Evaluating a river's ecological health: A multidimensional approach

Abstract

Evaluating the health of river surface water is essential, as rivers support significant biological resources and serve as vital drinking water sources. While the Water Quality Index (WQI) is commonly employed to evaluate surface water quality, it fails to consider biodiversity and does not fully capture the ecological health of rivers. Here we show a comprehensive assessment of the ecological health of surface water in the lower Yangtze River (LYR), integrating chemical and biological metrics. According to traditional WQI metrics, the LYR's surface water generally meets China's Class II standards. However, it also contains 43 high-risk emerging contaminants; nitrobenzenes are found at the highest concentrations, representing 25-90% of total detections, while polycyclic aromatic hydrocarbons present the most substantial environmental risks, accounting for 81-93% of the total risk quotient. Notably, the plankton-based index of biological integrity (P-IBI) rates the ecological health of the majority of LYR water samples (59.7%) as 'fair', with significantly better health observed in autumn compared to other seasons (p < 0.01). Our findings suggest that including emerging contaminants and P-IBI as additional metrics can enhance the traditional WQI analysis in evaluating surface water's ecological health. These results highlight the need for a multidimensional assessment approach and call for improvements to LYR's ecological health, focusing on emerging contaminants and biodiversity rather than solely on reducing conventional indicators.

Keywords: Biological integrity; Ecological risk; High-risk emerging contaminants; Plankton; The lower reaches of the Yangtze River.

Graphical abstract

Fig. 1

Sampling locations in the LYR. According to the geographical distribution characteristics, the study area was divided into the upper reaches (S1–S7), the middle reaches (S8–S15 and S19), and the lower reaches (S16–S18 and S20–S22).

Fig. 2

The detected concentrations and frequencies of PCBs (a), PAHs (b), and NBs (c) in the LYR. The boxplot represents the concentration ranges, while the line charts depict the detection frequencies.

Fig. 3

Composition of PCBs (a), PAHs (b), and NBs (c). According to the different structural characteristics, PCBs were divided into Tri-CBs, Tera-CBs, Penta-CBs, hexachlorobiphenyls (Hexa-CBs), and heptachlorobiphenyls (Hepta-CBs); PAHs were divided into 2–3, four, and five rings; and NBs were divided into nitrobenzene (NB), nitrotoluene (NT), chloro-dinitrobenzene (CNB), dinitrobenzene (DNB), dinitrotoluene (DNT), and trinitrotoluene (TNT).

Fig. 4

Temporal and spatial variation of plankton abundance. a, Temporal distribution of phytoplankton abundance; b, Spatial distribution of phytoplankton abundance; c, Temporal distribution of zooplankton abundance; d, Temporal distribution of zooplankton abundance.

Fig. 5

Distribution of evaluation results for three methods. a, The spatiotemporal distribution of WQI; b, Annual average RQ value of PCBs, PAHs, and NBs; c, The spatiotemporal distribution of P-IBI health level.

Fig. 6

Correlation analysis between water quality and biological indicators. In the figure, the ∑RQs and WQIs were standardized values, and Z-IBI represented zooplankton-IBI (∗p < 0.05, ∗∗p < 0.01).