Ecosystem health assessment in coastal waters by considering spatio-temporal variations with intense anthropogenic disturbance

Zhang, Feng; Sun, Xiaoxiao; Zhou, Yan; Zhao, Congjiao; Du, Zhenhong; Liu, Renyi

doi:10.1016/j.envsoft.2017.06.052

Cited by 32 publications

(20 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The eutrophication index (EI) is a critical eutrophication level that defines the trophic eutrophication status. This index, which is widely used by the State Oceanic Administrative of China (SOA), has been implemented in many earlier studies (e.g., Quan et al ; Yu et al ; Zhang et al ). Using the COD, DIN, and DIP concentration maps generated by BME over the entire study region, the corresponding EI was calculated by.

EI = \frac{C_{COD} C_{DIN} C_{DIP}}{4500} 10^{6},

where C COD , C DIN , and C DIP denote, respectively, the COD, DIN, and DIP concentrations of surface seawater; and EI is a unitless indicator used in the classification of the eutrophication level.…”

Section: Methodsmentioning

confidence: 99%

“…The eutrophication index (EI) is a critical eutrophication level that defines the trophic eutrophication status. This index, which is widely used by the State Oceanic Administrative of China (SOA), has been implemented in many earlier studies (e.g., Quan et al 2005;Yu et al 2013;Zhang et al 2017). Using the COD, DIN, and DIP concentration maps generated by BME over the entire study region, the corresponding EI was calculated by.…”

Section: Eutrophication Indexmentioning

confidence: 99%

“…With the rapid economic development of coastal areas in China, eutrophication in marine waters became more severe in coastal regions with high population densities and industrial activities. The last two characteristics result to an increasing flow of anthropogenic nutrients into rivers and accumulation in estuaries and harbors (Chen et al ; Kong et al ; Zhang et al ). Assessment of the eutrophication status is indispensable for environmental conservation management and policy making purposes.…”

mentioning

confidence: 99%

“…characteristics result to an increasing flow of anthropogenic nutrients into rivers and accumulation in estuaries and harbors (Chen et al 2016;Kong et al 2017;Zhang et al 2017). Assessment of the eutrophication status is indispensable for environmental conservation management and policy making purposes.…”

mentioning

confidence: 99%

See 3 more Smart Citations

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

Jiang

et al. 2018

Limnology & Oceanography

View full text Add to dashboard Cite

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.

show abstract

EI = \frac{C_{COD} C_{DIN} C_{DIP}}{4500} 10^{6},

Section: Methodsmentioning

confidence: 99%

“…The eutrophication index (EI) is a critical eutrophication level that defines the trophic eutrophication status. This index, which is widely used by the State Oceanic Administrative of China (SOA), has been implemented in many earlier studies (e.g., Quan et al 2005;Yu et al 2013;Zhang et al 2017). Using the COD, DIN, and DIP concentration maps generated by BME over the entire study region, the corresponding EI was calculated by.…”

Section: Eutrophication Indexmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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

Jiang

et al. 2018

Limnology & Oceanography

View full text Add to dashboard Cite

show abstract

“…Globally, coastal water quality is deteriorating due to anthropogenic influences [4]. Unfortunately, despite everything previously mentioned, human activities still negatively influence water quality and aquatic ecosystem functions, resulting in a decline of water quality, biodiversity, loss of critical habitats and an overall decrease in the quality life of all species.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Water Quality on the Structural Development of Vessels: Smart Dimensioning Process

Lousada¹,

Gouveia²,

Castanho³

2020

New Innovations in Engineering Education and Naval Engineering

View full text Add to dashboard Cite

In fact, it is pivotal the development, use, and management of the best and most suitable coatings to be supplied to vessels-especially those designed for long journeys; not only to increase their stability and safety but also to minimize their maintenance cost. In this regard, it should be also considering the function, the vessel typology and its routes, as well as the quality of the waters by which it will navigate. Topics that are critical to promote a better dimensioning process of vessels. Thus, the present chapter, via an extensive literature review articulated with practical approaches, aims to define relevant directions for vessels structural development processes regarding the water quality (sea or river waters), where they will outline their routes. Therefore, the study looks for a relationship between the vessels structural coating design process and the quality of the water where they navigate. Moreover, such a process not only will optimize/minimize the costs with the periodic maintenance of the vessels linings, but also to relate it with its routes-contributing to the revitalization of their structural dimensioning.

show abstract

Quantifying the spatial nonstationary response of influencing factors on ecosystem health based on the geographical weighted regression (GWR) model: an example in Inner Mongolia, China, from 1995 to 2020

Guo

2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

The identification of ecosystem health and its influencing factors is crucial to the sustainable management of ecosystems and ecosystem restoration. Although numerous studies on ecosystem health have been carried out from different perspectives, few studies have systematically investigated the spatiotemporal heterogeneity between ecosystem health and its influencing factors. Considering this gap, the spatial relationships between ecosystem health and its factors concerning climate, socioeconomic, and natural resource endowment at the county level were estimated based on a geographically weighted regression (GWR) model. The spatiotemporal distribution pattern and driving mechanism of ecosystem health were systematically analysed. The results showed the following: (1) the ecosystem health level in Inner Mongolia spatially increases from northwest to southeast, displaying notable global spatial autocorrelation and local spatial aggregation. (2) The factors influencing ecosystem health exhibit significant spatial heterogeneity. Annual average precipitation (AMP) and biodiversity (BI) are positively correlated with ecosystem health, and annual average temperature (AMT) and land use intensity (LUI) are estimated to be negatively correlated with ecosystem health. (3) Annual average precipitation (AMP) significantly improves ecosystem health, whereas annual average temperature (AMT) significantly worsens eco-health in the eastern and northern regions. LUI negatively impacts ecosystem health in western counties (such as Alxa, Ordos, and Baynnur). This study contributes to extending our understanding of ecosystem health depending on spatial scale and can inform decision-makers about how to control various influencing factors to improve the local ecology under local conditions. Finally, this study also proposes some relevant policy suggestions and provides effective ecosystem preservation and management support in Inner Mongolia.

show abstract

Ecosystem health assessment in coastal waters by considering spatio-temporal variations with intense anthropogenic disturbance

Cited by 32 publications

References 41 publications

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

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

The Influence of Water Quality on the Structural Development of Vessels: Smart Dimensioning Process

Quantifying the spatial nonstationary response of influencing factors on ecosystem health based on the geographical weighted regression (GWR) model: an example in Inner Mongolia, China, from 1995 to 2020

Contact Info

Product

Resources

About