Article ; Online: Reconstruction of Daily Courses of SO 4 2− , NO 3 − , NH 4 + Concentrations in Precipitation from Cumulative Samples
Atmosphere, Vol 13, Iss 7, p
2022 Volume 1049
Abstract: 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 ... ...
Abstract | 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 (SO 4 2− ), nitrates (NO 3 − ) and ammonium (NH 4 + ) 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 SO 4 2− , milder decreases in NO 3 − and steady states in NH 4 + concentrations in precipitation. Statistically significant decreasing time trends in SO 4 2− and NO 3 − 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 NO 3 − for the SVR site, for both past and current samples, whereas no distinct seasonal behaviour was recorded for NH 4 + at the RUD and SO 4 2− at the SVR sites. By applying Bayesian modelling and the Integrated Nested Laplace Approximation approach, we were able to reconstruct the daily patterns of SO 4 2− , NO 3 − and NH 4 + 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. |
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Keywords | precipitation chemistry ; Central Europe ; long-term trends ; time series ; data disaggregation ; Bayesian modelling ; Meteorology. Climatology ; QC851-999 |
Subject code | 333 ; 290 |
Language | English |
Publishing date | 2022-06-01T00:00:00Z |
Publisher | MDPI AG |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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