Carbon to chlorophyll-a ratio in modeling long-term eutrophication phenomena

Jin, Haisheng; Egashira, Shinji; Chau, K. W.

doi:10.2166/wst.1998.0472

Cited by 7 publications

(5 citation statements)

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“…THAP resulted in a significant reduction of pollutant loading which in turn improved the water quality. Moreover, both field and process-based modeling efforts have been made to address the eutrophication phenomenon and dissolved oxygen dynamics in Tolo Harbour (e.g., Chan and Hodgkiss, 1987;Chau and Jin, 1998;Jin et al, 1998;Chau, 2004;Chau, 2005;Xu et al, 2004).…”

Section: The Real-world Case Studymentioning

confidence: 99%

Machine-learning paradigms for selecting ecologically significant input variables

Muttil

Chau

2007

Engineering Applications of Artificial Intelligence

168

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Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1-2 weeks.

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Section: The Real-world Case Studymentioning

confidence: 99%

Machine-learning paradigms for selecting ecologically significant input variables

Muttil

Chau

2007

Engineering Applications of Artificial Intelligence

168

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“…cit. Baumert 1996; see also Geider et al 1998;Jin et al 1998;Ollinger 1999;Riebesell and Wolf-Gladrow 2002) such that an efficient discussion of ecologic or biotechnological problems is not easy. Language matters!…”

Section: Introduction Generalmentioning

confidence: 99%

The role of temperature, cellular quota and nutrient concentrations for photosynthesis, growth and light–dark acclimation in phytoplankton

Baumert

Petzoldt

2008

Limnologica

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The quantitative description of phytoplankton growth, dynamic photoacclimation and photosynthesis in dependence on temperature, irradiance, trace element and nutrient availability is still a matter of debate and competing ideas in the literature, mainly due to a lack of new critical experiments and to non-unique mathematical descriptions. This paper presents a closed theory of the above processes in a mathematically unique language. Notions like balanced and rapid growth, lower and upper compensation points are defined precisely and combined with successful validations of the system of equations against laboratory experiments and field observations taken from the literature. The minimum number of parameters of the present model of growth in dependence on light, temperature and one nutrient is 16 whereby 5 of them seem to have universal (species-independent) values.

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“…Untreated sewage is discharged in an expeditious rate and large quantities of various pollutants have been released, largely via five outlets, namely, Hu men, Jiao men, Hongqi men, Heng men, and Shenzhen River. The water quality within the delta has been deteriorating and the frequency of the occurrence of algae blooms has been increasing Sin and Chau, 1992;Jin et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Characterization of transboundary POP contamination in aquatic ecosystems of Pearl River delta

Chau

2005

Marine Pollution Bulletin

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During the past two decades, the rapid development of the Pearl River delta leads to substantial accumulation of various toxic organic compounds. This study aims to give a preliminary characterization of the existing state of contamination in this region and to provide insight into the possible fate of persistent organic pollutants (POPs) in this estuary. The available data on POPs in water, river, estuarine sediments, soil, and marine organisms within the Pearl River Delta are compiled. It is shown that it may lead to transboundary POP pollution problems at both Hong Kong and Macau Special Administration Regions located at the downstream end of the region. It is noted that the levels of DDTs and HCHs in various environmental media are at alerting levels and that fresh DDT might still be applied illegally within the region. A systematic research is required to determine both the temporal and spatial variations of all POPs in various carrying media of the Pearl River delta as a whole.

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Carbon to chlorophyll-a ratio in modeling long-term eutrophication phenomena

Cited by 7 publications

References 0 publications

Machine-learning paradigms for selecting ecologically significant input variables

Machine-learning paradigms for selecting ecologically significant input variables

The role of temperature, cellular quota and nutrient concentrations for photosynthesis, growth and light–dark acclimation in phytoplankton

Characterization of transboundary POP contamination in aquatic ecosystems of Pearl River delta

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