Assessing the severe eutrophication status and spatial trend in the coastal waters of Zhejiang province (China)

Abstract

The eutrophication of the coastal waters of Zhejiang Province has become one of the main contamination threats to the region's coastal marine ecosystems. Accordingly, the comprehensive characterization of the eutrophication status in terms of improved quantitative methods is valuable for local risk assessment and policy making. A novelty of this work is that the spatial distributions of chemical oxygen demand, dissolved inorganic nitrogen, and dissolved inorganic phosphorus were estimated across space by the Bayesian maximum entropy (BME) method. The BME estimates were found to have the best cross‐validation performance compared to ordinary kriging and inverse distance weighted techniques. Based on the BME maps, it was found that about 25.95%, 19.18%, 20.53%, and 34.34% of these coastal waters were oligotrophic, mesotrophic, eutrophic, and hypereutrophic. Another novelty of the present work is that comprehensive stochastic site indicators (SSI) were introduced in the quantitative characterization of the eutrophication risk in the Zhejiang coastal waters under conditions of in situ uncertainty. The results showed that the level of the eutrophication index (EI) increased almost linearly with increasing threshold values; and that 71%, 51%, and 19% of coastal locations separated by various spatial lags experience considerable mesotrophic, eutrophic, and hypereutrophic risks, respectively. The average EI values over the subregions of the Zhejiang coastal waters graded as “oligotrophic or higher,” “eutrophic or higher,” and “hypereutrophic” were about 11.14, 14.28, and 25.34, respectively. Our results also revealed that the joint eutrophication strength between coastal locations in the Zhejiang region was consistently greater than the combined strength of independent eutrophications at these locations (we termed this situation “positive quadrant eutrophication dependency”). It was found that a critical eutrophication threshold ζcr ≈ 8.38 exists so that below ζcr the spatial eutrophication dependency in the Zhejiang coastal waters increases with ζ, whereas above ζcr the opposite is true. Moreover, the eutrophication dependency decreases as the separation distance δs increases. Interestingly, at distances δs smaller than a critical distance δscr ≈ 15 km, the eutrophication locations are concentrated in the coastal waters of the Zhejiang province rather than being dispersed (this observation holds even for large thresholds ζ). Elasticity analysis of eutrophication indicators offered a quantitative measure of the excess eutrophication change in the Zhejiang coastal waters caused by a threshold change (the larger the elasticity is, the more sensitive eutrophication is to threshold changes). The above findings can contribute to an improved understanding of seawater quality and provide a practical approach for the identification of critical coastal water regions.

Space‐Time Characterization and Risk Assessment of Nutrient Pollutant Concentrations in China's Near Seas

“…Regarding the BME implementation, as has been shown in several previous studies (e.g., Gao et al, ; Jiang et al, ; Lee et al, ; Yu et al, ), BME can provide more accurate attribute estimates than the mainstream geostatistical techniques for space‐time analysis and SSI assessment purposes; it can produce useful information based on limited data sets; it is easily implemented and cost‐effective compared to other numerical modeling techniques; and it has the distinctive ability to assimilate various types of environmental information, both core and site‐specific. Accordingly, given its successes in other scientific fields, the potential usefulness of BME modeling in oceanography deserves serious consideration (e.g., ocean models, considered as core knowledge in the BME framework, can be combined with remote sensing data, considered as site‐specific data, to improve the large‐scale prediction accuracy of ocean attributes).…”

“…The space‐time prediction of nitrate and phosphate concentrations was based on the BME theory (Christakos, ), which has been applied successfully in various fields, including water quality, air pollution, and epidemiology (e.g., Coulliette et al, ; Fei et al, ; Jiang et al, ). In the BME modeling context, let NP ( p ) be the spatiotemporal random field model (Christakos, ) representing nutrient (nitrate or phosphate) pollutant concentrations in China's near seas during 2015, where the vector p = ( s , t ) determines a point within the space‐time domain D , with s denoting geographical location and t time.…”

Probabilistic logic analysis of the highly heterogeneous spatiotemporal HFRS incidence distribution in Heilongjiang province (China) during 2005-2013

“…That is to say, the spatiotemporal dependence of HFRS incidences can be assessed quantitatively in terms of stochastic (probabilistic) indicators linking categorical HFRS incidences at points p and p ′ in various yet complementary ways. Similar stochastic indicators have been used to characterize the space-time variation of population health status, environmental pollution and ocean health (e.g., [32–38]). The stochastic HFRS indicators considered in this work are based on the probability logic theory of medical reasoning developed in [29] and provide intuitive measures of relatedness or logical correlations between two categorical HFRS incidences at points p and p ′, and they may be estimated along multiple directions (anisotropic relatedness) or omnidirectionally (isotropic relatedness).…”

Barriers and Bridges in Abating Coastal Eutrophication

“…Eutrophication is pervasive and worsening along many parts of the coast of China (Jiang et al, 2018), prompting public and media attention, expanded research, and incipient environmental policies. Reminiscent of the Mississippi River and the northern Gulf of Mexico, a zone of seasonally hypoxic bottom water in the East China Sea associated with the plume of the China's largest river, the Chang Jiang, has developed seasonally since at least 1993 (Zhu et al, 2011).…”