Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

Balachandran, K.K.; Jayalakshmy, K.V.; Laluraj, C. M.; Nair, M.; Joseph, T.; Sheeba, P.

doi:10.1007/s10661-007-9829-5

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…Monitoring programs result in extensive and complex databases, covering several aspects which are difficult to analyze and interpret due to the interrelationship among the different variables (Huang et al, 2011). Given the complexity and dynamism of ecological interactions, especially in wastewater biological treatment systems, studies are increasingly searching for the aid of sophisticated techniques such as multivariate statistics (Balachandran et al, 2008). The multivariate statistics is one of the main tools to relate environmental variables and the density of phytoplankton populations (Thangaradjou et al, 2012).…”

Section: Statistical Analysesmentioning

confidence: 99%

Algae/bacteria consortium in high rate ponds: Influence of solar radiation on the phytoplankton community

Assemany

Calijuri

Couto

et al. 2015

Ecological Engineering

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Using multivariate statistical tools, the composition of the phytoplankton community was related to the characteristics of the domestic sewage used as culture medium in three high rate ponds (HRPs) submitted to different solar radiation levels. A total of 32 genera of phytoplankton were identified in the ponds; the class Chlorophyceae was the most abundant during the entire sampling period, with a larger number of individuals of the genus Desmodesmus in the summer and fall, and of the genus Chlorella in the winter and spring. The lowest occurrence of phytoplankton was observed in the fall, with behavior similar to the evolution of solar radiation throughout the year. Blocking over 30% of the solar radiation allowed for less variability of the phytoplankton community and favored the growth of biomass with higher density of individuals, as well as higher concentrations of chlorophyll-a and dissolved oxygen. On the other hand, the pond with 80% of shading presented the lowest mean density of organisms; from the perspective of wastewater treatment, however, it can be considered the most efficient in terms of organic matter and nutrient removal. According to the regression analysis, the algal biomass in HRPs can be maximized mostly if we consider the positive effect of carbon and phosphorus and the limiting effect of nitrogen and non-biodegradable organic load. For the conditions evaluated in this study, the photoinhibition phenomenon was not observed. Other aspects such as competition with other microorganisms for space and nutrients, or predation by zooplankton, seemed to be more significant for the growth and development of algal biomass. 2015 Elsevier B.V. All rights reserved.

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Section: Statistical Analysesmentioning

confidence: 99%

Algae/bacteria consortium in high rate ponds: Influence of solar radiation on the phytoplankton community

Assemany

Calijuri

Couto

et al. 2015

Ecological Engineering

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“…Hence the growing and declining condition of algal bloom can be described by the spatial and temporal variation of chlorophyll a. There are many study methods to the relationship between chlorophyll a and physicochemical factors such as stepwise multiple regression analysis [2], grey relative analysis [3] and artificial neural network [4]. Chlorophyll a can be related to the environmental parameters by means of linear regression, though it provides only the prediction efficiency of a single factor at a time [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…But the multivariate interaction and nonlinear process in the algal bloom were not considered in the previous work. So this present paper aims to study: (1) controlling and interactive factors of chlorophyll a, (2) nonlinear interrelationship between physicochemical parameters and chlorophyll a of spring algal bloom in Xiangxi Bay. In order to achieve this, stepwise multiple binomial regression method and grey relative analysis are adopted.…”

Section: Introductionmentioning

confidence: 99%

Influence Factors Analysis to Chlorophyll a of Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir

Luo¹,

Liu²,

Ji³

et al. 2009

JWARP

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To study the relationship between environmental variables and chlorophyll a of spring algal bloom in Xiangxi Bay of Three Gorges Reservoir, stepwise multiple binomial regression and grey relative analysis methods were adopted. In surveys, 13 stations have been investigated and 143 samples were collected weekly from March 4 to May 13 in 2007. The study shows environmental variables (turbidity, total nitrogen, dissolved oxygen, total phosphates and silicate) are key factors during algal bloom. The grey relative values and their permutation indicated that turbidity was the most important factor and had comprehensive effect on chlorophyll a. The more number of interactive variables is found to be an indication of biochemical activity during spring algal bloom in Xiangxi Bay such as DO×TN, Turb×TP and so on. There was good linear relationship between chlorophyll a and the interaction of DO with TN ( , ).The interac-tion of nutrients (TP×TN, TP×SiO4, TN×SiO4) had significant influence to chlorophyll a and probably determined the inter-specific competition at different nutrient concentrations

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“…Another study using multiple regression models off Kochi revealed that chlorophyll production depends not only on nutrients, but also on their physiological regulations like responses to nutrients, pH, temperature and salinity. As the computed and measured chlorophyll-a values are in good agreement, the step-up multiple regression model can be applied for the coastal waters to understand the influence of environmental variables on the production of phytoplankton [62]. Carbon-based ocean productivity and phytoplankton physiology were investigated by using satellite data [63].…”

Section: Biogeochemical Processes In Mudbanksmentioning

confidence: 99%

Characteristics of Mudbanks along the Southwest Coast of India

2021

EESRR

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Mudbank is a unique phenomenon observed along the south-west coast of India among all the Indian coastal regions during the Southwest Monsoon Season. The scientific reasons behind the formation and its persistence are vague. Mudbank is considered as a boon to the fishermen of Kerala, as they are getting reasonable catches during the occurrence of mudbanks using indigenous boats from the calm sea, which otherwise is in a fury during the rough Southwest Monsoon Season. Since the region, the Arabian Sea, is significant as a carbon sink due to its very high productivity because of different coastal ocean features, variations in the smaller and highly restricted coastal processes due to climate change can have a significant impact on the rates of global warming. Here, we consolidate the previous publications on various aspects of mudbanks with the reports of the occurrence of mudbanks on the south-west coast of India. A model for the prediction of the formation and location of mudbanks, inclusive of all the intrinsic and extrinsic parameters involved, through an extensive study is much relevant as far as the socio-economic and food security significance is concerned. Here, we discuss the most conducive factors for the formation of mudbank and its characteristics with special reference to Alappuzha, where the frequency of occurrence of mudbanks is maximum among the Indian coastal regions.

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Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

Cited by 10 publications

References 18 publications

Algae/bacteria consortium in high rate ponds: Influence of solar radiation on the phytoplankton community

Algae/bacteria consortium in high rate ponds: Influence of solar radiation on the phytoplankton community

Influence Factors Analysis to Chlorophyll a of Spring Algal Bloom in Xiangxi Bay of Three Gorges Reservoir

Characteristics of Mudbanks along the Southwest Coast of India

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