Development of composting process of oil palm industrial wastes by multi-enzymatic fungal system

There is an increased interest in composting as an effective means of handling large amounts of organic wastes generated by oil palm industries in Malaysia. However, very few studies have been conducted to develop an effective composting process using the multi-enzymatic system. This study demonstrates an effective composting process of EFB (empty fruit bunch) with POME (palm oil mill effluent), using the optimized process parameters and compatible multi-enzymatic fungal system. A higher decrease (3 %) of organic matter was achieved in the fungal treated system, almost double that of the control (without inoculum). The lowest C/N ratio and soluble protein content recorded were about 17 and 128.82 g/kg, respectively. The maximum germination index obtained was 116 % at day 50 of treatment, which is considered high compared to the control (uninoculated). Furthermore, the maximum activity of ligninase enzyme was found to be 25.95 U/g and the highest cellulase activity was recorded at 0.975 U/g.     

Novel Bioactive Chiral Poly(amide–imide)s Containing Different Amino Acids Linkages: Studies on Synthesis, Characterization and Biodegradability

In this paper chiral bioactive poly(amide–imide)s (PAI)s were synthesized from four different diacids containing chiral amino acids with 4,4′-methylene bis(3-chloro 2,6-diethylaniline) as a diamine via direct polycondensation reaction in a system of tetra-n-butylammonium bromide and triphenyl phosphite as a condensing agent. The structures of these polymers were confirmed by FT-IR, ¹H-NMR, specific rotation, elemental and thermogravimetric analysis (TGA) techniques. TGA showed that the 10 % weight loss temperature in a nitrogen atmosphere was more than 378 °C, which indicates that the resulting PAIs have a good thermal stability. The biodegradability of the monomers and prepared polymers was investigated in culture media and soil burial test for assessment of the susceptibility of these compounds to microbial degradation. The results showed that the synthesized monomers and theirs derived polymers are biologically active and nontoxic to microbial growth.     

Health risk assessment due to heavy metal exposure from commonly consumed fish and vegetables

Contamination of heavy metals in fish and vegetables is regarded as a major crisis globally, with a large share in many developing countries. In Bogra District of Bangladesh, concentrations of six heavy metals, i.e., chromium (Cr), nickel (Ni), copper (Cu), arsenic (As), cadmium (Cd) and lead (Pb), were evaluated in the most consumed vegetables and fish species. The sampling was conducted during February–March 2012 and August–September 2013. The levels of metals varied between different fish and vegetable species. Elevated concentrations of As, Cd and Pb were observed in vegetable species (Solanum tuberosum, Allium cepa and Daucus carota), and fish species (Anabas testudineus and Heteropneustes fossilis) were higher than the FAO/WHO permissible limits, indicating these three metals might pose risk from the consumption of these vegetable and fish species. The higher concentration of heavy metals in these vegetable species might be due to the higher uptake from soil and sediment ingestion behavior in fish species. Multivariate principal component analysis (PCA) showed significant anthropogenic contributions of Cr, Ni, Cu and Pb in samples as the PCA axis scores were correlated with scores of anthropogenic activities. Target hazard quotients showed that the intakes of Cu, As and Pb through vegetables and fish were higher than the recommended health standards, indicated non-carcinogenic risk. Therefore, intakes of these elements via fish and vegetables for Bangladeshi people are a matter of concern.     

River water quality assessment using environmentric techniques: case study of Jakara River Basin

Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH₄). Partial correlation analysis (r _p) results showed that there is a strong relationship between salinity and turbidity (r _p?=?0.930, p?=?0.001) and BOD₅ and COD (r _p?=?0.839, p?=?0.001) controlling for the linear effects of conductivity and NH₄, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD₅, COD, SS, NH₄, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.     

Abundance of denitrification genes under different peizometer depths in four Irish agricultural groundwater sites

This study examined the relationship between the abundance of bacterial denitrifiers in groundwater at four sites, differing with respect to overlaying land management and peizometer depth. Groundwater was sourced from 36 multilevel piezometers, which were installed to target different groundwater zones: (1) subsoil, (2) subsoil to bedrock interface, and (3) bedrock. The gene copy concentrations (GCCs), as gene copies per liter, for bacterial 16S rRNA genes and the denitrifying functional genes, nirK, nirS, and nosZ, were determined using quantitative polymerase chain reaction assays. The results were related to gaseous nitrogen emissions and to the physicochemical properties of the four sites. Overall, nirK and nirS abundance appeared to show no significant correlation to N₂O production (P?=?0.9989; P?=?0.3188); and no significant correlation was observed between nosZ and excess N₂ concentrations (P?=?0.0793). In the majority of piezometers investigated, the variation of nirK and nirS gene copy concentrations was considered significant (P?<?0.0001). Dissolved organic carbon (DOC) decreased with aquifer depth and ranged from 1.0–4.0 mg l^?1, 0.9–2.4 mg l^?1, and 0.8–2.4 mg l^?1 within piezometers located in the subsoil, subsoil/bedrock interface, and bedrock depths, respectively. The availability of increasing DOC and the depth of the water table were positively correlated with increasing nir and nosZ GCCs (P?=?0.0012). A significant temporal correlation was noted between nirS and piezometer depth (P?<?0.001). Interestingly, the nirK, nirS, and nosZ GCCs varied between piezometer depths within specific sites, while GCCs remained relatively constant from site to site, thus indicating no direct impact of agricultural land management strategies investigated on denitrifier abundance.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Effect of fungicides on plant growth promoting activities of phosphate solubilizing Pseudomonasputida isolated from mustard (Brassica compestris) rhizosphere

This study was navigated to examine the effects of fungicide-stress on the activities of plant-growth-promoting rhizobacterium Pseudomonasputida with inherent phosphate solubilizing activity. The fungicide-tolerant and phosphate solubilizing P.putida strain PS9 was isolated from the mustard rhizosphere and tentatively identified following standard morphological, physiological and biochemical tests. To further consolidate the identity of the strain PS9, the 16S rDNA sequence analysis was performed. Following the BLAST program, the strain PS9 was identified as P.putida. In the presence of the varying concentrations (0-3200 μg mL⁻¹; at a two fold dilution interval) of four fungicides of different chemical families (tebuconazole, hexaconazole, metalaxyl and kitazin) amended in minimal salt agar medium, the P.putida strain PS9 showed a variable tolerance levels (1400-3200 μg mL⁻¹) against the tested fungicides. The strain PS9 produced plant-growth-promoting (PGP) substances in significant amount in the absence of fungicides. In general, fungicides applied at the recommended, two and three times of the recommended rates, decreased the PGP attributes of P.putida the strain PS9 and affected the PGP activities in concentration-dependent manner. Fungicides at the recommended dose had minor reducing effect while the doses higher than the recommended dose significantly reduced the PGP activities (phosphate solubilization, salicylic acid, 2,3-dihydroxy benzoic acid, and indole-3-acetic acid production except exo-polysaccharides, hydrogen cyanate and ammonia production). Of the four fungicides, tebuconazole generally, showed maximum toxicity to the PGP activities of the strain PS9. This study inferred that fungicides must be examined in vitro for their possible adverse effects on soil micro flora before their application in agricultural fields. Moreover, the results also suggested the prerequisite of application of fungicide-tolerant PGPR strains as bioinoculants so that their PGP activities may not be suppressed under fungicide stress.     

Chronic exposure of arsenic via drinking water and its adverse health impacts on humans

Worldwide chronic arsenic (As) toxicity has become a human health threat. Arsenic exposure to humans mainly occurs from the ingestion of As contaminated water and food. This communication presents a review of current research conducted on the adverse health effects on humans exposed to As-contaminated water. Chronic exposure of As via drinking water causes various types of skin lesions such as melanosis, leucomelanosis, and keratosis. Other manifestations include neurological effects, obstetric problems, high blood pressure, diabetes mellitus, diseases of the respiratory system and of blood vessels including cardiovascular, and cancers typically involving the skin, lung, and bladder. The skin seems to be quite susceptible to the effects of As. Arsenic-induced skin lesions seem to be the most common and initial symptoms of arsenicosis. More systematic studies are needed to determine the link between As exposure and its related cancer and noncancer end points.     

Monitoring and assessment of seasonal land cover changes using remote sensing: a 30-year (1987–2016) case study of Hamoun Wetland,Iran

The availability of Landsat data allows improving the monitoring and assessment of large-scale areas with land cover changes in rapid developing regions. Thus, we pretend to show a combined methodology to assess land cover changes (LCCs) in the Hamoun Wetland region (Iran) over a period of 30-year (1987–2016) and to quantify seasonal and decadal landscape and land use variabilities. Using the pixel-based change detection (PBCD) and the post-classification comparison (PCC), four land cover classes were compared among spring, summer, and fall seasons. Our findings showed for the water class a higher correlation between spring and summer (R²?=?0.94) than fall and spring (R²?=?0.58) seasons. Before 2000, ~?50% of the total area was covered by bare soil and 40% by water. However, after 2000, more than 70% of wetland was transformed into bare soils. The results of the long-term monitoring period showed that fall season was the most representative time to show the inter-annual variability of LCCs monitoring and the least affected by seasonal-scale climatic variations. In the Hamoun Wetland region, land cover was highly controlled by changes in surface water, which in turn responded to both climatic and anthropogenic impacts. We were able to divide the water budget monitoring into three different ecological regimes: (1) a period of high water level, which sustained healthy extensive plant life, and approximately 40% of the total surface water was retained until the end of the hydrological year; (2) a period of drought during high evaporation rates was observed, and a mean wetland surface of about 85% was characterized by bare land; and (3) a recovery period in which water levels were overall rising, but they are not maintained from year to year. After a spring flood, in 2006 and 2013, grassland reached the highest extensions, covering till more than 20% of the region, and the dynamics of the ecosystem were affected by the differences in moisture. The Hamoun wetland region served as an important example and demonstration of the feedbacks between land cover and land uses, particularly as pertaining to water resources available to a rapidly expanding population.     

Effects of water shortage in Kabul River network on the plain areas of Khyber Pakhtunkhwa,Pakistan

Kabul River is a shared resource of Pakistan and Afghanistan and is a major source of economy for both countries. It is used for irrigation, power generation, fishery, hunting, and recreation. This study explored human-induced impact on Kabul River and its associated wetlands. For an in-depth study, the area situated between the two tributaries of Kabul River, i.e., Shalam and Naguman Rivers, was selected. The focus of the study was to find out reduction in the wetland areas, its causes, and associated impacts. For this purpose, a survey was conducted in 10 villages selected randomly in 10 union councils. The study revealed that anthropogenic activities, such as wetland farming, grazing residential encroachment, and industrial development, have caused severe alteration in the wetland of the study areas. The results revealed that before the 1990s, 33.55% area of the study area was wetland. Due to regular decrease in the flow of the Kabul River System and human encroachment, it has engulfed 68.40% of the total wetland leaving behind only 10.60% wetland. This can be termed as a big ecological disturbance. In the long run, this will have negative effects on both the countries. It is therefore recommended to give proper attention to this important wetland, positioning between Shalam and Naguman Rivers and to conserve it. One of the solutions is to regulate the natural flow of Kabul River.