Estimation of underwater visibility in coastal and inland waters using remote sensing data

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z _sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z _sd , namely, turbidity and length attenuation coefficient (1/(c + K _d ), c = beam attenuation coefficient; K _d  = diffuse attenuation coefficient at 531 nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R _rs (443) + R _rs (490))/(R _rs (555) + R _rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531 nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K _d ) at 531 nm, K _d (490) is estimated as a function of reflectance ratio (R _rs (670)/R _rs (490)), which provides the bio-optical link between chlorophyll concentration and K _d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z _sd and in situ Z _sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z _sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z _sd and in situ Z _sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z _sd and in situ Z _sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z _sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z _sd products. The modeled and satellite-derived Z _sd values were compared with measurement data which yielded RMSE = 0.079, MRE = ?0.016, and R ²  = 0.95 for the modeled Z _sd and RMSE = 0.075, MRE = 0.020, and R ²  = 0.95 for the satellite-derived Z _sd products.     

A regional algorithm to model mesozooplankton biomass along the southwestern Bay of Bengal

A three-dimensional regression analysis attempted to model mesozooplankton (MSP) biomass using sea surface temperature (SST) and chlorophyll-a (Chl-a). The study was carried out from January 2014 to July 2015 in the southwestern Bay of Bengal (BoB) and sampling was carried out on board Sagar Manjusha and Sagar Purvi. SST ranged from 26.2 to 33.1 °C while Chl-a varied from 0.04 to 6.09 μg L^?1. During the course of the study period, there was a weak correlation (r?= 0.32) between SST and Chl-a statistically. MSP biomass varied from 0.42 to 9.63 mg C m^?3 and inversely related with SST. Two kinds of approaches were adopted to develop the model by grouping seasonal datasets (four seasonal algorithms) and comprising all datasets (one annual algorithm). Among the four functions used (linear, paraboloid, the Lorentzian and the Gaussian functions), paraboloid model was best suited. The best seasonal and annual algorithms were applied in the synchronous MODIS-derived SST and Chl-a data to estimate the MSP biomass in the southwestern BoB. The modelled MSP biomass was validated with field MSP biomass and the result was statistically significant, showing maximum regression coefficient for the seasonal algorithms (R²?=?0.60; p?=?0.627; α?= 0.05), than the annual algorithm (R²?=?0.52; p?=?0.015, α?=?0.05).     

Dynamic Modeling of Biological Treatment of Leachates from Solid Wastes

Leachates from municipal solid wastes are liquids formed by organic and inorganic components suitable for biological treatment. In biological treatment plants, prediction of the output parameter value due to dynamic variation is essential. A dynamic model based on ASM1 has been applied for prediction of effluent COD value at real scale, using three different composition leachates. Tuning between dynamic variables and predicted parameter is tested for a correct proposal of the model. Dynamic model predicts correctly fluctuations in the output COD value and tempers dynamism caused by variation of temperature and biomass concentration in the case of high organic loaded leachates. Kinetic parameters Y _H and K _s are fixed to solve the model, and dynamic variables μ _H and X_BH are changing in time. μ_H,max is obtained in simulation (0.37–1.82 day^?1) for minimum error between predicted values and experimental data. μ_H,max value is much lower compared with the value for domestic wastewater treatment due to lower biodegradability of leachate.     

Integrating multiple vegetation indices via an artificial neural network model for estimating the leaf chlorophyll content of <Emphasis Type="Italic">Spartina alterniflora</Emphasis> under interspecies competition

The invasive species Spartina alterniflora and native species Phragmites australis display a significant co-occurrence zonation pattern and this co-exist region exerts most competitive situations between these two species, competing for the limited space, directly influencing the co-exist distribution in the future. However, these two species have different growth ratios in this area, which increase the difficulty to detect the distribution situation directly by remote sensing. As chlorophyll content is a key indicator of plant growth and physiological status, the objective of this study was to reduce the effect of interspecies competition when estimating Cab content; we evaluated 79 published representative indices to determine the optimal indices for estimating the chlorophyll a and b (Cab) content. After performing a sensitivity analysis for all 79 spectral indices, five spectral indices were selected and integrated using an artificial neural network (ANN) to estimate the Cab content of different competition ratios: the Gitelson ratio green index, the transformed chlorophyll absorption ratio index/optimized soil-adjusted vegetation index, the modified normalized difference vegetation index, the chlorophyll fluorescence index, and the Vogelmann chlorophyll index. The ANN method yielded better results (R ² = 0.7110 and RMSE = 8.3829 μg cm^?2) on average than the best single spectral index (R ² = 0.6319 and RMSE = 9.3535 μg cm^?2), representing an increase of 10.78% in R ² and a decrease of 10.38% in RMSE. Our results indicated that integrating multiple vegetation indices with an ANN can alleviate the impact of interspecies competition and achieve higher estimation accuracy than the traditional approach using a single index.     

Seasonal variations and source profile of <Emphasis Type="Italic">n</Emphasis>-alkanes in particulate matter (PM<Subscript>10</Subscript>) at a heavy traffic site,Delhi

Delhi is one of the most polluted cities in the world. The generation of aerosols in the lower atmosphere of the city is mainly due to a large amount of natural dust advection and sizable anthropogenic activities. The compositions of organic compounds in aerosols are highly variable in this region and need to be investigated thoroughly. Twenty-four-hour sampling to assess concentrations of n-alkanes (ng/m³) in PM₁₀ was carried out during January 2015 to June 2015 at Indira Gandhi Delhi Technical University for Women (IGDTUW) Campus, Delhi, India. The total average concentration of n-alkanes, 243.7 ± 5.5 ng/m³, along with the diagnostic tools has been calculated. The values of CPI₁, CPI₂, and CPI₃ for the whole range of n-alkanes series, petrogenic n-alkanes, and biogenic n-alkanes were 1.00, 1.02, and 1.04, respectively, and C _max were at C₂₅ and C₂₇. Diagnostic indices and curves indicated that the dominant inputs of n-alkanes are from petrogenic emissions, with lower contribution from biogenic emissions. Significant seasonal variations were observed in average concentrations of n-alkanes, which is comparatively higher in winter (187.4 ± 4.3 ng/m³) than during the summer season (56.3 ± 1.1 ng/m³).     

Investigation of polar organic chemical integrative sampler (POCIS) flow rate dependence for munition constituents in underwater environments

Munition constituents (MC) are present in aquatic environments throughout the world. Potential for fluctuating release with low residence times may cause concentrations of MC to vary widely over time at contaminated sites. Recently, polar organic chemical integrative samplers (POCIS) have been demonstrated to be valuable tools for the environmental exposure assessment of MC in water. Flow rate is known to influence sampling by POCIS. Because POCIS sampling rates (R_s) for MC have only been determined under quasi-static conditions, the present study evaluated the uptake of 2,4,6-trinitrotoluene (TNT), RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine), and 2,4- and 2,6-dinitrotoluenes (DNT), by POCIS in a controlled water flume at 7, 15, and 30 cm/s in 10-day experiments using samplers both within and without a protective cage. Sampling rate increased with flow rate for all MC investigated, but flow rate had the strongest impact on TNT and the weakest impact on RDX. For uncaged POCIS, mean R_s for 30 cm/s was significantly higher than that for 7 cm by 2.7, 1.9, 1.9, and 1.3 folds for TNT, 2,4-DNT, 2,6-DNT, and RDX, respectively. For all MC except RDX, mean R_s for caged POCIS at 7 cm/s were significantly lower than for uncaged samplers and similar to those measured at quasi-static condition, but except for 2,6-DNT, no caging effect was measured at the highest flow rate, indicating that the impact of caging on R_s is flow rate-dependent. When flow rates are known, flow rate-specific R_s should be used for generating POCIS-derived time-averaged concentrations of MC at contaminated sites.     

Epilithic diatom assemblages and their relationships with environmental variables in the Nilüfer Stream Basin,Bursa, Turkey

Patterns of epilithic diatom species distribution in relation to environmental variables from 12 sampling sites on the main stream and some of its tributaries in the Nilüfer Stream Basin were determined using multivariate statistical techniques. The stream basin has been heavily influenced by anthropogenic effects. The upper part of the basin that is distant from pollution sources mostly has a spring water quality, while the lower part where the stream flows through the urban area and receives domestic and industrial wastewater has a quite low quality. Ordination techniques using both diatom taxa and 21 environmental variables revealed non- to slightly polluted upper basin sites and highly polluted lower basin sites along the stream. The results showed that the stream catchment is polluted gradually from upstream to downstream and that most of the downstream sites have very low water quality especially in summer months. A total of 134 epilithic diatom taxa belonging to 50 genera were recorded for 12 sample sites. Partial CCA results indicated that water temperature (T), discharge (Q), and total phosphorus (TP) were the most important variables affecting the distribution of diatom species. Unpolluted or slightly polluted upper basin sites were dominated by Achnanthidium minutissimum, Cocconeis placentula var. euglypta, Gomphonema olivaceum, and Navicula tripunctata. Highly polluted lower basin sites were characterized by high levels of organic and inorganic matters and low dissolved oxygen (DO) values. Species widespread in the highly polluted lower basin sites such as Nitzschia umbonata, Nitzschia amphibia, Nitzschia capitellata, Nitzschia palea, Nitzschia paleacea, Luticola mutica, and Stephanodiscus niagarae were mostly related to pollution indicator variables such as ammonium nitrogen (NH₄ ⁺–N), sodium (Na⁺), total phosphorus (TP), and total organic matter (TOM).     

Large-scale carbon stock assessment of woody vegetation in tropical dry deciduous forest of Sathanur reserve forest,Eastern Ghats,India

Tropical dry forests are one of the most widely distributed ecosystems in tropics, which remain neglected in research, especially in the Eastern Ghats. Therefore, the present study was aimed to quantify the carbon storage in woody vegetation (trees and lianas) on large scale (30, 1 ha plots) in the dry deciduous forest of Sathanur reserve forest of Eastern Ghats. Biomass of adult (≥10 cm DBH) trees was estimated by species-specific allometric equations using diameter and wood density of species whereas in juvenile tree population and lianas, their respective general allometric equations were used to estimate the biomass. The fractional value 0.4453 was used to convert dry biomass into carbon in woody vegetation of tropical dry forest. The mean aboveground biomass value of juvenile tree population was 1.86 Mg/ha. The aboveground biomass of adult trees ranged from 64.81 to 624.96 Mg/ha with a mean of 245.90 Mg/ha. The mean aboveground biomass value of lianas was 7.98 Mg/ha. The total biomass of woody vegetation (adult trees + juvenile population of trees + lianas) ranged from 85.02 to 723.46 Mg/ha, with a mean value of 295.04 Mg/ha. Total carbon accumulated in woody vegetation in tropical dry deciduous forest ranged from 37.86 to 322.16 Mg/ha with a mean value of 131.38 Mg/ha. Adult trees accumulated 94.81% of woody biomass carbon followed by lianas (3.99%) and juvenile population of trees (1.20%). Albizia amara has the greatest biomass and carbon stock (58.31%) among trees except for two plots (24 and 25) where Chloroxylon swietenia contributed more to biomass and carbon stock. Similarly, Albizia amara (52.4%) showed greater carbon storage in juvenile population of trees followed by Chloroxylon swietenia (21.9%). Pterolobium hexapetalum (38.86%) showed a greater accumulation of carbon in liana species followed by Combretum albidum (33.04%). Even though, all the study plots are located within 10 km radius, they show a significant spatial variation among them in terms of biomass and carbon stocks which could be attributed to variation in anthropogenic pressures among the plots as well as to changes in tree density across landscapes. Total basal area of woody vegetation showed a significant positive (R ² = 0.978; P = 0.000) relationship with carbon storage while juvenile tree basal area showed the negative relationship (R ² = 0.4804; P = 0.000) with woody carbon storage. The present study generates a large-scale baseline data of dry deciduous forest carbon stock, which would facilitate carbon stock assessment at a national level as well as to understand its contribution on a global scale.     

Discriminant analysis for the prediction of sand mass distribution in an urban stormwater holding pond using simulated depth average flow velocity data

The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m³ s^?1, representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size?>?0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5 %. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20 %) compared to group 2 (16.90 to 45.55 %). Two Fisher’s linear discriminant functions, F ₁ and F ₂, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F ₁?=??9.405?+?4232.119?×?A???1795.805?×?B?+?281.224?×?C, and F ₂?=??2.842?+?2725.137?×?A???1307.688?×?B?+?231.353?×?C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m³ s^?1, respectively. The model correctly predicts 88.9 and 100.0 % of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8 % are correctly classified. The model predicts that 31.4 % of the model domain areas consist of high-sand mass composition areas and the remaining 68.6 % comprise low-sand mass composition areas.     

Stochastic modeling approaches based on neural network and linear–nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers

This paper presents a new mathematical model and a two-layer neural network approach to predict the single droplet collection efficiency (SDCE), η _d, of countercurrent spray towers. SDCE values were calculated using MATLAB^® algorithm for 205 different artificial scenarios given in a large range of operating conditions. Theoretical results were compared with outputs obtained from a two-layer neural network and DataFit^® scientific software. The predicted model developed from linear–nonlinear regression analysis and network outputs agreed with the theoretical data, and all predictions proved to be satisfactory with a correlation coefficient of about 0.921 and 0.99, respectively. By using the proposed model, iterations between Reynolds number (Re), drag coefficient (C _D) and terminal velocity values (v _T) were neglected for a large range of operating conditions. SDCE values were also obtained speedily and practically for five main operating inputs used in the model.     

Variation in biochemical constituents and master elements in common seaweeds from Alexandria Coast,Egypt, with special reference to their antioxidant activity and potential food uses: prospective equations

Biochemical constituents and master elements (Pb, Cr, Cd, Fe, Cu, Zn, Hg, B, Al, SO₄ ^2?, Na, K, Li, Ca, Mg, and F) were investigated in six different seaweed species from Abu Qir Bay in the Egyptian Mediterranean Sea coast. The moisture level ranged from 30.26% in Corallina mediterranea to 77.57% in Padina boryana. On dry weight basis, the ash contents varied from 25.53% in Jania rubens to 88.84% in Sargassum wightii. The protein contents fluctuated from 8.26% in S. wightii to 28.01% in J. rubens. Enteromorpha linza showed the highest lipids (4.66%) and carbohydrate contents (78.95%), whereas C. mediterranea had the lowest lipid (0.5%), and carbohydrate contents (38.12%). Chlorophylls and carotenoid contents varied among the species. Total antioxidant capacity of the tested green seaweeds had the highest activities followed by brown and red seaweeds which had a similar trend of phenol and tannins contents. High reducing power was observed in all tested seaweeds extract except Ulva lactuca. Brown species had the highest amount of elements followed by red and green seaweeds. Notably, SO₄ ^2? recorded the highest level in the tested green species (108.05 mg/g dry weight (DW)). The Ca/Mg and K/Na ratios reflected highly significant difference between seaweed species. This study keeps an eye on 29 parameters and by applying stepwise multiple regression analysis, prospective equations have been set to describe the interactions between these parameters inside seaweeds. Accordingly, the tested seaweeds can be recommended as a source of healthy food with suitable ion quotient and estimated daily intake values.     

<Emphasis Type="Italic">Synechococcus</Emphasis> production and grazing loss rates in nearshore tropical waters

Temporal variation of Synechococcus, its production (μ) and grazing loss (g) rates were studied for 2 years at nearshore stations, i.e. Port Dickson and Port Klang along the Straits of Malacca. Synechococcus abundance at Port Dickson (0.3–2.3 × 10⁵ cell ml^?1) was always higher than at Port Klang (0.3–7.1 × 10⁴ cell ml^?1) (p < 0.001). μ ranged up to 0.98 day^?1 (0.51 ± 0.29 day^?1), while g ranged from 0.02 to 0.31 day^?1 (0.15 ± 0.07 day^?1) at Port Klang. At Port Dickson, μ and g averaged 0.47 ± 0.13 day^?1 (0.29–0.82 day^?1) and 0.31 ± 0.14 day^?1 (0.13–0.63 day^?1), respectively. Synechococcus abundance did not correlate with temperature (p > 0.25), but nutrient and light availability were important factors for their distribution. The relationship was modelled as log Synechococcus = 0.37Secchi ? 0.01DIN + 4.52 where light availability (as Secchi disc depth) was a more important determinant. From a two-factorial experiment, nutrients were not significant for Synechococcus growth as in situ nutrient concentrations exceeded the threshold for saturated growth. However, light availability was important and elevated Synechococcus growth rates especially at Port Dickson (F = 5.94, p < 0.05). As for grazing loss rates, they were independent of either nutrients or light intensity (p > 0.30). In nearshore tropical waters, an estimated 69 % of Synechococcus production could be grazed.     

Optimizing the flow adjustment of constituent concentrations via LOESS for trend analysis

Trend analysis of stream constituent concentrations requires adjustment for exogenous variables like discharge because concentrations often have variable relations with flow. To remove the influence of flow on stream water quality data, an accurate characterization of the relationship between the constituent and streamflow is needed. One popular method, locally weighted regression (LOESS), provides an effective means for flow-adjusting concentrations. The LOESS fit can be tailored to the data via the smoothing parameter (f), so that the user can avoid overfitting or oversmoothing the data. However, it is a common practice to use a single f value when flow-adjusting water quality data for trend analysis. This study provides a robust, automated method for determining the optimal f value (f_opt) for each dataset via an iterative K-fold cross-validation procedure that minimizes prediction error in LOESS. The method is developed by analyzing datasets of seven different constituents across 17 sites (119 datasets total) from a stream monitoring program in northwest Arkansas (USA). We recommend using 10 iterations of 10-fold cross-validation (10?×?10 CV) in order to select f_opt when flow-adjusting water quality data with LOESS. The use of a default f value did not produce different trend interpretations for the data used here; however, the proposed approach may be helpful in other water quality studies which employ similar statistical fitting methods. Additionally, we provide an implementation of the method in the R statistical computing environment.     

Spatiotemporal analysis of the effect of climate change on vegetation health in the Drakensberg Mountain Region of South Africa

The impact of climate change on mountain ecosystems has been in the spotlight for the past three decades. Climate change is generally considered to be a threat to ecosystem health in mountain regions. Vegetation indices can be used to detect shifts in ecosystem phenology and climate change in mountain regions while satellite imagery can play an important role in this process. However, what has remained problematic is determining the extent to which ecosystem phenology is affected by climate change under increasingly warming conditions. In this paper, we use climate and vegetation indices that were derived from satellite data to investigate the link between ecosystem phenology and climate change in the Namahadi Catchment Area of the Drakensberg Mountain Region of South Africa. The time series for climate indices as well as those for gridded precipitation and temperature data were analyzed in order to determine climate shifts, and concomitant changes in vegetation health were assessed in the resultant epochs using vegetation indices. The results indicate that vegetation indices should only be used to assess trends in climate change under relatively pristine conditions, where human influence is limited. This knowledge is important for designing climate change monitoring strategies that are based on ecosystem phenology and vegetation health.     

Bioindicator responses and performance of plant species along a vehicular pollution gradient in western Himalaya

Loss of green cover, and increasing pollution is a prime global concern. The problem calls for screening of pollution-tolerant tree species that can be integrated into plantation drives. Recognizing this, the study analyzed bio-indicator responses and performance of commonly occurring plant species along a pollution gradient in western Himalaya. Based on distance from the road, three sites viz., highly polluted (HP), moderately polluted (MP), and least polluted (LP), were identified. From these sites, leaves of commonly occurring 26 tree species were collected and analyzed for dust accumulation, total chlorophyll, relative water content (RWC), ascorbic acid, and pH using standard protocols. Later, assessment of Air Pollution Tolerance Index (APTI) and Anticipated Performance Indices (API) was carried out. The results revealed variations in biochemical characteristics. The pH, RWC, and total chlorophyll increased with decreasing pollution while ascorbic acid increased with increasing pollution. Dust capturing potential of Ficus carica (1.191 mg/m²) and Toona ciliata (0.820 mg/m²) was relatively higher. Based on the results of APTI, Grevillea robusta was classified as tolerant. It scored significantly higher values (21.06, 21.19, and 19.61 in LP, MP, and HP sites, respectively). Quercus floribunda, G. robusta (68.75% each), Juglans regia (68.7%), and T. ciliata (62.50%) were good performers in HP sites. Acer caesium, Betula utilis, and Morus alba that had low API scores (43.75%) were predicted as poor performers. Thus, G. robusta, Q. floribunda, J. regia, T. ciliata, and F. carica were evaluated as best performers. They could be integrated into plantations drives for environmental management.     

Organismic-level acute toxicology profiling of reactive azo dyes

In the present study, organismic-level acute toxicology profile of three reactive azo dyes, viz. Reactive Blue 221, Reactive Red 195, and Reactive Yellow 145, was investigated, by using bacterial (Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Listeria monocytogenes, and Bacillus subtilis), fungal (Trichoderma asperellum, Aspergillus flavus, Fusarium fujikuroi, and Rhizoctonia solani), plant (Raphanus sativus, Triticum aestivum, Sorghum bicolor, and Phaseolus mungo), and aquatic (Artemia salina and Daphnia magna) specimens. Microbial test organisms (all the six bacteria and two fungi, i.e., T. asperellum and A. flavus) and D. magna were found to be relatively more sensitive towards the reactive azo dyes and their mixture, as the EC50 values were in the range of 80–330, 135–360, and 108–242 ppm for bacteria, fungi, and D. magna, respectively (but the effect was not acutely toxic). Moreover, the effect of dye mixture was comparable tothe individual dyes in almost all the tested microbial specimens. For plant seeds, the dye mixture was found to be relatively more inhibitory towards T. aestivum and R. sativus than the individual dyes. For S. bicolor and P. mungo seeds, the effect of the dye mixture was almost identical to the individual dyes. However, in all cases, EC50 values were in the range of 950–3500 ppm, which indicates a non-toxic effect on plant seed germination potential. Likewise, the dyes and their mixture were not acutely toxic for Artemia salina larvae (more sensitive to the dye mixture) and Daphnia magna neonates (EC50, 516–950 and 108–242 ppm, respectively).     

Assessment of physicochemical parameters and prevalence of virulent and multiple-antibiotic-resistant <Emphasis Type="Italic">Escherichia coli</Emphasis> in treated effluent of two wastewater treatment plants and receiving aquatic <Emphasis Type="Italic">milieu</Emphasis> in Durban,South Africa

The poor operational status of some wastewater treatment plants often result in the discharge of inadequately treated effluent into receiving surface waters. This is of significant public health concern as there are many informal settlement dwellers (ISDs) that rely on these surface waters for their domestic use. This study investigated the treatment efficiency of two independent wastewater treatment plants (WWTPs) in Durban, South Africa and determined the impact of treated effluent discharge on the physicochemical and microbial quality of the receiving water bodies over a 6-month period. Presumptive Escherichia coli isolates were identified using biochemical tests and detection of the mdh gene via PCR. Six major virulence genes namely eae, hly, fliC, stx1, stx2, and rfbE were also detected via PCR while antibiotic resistance profiles of the isolates were determined using Kirby-Bauer disc diffusion assay. The physicochemical parameters of the wastewater samples ranged variously between 9 and 313.33 mg/L, 1.52 and 76.43 NTUs, and 6.30 and 7.87 for COD, turbidity, and pH respectively, while the E. coli counts ranged between 0 and 31.2?×?10³ CFU/ml. Of the 200 selected E. coli isolates, the hly gene was found in 28 %, fliC in 20 %, stx2 in 17 %, eae in 14 %, with stx1 and rfbE in only 4 % of the isolates. Notable resistance was observed toward trimethoprim (97 %), tetracycline (56 %), and ampicillin (52.5 %). These results further highlight the poor operational status of these WWTPs and outline the need for improved water quality monitoring and enforcement of stringent guidelines.     

Variation of phytoplankton assemblages of Kongsfjorden in early autumn 2012: a microscopic and pigment ratio-based assessment

Phytoplankton species distribution and composition were determined by using microscopy and pigment ratios in the Kongsfjorden during early autumn 2012. Variation in sea surface temperature (SST) was minimal and matched well with satellite-derived SST. Nutrients were generally limited. Surface phytoplankton abundance ranged from 0.21?×?10³ to 10.28?×?10³ cells L^?1. Phytoplankton abundance decreased with depth and did not show any significant correlation with chlorophyll a (chl a). Column-integrated phytoplankton cell counts (PCC) ranged from 94.3?×?10⁶ cells m^?2 (Kf4) to 13.7?×?10⁶ cells m^?2 (Kf5), while chl a was lowest at inner part of the fjord (6.3 mg m^?2) and highest towards the mouth (24.83 mg m^?2). Biomass from prymnesiophytes and raphidophytes dominated at surface and 10 m, respectively. The contribution of Bacillariophyceae to biomass was low. Generally, heterotrophic dinoflagellates were great in abundance (12.82 %) and ubiquitous in nature and were major contributors to biomass. Various chl pigments (chl b, chl c, phaeopigments (phaeo)) were measured to obtain pigment/chl a ratios to ascertain phytoplankton composition. Phaeo were observed only in inner fjord. Chl b:a ratios and microscopic observations indicated dominance of Chlorophyceae at greater depths than surface. Furthermore, microscopic observations confirmed dominance of chl c containing algae throughout the fjord. The study indicates that pigment ratios can be used as a tool for preliminary identification of major phytoplankton groups. However, under the presence of a large number of heterotrophic dinoflagellates such as Gymnodinium sp. and Gyrodinium sp., pigment signatures need to be supplemented by microscopic observations.     

Genotoxic effects and gene expression in <Emphasis Type="Italic">Danio rerio</Emphasis> (Hamilton 1822) (Cypriniformes: Cyprinidae) exposed to mining-impacted tributaries in Manizales,Colombia

The zebrafish (Danio rerio) is one of the most studied aquatic organisms for water biomonitoring, due to its sensitivity to environmental degradation and resistance to toxic substances. This study determined the presence of micronuclei and nuclear abnormalities in peripheral blood erythrocytes, and assessed the gene expression of caspase-3 (CASP-3) and metallothionein 1 (MT-1) in the gills and liver of D. rerio. The study fish (n = 45) were exposed to water collected from two stations with mining impact (E2 and E3) and a reference station without evident mining contamination (E1), all located in La Elvira stream (Manizales-Colombia). In addition, a positive control (PC) with HgCl₂ (50 μg/L) and negative control (NC) with tap water were included. The fish from the PC and E2 and E3 treatments displayed genotoxic effects and changes in gene expression, with significant differences in micronuclei formation and the presence of blebbed nuclei. The cytochrome oxidase subunit I (COI) gene was used as reference and proved to be stable compared to the β-actin and 28S ribosomal RNA (28S) genes. In gills, CASP-3 expression was higher in the PC, and MT-1 expression was higher in the PC and E3 treatment. In liver, CASP-3 was expressed in the E2 treatment, and MT-1 expression was low. These results show that the genotoxic effects and differential gene expression observed in fish exposed to water from La Elvira stream could also be affecting the organisms present in this habitat.     

Singular Spectrum Analytic (SSA) Decomposition and Reconstruction of Flowering: Signatures of Climatic Impacts

An empirical approach for the decomposition and reconstruction of long-term flowering records of eight eucalypt species is presented. Results from singular spectrum analysis (SSA) allow for characterisation of the dynamic and complex flowering patterns in response to temperature and rainfall throughout the year. SSA identified trend, annual, biennial and other sub-components of flowering. The ability to discriminate flowering and climate relationships is demonstrated based on SSA (cross-)correlation analysis. SSA also identified the cyclical influence of temperature and rainfall on peak flowering. For each species, there is, on average, 6 months of the annual cycle when temperature positively influences flowering and 6 months when the influence of temperature is negative. For all species, rainfall exerts a negative influence when temperature is positive. Investigation of short-term and long-term lags of climate on flowering provided best-case climatic scenarios for each species’ flowering; e.g. more intense peak flowering is likely in Eucalyptus leucoxylon when cool, wet conditions coincide with peak flowering and is further enhanced if the preceding autumn and winter were warm and dry, and the previous spring and summer cool and wet. Three clear species groupings, according to similar SSA (cross-)correlation signatures, were identified: (1) E. leucoxylon and E. tricarpa; (2) E. camaldulensis, E. melliodora and E. polyanthemos and (3) E. goniocalyx, E. microcarpa and E. macrorhyncha. Lastly, change point years for flowering based on SSA sub-components in four of the species seem to align with years of major shift in global ENSO signal (1951/1957/1958) as indicated by the extended multivariate ENSO index.