<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.     

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.     

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.     

Does Globalisation Worsen Environmental Quality in Developed Economies?

We examine the causal relationship between globalisation and CO₂ emissions for 25 developed economies in Asia, North America, Western Europe and Oceania using both time series and panel data techniques, spanning the annual data period of 1970–2014. Because of the presence of cross-sectional dependence in the panel, we employ Pesaran’s Journal of Applied Econometrics 22, 265–312 (2007) cross-sectional augmented panel unit root (CIPS) test to ascertain unit root properties. The Westerlund Oxford Bulletin of Economics and Statistics 69, 709–748 (2007) cointegration test is also used to ascertain the presence of a long-run association between globalisation and carbon emissions. The long-run heterogeneous panel elasticities are estimated using the Pesaran Econometrica, 74(4), 967–1012 (2006) common correlated effect mean group (CCEMG) estimator and the Eberhardt and Teal Productivity analysis in global manufacturing production (2010) augmented mean group (AMG) estimator. The causality between the variables is examined by employing the Dumitrescu and Hurlin Economic Modelling, 29, 1450–1460 (2012) and Emirmahmutoglu and Kose Economic Modelling, 28, 870–876 (2011) Granger causality tests. The empirical results reveal that globalisation increases carbon emissions, and thus, the globalisation-driven carbon emission hypothesis is valid. This empirical analysis suggests insightful policy guidelines for policy makers using ‘globalisation’ as an economic tool for better long-run environmental policy.     

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.     

<Emphasis Type="Italic">Mazzaella laminarioides</Emphasis> and <Emphasis Type="Italic">Sarcothalia crispata</Emphasis> as possible bioindicators of heavy metal contamination in the marine coastal zone of Chile

The suitability of Mazzaella laminarioides and Sarcothalia crispata as heavy metal biomonitors of Cd, Cu, Hg, Pb, and Zn was assessed by comparing bioaccumulation of these elements in different life stages and frond sizes in samples from three locations, San Vicente Bay (industrial area), Coliumo, and Quidico (the latter as a reference station), where different degrees of heavy metal pollution are recorded. Bioaccumulation and bioconcentration factors of Cd, Cu, Hg, Pb, and Zn were evaluated. The two macroalgae species showed similar patterns, with higher values of Cu, Hg, Pb, and Zn in polluted areas. M. laminarioides bioaccumulated higher concentrations of all metals assessed than S. crispata, independent of life stage and frond size. The results also showed significantly higher Cu, Hg, Pb, and Zn concentrations (p < 0.05) in water samples from San Vicente Bay than those measured in Coliumo and Quidico. Concentrations of Cd, Hg, Pb, and Zn in San Vicente Bay and Cd, Hg, and Pb in Coliumo and Quidico exceed the mean values considered to represent natural concentrations (Cu = 3.00 μg L^?1; Zn = 5.00 μg L^?1; Pb = 0.03 μg L^?1; Cd = 0.05 μg L^?1; Hg = 0.05 μg L^?1); however, the concentrations recorded do not cause negative effects on the growth and survival of macroalgae. The assessment of heavy metals bioaccumulated in M. laminarioides and S. crispata, particularly Hg, Pb, and Zn, offers a reliable approach for pollution assessment in rocky intertidal environments. Cu and Cd concentrations in seawater samples from San Vicente and Coliumo Bays were significantly higher than in those from Quidico (p value < 0.05); no significant differences in Cd concentrations were observed between San Vicente and Coliumo Bays (p < 0.05). Exceptionally, Cd is bioaccumulated at high levels independent of its availability in the water, thus reaching high concentrations in control areas. High concentrations of metals like Cu and Zn may limit or inhibit Cd uptake in macroalgae, since the transport channels are saturated by some metals, reducing the accumulation of others. These macroalgae species offer good potential for the development of suitable heavy metal pollution survey tools in rocky intertidal environments.     

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.     

Seasonal and spatial variation of reservoir water quality in the southwest of Ethiopia

This research investigated the spatiotemporal variation of water quality in the Gilgel Gibe reservoir, Ethiopia, using physicochemical water quality parameters. Nonparametric tests and multivariate statistical techniques were used to evaluate data sets measured during dry and rainy seasons. Electrical conductivity (EC), pH, biochemical oxygen demand (BOD₅), total phosphorus (TP), total nitrogen (TN), nitrate (NO₃^?), total dissolved solids (TDSs), and total suspended solids (TSSs) were all significantly different among seasons (Mann-Whitney U test, p?<?0.01). In addition, principal component analysis distinguished dry season samples from wet season samples. The dry season was positively associated with EC, pH, TP, TN, NO₃^?, TDS, and TSS and negatively associated with BOD₅. The wet season was in contrast associated with high values of turbidity, soluble reactive phosphorus (SRP), water temperature, and dissolved oxygen (DO). Within the reservoir, spatial variation was observed for some of the water quality parameters, with significant difference at p?=?<?0.05. Overall, high nutrient concentrations suggest eutrophic conditions, likely due to high nutrient loading from the watershed. Levels of TSS, attributed to inputs from tributaries, have been excessive enough to inhibit light penetration and thus have a considerable impact on the aquatic food web. Our findings indicate that the reservoir is at high risk of eutrophication and siltation, and hence, urgent action should target the planning and implementation of integrated watershed management for this and similar reservoirs in the region.     

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.     

Soil tillage erosion estimated by using magnetism of soils—a case study from Bulgaria

A detailed field and laboratory study on small 0.84-ha test site of agricultural land near Sofia (Bulgaria) has been carried out in order to test the applicability of magnetic methods in soil erosion estimation in the particular case of strongly magnetic parent material. Field measurements of magnetic susceptibility were carried out with grid size of 6 m, resulting in 258 data points. Bulk soil material was gathered from 78 grid points. Natural, non-disturbed soil section was sampled near the agricultural field for reference profile of complete undisturbed soil. Surface susceptibility measurements reveal well-defined maxima down slope which, however, cannot be assigned directly to a certain depth interval, corresponding with susceptibilities along the non-disturbed soil profile. This is caused by the high magnetic susceptibility of the lithogenic coarse-grained magnetic fraction. Non-uniqueness is resolved by using magnetic susceptibility of coarse (1 mm >?d?> 63 μm) and fine (d < 63 μm) mechanical fractions and the parameter Δχ = 100*(χ _coarse???χ _fine)/χ _bulk (%). It shows increased values in the C-horizon of undisturbed soil profile, which corresponds to a certain part of the studied area. After the application of an empirical model to predict the values of magnetic parameter after tillage homogenization and removal of soil material from the surface, the amount of soil loss is estimated.     

Permutation/randomization-based inference for environmental data

Quantitative inference from environmental contaminant data is almost exclusively from within the classic Neyman/Pearson (N/P) hypothesis-testing model, by which the mean serves as the fundamental quantitative measure, but which is constrained by random sampling and the assumption of normality in the data. Permutation/randomization-based inference originally forwarded by R. A. Fisher derives probability directly from the proportion of the occurrences of interest and is not dependent upon the distribution of data or random sampling. Foundationally, the underlying logic and the interpretation of the significance of the two models vary, but inference using either model can often be successfully applied. However, data examples from airborne environmental fungi (mold), asbestos in settled dust, and 1,2,3,4-tetrachlorobenzene (TeCB) in soil demonstrate potentially misleading inference using traditional N/P hypothesis testing based upon means/variance compared to permutation/randomization inference using differences in frequency of detection (Δf _d). Bootstrapping and permutation testing, which are extensions of permutation/randomization, confirm calculated p values via Δf _d and should be utilized to verify the appropriateness of a given data analysis by either model.     

How to Take into Account Uncertainty in Species Extinction Probabilities for Phylogenetic Conservation Prioritization

In this article, we are concerned with the general problem of choosing from a set of taxa T a subset S to protect in order to try to contribute to halting biodiversity loss as efficiently as possible given limited resources. The protection of a taxon decreases its extinction probability, and the impact of protecting the taxa of S is measured by the resulting expected phylogenetic diversity (ePD) of the set T. The primary challenge posed by this approach lies in determining the extinction probability of a taxon (protected or unprotected). To deal with this difficulty, the uncertainty about the extinction probabilities can be described through a set of possible scenarios, each corresponding to different extinction probability values for each taxon. We show how to determine an “optimal robust set” of taxa to protect, defined as the set of taxa that minimizes the maximum “regret,” i.e., the maximum relative gap, over all the scenarios, between (1) the ePD of T obtained by protecting the taxa of this set and (2) the ePD of T which would be produced by protecting the subset of taxa optimal for the considered scenario. In our experimental conditions covering 100 cases, this gap is almost always less than 1%. Consequently, the ePD of T obtained by protecting the taxa of the optimal robust set is not far from the maximum ePD of T that could have been obtained if we had known the true scenario. In other words, a way of escaping (in large part, at least) from the uncertainty related to the extinction probabilities of the taxa consists of choosing to protect those belonging to the optimal robust set. We also compare the optimal robust set to other relevant subsets of T.     

Identification and detection sensitivity of <Emphasis Type="Italic">Microcystis aeruginosa</Emphasis> from mixed and field samples using MALDI-TOF MS

To verify the applicability of identifying Microcystis aeruginosa by matrix-assisted laser desorption-ionization-time-of-flight mass spectrometry (MALDI-TOF MS), mixed and field samples were employed to study the sensitivity and the analysis power, respectively. Series diluted samples and artificially mixed samples by the M. aeruginosa NIES-843 strain were designed to verify the sensitivity. The lowest detection limit was 1.955?×?10⁶ cells in pure samples, while for mixed samples, the lowest detection limit and ratio of NIES-843 strain were 2.88?×?10⁶ cells and 33.7%, respectively. The results provided a reference for the reasonable volume of the water sample in which the M. aeruginosa could be detected. Ribosomal protein biomarkers for identifying M. aeruginosa which were successfully detected from the field samples in Taihu Lake, indicated that the identification of M. aeruginosa by MALDI-TOF MS could be applied in field samples. Furthermore, different genetic types of M. aeruginosa strains were also detected at different locations in Taihu Lake, which revealed the diversity of M. aeruginosa and the detection power of MALDI-TOF MS at the strain level for the field samples. The sensitivity and detection power in the analysis of M. aeruginosa by the MALDI-TOF MS demonstrated the applicability of this method in routine environmental monitoring.     

Association of textile industry effluent with mutagenicity and its toxic health implications upon acute and sub-chronic exposure

Complex industrial discharges pose certain risks to the ecosystem. This study was aimed at identifying acute and sub-chronic toxicological effects of the textile industry wastewater. The textile wastewater was evaluated for the metals and organic pollutants by atomic absorption spectrophotometer and GC-MS respectively. In vitro genotoxicity and mutagenicity were assessed by Comet assay in peripheral lymphocytes isolated from Ovis aries and Ames test in Salmonella typhimurium strains TA-100 and 102 respectively. Physiological and behavioral changes along with systemic toxicity were determined in Rattus norvegicus albinus following acute and sub-chronic exposure. High amount of heavy metals such as Cr, Pb, Hg, As, and Cd were detected in textile wastewater. Organic pollutants such as 25-deacetoxy cucurbitacin-b, E-14-Hexadecenal, 11-Tricosene, and phthalates were also found. In vitro genotoxicity assessment in lymphocytes showed statistically significant DNA damaging potential of textile wastewater. Textile wastewater also showed significantly higher (p? 0.05) mutagenic potential in Salmonella TA-100 and TA-102 strains than sodium azide and 2-amino anthracycline. Acute exposure of textile wastewater to Rattus norvegicus was associated with several physiological changes and behavioral symptoms. Sub-chronic exposure of textile wastewater in Rattus norvegicus instigated the degeneration and necrosis of epithelial cells in renal tubules, hydropic degeneration and necrosis of hepatocytes, peri-bronchiolar infiltration and emphysema of the alveoli, and the degradation of myocardial cells. This study concludes that the textile wastewater may cause genotoxicity and mutagenicity, result in physiological and behavioral changes upon acute exposure, and inflict various pathological lesions upon sub-chronic exposure.     

The influence of chemical protection on the content of heavy metals in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) growing on the soil enriched with granular sludge

The presence of heavy metals in Triticum aestivum L. growing on the soil enriched with granular sludge after chemical protection was observed. The five variants of treatments using herbicide (Chwastox Turbo 340SL) and four fungicides (Topsin M 500SC, Amistar 250SC, Artea 330EC, and Falcon 460EC) were performed. On control and experimental plots, the concentration of Ni, Pb, Cr, and Cu in wheat leaves were in the range 0.32–0.99, 0.92–1.57, 0.89–6.31, and 7.08–12.59 mg/kg and in grains 0.03 to 0.11, 0.14–0.25, 0.11–0.76, and 1.06–1.46 mg/kg, respectively. The concentration of Pb in grain protected by MCPA and 2,4-D with thiophanate-methyl and azoxystrobin was higher than the maximum levels of 0.20 mg/kg D.M. The bioconcentration factor (BCF) differed and depended on chemical protection. The highest value of BCF was achieved for Cd. The statistical analysis showed a significant correlation between concentration of metals and quality parameters of wheat. One observed significant negative correlations between Ni/Zeleny sedimentation value (r = ?0.51) and between Pb/starch content (r = ?0.57). Positive correlations were observed between Cd/yield, the number of grains/ergosterol concentration (respectively, r = 0.41, r = 0.55, r = 0.56), and Zn/thousand grain weight (r = 0.50) at a p ≤ 0.05.     

Mapping aboveground woody biomass using forest inventory,remote sensing and geostatistical techniques

Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m?×?31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10–40, 40–70 and >70 % of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m³ ha^?1. The total growing stock of the forest was found to be 2,024,652.88 m³. The AGWB ranged from 143 to 421 Mgha^?1. Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE)?=?42.25 Mgha^?1) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha^?1 respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha^?1. The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.     

Relative proportions of <Emphasis Type="Italic">E. coli</Emphasis> and <Emphasis Type="Italic">Enterococcus spp.</Emphasis> may be a good indicator of potential health risks associated with the use of roof harvested rainwater stored in tanks

A total of 285 water samples were collected from 71 roof harvested rainwater tanks from four villages in different provinces over a two-year (2013–2014) period during the early (October to December) and late (January to March) rainy season. Water quality was evaluated based on Escherichia coli, faecal coliforms and Enterococcus spp. prevalence using the IDEXX Quanti-Tray quantification system. Real-Time PCR was used to analyse a subset of 168 samples for the presence of Shigella spp., Salmonella spp. and E. coli virulence genes (stx1, stx2 and eaeA). Escherichia coli were detected in 44.1% of the samples, Enterococcus spp. in 57.9% and faecal coliforms in 95.7%. The most prevalent E. coli concentrations in harvested rainwater were observed in 29.1% of samples and 22.5% for Enterococcus spp. and, were within 1–10 cfu/100 ml and 10–100 cfu/100 ml, respectively, whereas those for faecal coliforms (36.6%) were within 100–1000 cfu/100 ml. On average 16.8% of the samples had neither E. coli nor Enterococcus spp. detected, while 33.9% had only Enterococcus spp. and 23.7% had only E. coli. E. coli and Enterococcus spp. were detected together in 25.5% of the samples. Evaluation of samples for potential pathogenic bacteria showed all tested samples to be negative for the Shigella spp. ipaH gene, while five tested positive for Salmonella ipaB gene. None of the samples tested positive for the stx1 and stx2 genes, and only two tested positive for the eaeA gene. These findings are potentially useful in the development of a simplified risk assessment strategy based on the concentrations of indicator bacteria.     

Physicochemical properties associated with the presence of <Emphasis Type="Italic">Burkholderia pseudomallei</Emphasis> in small ruminant farm water supplies in Peninsular Malaysia

Burkholderia pseudomallei causes melioidosis, a life-threatening infection in both humans and animals. Water is an important reservoir of the bacteria and may serve as a source of environmental contamination leading to infection. B. pseudomallei has an unusual ability to survive in water for a long period. This paper investigates physicochemical properties of water associated with the presence of B. pseudomallei in water supply in small ruminant farms in Peninsular Malaysia. Physicochemical properties of water samples taken from small ruminant farms that included temperature, pH, dissolved oxygen (DO₂), optical density (OD), and chemical oxygen demand (COD) were measured after which the samples were cultured for B. pseudomallei. Multivariable logistic regression model revealed that slightly acidic water pH and higher COD level were significantly associated with the likelihood of the B. pseudomallei presence in the water.