scholarly journals High chlorophyll a concentration in a low nutrient context: discussions in a subtropical lake dominated by Cyanobacteria

2016 ◽  
Author(s):  
Mariana C. Hennemann ◽  
Mauricio M. Petrucio
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Cylindrospermopsis Raciborskii ◽  
P Availability ◽  
Chlorophyll A Concentration ◽  
Subtropical Lake ◽  
Total P

<p>Temporal variability in some water quality parameters can play an important role in determining the presence and abundance of primary producers, and consequently in the trophic state and other characteristics and uses of lake ecosystems. In this sense, the present study aimed at understanding temporal dynamics of some trophic relevant water quality parameters in different time scales and their correlation and influence in phytoplankton biomass (chlorophyll <em>a</em>) in a shallow subtropical coastal lake. Peri Lake is located in Florianópolis island in Southern Brazil and samples were taken monthly between March 2007 and February 2013. The lake showed low dissolved nutrients concentration, especially phosphorus (P) (median dissolved P: 2.0 µg.l<sup>-1</sup>)  and high chlorophyll <em>a</em> (median: 20.8 µg.l<sup>-1</sup>) concentration. Total nitrogen (TN) concentration varied broadly, with a median of 672.8 µg L<sup>-1</sup>, and total P (TP) concentration was low (median: 13.5 µg L<sup>-1</sup>). A seasonal pattern of variation concerning dissolved and total P and chlorophyll a concentration was observed, associated mainly with temperature and wind speeds, but no clear pattern was observed for nitrogen (N) fractions. Significant differences were observed in different years for some parameters, with higher chlorophyll a and lower N concentration in the last three years sampled. The lake was considered potentially P limited during the majority of the study period and a positive correlation was found between chlorophyll <em>a</em> and total and dissolved P concentration. Phytoplankton biomass (as chlorophyll <em>a</em>) was apparently controlled by water temperature and P availability (TN:TP ratio and dissolved P). Water transparency (as Secchi depth) was strongly and negatively influenced by chlorophyll <em>a</em> concentration. <em>Cylindrospermopsis raciborskii</em> abilities to compete for P and light seem to be important factors determining its success and dominance in this low P coastal ecosystem. The fluctuating P supply, probably associated to sediment resuspension by wind in this shallow waterbody, is an advantageous factor for cyanobacteria and has an important role in chlorophyll <em>a</em> dynamics. Thus, high chlorophyll <em>a</em> concentration in this subtropical lake seems to be related to the P-limited condition, shallowness and low water column transparency, which are probably favouring the dominance of <em>C. raciborskii</em>, especially in higher summer temperatures, and leading to high chlorophyll <em>a</em> concentration even in a low dissolved nutrient environment.</p>

Correlation of Water Quality Parameters in Tolo Harbour, Hong Kong

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2555-2558 ◽  
Author(s):  
K. W. Chau ◽  
Y. S. Sin
Keyword(s):  
Water Quality ◽  
Hong Kong ◽  
Chlorophyll A ◽  
Secchi Depth ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Phosphorus Concentration ◽  
Control Zone ◽  
Chlorophyll A Concentration ◽  
Tolo Harbour

In this paper, long-term biweekly measurements on the various water quality parameters in Tolo Harbour from year 1982 to 1990, subsequent to the declaration of the area as water control zone, were analyzed and correlated. Correlations have been demonstrated between surface chlorophyll-a concentration with secchi depth and with total nitrogen concentration (TN) in the three sub-zones of Tolo Harbour in Hong Kong with different water quality objectives. The correlation between chlorophyll-a concentration and total phosphorus concentration (TP) is less significant which can be explained by the TN/TP ratio. The correlations are useful for water management, planning and effective pollution control on the land-locked estuary.

Novel application of a back-propagation artificial neural network model formulated to predict algal bloom

1997 ◽  
Vol 36 (5) ◽  
pp. 89-97 ◽  
Author(s):  
Ken-ichi Yabunaka ◽  
Masaaki Hosomi ◽  
Akihiko Murakami
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Algal Bloom ◽  
Back Propagation ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Ann Model ◽  
Chlorophyll A Concentration

This paper describes the novel application of an artificial neural network (ANN) model based on the back-propagation method formulated to predict algal bloom by simulating the future growth of five phytoplankton species and the chlorophyll a concentration in the second largest lake in Japan: eutrophic freshwater Lake Kasumigaura. Comparison of observed and calculated values showed that (i) seasonal variations in the biomass of Microcystis spp. were well-predicted with respect to the timing and magnitude of algal bloom, and (ii) the concentration of chlorophyll a, as an indicator of the total biomass of phytoplankton, was well predicted in general. The resultant correlations for the other species, however, showed that model learning was insufficient to effectively predict species biomass; thereby indicating that some unknown factors which are not represented by the set of water quality parameters used as model input data affect phytoplankton growth. A sensitivity analysis performed on input parameters showed that chlorophyll a concentration was mainly affected by PO4-P concentration, while cyanobacteria and diatom species were affected by NO3-N and NH4-N concentrations, respectively. These results indicate that the “algal bloom” ANN model achieved reasonable effectiveness with respect to learning the relationship between the selected water quality parameters and algal bloom.

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

scholarly journals The role of annual periodic behavior of water quality parameters in primary production – Chlorophyll-a estimation

2017 ◽  
Vol 78 ◽  
pp. 311-321 ◽  
Author(s):  
József Kovács ◽  
Péter Tanos ◽  
Gábor Várbíró ◽  
Angéla Anda ◽  
Sándor Molnár ◽  
...  
Keyword(s):  
Water Quality ◽  
Primary Production ◽  
Chlorophyll A ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Periodic Behavior

scholarly journals ASSESSMENT OF SEAGRASS PERCENT COVER AND WATER QUALITY USING UAV IMAGES AND FIELD MEASUREMENTS IN BOLINAO, PANGASINAN

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 233-240
Author(s):  
M. K. M. R. Guerrero ◽  
J. A. M. Vivar ◽  
R. V. Ramos ◽  
A. M. Tamondong
Keyword(s):  
Water Quality ◽  
Regression Analysis ◽  
Chlorophyll A ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Support Vector ◽  
Percent Cover ◽  
Chl A ◽  
Relationship Of ◽  
The Relationship

Abstract. The sensitivity to changes in water quality inherent to seagrass communities makes them vital for determining the overall health of the coastal ecosystem. Numerous efforts including community-based coastal resource management, conservation and rehabilitation plans are currently undertaken to protect these marine species. In this study, the relationship of water quality parameters, specifically chlorophyll-a (chl-a) and turbidity, with seagrass percent cover is assessed quantitatively. Support Vector Machine, a pixel-based image classification method, is applied to determine seagrass and non-seagrass areas from the orthomosaic which yielded a 91.0369% accuracy. In-situ measurements of chl-a and turbidity are acquired using an infinity-CLW water quality sensor. Geostatistical techniques are utilized in this study to determine accurate surfaces for chl-a and turbidity. In two hundred interpolation tests for both chl-a and turbidity, Simple Kriging (Gaussian-model type and Smooth- neighborhood type) performs best with Mean Prediction equal to −0.1371 FTU and 0.0061 μg/L, Root Mean Square Standardized error equal to −0.0688 FTU and −0.0048 μg/L, RMS error of 8.7699 FTU and 1.8006 μg/L and Average Standard Error equal to 10.8360 FTU and 1.6726 μg/L. Zones are determined using fishnet tool and Moran’s I to calculate for the seagrass percent cover. Ordinary Least Squares (OLS) is used as a regression analysis to quantify the relationship of seagrass percent cover and water quality parameters. The regression analysis result indicates that turbidity has an inverse relationship while chlorophyll-a has a direct relationship with seagrass percent cover.

scholarly journals Exploring the Influence Mechanism of Meteorological Conditions on the Concentration of Suspended Solids and Chlorophyll-a in Large Estuaries Based on MODIS Imagery

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 375
Author(s):  
Cheng He ◽  
Youru Yao ◽  
Xiaoman Lu ◽  
Mingnan Chen ◽  
Weichun Ma ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Suspended Solids ◽  
Quality Parameters ◽  
Meteorological Conditions ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Yangtze River Estuary ◽  
Modis Imagery ◽  
Chla Concentration

In estuary areas, meteorological conditions have become unstable under the continuous effects of climate change, and the ecological backgrounds of such areas have strongly been influenced by anthropic activities. Consequently, the water quality of these areas is obviously affected. In this research, we identified periods of fluctuation of the general meteorological conditions in the Yangtze River Estuary using a wavelet analysis. Additionally, we performed a spatiotemporal evaluation of the water quality in the fluctuating period by using remote sensing modeling. Then, we explored how the fluctuating meteorological factors affect the distribution of total suspended solids (TSS) and chlorophyll-a (Chla) concentration. (1) The results show that from 2000 to 2015, temperature did not present significant fluctuations, while wind speed (WS) and precipitation (PR) presented the same fluctuation period from January 2012 to December 2012. (2) Based on the measured water sample data associated with Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, we developed a water quality algorithm and depicted the TSS and Chla concentrations within the WS and PR fluctuating period. (3) We found that the TSS concentration decreased with distance from the shore, while the Chla concentration showed an initially decreasing trend followed by an increasing trend; moreover, these two water quality parameters presented different inter-annual variations. Then, we discussed the correlation between the changes in the TSS and Chla concentrations and the WS and PR variables. The contribution of this research is reflected in two aspects: 1. variations in water quality parameters over a wide range of water bodies can be evaluated based on MODIS data; 2. data from different time periods showed that the fluctuations of meteorological elements had different impacts on water bodies based on the distance from the shore. The results provide new insights for the management of estuary water environments.

scholarly journals Comparing PlanetScope to Landsat-8 and Sentinel-2 for Sensing Water Quality in Reservoirs in Agricultural Watersheds

2021 ◽  
Vol 13 (9) ◽  
pp. 1847
Author(s):  
Abubakarr S. Mansaray ◽  
Andrew R. Dzialowski ◽  
Meghan E. Martin ◽  
Kevin L. Wagner ◽  
Hamed Gholizadeh ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Water Quality Monitoring ◽  
Agricultural Runoff ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Sentinel 2

Agricultural runoff transports sediments and nutrients that deteriorate water quality erratically, posing a challenge to ground-based monitoring. Satellites provide data at spatial-temporal scales that can be used for water quality monitoring. PlanetScope nanosatellites have spatial (3 m) and temporal (daily) resolutions that may help improve water quality monitoring compared to coarser-resolution satellites. This work compared PlanetScope to Landsat-8 and Sentinel-2 in their ability to detect key water quality parameters. Spectral bands of each satellite were regressed against chlorophyll a, turbidity, and Secchi depth data from 13 reservoirs in Oklahoma over three years (2017–2020). We developed significant regression models for each satellite. Landsat-8 and Sentinel-2 explained more variation in chlorophyll a than PlanetScope, likely because they have more spectral bands. PlanetScope and Sentinel-2 explained relatively similar amounts of variations in turbidity and Secchi Disk data, while Landsat-8 explained less variation in these parameters. Since PlanetScope is a commercial satellite, its application may be limited to cases where the application of coarser-resolution satellites is not feasible. We identified scenarios where PS may be more beneficial than Landsat-8 and Sentinel-2. These include measuring water quality parameters that vary daily, in small ponds and narrow coves of reservoirs, and at reservoir edges.

scholarly journals Retrieval of Water Quality Parameters from Hyperspectral Images Using Hybrid Bayesian Probabilistic Neural Network

2020 ◽  
Vol 12 (10) ◽  
pp. 1567
Author(s):  
Yishan Zhang ◽  
Lun Wu ◽  
Huazhong Ren ◽  
Licui Deng ◽  
Pengcheng Zhang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Chlorophyll A ◽  
Probabilistic Neural Network ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Bayesian Probability ◽  
Proposed Model ◽  
Probability Methods

The protection of water resources is of paramount importance to human beings’ practical lives. Monitoring and improving water quality nowadays has become an important topic. In this study, a novel Bayesian probabilistic neural network (BPNN) improved from ordinary Bayesian probability methods has been developed to quantitatively predict water quality parameters including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a. The proposed method, based on conventional Bayesian probability methods, involves feature engineering and deep neural networks. Additionally, it extracts significant information for each endmember from combinations of spectra by feature extraction, with spectral unmixing based on mathematical and statistical analysis, and calculates each of the water quality parameters. The experimental results show the great performance of the proposed model with all coefficient of determination R 2 over 0.9 greater than the values (0.6–0.8) from conventional methods, which are greater than ordinary Bayesian probability analysis. The mean percent of absolute error (MPAE) is taken into account as an important statistical criterion to evaluate model performance, and our results show that MPAE ranges from 4% (nitrogen) to 10% (COD). The root mean squared errors (RMSEs) of phosphorus, nitrogen, COD, BOD, and chlorophyll-a (Chla) are 0.03 mg/L, 0.28 mg/L, 3.28 mg/L, 0.49 mg/L, and 0.75 μg/L, respectively. In comparison with other deep learning methods, this study takes a relatively small amount of data as training data to train the proposed model and the proposed model is then tested on the same amount of testing data, achieving a greater performance. Thus, the proposed method is time-saving and more effective. This study proposes a more compatible and effective method to assist with decomposing combinations of hyperspectral signatures in order to calculate the content level of each water quality parameter. Moreover, the proposed method is practically applied to hyperspectral image data on board an unmanned aerial vehicle in order to monitor the water quality on a large scale and trace the location of pollution sources in the Maozhou River, Guangdong Province of China, obtaining well-explained and significant results.

Correlation Analysis between Primary Productivity Factors and Water Environment Factors

2013 ◽  
Vol 448-453 ◽  
pp. 902-907
Author(s):  
Shih Chieh Chen ◽  
Chao Cheng Chung ◽  
Wen Liang Lai ◽  
Chung Yi Chung ◽  
Hwa Sheng Gau ◽  
...  
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Chlorophyll A ◽  
Primary Productivity ◽  
Explanatory Power ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Canonical Variables ◽  
The Relationship

In this study, we use canonical correlation analysis to interpret the relationship between water quality parameters (T, Alk, Cl, EC, TN, TP, UV-254, pH, HPC, DO) and primary productivity parameters (algae and chlorophyll-a). In these two sets of constructed canonical variables, the water quality parameters can account for 39.25% of the total variance of primary productivity. The majority of the explanatory power is from the first set of canonical variables, which has a correlation coefficient of 0.84. The main factors that control chlorophyll-a are HPC, Alk, T, TN, and pH.

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