordingly.
  
  Citation: https://doi.org/10.5194/ar-2026-15-CC1
RC2:
'Comment on ar-2026-15', Anonymous Referee #2, 18 May 2026

find attached

Citation: https://doi.org/10.5194/ar-2026-15-RC2
- CC2: 'Reply on RC2', Radek Lhotka, 20 May 2026
  
  We sincerely thank the reviewer for his detailed and constructive comments. We will carefully consider all suggestions described in the comments.
  We will make linguistic and stylistic adjustments, acknowledging this deficiency in the manuscript, and will pay special attention to the results section.
  
  Citation: https://doi.org/10.5194/ar-2026-15-CC2
AC1: 'Comment on ar-2026-15', Adéla holubová, 21 Jun 2026

The response to the comments is attached. The new text is marked in green; the authors' answers are in yellow.

Citation: https://doi.org/10.5194/ar-2026-15-AC1

Status: closed

RC1:
'Comment on ar-2026-15', Anonymous Referee #1, 13 May 2026
This study measures the long-term trends of atmospheric heavy metals (HMs) at various station types in the Czech Republic and applies the dispersion normalization method to demonstrate that, in some cases, meteorological variability can mask the true level of emissions. The study was conducted on a robust temporal (12-year) and spatial series, with statistically significant data for measured heavy metal concentrations and correlation analyses between stations confirming the representativeness of the groups by environmental type.
Except for Ni at the industrial site, the results demonstrate statistically significant (p < 0.001) decreasing trends at all station types and for all monitored heavy metals, confirming that dispersion normalization is a robust method capable of providing clearer results on the effectiveness of legislative emission control measures.

The article presents good data quality obtained using an appropriate methodology, therefore requiring minor revisions, as suggested below:
Improve the clarity of the text in the introduction from lines 36-44.

Check and correct some errors in station abbreviations. For example, the OPR station in the text is listed as Industrial, while in Table S2 it is BS (background/suburban); in Table S2, the Industrial station is listed as Ostrava-Přívoz, which is designated POP; in all other Tables S2-S10, check the abbreviation OPO

It is recommended to improve the clarity of Figure S3 (in the Supplementary Information), for example by dividing it into multiple figures by station type or, at least, increasing the font size for easier reading
Citation: https://doi.org/10.5194/ar-2026-15-RC1
- CC1: 'Reply on RC1', Radek Lhotka, 13 May 2026
  
  We sincerely thank the reviewer for his/her thorough and constructive assessment of our manuscript. We are pleased that the reviewer found the study to be of high quality in terms of data, conducted on a robust temporal and spatial basis, and requiring only minor revisions. We will carefully consider all comments made in the comments and will revise our study accordingly.
  
  Citation: https://doi.org/10.5194/ar-2026-15-CC1
RC2:
'Comment on ar-2026-15', Anonymous Referee #2, 18 May 2026

find attached

Citation: https://doi.org/10.5194/ar-2026-15-RC2
- CC2: 'Reply on RC2', Radek Lhotka, 20 May 2026
  
  We sincerely thank the reviewer for his detailed and constructive comments. We will carefully consider all suggestions described in the comments.
  We will make linguistic and stylistic adjustments, acknowledging this deficiency in the manuscript, and will pay special attention to the results section.
  
  Citation: https://doi.org/10.5194/ar-2026-15-CC2
AC1: 'Comment on ar-2026-15', Adéla holubová, 21 Jun 2026

The response to the comments is attached. The new text is marked in green; the authors' answers are in yellow.

Citation: https://doi.org/10.5194/ar-2026-15-AC1

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

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https://doi.org/10.5194/ar-2026-15-supplement

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

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Aug 2026	4	8	4	16

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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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