- Tytuł:
- Monthly changes in physicochemical parameters of the groundwater in Nida valley, Poland (case study)
- Autorzy:
-
Phan, Cong Ngoc
Strużyński, Andrzej
Kowalik, Tomasz - Powiązania:
- https://bibliotekanauki.pl/articles/2203556.pdf
- Data publikacji:
- 2023
- Wydawca:
- Instytut Technologiczno-Przyrodniczy
- Tematy:
-
groundwater
Nida valley
physicochemical water property
statistical method
water quality classification - Opis:
- The groundwater of the Nida valley was investigated to assess the quality of water source and monthly variations of the physicochemical parameters. A total of 70 water samples were collected from 7 sampling sites during a 10 months period from June 2021 to March 2022. Sampling frequency was once per month. The parameters such as temperature (T), electrical conductivity (EC), dissolved oxygen (DO), pH, total dissolved solids (TDS) were measured in-situ by using handheld device. Meanwhile, total nitrogen (TN), total phosphorus (TP), chloride (Cl – ), sulphate (SO42– ), manganese (Mn), iron (Fe), zinc (Zn), cadmium (Cd), lead (Pb), copper (Cu), chemical oxygen demand (COD) were analysed in the laboratory. According to the classification of Ministry of Marine Economy and Inland Navigation in Poland (2019), some investigated parameters are classified as unsatisfactory quality waters (class 4) and poor-quality waters (class 5) for a few specific months. Such as, TP concentrations obtained in June and January are classified as class 4, SO42– concentrations corresponded to classes 4 and 5 in June, July and August, and Mn concentrations (except in January) are settled in class 5. The high values of Fe in November are arranged in class 5 and in June, July to September and March are classified in class 4. Statistical methods were used as: Shapiro-Wilk test (α = 0.05), ANOVA test and post-hoc Tukey test (α = 0.05), Kruskal-Wallis test and Wilcoxon (Mann-Whitney) rank sum test (α = 0.05) estimated the significant differences in sampling months. Pearson correlation analysis (α = 0.01 and 0.05), principal component analysis (PCA) and cluster analysis showed correlation between the parameters and sampling months.
- Źródło:
-
Journal of Water and Land Development; 2023, 56; 220--234
1429-7426
2083-4535 - Pojawia się w:
- Journal of Water and Land Development
- Dostawca treści:
- Biblioteka Nauki
Artykuł