Monitoring, modeling, and forecasting long-term changes in coastal seawater quality due to climate change

Abstract

As climate change affects the physicochemical properties of coastal water, the resulting element re-exposure may override the emission reductions achieved by human pollution control efforts. Here, we conduct an analysis the water quality-climate effect over eight consecutive years from 2015 to 2022 along the South China coast combined with CMIP6 Scenario Model Intercomparison Project. Then we utilized a data-driven model to predict the concentrations of trace metals and nutrients over the next 80 years. It is suggested that the acidification process carries the risk of triggering the ocean's buffering mechanisms. During this alkalinity replenishment process, trace metals, such as Cd, Cr, Cu, Fe, Hg, Mn, Pb, and Zn, in the sediment are released into the water phase, along with Ca²⁺ and Mg²⁺. Here, the aim of this study is to show that the nexus of re-exposure-eutrophication-emission reduction with human activities and climate feedback, cannot be ignored in the pursuit of effective environmental governance.

Fig. 1. The correlation of indicators and the sensitivity of pollutant species to environmental variables are presented in (a) and (b) respectively.

a The correlation of each index to these three variables. Double correlation analysis of key response factors of climate change and elements in coastal water bodies. Positive correlations are indicated by purple, while negative correlations by blue. The intensity or darkness of the color represents the strength of the correlation, with darker colors signaling a stronger correlation; (b) The feature importance of each index to these three variables. It depends on the use, chemical properties, and migration trend of the elements. According to the difference in response to variables, elements are divided into three categories, in which red represents sea surface temperature (SST)-sensitive elements, blue represents sedimentary-sensitive elements, and purple represents elements that play multiple roles in the geochemical process.

Fig. 2. Trends in simulated results (blue line) and measured values (purple line) of the receptor model (2015 − 2022).

Error bars represent parallel samples of multiple sampling points. a Represents nutrients, and part (b) represents trace matals (TMs).

Fig. 3. The trend of dissolved phase concentration of some water quality indices in 2015–2099.

Error bars represent parallel samples of multiple sampling points. a Represents the changes in biological elements and their ratios, and (b) represents the TMs.

Fig. 4. The monthly variation of metal concentrations in the aqueous phase of the study area.

The annual range of concentrations represents the ratio of the difference between the maximum and minimum months to the annual average concentration ($R=\frac{C_{\max }-C_{\min }}{C_{\mathrm{avg}}}$).