Seaweeds as bioindicators of heavy metals off a hot spot area on the Egyptian Mediterranean Coast during 2008–2010

Concentrations of Cu, Zn, Cd, Pb, Ni, Co, Fe, Mn, and Hg were measured successively in water, sediments, and six macroalgal species belonging to three algal classes during 3 years (2008–2010) from Abu Qir Bay, Alexandria, Egypt: Chlorophyceae (Enteromorpha compressa, Ulva fasciata), Phaeophyceae (Padina boryana), and Rhodophyceae (Jania rubens, Hypnea musciformis, Pterocladia capillacea). The study aimed to assess the bioaccumulation potential of the seaweeds, as well as to evaluate the extent of heavy metal contamination in the selected study site. Metals were analyzed using atomic absorption spectrophotometry coupled with MH-10 hydride system. The obtained data showed that the highest mean concentrations of Cu, Zn, Fe, and Mn were recorded in E. compressa; Cd, Ni, and Hg exhibited their highest mean concentrations in P. boryana, while Pb and Co were found in J. rubens. Abundance of the heavy metals in the algal species was as follow: Fe?>?Mn?>?Zn?>?Pb?>?Ni?>?Co?>?Cu?>?Cd?>?Hg. E. compressa showed the maximum metal pollution index (MPI) which was 11.55. Bioconcentration factor (BCF) for the metals in algae was relatively high with a maximum value for Mn. The Tomlinson pollution load index (PLI) values for the recorded algal species were low, which ranged between 1.00 in P. boryana and 2.72 in E. compressa. Enrichment factors for sediments were low fluctuating between 0.43 for Hg to 2.33 for Mn. Accordingly, the green alga E. compressa, brown alga P. boryana, and red alga J. rubens can be nominated as bioindicators. Based on MPI and PLI indices, Abu Qir Bay in the present study is considered as low-contaminated area.     

GC estimation of organic hydrocarbons that threaten shallow Quaternary sandy aquifer Northwestern Gulf of Suez,Egypt

Soil and groundwater contamination is one of the important environmental problems at petroleum-related sites, which causes critical environmental and health defects. Severe petroleum hydrocarbon contamination from coastal refinery plant was detected in a shallow Quaternary sandy aquifer is bordered by Gulf in the Northwestern Gulf of Suez, Egypt. The overall objective of this investigation is to estimate the organic hydrocarbons in shallow sandy aquifers, released from continuous major point-source of pollution over a long period of time (91 years ago). This oil refinery contamination resulted mainly in the improper disposal of hydrocarbons and produced water releases caused by equipment failures, vandalism, and accidents that caused direct groundwater pollution or discharge into the gulf. In order to determine the fate of hydrocarbons, detailed field investigations were made to provide intensive deep profile information. Eight composite randomly sediment samples from a test plot were selected for demonstration. The tested plot was 50 m long?×?50 m wide?×?70 cm deep. Sediment samples were collected using an American auger around the point 29° 57′ 33″ N and 32° 30′ 40″ E in 2012 and covered an area of 2,500 m² which represents nearly 1/15 of total plant area (the total area of the plant is approximately 3.250 km²). The detected total petroleum hydrocarbons (TPHs) were 2.44, 2.62, 4.54, 4.78, 2.83, 3.22, 2.56, and 3.13 wt%, respectively. TPH was calculated by differences in weight and subjected to gas chromatography (GC). Hydrocarbons were analyzed on Hewlett–Packard (HP-7890 plus) gas chromatograph equipped with a flame ionization detector (FID). The percentage of paraffine of the investigated TPH samples was 7.33, 7.24, 7.58, 8.25, 10.25, 9.89, 14.77, and 17.53 wt%, respectively.     

Systematic Development of an Artificial Neural Network Model for Real-Time Prediction of Ground-Level Ozone in Edmonton,Alberta, Canada

Abstract

Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O₃) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O₃ events. The models are used to determine the meteorological conditions and precursors that most affect O₃ concentrations. O₃ time-series effects and the efficacy of the systematic approach are also assessed. The developed models showed good predictive success, with coefficient of multiple determination values ranging from 0.75 to 0.94 for forecasts up to 2 hr in advance. The inputs most important for O₃ prediction were temperature and concentrations of nitric oxide, total hydrocarbons, sulfur dioxide, and nitrogen dioxide.     

Development of an integrated sensor to measure odors

Odorous air samples collected from several sources were presented to an olfactometer, an electronic nose, a hydrogen sulfide (H(2)S) detector and an ammonia (NH(3)) detector. The olfactometry measurements were used as the expected values while measurements from the other instrumentation values became input variables. Five hypotheses were established to relate the input variables and the expected values. Both linear regression and artificial neural network analyses were used to test the hypotheses. Principal component analysis was utilized to reduce the dimensionality of the electronic nose measurements from 33 to 3 without significant loss of information. The electronic nose or the H(2)S detector can individually predict odor concentration measurements with similar accuracy (R (2) = 0.46 and 0.50, respectively). Although the NH(3) detector alone has a very poor relationship with odor concentration measurements, combining the H(2)S and NH(3) detectors can predict odor concentrations more accurately (R (2) = 0.58) than either individual instrument. Data from the integration of the electronic nose, H(2)S, and NH(3) detectors produce the best prediction of odor concentrations (R (2) = 0.75). With this accuracy, odor concentration measurements can be confidently represented by integrating an electronic nose, and H(2)S and NH(3) detectors.     

Could curcumin ameliorate titanium dioxide nanoparticles effect on the heart? A histopathological,immunohistochemical, and genotoxic study

Environmental Science and Pollution Research - The evaluation of the toxicological effects of titanium dioxide nanoparticles (TiO2NPs) is increasingly important due to their growing occupational...     

Systematic development of an artificial neural network model for real-time prediction of ground-level ozone in Edmonton, Alberta, Canada

Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O3) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O3 events. The models are used to determine the meteorological conditions and precursors that most affect O3 concentrations. O3 time-series effects and the efficacy of the systematic approach are also assessed. The developed models showed good predictive success, with coefficient of multiple determination values ranging from 0.75 to 0.94 for forecasts up to 2 hr in advance. The inputs most important for O3 prediction were temperature and concentrations of nitric oxide, total hydrocarbons, sulfur dioxide, and nitrogen dioxide.     

Nano-sized Prussian blue immobilized costless agro-industrial waste for the removal of cesium-137 ions

Environmental Science and Pollution Research - For human health and safety, it is of great importance to develop innovative materials with a vast capacity for powerful removal of radioactive ions...     

Impact of petroleum industry on goats in Saudi Arabia: heavy metal accumulation,oxidative stress,and tissue injury

Environmental Science and Pollution Research - Heavy metals (HMs) constitute a group of persistent toxic pollutants, and the petroleum industry is one of the sources of these metals. This study...     

Efficacy and safety of doravirine in treatment-naive HIV-1-infected adults: a systematic review and meta-analysis

Environmental Science and Pollution Research - This research aims to study the safety and efficacy of doravirine in the treatment of HIV-1 (human immunodeficiency virus) patients. We conducted an...