A Low-Cost Digital Colorimetry Setup to Investigate the Relationship between Water Color and Its Chemical Composition.

Developments in digital image acquisition technologies and citizen science lead to more water color observations and broader public participation in environmental monitoring. However, the implications of the use of these simple water color indices for water quality assessment have not yet been fully evaluated. In this paper, we build a low-cost digital camera colorimetry setup to investigate quantitative relationships between water color indices and concentrations of optically active constituents (OACs). As proxies for colored dissolved organic matter (CDOM) and phytoplankton, humic acid and algae pigments were used to investigate the relationship between water chromaticity and concentration. We found that the concentration fits an ascending relationship with xy chromaticity values and a descending relationship with hue angle. Our investigations permitted us to increase the information content of simple water color observations, by relating them to chemical constituent concentrations in observed waters.

Exploring the Environmental Exposure to Methoxychlor, α-HCH and Endosulfan-sulfate Residues in Lake Naivasha (Kenya) Using a Multimedia Fate Modeling Approach.

Distribution of pesticide residues in the environment and their transport to surface water bodies is one of the most important environmental challenges. Fate of pesticides in the complex environments, especially in aquatic phases such as lakes and rivers, is governed by the main properties of the contaminants and the environmental properties. In this study, a multimedia mass modeling approach using the Quantitative Water Air Sediment Interaction (QWASI) model was applied to explore the fate of organochlorine pesticide residues of methoxychlor, α-HCH and endosulfan-sulfate in the lake Naivasha (Kenya). The required physicochemical data of the pesticides such as molar mass, vapor pressure, air-water partitioning coefficient (KAW), solubility, and the Henry's law constant were provided as the inputs of the model. The environment data also were collected using field measurements and taken from the literature. The sensitivity analysis of the model was applied using One At a Time (OAT) approach and calibrated using measured pesticide residues by passive sampling method. Finally, the calibrated model was used to estimate the fate and distribution of the pesticide residues in different media of the lake. The result of sensitivity analysis showed that the five most sensitive parameters were KOC, logKow, half-life of the pollutants in water, half-life of the pollutants in sediment, and KAW. The variations of outputs for the three studied pesticide residues against inputs were noticeably different. For example, the range of changes in the concentration of α-HCH residue was between 96% to 102%, while for methoxychlor and endosulfan-sulfate it was between 65% to 125%. The results of calibration demonstrated that the model was calibrated reasonably with the R2 of 0.65 and RMSE of 16.4. It was found that methoxychlor had a mass fraction of almost 70% in water column and almost 30% of mass fraction in the sediment. In contrast, endosulfan-sulfate had highest most fraction in the water column (>99%) and just a negligible percentage in the sediment compartment. α-HCH also had the same situation like endosulfan-sulfate (e.g., 99% and 1% in water and sediment, respectively). Finally, it was concluded that the application of QWASI in combination with passive sampling technique allowed an insight to the fate process of the studied OCPs and helped actual concentration predictions. Therefore, the results of this study can also be used to perform risk assessment and investigate the environmental exposure of pesticide residues.

Using hydrodynamic and water quality variables to assess eutrophication in a tropical hydroelectric reservoir.

Few studies have examined the influence of reservoir hydrodynamics on the water quality of its limnological zones. In this study, the relationships between the operational phases and the water quality of the limnological zones were assessed for the Amazonian reservoir Tucuruí. Limnological zones were clustered by means of an artificial neural network technique, and inputs used were water quality variables, measured at twelve stations between 2006 and 2016. Generalized Linear Models (GLMs) were then used to identify the influence of the operational phases of the reservoir on the water quality of its limnological zones. The GLM with a gamma-distributed response variable indicated that Chlorophyll-a concentrations in the riverine and transitional zones differed notably from those observed in the lacustrine zone. Chlorophyll-a concentrations were significantly lower during the operational falling water phase than in the low water phase (p < 0.05). The GLM with an inverse Gaussian-distributed response variable indicated that Secchi depth was significantly lower in the riverine than in the lacustrine limnological zone (p < 0.05). Our results suggest that more eutrophic conditions occur during the operational rising water phase, and that the area most vulnerable to eutrophication is the transitional zone. We demonstrate that the use of GLMs is suitable for determining areas and operational phases most vulnerable to eutrophication. We envisage that this information will be useful to decision-makers when monitoring the water quality of hydroelectric reservoirs with dendritic patterns and dynamic operational phases.

Conjunctive use of in situ gas sampling and chromatography with geospatial analysis to estimate greenhouse gas emissions of a large Amazonian hydroelectric reservoir.

Hydroelectric power reservoirs are considered potential contributors to the greenhouse effect in the atmosphere through the emittance of methane and carbon dioxide. We combined in situ sampling and gas chromatography with geostatistical and remote sensing approaches to estimate greenhouse gas (GHG) emissions of a large hydropower reservoir. We used remote sensing data to estimate the water surface and geospatial interpolation to calculate total emissions as a function of reservoir surface area. The CH4 and CO2 gas concentrations were linearly correlated to sampling time, confirming the adequacy of the in situ sampling method to measure GHG diffusive fluxes from reservoir water surfaces. The combination of high purity (99.99%) ISO-norm gas standards with a gas chromatograph, enabled us to achieve low measurement detection limits of 0.16 and 0.60 µmol mol-1, respectively, for CH4 (using a flame ionization or FID detector) and CO2 (using a thermal conductivity or TCD detector). Our results show that CO2 emissions are significantly (an order of 5.102-103) higher than those of CH4 in both the spatial and temporal domain for this reservoir. The total diffusive GHG emissions over a year (June 2011 to May 2012) of the Tucuruí hydropower reservoir being in operation, in units of tons of carbon, added up to 6.82 × 103 for CH4 and 1.19 × 106 for CO2. We show that in situ GHG sampling using small floating gas chambers and high precision gas chromatography can be combined with geospatial interpolation techniques and remote sensing data to obtain estimates of diffusive GHG emissions from large water bodies with fluctuating water surfaces such as hydropower reservoirs. We recommend that more measurements and observations on these emissions are pursued in order to support and better quantify the ongoing discussions on estimates and mitigation of GHG emissions from reservoirs in the Amazon region and elsewhere in the world.

Correction to: Evaluating organochlorine pesticide residues in the aquatic environment of the Lake Naivasha River basin using passive sampling techniques.

The original version of this article contained a mistake. The pesticides concentrations units have to be updated to ng/L (nanogram per liter) in the text and partly in Fig. 5 (left part).

Evaluating organochlorine pesticide residues in the aquatic environment of the Lake Naivasha River basin using passive sampling techniques.

Passive sampling techniques can improve the discovery of low concentrations by continuous collecting the contaminants, which usually go undetected with classic and once-off time-point grab sampling. The aim of this study was to evaluate organochlorine pesticide (OCP) residues in the aquatic environment of the Lake Naivasha river basin (Kenya) using passive sampling techniques. Silicone rubber sheet and Speedisk samplers were used to detect residues of α-HCH, ß-HCH, γ-HCH, δ-HCH, heptachlor, aldrin, heptachlor epoxide, pp-DDE, endrin, dieldrin, α-endosulfan, ß-endosulfan, pp-DDD, endrin aldehyde, pp-DDT, endosulfan sulfate, and methoxychlor in the Malewa River and Lake Naivasha. After solvent extraction from the sampling media, the residues were analyzed using gas chromatography electron capture detection (GC-ECD) for the OCPs and gas chromatography-mass spectrometry (GC-MS) for the PCB reference compounds. Measuring the OCP residues using the silicone rubber samplers revealed the highest concentration of residues (∑OCPs of 81 (± 18.9 SD) ng/L) to be at the Lake site, being the ultimate accumulation environment for surficial hydrological, chemical, and sediment transport through the river basin. The total OCP residue sums changed to 71.5 (± 11.3 SD) ng/L for the Middle Malewa and 59 (± 12.5 SD) ng/L for the Upper Malewa River sampling sites. The concentration sums of OCPs detected using the Speedisk samplers at the Upper Malewa, Middle Malewa, and the Lake Naivasha sites were 28.2 (± 4.2 SD), 31.3 (± 1.8 SD), and 34.2 (± 6.4 SD) ng/L, respectively. An evaluation of the different pesticide compound variations identified at the three sites revealed that endosulfan sulfate, α-HCH, methoxychlor, and endrin aldehyde residues were still found at all sampling sites. However, the statistical analysis of one-way ANOVA for testing the differences of ∑OCPs between the sampling sites for both the silicone rubber sheet and Speedisk samplers showed that there was no significant difference from the Upper Malewa to the Lake site (P < 0.05). Finally, the finding of this study indicated that continued monitoring of pesticides residues in the catchment remains highly recommended.