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A Methodological Approach for Spatiotemporally Analyzing Water‐Polluting Effluents in Agricultural Landscapes Using Partial Triadic Analysis
Author(s) -
Jiménez J. J.,
DarwicheCriado N.,
Sorando R.,
Comín F. A.,
SánchezPérez J. M.
Publication year - 2015
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2014.09.0377
Subject(s) - environmental science , water quality , watershed , pollution , surface runoff , land use , hydrology (agriculture) , agriculture , land cover , drainage basin , sampling (signal processing) , effluent , agricultural land , water resource management , environmental engineering , geography , ecology , computer science , geotechnical engineering , archaeology , cartography , filter (signal processing) , machine learning , engineering , computer vision , biology
Multivariate techniques for two‐dimensional data matrices are normally used in water quality studies. However, if the temporal dimension is included in the analysis, other statistical techniques are recommended. In this study, partial triadic analysis was used to investigate the spatial and temporal variability in water quality variables sampled in a northeastern Spain river basin. The results highlight the spatiality of the physical and chemical properties of water at different sites along a river over 1 yr. Partial triadic analysis allowed us to clearly identify the presence of a stable spatial structure that was common to all sampling dates across the entire catchment. Variables such as electrical conductivity and Na + and Cl − ions were associated with agricultural sources, whereas total dissolved nitrogen, NH 4 + –N concentrations, and NO 2 − –N concentrations were linked to polluted urban sites; differences were observed between irrigated and nonirrigated periods. The concentration of NO 3 − –N was associated with both agricultural and urban land uses. Variables associated with urban and agricultural pollution sources were highly influenced by the seasonality of different activities conducted in the study area. In analyzing the impact of land use and fertilization management on water runoff and effluents, powerful statistical tools that can properly identify the causes of pollution in watersheds are important. Partial triadic analysis can efficiently summarize site‐specific water chemistry patterns in an applied setting for land‐ and water‐monitoring schemes at the landscape level. The method is recommended for land‐use decision‐making processes to reduce harmful environmental effects and promote sustainable watershed management. Core Ideas The spatiality of the physicochemical properties of water along a river is demonstrated. PTA can efficiently summarize site‐specific water chemistry patterns. Significant positive and negative autocorrelation at several distance lags was shown. PTA is useful for evaluating and monitoring water quality.