Describe Water Quality Research Articles

Analysis of occurrence, bioaccumulation and molecular targets of arsenic and other selected volcanic elements in Argentinean Patagonia and Antarctic ecosystems

In Latin America, the high proportion of arsenic (As) in many groundwaters and phreatic aquifers is related to the volcanism of the Andean Range. Nevertheless, there is still very little published research on As and other elements occurrence, and/or transference to biota in Southern regions such as Argentinean Patagonia and the South Shetland Islands in Antarctica, where there are active volcanoes and geothermal processes. Therefore, this study was aimed to describe water quality from the main rivers of Argentinean Northern Patagonia through physicochemical analysis. The Patagonian and Antarctic biota (including samples of animal, plants, algae and bacteria) was characterized through the analysis of their As and other elemental concentrations (P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, Se, Br, Rb and Sr), by synchrotron radiation x-ray fluorescence spectroscopy (SRXRF). Finally, the analysis of metal and As-proteins associations in As-accumulating organisms was performed by SRXRF after sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). A wide range of metal concentration including As (up to 950 μg/L As) was found in water samples from Patagonian rivers. A hierarchical cluster analysis revealed that the elemental concentration of analysed biological samples was related to volcanic environments and their place in the trophic chain. Moreover, the results suggest that Se, Co, Cu, Br, and Cl are strong predictors of As in biota. On the other hand, As was not detected in proteins from the studied samples, suggesting biotransformation into soluble As-organic compounds. This is the first study to describe environmental pollution as a consequence of active volcanism, and its influence on water quality and elemental composition of biota in Argentinean Northern Patagonia and Antarctica.

Hydro-chemical and microbiological characterization of Lower Cretaceous waters in a semi-arid zone Beni-Ounif syncline, South-West of Algeria

Abstract The article analyses the water quality of the Lower Cretaceous aquifer in the Beni-Ounif syncline. To this end, 42 samples were taken for physico-chemical analysis and 28 for microbiological analysis in March, May and October 2017 from 14 sampling points. The results of physico-chemical analysis were processed by multi-variety statistical analysis methods: principal component analysis (PCA) and hierarchical cluster analysis (HCA) coupled to hydro chemical methods: Piper diagram. The PCA allowed us to explore the connections between physico-chemical parameters and similarities between samples and to identify the most appropriate physico-chemical elements to describe water quality. The HCA allowed us to classify the sampling points according to the similarity between them and thus reduce them for the next follow-up analysis. Waters of the syncline are characterized by medium to low mineralization (320 < EC < 7600 μS∙cm–1 and 200 < RS < 4020 mg∙dm−3) and hardness of between 22 and 123°f. Only 19% of the samples show NO3 concentrations exceeding the Algerian standards. Microbiologically, the study reports the presence of bacteria: coliforms (<8 CFU∙0.1 dm−3), Streptococcus D (<1100 CFUꞏ0.1 dm–3), Clostridium sulphito-reducer of vegetative form (<90 CFUꞏ0.02 dm–3) and sporulate (<4 CFUꞏ0.02 dm–3), total aerobic mesophilic flora at 22°C (<462 CFUꞏ0.001 dm–3) and at 37°C (<403 CFUꞏ0.001 dm–3). It must be noted that no presence of thermo-tolerant coliforms is observed.

Mathematical Model of the Water Quality in Kalibaru Watershed

The Kalibaru Watershed is one of the watersheds which needs to be concerned because it is suspected to have decreased water quality, due to the high activity of the households, industry, livestock, and agriculture. This condition implies that the water quality as the embodiment of the environmental capacity of Kalibaru Watershed needs to be studied, so that it can be known how far it benefits the lives of the society.The aim of the research were to determine the water quality related to physical properties (pH), chemical properties (BOD, PO4P, NO2N) and microbiological properties (Fecal coli) from observation stations upstream, central and downstream of Kalibaru River watershed. The research approach framework was carried out by: (1) data collection to describe water quality problems in the Kalibaru watershed from the Sampean Baru River Area Water Resources Management Center in Bondowoso. The data such as the results of laboratory analysis of physical, chemical and microbiological properties in 3 (three) observation points of the Kalibaru watershed. The results of the analysis were processed using the regression method became a model for decrease in water quality as seen from the 3 parameters above and compared with the Republic of Indonesia Government Regulation No. 82 of 2001. Data analysis of the above parameters from January to December 2017 with the Polynomial equation of order 6. Research parameters include pH, BOD, PO4N, NO2N with MAXR method. The parameters of pH observation stasion in upstream, middle and downstream get result R Max respectively are 0,76; 0,60; 0,57. BOD parameter values respectively are 0,59: 0,42: 0,18. PO4P paramater values respectively are 0,52; 0,47; 0,48. NO2N paramater values respectively are 0,66; 0,71; 0,77. Fecal Coli parameter values respectively are 0,57; 0,85; 0,87.

Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery.

Chlorophyll-a (Chl-a) concentration is a key parameter to describe water quality in marine and freshwater environments. Nowadays, several products with Chl-a have derived from satellite imagery, but they are not available or reliable sometimes for coastal and/or small water bodies. Thus, in the last decade several methods have been described to estimate Chl-a with high-resolution (30 m) satellite imagery, such as Landsat, but a standardized method to estimate Chl-a from Landsat imagery has not been accepted yet. Therefore, this study evaluated the predictive performance of regression models (Simple Linear Regression [SLR], Multiple Linear Regression [MLR] and Generalized Additive Models [GAMs]) to estimate Chl-a based on Landsat imagery, using in situ Chl-a data collected (synchronized with the overpass of Landsat 8 satellite) and spectral reflectance in the visible light portion (bands 1–4) and near infrared (band 5). These bands were selected because of Chl-a absorbance/reflectance properties in these wavelengths. According to goodness of fit, GAM outperformed SLR and MLR. However, the model validation showed that MLR performed better in predicting log-transformed Chl-a. Thus, MLR, constructed by using four spectral bands (1, 2, 3, and 5), was considered the best method to predict Chl-a. The coefficients of this model suggested that log-transformed Chl-a concentration had a positive linear relationship with bands 1 (coastal/aerosol), 3 (green), and 5 (NIR). On the other hand, band 2 (blue) suggested a negative relationship, which implied high coherence with Chl-a absorbance/reflectance properties measured in the laboratory, indicating that Landsat 8 images could be applied effectively to estimate Chl-a concentrations in coastal environments.

Forest cover correlates with good biological water quality. Insights from a regional study (Wallonia, Belgium)

Forested catchments are generally assumed to provide higher quality water in opposition to agricultural and urban catchments. However, this should be tested in various ecological contexts and through the study of multiple variables describing water quality. Indeed, interactions between ecological variables, multiple land use and land cover (LULC) types, and water quality variables render the relationship between forest cover and water quality highly complex. Furthermore, the question of the scale at which land use within stream catchments most influences stream water quality and ecosystem health remains only partially answered. This paper quantifies, at the regional scale and across five natural ecoregions of Wallonia (Belgium), the forest cover effect on biological water quality indices (based on diatoms and macroinvertebrates) at the riparian and catchment scales. Main results show that forest cover – considered alone – explains around one third of the biological water quality at the regional scale and from 15 to 70% depending on the ecoregion studied. Forest cover is systematically positively correlated with higher biological water quality. When removing spatial, local morphological variations, or population density effect, forest cover still accounts for over 10% of the total biological water quality variation. Partitioning variance shows that physico-chemical water quality is one of the main drivers of biological water quality and that anthropogenic pressures often explain an important part of it (shared or not with forest cover). The proportion of forest cover in each catchment at the regional scale and across all ecoregions but the Loam region is more positively correlated with high water quality than when considering the proportion of forest cover in the riparian zones only. This suggests that catchment-wide impacts and a fortiori catchment-wide protection measures are the main drivers of river ecological water quality. However, distinctive results from the agricultural and highly human impacted Loam region show that riparian forests are positively linked to water quality and should therefore be preserved.

Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake

Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (CSPM) is a key parameter describing water quality. This study, using in-situ spectral and CSPM measurements as well as Sentinel 2 Multispectral Imager (MSI) images, aimed to develop CSPM retrieval models and further to estimate the CSPM values of Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate and the left samples were used to validate CSPM retrieval models, respectively. The developed models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the derived CSPM values were compared with those derived from MODIS B1 (λ = 645 nm). Results showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which explained 81–93% of the variation of CSPM. The validation results also showed the reliability of these six models, and the estimated CSPM explained 77–93% of the variation of measured CSPM with the mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model based on B7 (λ = 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived CSPM values were generally consistent in spatial distribution and magnitude with those derived from MODIS. The CSPM derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on 15 August 2016, while the Sentinel 2 MSI B4 (λ = 665 nm) produced the highest consistency with MODIS on 2 April 2017. Overall, this study demonstrated the applicability of Sentinel 2 MSI for CSPM retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high loadings of SPM, respectively.

Improved retrieval of Secchi depth for optically-complex waters using remote sensing data

Water transparency is one of the ecological indicators for describing water quality and the underwater light field which determines its productivity. In the European Water Framework Directive (WFD) as well as in the European Marine Strategy Framework Directive (MSFD) water transparency is used for ecological status classification of inland, coastal and open sea waters and it is regarded as an indicator for eutrophication in Baltic Sea management (HELCOM, 2007). We developed and compared different empirical and semi-analytical algorithms for lakes and coastal Nordic waters to retrieve Secchi depth (ZSD) from remote sensing data (MERIS, 300m resolution). The algorithms were developed in water bodies with high coloured dissolved organic matter absorption (aCDOM(442) ranging 1.7–4.0m−1), Chl a concentration (0.5–73mgm−3) and total suspended matter (0.7–37.5gm−3) and validated against an independent data set over inland and coastal waters (0.6m<ZSD<14.8m). The results indicate that for empirical algorithms, using longer wavelengths in the visible spectrum as a reference band decreases the RMSE and increases the coefficient of determination (R2). The accuracy increased (R2=0.75, RMSE=1.33m, n=134) when ZSD was retrieved via an empirical relationship between ZSD and Kd(490). The best agreement with in situ data was attained when ZSD was calculated via both the diffuse and the beam attenuation coefficient (R2=0.89, RMSE=0.77m, n=89). The results demonstrate that transparency can be retrieved with high accuracy over various optical water types by the means of ocean color remote sensing, improving both the spatial and temporal coverage. The satellite derived ZSD product could be therefore used as an additional source of information for WFD and MSFD reporting purposes.

Developing alternative regression models for describing water quality using a self-organizing map

Statistical models play an important role in elucidating the dynamic behaviors of surface water quality, given limited data on a large scale. In this study, we examine alternative approaches to develop regression models that predict fecal coliform (FC) concentrations in a river using different methods for selecting important variables provided by a self-organizing map (SOM). The raw data used as input to the SOM included 11 water quality, 6 meteorological, and 7 land use parameters that were monitored along the Yeongsan River in Korea on various time scales (from daily to half a decade) during 1996–2008. In both test and validation data sets, (multiple) regressions using backward elimination were compared against regression models via forced entry, which included a set of ranked variables simultaneously based on four indices in the SOM (i.e. structuring index, relative importance, cluster description, and Spearman’s rank correlation). Results showed that the SOM effectively illustrated the complex relationship between FC and the remaining variables in the entire data set. This relationship was seen more clearly in homogeneous clusters, indicating that the regression models became more robust in each subdivided group. While the original backward elimination model (R2 = 0.66) had much better performance than the models with four indices (R2 = 0.40–0.45) in the test data set, its performance (R2 = 0.42) was quite comparable to the relative importance model (R2 = 0.38) in the validation data set. Based on this preliminary study, we recommend further investigation of these indices for a reliable regression analysis, as the t values currently used for the variable selection in regressions provide only a locally optimal solution for the final model. The proposed methodology, if verified successfully, would be useful in developing early warning models that control mortality or disease rates of fishes in high-density aquafarms via water quality.

The baseline of coral reef water quality in Xisha Islands waters of South China Sea under southwest monsoon

The Xisha Islands, located off Sanya, South China Sea, are a group of islands administered by Hainan Province of the People's Republic of China. Due to little human activity, they are well preserved. Such conditions suggest that changes in ecosystem are mostly related to natural behavior. A baseline of water quality was established in May and July, 2012. The results of multivariate analysis showed changes in nutrients as the most significant variable in describing water quality. Hydrologic heterogeneity showed little change, but temporal characteristics presented distinguishable variations. This information may be valuable for socioeconomic development and human health in Xisha island waters.

Analysis of the Temporal and Spatial Distribution of Lake and Reservoir Water Quality in China and Changes in Its Relationship with GDP from 2005 to 2010

We analyzed the spatial distribution of lake and reservoir water quality in China, and the trends from 2005 to 2010, based on monitoring data from 28 large Chinese lakes and reservoirs. We used a comprehensive water pollution index (WPI) to describe water quality and also identified the major pollutants. Using GDP data, we analyzed the relationships between economic factors and water quality. We found that although the water quality of large reservoirs is improving or remaining stable, despite economic growth, the water quality of most lakes either did not change or worsened. The outlook is pessimistic, as water quality in most lakes has decreased to Grade V or worse. The water quality was lowest for northern lakes and highest for southern lakes due to a combination of the local industrial structure and lower rainfall in the north. The primary pollutants generally remained stable during the study period. For some lakes, fluoride and volatile phenols became the primary pollutants, indicating more diverse sources of contamination. We divided the 28 bodies of water into four types based on the median WPI and GDP. The dominant combinations were low WPI with low GDP and high WPI with high GDP, as a result of the balance among economic development, the natural environment and environmental policy.

Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake, China

Suspended particulate matter (SPM) is a dominant water constituent of case-II waters, and SPM concentration (CSPM) is a key parameter describing water quality. This study, using Landsat 8 Operational Land Imager (OLI) images, aimed to develop the CSPM retrieval models and further to estimate the CSPM values of Dongting Lake. One Landsat 8 OLI image and 53 CSPM measurements were employed to calibrate Landsat 8-based CSPM retrieval models. The CSPM values derived from coincident Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) images were compared to validate calibrated Landsat 8-based CSPM models. After the best stable Landsat 8-based CSPM retrieval model was further validated using an independent Landsat 8 OLI image and its coincident CSPM measurements, it was applied to four Landsat 8 OLI images to retrieve the CSPM values in the South and East Dongting Lake. Model calibration results showed that two exponential models of the red band explained 61% (estimated standard error (SE) = 7.96 mg l–1) and 67% (SE = 6.79 mg l–1) of the variation of CSPM; two exponential models of the red:panchromatic band ratio obtained 81% (SE = 5.48 mg l–1) and 77% (SE = 4.96 mg l–1) fitting accuracy; and four exponential and quadratic models of the infrared band explained 72–83% of the variation of CSPM (SE = 5.18–5.52 mg l–1). By comparing the MODIS- and Landsat 8-based CSPM values, an exponential model of the Landsat 8 OLI red band (CSPM = 1.1034 × exp(23.61 × R)) obtained the best consistent CSPM estimations with the MODIS-based model (r = 0.98, p < 0.01), and its further validation result using an independent Landsat 8 OLI image showed a significantly strong correlation between the measured and estimated CSPM values at a significance level of 0.05 (r = 0.91, p < 0.05). The CSPM spatiotemporal distribution derived from four Landsat 8 images revealed a clear spatial distribution pattern of CSPM in the South and East Dongting Lake, which was caused by natural and anthropogenic factors together. This study confirmed the potential of Landsat 8 OLI images in retrieving CSPM and provided a foundation for retrieving the spatial distribution of CSPM accurately from this new data source in Dongting Lake.

GIS and Artificial Neural Network–Based Water Quality Model for a Stream Network in the Upper Green River Basin, Kentucky, USA

AbstractThe prediction of stream water quality (WQ) is essential to understand and quantitatively describe water quality parameters (which include physical characteristics, inorganic metallic, and nonmetallic concentrations) and their structure, watershed health, biodiversity, and ecology of a basin. The spatial variability and temporal randomness of stream water quality parameters makes the problem a complex modeling task by ordinary statistical regression methods. The determination of water quality parameters and their spatial and temporal description in stream networks is even more complex due to the stochastic nature of water flow, atmospheric conditions, meteorological patterns, and nonlocal effects of precipitation and temperature. In this paper, a statistical, geographic information system (GIS) and a neural network based water quality model is developed to study stream water quality parameter structure in a geographic framework in the United States of America (USA) consisting of stream network, wa...

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach.

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively, and cluster analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments, and Vaqueros and La Caldera rivers were the most similar. Canonical correlation analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and principal component analysis allowed finding relationships among the nine measured variables in all aquatic environments. Variable's loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros rivers were influenced by recreational activities. Discriminant analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved.

The Implications of Monitoring Frequency for Describing Riverine Water Quality Regimes

AbstractStrategies to characterize water quality vary widely, but regulatory approaches mainly consider single‐value thresholds for individual parameters (e.g. minimum dissolved oxygen concentration). Distributions of parameter values across multiple temporal and spatial scales, commonly referred to as regimes, add greater context and interpretability to point measurements. Although uncommon among monitoring programmes, continuous water quality data collected at high frequency (e.g. hourly) can characterize waterbody health more accurately than infrequent point measurements. We used multivariate analysis to describe water quality regimes based on hourly measurements of dissolved oxygen, pH, specific conductance and water temperature from three divergent stream types in Southeast Alaska national parks. We also assessed whether less frequent measurements drawn from the original hourly data set resulted in similar water quality regime descriptions. The monthly means and standard deviations of the four water quality parameters created ordinations with interpretable, stream‐specific environmental gradients. Procrustean analysis revealed that ordination results were strikingly similar across all temporal monitoring frequencies. Univariate medians and distributions of weekly, twice monthly and monthly measurements were similar across all parameters, but hourly monitoring was necessary to accurately characterize extreme values. These analyses demonstrated the ability of commonly collected water quality parameters to define unique physical–chemical properties across regional stream types and present scientists with common analytical tools to determine appropriate monitoring scales for accurately characterizing water quality regimes. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Validation of a new measuring system for water turbidity field measurements

Turbidity is an essential parameter for describing water quality by direct and indirect impacts on fish, invertebrates and aquatic plants. Currently, environmental monitoring measurements are carried out with appropriate quality by accredited testing laboratories, but there is also a need for employing the third sector, i.e. citizens and voluntaries in environmental monitoring. A device called “Secchi3000” was developed as a low-cost and simple-to-operate tool so that water quality measurements can also be carried out by non-experts and citizens. The measurement using the new device is simple: The user fills the container with water and places the measurement structure in the container. The user takes a photograph with the camera on a mobile phone through a hole in the lid. The software sends the photograph to a server, which analyses the photographs automatically. Finally, the results are returned to the user’s mobile phone and stored on a database for further analysis. In this study, the measuring system for turbidity measurements in natural waters was validated. Validation included an estimation of the limit of quantification, investigations of the influence of water colour and illumination conditions on turbidity measurement values and the estimation of measurement uncertainty. A comparison of turbidity results obtained with the new device and laboratory instrument in natural water samples was carried out, and turbidity values obtained with different mobile phones were compared. According to the validation results, the new device was appropriate for the measurement of turbidity lower than 7 FNU (Formazine Nephelometric Unit). An algorithm applied for present turbidity calculations is not fully suitable for higher turbidities. For potential routine use, this is not a major problem, since most Finnish natural waters have turbidities lower than 7 FNU. For official monitoring purposes, the limit of the quantification needs to be lower than presently achieved (1.7 FNU). Although the present configuration of the Secchi3000 device is not yet fully suitable for official environmental monitoring, it will already enable the involvement of the third sector in water quality monitoring, and in this way, citizens’ observations could then serve at least as supplementary information for reporting and surveys.

Developing MODIS-based retrieval models of suspended particulate matter concentration in Dongting Lake, China

To case-II waters, suspended particulate matter (SPM) is one of the dominant water constituents, SPM concentration (CSPM) is a key parameter describing water quality, and developing remote sensing-based CSPM retrieval models is foundation for obtaining its spatiotemporal distributions. This study aimed to develop moderate resolution imaging spectroradiometer (MODIS)-based CSPM empirical retrieval models in Dongting Lake, China. The 95 CSPM measurements on 31 August 2012 and 14 June 2013 and their corresponding MODIS Terra images were used to calibrate models, and the model calibration results showed that the 250m MODIS red band obtained better fitting accuracies than the near infrared band; the quadratic and exponential models of single red band explained 75% (estimated standard errors (SE)=6.19mg/l) and 71% (SE=6.54mg/l) of the variation of CSPM; and the quadratic and exponential models of red minus shortwave infrared (SWIR) band at 1240 and 1640nm explained 72–73% (SE=6.43–6.48mg/l) and 68–69% (SE=6.83–6.96mg/l) of the variations of CSPM, respectively. The quadratic and exponential models of red band and red minus SWIR band were applied to the MODIS Terra image on 16 September 2013 to estimate CSPM values. By comparing the estimated CSPM values on 16 September 2013 and the measured ones on 17 September 2013 at 40 sampling points for model validations, the results indicated that there exited significantly strong correlations between the measured and estimated CSPM values at a significance level of 0.05 for all models, and the exponential model of red minus SWIR band at 1240nm achieved the best estimation result within all models. Such result provided foundation for obtaining the spatiotemporal distribution information of CSPM from MODIS images in Dongting Lake, which will be helpful for understanding, managing and protecting this ecosystem.

Water Quality Assessment of the Lower West Branch – Susquehanna River: Focus on Sewage Treatment

ABSTRACTThe object of this study was to describe and determine the water quality of the Lower West Branch of the Susquehanna River between Lock Haven and Sunbury. Sites were selected in relation to location of sewage treatment plants along this stretch of river. Water chemistry data (pH, alkalinity, nitrate nitrogen, nitrite nitrogen, dissolved oxygen, temperature, conductivity, orthophosphorus, total phosphorus, total dissolved solids, and turbidity) are presented from 2005 to 2013. Macroinvertebrate kick samples were collected from sample sites in the summer of 2013. These data were subjected to the EPA Rapid Bioassessment Protocol II (RBA-Family Level), Hillsenhoff Biotic Index and Shannon-Wiener Diversity Index in an attempt to describe water quality. All nine of the sewage treatment plants in this section of the river have made improvements to address discharge and combined sewer overflows (CSO's) concerns in the last decade. The overall quality of the water appears to have improved as a number of Chesapeake Bay Initiatives on sewage treatment plants has taken place. Noticeable success of the new standards for sewage treatment plants that have been or are currently being upgraded will need continued monitoring to demonstrate overall water quality improvements.

Fluctuaciones poblacionales de &lt;i&gt;Pyrodinium bahamense&lt;/i&gt; y &lt;i&gt;Ceratium furca&lt;/i&gt; (Dinophyceae) en Laguna Grande, Puerto Rico, y variables ambientales asociadas durante un periodo de tres años

Bioluminescent bays and lagoons are unique natural environments and popular tourist attractions. However, the bioluminescence in many of these water bodies has declined, principally due to anthropogenic activities. In the Caribbean, the bioluminescence in these bays and lagoons is mostly produced by the dinoflagellate Pyrodinium bahamense var. bahamense. Laguna Grande is one of the three year-round bioluminescent water bodies in Puerto Rico that are known to remain but P. bahamense var. bahamense density fluctuations have not been studied. In this study we describe water quality parameters and density fluctuations of the most common dinoflagellates in Laguna Grande, P. bahamense var. bahamense and Ceratium furca, over a three-year period. For this, three sampling stations were established in Laguna Grande from which water samples were collected in triplicate and analyzed for temperature, phosphates, nitrates, salinity, water transparency, fluorescence, and dinoflagellate densities, at the water surface and at 2m depth, from May 2003 to May 2006. The results showed a density fluctuation pattern for P. bahamense var. bahamense, where higher densities were observed mainly from April to September, and lower densities from October to February. Density fluctuations of C. furca were more erratic and a repetitive pattern was not observed. Densities of P. bahamense var. bahamense ranged from 0.48 to 90978 cells/L and densities of C. furca ranged from 0 to 11,200 cells/L. The mean population density throughout the sampling period was significantly higher in P. bahamense var. bahamense (mean = 18,958.5 cells/L) than in C. furca (mean = 2601.9 cells/L). Population densities of P. bahamense var. bahamense were negatively correlated with C. furca densities during the first year of sampling; however, they were positively correlated during the third year. Non-significant differences between surface and 2m depth samples were observed for temperature, phosphates, nitrates, salinity, fluorescence, and densities of P. bahamense var. bahamense and C. furca, suggesting a vertically mixed water column. Water transparency was positively correlated with salinity and negatively correlated with fluorescence. Fluorescence was negatively correlated with salinity. The mean population densities of P. bahamense var. bahamense and C. furca observed in this study were within the range of previous reports in other bioluminescent water bodies in Puerto Rico and Florida, U.S.A. In order to conserve the continuous P. bahamense var. bahamense populations in Laguna Grande, as well as its bioluminescence, it is recommended to maintain the existing water flow levels in the 1.5 km long inlet/outlet channel; to maintain unpolluted water quality parameters within the bay, the hydrographical basin and adjacent waters, and to preserve mangrove communities within the basin and adjacent areas. Results of this study may help to develop management plans aiming to conserve P. bahamense, its bioluminescence and the lagoon attraction.

A simple framework for selection of water quality models

Water quality modeling is no longer just the preserve of specialists who seek to describe water quality processes but also for use by non specialists in everyday water quality management issues. With so many models already developed, it becomes prudent to adapt them to a situation than to develop a completely new model that would probably do the same simulations. The question is: which is the most appropriate model to apply to a situation? The specialist can always draw on past experiences to make a decision. However, this is not the case for the non specialist. A lot of different criteria can be used to decide which model to use for a particular situation based on some important factors. The objectives of modeling exercises differ and each water body is unique so there cannot be hard and fast rules on which is the best criteria for selecting the appropriate model. Furthermore, there is usually hardly any time on the project work plan allocated for model selection. Therefore there is need for a simple procedure to select the appropriate model. The objective of this paper was to develop a simple framework for selecting water quality models to aid the non specialist. The framework was then applied to a case study in order to evaluate its usefulness. The results from the case study show that after a thorough literature review, models can be evaluated against chosen criteria and the most appropriate model singled out. It was concluded that the framework is only effective if the research objective is adequately defined and the models are reviewed thoroughly, but it saves time for the actual modeling exercise.

Turbidity Measurement Using An Optical Tomography System

Turbidity is used to describe water quality and it can be caused by the presence of suspended particles and organic matter such as algae, clay and silt. The measurement of turbidity level of water is vital to domestic water supplies since it is related to public health and water treatment process. This paper presents an investigation on an optical tomography system to estimate the turbidity level in a sample of water. The optical sensors consist of infrared light-emitting diodes (LED) as transmitters and photodiodes as the receivers where the projections of the sensors are designed in fan beam mode. The system was tested using a vertical flow pipe. The Independent Component Analysis (ICA) method was used to display the concentration profile. Results obtained proved that the technique can provide the concentration profile representing the turbidity level of water.