Reconstruction of Daily Courses of SO42−, NO3−, NH4+ Concentrations in Precipitation from Cumulative Samples

0559279 - ÚI 2023 RIV CH eng J - Journal Article
Hůnová, I. - Brabec, Marek - Malý, Marek - Škáchová, H.
Reconstruction of Daily Courses of SO42−, NO3−, NH4+ Concentrations in Precipitation from Cumulative Samples.
Atmosphere. Roč. 13, č. 7 (2022), č. článku 1049. E-ISSN 2073-4433
R&D Projects: GA TA ČR(CZ) SS02030031
Institutional support: RVO:67985807
Keywords : precipitation chemistry * Central Europe * long-term trends * time series * data disaggregation * Bayesian modelling * INLA
OECD category: Statistics and probability
Impact factor: 2.9, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.3390/atmos13071049

It is important to study precipitation chemistry to comprehend both atmospheric and environmental processes. The aim of this study was the reconstruction of daily concentration patterns of major ions in precipitation from samples exposed for longer and differing time periods. We explored sulphates (SO42−), nitrates (NO3−) and ammonium (NH4+) ions measured in precipitation within a nation-wide atmospheric deposition monitoring network in the Czech Republic during 1980–2020. We visualised the long-term trends at selected individual years for four stations, Praha 4-Libuš (LIB), Svratouch (SVR), Rudolice v Horách (RUD) and Souš (SOU), differing in geographical location and reflecting different environments. We found anticipated time trends reflecting the emission patterns of the precursors, i.e., sharp decreases in SO42−, milder decreases in NO3− and steady states in NH4+ concentrations in precipitation. Statistically significant decreasing time trends in SO42− and NO3− concentrations in precipitation between 1990 and 2015 were revealed for the LIB and SVR sites. Spring maxima in April were found for all major ions at the LIB site and for NO3− for the SVR site, for both past and current samples, whereas no distinct seasonal behaviour was recorded for NH4+ at the RUD and SO42− at the SVR sites. By applying Bayesian modelling and the Integrated Nested Laplace Approximation approach, we were able to reconstruct the daily patterns of SO42−, NO3− and NH4+ concentrations in precipitation, which might be further utilised for a wide range of tasks, including comparison of magnitudes and shapes between stations, grouping the decomposed daily data into the ecologically motivated time periods, as well as for logical checks of sampling and measurement reliability.
Permanent Link: https://hdl.handle.net/11104/0332609