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.     

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.     

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).     

Improve Compliance with Limited Resources by a Three-Group Inspection Regime

Frequent monitoring and relatively high fines are usually necessary to bring about improvements in environmental quality, but more challenging for many countries with limited human, material, and financial resources is to put them into practice. This paper developed a three-group model of a state-dependent enforcement in a repeated game to improve the policy implementation under limited inspection capacities. A certain number of firms are grouped (group 1, group 2, group 3) for different supervision intensity (e.g., the order of inspection probability corresponding to each group is p ₁?<?p ₂?<?p ₃) based on their environmental performance. The optimal policy parameters, such as inspection probability of each group and the probability that a firm found in compliance is moved to a better reputation group, were obtained as the basis for regulator’s policy making. Numerical simulations indicated that the three-group inspection regime can significantly increase compliance rate as compared with static enforcement with the same monitoring probability. Among the number of firms in each group under steady state conditions, group 2 had the most, group 1 was the second, and group 3 had the smallest. Analysis and prediction of a three-group reputation example provided a good experiment for the model. The results give a practical reference for the policy makers with inspection capacity constraints to achieve higher compliance rate.     

Assessing uncertainty in annual nitrogen,phosphorus, and suspended sediment load estimates in three agricultural streams using a 21-year dataset

Accurate estimation of constituent loads is important for studies of ecosystem mass balance or total maximum daily loads. In response, there has been an effort to develop methods to increase both accuracy and precision of constituent load estimates. The relationship between constituent concentration and stream discharge is often complicated, potentially leading to high uncertainty in load estimates for certain constituents, especially at longer-term (annual) scales. We used the loadflex R package to compare uncertainty in annual load estimates from concentration vs. discharge relationships in constituents of interest in agricultural systems, including ammonium as nitrogen (NH₄-N), nitrate as nitrogen (NO₃-N), soluble reactive phosphorus (SRP), and suspended sediments (SS). We predicted that uncertainty would be greatest in NO₃-N and SS due to complex relationships between constituent concentration and discharge. We also predicted lower uncertainty with a composite method compared to regression or interpolation methods. Contrary to predictions, we observed the lowest uncertainty in annual NO₃-N load estimates (relative error 1.5–23%); however, uncertainty was greatest in SS load estimates, consistent with predictions (relative error 19–96%). For all constituents, we also generally observed reductions in uncertainty by up to 34% using the composite method compared to regression and interpolation approaches, as predicted. These results highlight differences in uncertainty among different constituents and will aid in model selection for future studies requiring accurate and precise estimates of constituent load.     

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).     

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.     

2019年江苏省PM2.5和O3多模式集合预报算法效果评估

基于江苏省重污染天气监测预报预警系统多模式预报结果,分析了不同数值模式对江苏省13个城市细颗粒物(PM_2.5)和臭氧(O₃)的预报偏差特征,发展了多模式集合预报算法,并对其进行了评估。结果表明,相较于单一数值模式,集合预报算法显著改善了PM_2.5和O₃预报的准确率,其对江苏省PM_2.5和O₃空气质量分指数等级的预报准确率超过了80%。就江苏省整体而言,PM_2.5集合预报的准确率相比最优单一数值模式提升了6%。O₃浓度较低时,集合预报能有效改善各模式存在的高估现象。但受限于目前的校正策略,出现高浓度O₃污染时,集合预报对预报效果的提升相对有限。     

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.     

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.     

Assessment of multi-contaminant exposure in a cancer treatment center: a 2-year monitoring of molds,mycotoxins, endotoxins,and glucans in bioaerosols

Indoor air quality in health care facilities is a major public health concern, particularly for immunocompromised patients who may be exposed to microbiological contaminants such as molds, mycotoxins, endotoxins, and (1,3)-ß-D-glucans. Over 2 years, bioaerosols were collected on a monthly basis in a cancer treatment center (Centre F. Baclesse, Normandy, France), characterized from areas where there was no any particular air treatment. Results showed the complexity of mycoflora in bioaerosols with more than 100 fungal species identified. A list of major strains in hospital environments could be put forward due to the frequency, the concentration level, and/or the capacity to produce mycotoxins in vitro: Aspergillus fumigatus, Aspergillus melleus, Aspergillus niger, Aspergillus versicolor, Cladosporium herbarum, Purpureocillium lilacinum, and Penicillium brevicompactum. The mean levels of viable airborne fungal particles were less than 30.530 CFU per m³ of air and were correlated to the total number of 0.30 to 20 μm particles. Seasonal variations were observed with fungal particle peaks during the summer and autumn. Statistical analysis showed that airborne fungal particle levels depended on the relative humidity level which could be a useful indicator of fungal contamination. Finally, the exposure to airborne mycotoxins was very low (only 3 positive samples), and no mutagenic activity was found in bioaerosols. Nevertheless, some fungal strains such as Aspergillus versicolor or Penicillium brevicompactum showed toxigenic potential in vitro.     

Eco-friendly synthesis of silver nanoparticles using green algae (<Emphasis Type="Italic">Caulerpa serrulata</Emphasis>): reaction optimization,catalytic and antibacterial activities

Stable colloidal silver nanoparticles (AgNPs) were synthesized using Caulerpa serrulata (green marine algae) aqueous extract as an efficient reducing and stabilizing agent. This method is considered to be a sustainable alternate to the more complicated chemical procedures. To achieve the optimization synthesis of AgNPs, several effects such as extract concentration, contact time, pH values, and temperature were examined. The synthesized AgNPs were characterized by UV–Vis spectroscopy, FT-IR, XRD, and HR-TEM. The synthesized AgNPs showed an intense surface plasmon resonance band at 412 nm at the optimal conditions (20% (v/v) extract and 95 °C). TEM reveal that higher extract concentration and higher temperature leading to the formation of spherical AgNPs with an average particle size of 10 ± 2 nm. The synthesized AgNPs showed excellent catalytic reduction activity of Congo red (CR) dye from aqueous solutions. The degradation percentage of CR with AgNPs accelerated by increasing either NaBH₄ concentration or catalytic dosage. The AgNPs synthesized at higher temperature (e.g., 10Ag-95) exhibited the highest catalytic activity. The reaction kinetics was found to be pseudo first order with respect to the dye concentration. Moreover, the AgNPs displayed antibacterial activity at lower concentration against Staphylococcus aureus, Pseudomonas aeruginosa, Shigella sp., Salmonella typhi, and Escherichia coli and may be a good alternative therapeutic approach. The outcomes of the current study confirmed that the synthesized AgNPs had an awesome guarantee for application in catalysis and wastewater treatment.     

Improved dissipation kinetic model to estimate permissible pre-harvest residue levels of pesticides in apples

Prediction of residual concentrations of applied pesticides during the pre-harvest period may be required to ensure the safety of agricultural products. In this study, time-dependent dissipation trends of carbaryl (CB), kresoxim-methyl (KM), flubendiamide (FB), flufenoxuron (FN), bitertanol (BT), and chlorantraniliprole (CN) applied to apples at recommended and threefold greater doses were modeled to estimate pre-harvest residue limit concentrations (C_PHRL) indicating permissible pesticide concentrations during the pre-harvest period. Double-exponential (DE) model results best fit the dissipation trends of all tested pesticides (correlation coefficients of 0.91–0.99) compared to zero-, first-, and second-order models. Among the pesticides examined, CB half-lives in apples of 2.9 and 6.6 days were the shortest, while those of FN (21.1–32.7 days) were the longest. The C_PHRL values for each pesticide in apples were estimated with DE model parameter values and could be used to determine harvest dates for safe apples with pesticide concentrations below their maximum residue limits. Application of the DE model for C_PHRL calculation provides more accurate information for farmers to produce agricultural products safe from pesticide residues.     

Dynamic extreme values modeling and monitoring by means of sea shores water quality biomarkers and valvometry

Water quality can be evaluated using biomarkers such as tissular enzymatic activities of endemic species. Measurement of molluscs bivalves activity at high frequency (e.g., valvometry) during a long time period is another way to record the animal behavior and to evaluate perturbations of the water quality in real time. As the pollution affects the activity of oysters, we consider the valves opening and closing velocities to monitor the water quality assessment. We propose to model the huge volume of velocity data collected in the framework of valvometry using a new nonparametric extreme values statistical model. The objective is to estimate the tail probabilities and the extreme quantiles of the distribution of valve closing velocity. The tail of the distribution function of valve closing velocity is modeled by a Pareto distribution with parameter ??_t,τ, beyond a threshold τ according to the time t of the experiment. Our modeling approach reveals the dependence between the specific activity of two enzymatic biomarkers (Glutathione-S-transferase and acetylcholinesterase) and the continuous recording of oyster valve velocity, proving the suitability of this tool for water quality assessment. Thus, valvometry allows in real-time in situ analysis of the bivalves behavior and appears as an effective early warning tool in ecological risk assessment and marine environment monitoring.     

Microphytobenthic biomass,species composition and environmental gradients in the mangrove intertidal region of the Andaman Archipelago,India

Monthly sampling in mangrove intertidal sediments of Andaman Archipelago was carried out during a 1-year study (January to December, 2013) in order to analyse the spatial and temporal distribution of microphytobenthos (MPB) and MPB biomass (sediment chlorophyll-a (chl-a)) in the surficial layer 0–1 cm. The MPB community was mainly composed of diatoms. The MPB biomass concentration in surface sediment (0–1 cm) ranged from 0.7 to 16.98 μg cm^?3. Population density of benthic diatoms varied from 78 to 224 ind cm^?3. This study identified 41 diatom taxa (27 pennate diatoms, 14 centric diatoms) in the sediment, and among all the diatom taxa, we distinguished few true planktonic species—Coscinodiscus centralis, Coscinodiscus marginatus, Leptocylindricus danicus, Planktoniella sol, Thalassiosira decipiens, Thalassionema nitzschioides and Thalassiothrix longissima. Overall, a high percentage of diatoms were pennate (81%) as opposed to centric. Based on benthic diatom abundance, species composition and distribution, MPB assemblages of sampling stations were grouped into two distinct clusters: one with St. 1 and St. 3 and another one with St. 2 and St. 4. Canonical correspondence analysis (CCA) revealed seasonality as the most important factor determining variability in diatom species composition among sampling sites. There was a distinct seasonal pattern in MPB biomass distribution and benthic diatom cell density during monsoon and post-monsoon seasons. Our results suggest that among various physical and chemical variables studied, greater levels of overlying water nutrients and sediment textures significantly correlated and were conducive factors for MPB. This is the first detailed study on the MPB from these mangrove sediments, providing benchmark data for future studies about these remote groups of Andaman and Nicobar Islands.     

A glimpse at short-term controls of evapotranspiration along the southern slopes of Kilimanjaro

Future climate characteristics of the southern Kilimanjaro region, Tanzania, are mainly determined by local land-use and global climate change. Reinforcing increasing dryness throughout the twentieth century, ongoing land transformation processes emphasize the need for a proper understanding of the regional-scale water budget and possible implications on related ecosystem functioning and services. Here, we present an analysis of scintillometer-based evapotranspiration (ET) covering seven distinct habitat types across a massive climate gradient from the colline savanna woodlands to the upper-mountain Helichrysum zone (940 to 3960 m.a.s.l.). Random forest-based mean variable importance indicates an outstanding significance of net radiation (R _net) on the observed ET across all elevation levels. Accordingly, topography and frequent cloud/fog events have a dampening effect at high elevations, whereas no such constraints affect the energy and moisture-rich submontane coffee/grassland level. By contrast, long-term moisture availability is likely to impose restrictions upon evapotranspirative net water loss in savanna, which particularly applies to the pronounced dry season. At plot scale, ET can thereby be approximated reasonably using R _net, soil heat flux, and to a lesser degree, vapor pressure deficit and rainfall as predictor variables (R ² 0.59 to 1.00). While multivariate regression based on pooled meteorological data from all plots proves itself useful for predicting hourly ET rates across a broader range of ecosystems (R ² = 0.71), additional gains in explained variance can be achieved when vegetation characteristics as seen from the NDVI are considered (R ² = 0.87). To sum up, our results indicate that valuable insights into land cover-specific ET dynamics, including underlying drivers, may be derived even from explicitly short-term measurements in an ecologically highly diverse landscape.     

A temporal and spatial monitoring of organotin pollution in a harborside region of Brazil by imposex and ecological quality ratio using <Emphasis Type="Italic">Leucozonia nassa</Emphasis>

This study represents the first use of Leucozonia nassa (Mollusca-Gastropoda) in a program of long-term monitoring. Specimens of L. nassa were collected between 2006 and 2014 and analyzed for the occurrence of imposex in two areas in Southeastern Brazil, one control area and another characterized by the presence of many ports and marinas. Imposex was evaluated in nine sites based on the percentage of females exhibiting imposex (I%), relative penis length index and vas deferens sequence index, classical indices to evaluate tributyltin (TBT) impacts. Moreover, this study uses for the first time in a tropical environment the ecological quality ratio (EQR), an approach to verify the imposex levels as an indicator of the impact by organotin compounds according to the EU Water Framework Directive. The results confirm the occurrence of high levels of imposex in L. nassa with all the indices evaluated. Furthermore, the results also indicate that, despite the international ban, antifouling paints based on TBT are still possibly used in Brazil. In addition, this study shows that L. nassa has the characteristics that make it a suitable bioindicator to monitor TBT pollution, and that the use of the EQR may create important data regarding this problem.     

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).     

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.