Impact of anthropogenic forcing on the environmental controls of phytoplankton dynamics between 1974 and 2017 in the Pearl River estuary, China

Niu, Lixia; Luo, Xiao; Zhou, Tianjun; Feng, Lian; Cai, Huayang; Ren, Lei; Ou, Suying; Zeng, Danna; Yang, Qingshu

doi:10.1016/j.ecolind.2020.106484

Cited by 34 publications

(51 citation statements)

References 62 publications

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“…Over time, DIN gradually increased from 1996 to 2018, with a fitted linear curve (R 2 = 0.31, N=6). Among the DIN compositions, NO 3 was the most abundant form (Supplementary Figure 4), as reported from other studies in this estuary (Huang et al, 2003;Zhang et al, 2016;Niu et al, 2020;Tao et al, 2020). The proportions of NO 2 and NH 4 were followed.…”

Section: Nutrient Enrichment In An Estuarine Environmentsupporting

confidence: 79%

“…The data preparations for Pearson correlation, linear regression, and PCA are conducted using the statistical package IBM SPSS Statistics 20 for Windows. The two end-member mixing model is introduced to differentiate biological processes from the effects of physical mixing, as used in the earlier studies of Han et al (2012), Wu et al (2016), Li et al (2017), and Niu et al (2020). These two end members have different nutrient conditions.…”

Section: Discussionmentioning

confidence: 99%

“…The mean value of NO 3 was 0.63 mg/L in 2006, 0.89 mg/L in 2008, 0.99 mg/L in 2016, and 1.03 mg/L in 2018, accounting for 75, 83, 72, and 81% of DIN, accordingly. NO 3 was mainly detected from the atmospheric deposition and agricultural applications (e.g., fertilizers, pesticides) in the Pearl River basin (Niu et al, 2020). The runoff transported the NO 3 from the upper channel to the estuary.…”

Section: Nutrient Enrichment In An Estuarine Environmentmentioning

confidence: 99%

“…It is critically important that we understand the quantitative cause-effect chains of biogeochemical properties of nutrient enrichment in the 1990s (as 1996, 1999), 2000s (as 2006, 2008), and 2010s (as 2016, 2018). Moreover, the period of 1990-2000 corresponded to the high intensity of human activity (e.g., land reclamation, channel dredging) in the Pearl River Delta (Niu et al, 2020). For this purpose, the objectives of this field investigation are designed to: (1) characterize the nutrient status and related ecosystem response over the past two decades in the PRE, spatially and temporally; (2) determine the driving factors (e.g., salinity, suspended sediment, DO, and COD) that influence nutrient accumulation; (3) differentiate the source-sink exchange of nutrients; and (4) evaluate the ecological significance for estuary management.…”

Section: Introductionmentioning

confidence: 99%

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Nutrient Pollution and Its Dynamic Source-Sink Pattern in the Pearl River Estuary (South China)

Tao¹,

Niu²,

Dong³

et al. 2021

Front. Mar. Sci.

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Nutrient enrichment and its quantitative cause-effect chains of the biogeochemical processes have scarcely been documented in the Pearl River Estuary (South China). Field investigations of nutrient samples taken between 1996 and 2018 showed significant differences in nitrogen and phosphorus with times and sites. The concentrations of DIN and DIP gradually increased over the past two decades, with good fitted linear curves (R2 = 0.31 for DIN, R2 = 0.92 for DIP); while the temporal variation in DSi was non-significant. Higher levels of nitrogen and silicate mainly appeared in the upper estuary because of the riverine influence. The phosphorus pollution was accumulated in the northeast (e.g., Shenzhen bay). The aquatic environment was highly sensitive to nutrient pollution and eutrophication risk, which accordingly corresponded to high phytoplankton production and biodiversity. Phosphorus was the limiting factor of phytoplankton growth in this estuary, and more frequently caused the eutrophication risks and blooms. The nutrient pollution was largely influenced by riverine inputs, quantified by PCA-generation, and the contributions of coastal emission and atmospheric deposition were followed. The two-end member mixing model differentiated the physical alterations from the biological activity and identified the dynamic source-sink patterns of nutrient species. Nitrogen and silicate had relatively conservative behaviors in the estuary and phosphate showed an active pattern.

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Section: Nutrient Enrichment In An Estuarine Environmentsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Nutrient Enrichment In An Estuarine Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nutrient Pollution and Its Dynamic Source-Sink Pattern in the Pearl River Estuary (South China)

Tao¹,

Niu²,

Dong³

et al. 2021

Front. Mar. Sci.

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“…Phosphate is a nutrient present in water that is associated with the proliferation of algae, derived from the amount of organic matter present, leachate from soils or some artificial source of pollution such as detergents, and is therefore directly related to the eutrophication of wetlands and swamp ecosystems, as set out in [15].…”

Section: Physical-chemical Parameters Of Watermentioning

confidence: 99%

Landsat-7 ETM+ Based Remote Sensing as a Tool for Assessing Lakes Water Quality Characteristics

Cabezas-Alzate¹,

Garcés-Gómez²,

Céspedes³

2021

Journal of Southwest Jiaotong University

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The article describes a new method using remote sensing techniques to set the mathematical models that allow the estimation of the most relevant parameters for water quality monitored in Laguna de Sonso lake, Valle del Cauca, determined using Landsat-7 ETM+ multispectral images. Chlorophyll-a (Chl-a), Turbidity, Dissolved Oxygen (DO), and Total Phosphorus (P) are the parameters chosen for this study. The annual dry and wet seasons were defined, from 2010 to 2017, with a total of 70 images. It was necessary to carry out a process of masking the water Buchón (Eichhornia crassipes) and replacing pixels using the statistical average of the two established annual seasons. For the case of Chl-a, the NDI ratio between the red and near-infrared (NIR) bands was the best correlated with an ; for turbidity, a regression with the red band, with an ; for DO, the ratio with the highest correlation was a simple ratio (SR) between the green and blue bands, with an ; and for P, a regression of the NIR band was enough, presenting an . Finally, the adjusted mathematical models were obtained for each established parameter, allowing the estimation of each parameter to monitor the lagoon water quality using images from the ETM+ sensor.

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Phytoplankton community dynamics in response to seawater intrusion in a tropical macrotidal river-estuary continuum

et al. 2022

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Impact of anthropogenic forcing on the environmental controls of phytoplankton dynamics between 1974 and 2017 in the Pearl River estuary, China

Cited by 34 publications

References 62 publications

Nutrient Pollution and Its Dynamic Source-Sink Pattern in the Pearl River Estuary (South China)

Nutrient Pollution and Its Dynamic Source-Sink Pattern in the Pearl River Estuary (South China)

Landsat-7 ETM+ Based Remote Sensing as a Tool for Assessing Lakes Water Quality Characteristics

Phytoplankton community dynamics in response to seawater intrusion in a tropical macrotidal river-estuary continuum

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