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Nutrients and Environmental Factors Cross Wavelet Analysis of River Yi in East China: A Multi-Scale Approach

The accumulation of nutrients in rivers is a major cause of eutrophication, and the change in nutrient content is affected by a variety of factors. Taking the River Yi as an example, this study used wavelet analysis tools to examine the periodic changes in nutrients and environmental factors, as wel...

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Detalles Bibliográficos
Autores principales: Wang, Lizhi, Song, Hongli, An, Juan, Dong, Bin, Wu, Xiyuan, Wu, Yuanzhi, Wang, Yun, Li, Bao, Liu, Qianjin, Yu, Wanni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819906/
https://www.ncbi.nlm.nih.gov/pubmed/36612818
http://dx.doi.org/10.3390/ijerph20010496
Descripción
Sumario:The accumulation of nutrients in rivers is a major cause of eutrophication, and the change in nutrient content is affected by a variety of factors. Taking the River Yi as an example, this study used wavelet analysis tools to examine the periodic changes in nutrients and environmental factors, as well as the relationship between nutrients and environmental factors. The results revealed that total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen ([Formula: see text] –N) exhibit multiscale oscillation features, with the dominating periods of 16–17, 26, and 57–60 months. The continuous wavelet transform revealed periodic fluctuation laws on multiple scales between nutrients and several environmental factors. Wavelet transform coherence (WTC) was performed on nutrients and environmental factors, and the results showed that temperature and dissolved oxygen (DO) have a strong influence on nutrient concentration fluctuation. The WTC revealed a weak correlation between pH and TP. On a longer period, however, pH was positively correlated with TN. The flow was found to be positively correct with N and P, while N and P were found to be negatively correct with DO and electrical conductance (EC) at different scales. In most cases, TP was negatively correlated with 5-day biochemical oxygen demand (BOD(5)) and permanganate index (COD(Mn)). The correlation between TN and COD(Mn) and BOD(5) was limited, and no clear dominant phase emerged. In a nutshell, wavelet analysis revealed that water temperature, pH, DO, flow, EC, COD(Mn), and BOD(5) had a pronounced influence on nutrient concentration in the River Yi at different time scales. In the case of the combination of environmental factors, pH and DO play the largest role in determining nutrient concentration.