Identification of long-term trends and seasonality in high-frequency water quality data from the Yangtze River basin, China

PLoS One. 2018 Feb 21;13(2):e0188889. doi: 10.1371/journal.pone.0188889. eCollection 2018.

Abstract

Comprehensive understanding of the long-term trends and seasonality of water quality is important for controlling water pollution. This study focuses on spatio-temporal distributions, long-term trends, and seasonality of water quality in the Yangtze River basin using a combination of the seasonal Mann-Kendall test and time-series decomposition. The used weekly water quality data were from 17 environmental stations for the period January 2004 to December 2015. Results show gradual improvement in water quality during this period in the Yangtze River basin and greater improvement in the Uppermost Yangtze River basin. The larger cities, with high GDP and population density, experienced relatively higher pollution levels due to discharge of industrial and household wastewater. There are higher pollution levels in Xiang and Gan River basins, as indicated by higher NH4-N and CODMn concentrations measured at the stations within these basins. Significant trends in water quality were identified for the 2004-2015 period. Operations of the three Gorges Reservoir (TGR) enhanced pH fluctuations and possibly attenuated CODMn, and NH4-N transportation. Finally, seasonal cycles of varying strength were detected for time-series of pollutants in river discharge. Seasonal patterns in pH indicate that maxima appear in winter, and minima in summer, with the opposite true for CODMn. Accurate understanding of long-term trends and seasonality are necessary goals of water quality monitoring system efforts and the analysis methods described here provide essential information for effectively controlling water pollution.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Seasons*
  • Water Quality*

Grants and funding

This study was sponsored by the National Natural Science Foundation of China (No. 41471460 and 41501552), the Natural Science Foundation of Jiangsu Province (Grants No BK20161612), the Talent Introduction Project of Nanjing Institute of Geography and Limnology, CAS (NIGLAS2015QD09), and “One Hundred Talents Program” of the Chinese Academy of Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.