Determination of mercury, lead, arsenic, cadmium and chromium in salt and water of Maharloo Lake, Iran, in different seasons

Background and purpose: Today, environmental pollutions are of the most important issues and hazardous in food safety around the world. Given the importance of salt and water in dietary, and extracting them from Maharloo Lake, Iran, this study aimed to investigate the concentrations of heavy metals, as environmental pollutions, in salt and water of this lake at different seasons. Materials and methods: In order to monitor heavy metals, including chromium (Cr), mercury (Hg), arsenic (As), cadmium (Cd) and lead (Pb), water and salt samplings were done in each season from August 2009 to May 2010. Using atomic absorption, the concentration of the mentioned metals was determined after digesting the samples. Results: The order of concentration of detected metals in different seasons in salt was Cr > As > Cd > Pb > Hg, and in water was Cr > As > Hg > Cd > Pb. In salt, the highest concentrations of Cr, Cd, Pb, and As were detected in spring and of Hg was seen in summer. Also in water, the highest concentrations of As and Cd were detected in spring, of Cr and Hg in summer, and of Pb in autumn. Conclusion: In summary, it could be concluded that the changes in the concentration of metals during different seasons was caused by the changes in the inlet water and human activities, especially agricultures. In addition, compared to Iranian standard, determined concentrations of Pb and Hg in salt samples were lower

Modeling Relationships between Surface Water Quality and Landscape Metrics Using the Adaptive Neuro-Fuzzy Inference System, A Case Study in Mazandaran Province

Landscape indices can be used as an approach for predicting water quality changes to monitor non-point source pollution. In the present study, the data collected over the period from 2012 to 2013 from 81 water quality stations along the rivers flowing in Mazandaran Province were analyzed. Upstream boundries were drawn and landscape metrics were extracted for each of the sub-watersheds at class and landscape levels. Principal component analysis was used to single out the relevant water quality parameters and forward linear regression was employed to determine the optimal metrics for the description of each parameter. The first five components were able to describe 96.61% of the variation in water quality in Mazandaran Province. Adaptive Neuro-fuzzy Inference System (ANFIS) and multiple linear regression were used to model the relationship between landscape metrics and water quality parameters. The results indicate that multiple regression was able to predict SAR, TDS, pH, NO3‒, and PO43‒ in the test step, with R2 values equal to 0.81, 0.56, 0.73, 0.44. and 0.63, respectively. The corresponding R2 value of ANFIS in the test step were 0.82, 0.79, 0.82, 0.31, and 0.36, respectively. Clearly, ANFIS exhibited a better performance in each case than did the linear regression model. This indicates a nonlinear relationship between the water quality parameters and landscape metrics. Since different land cover/uses have considerable impacts on both the outflow water quality and the available and dissolved pollutants in rivers, the method can be reasonably used for regional planning and environmental impact assessment in development projects in the region

Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals

The gut microbiome is shaped by diet and influences host metabolism; however, these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of 1,203 gut microbiomes from 1,098 individuals enrolled in the Personalised Responses to Dietary Composition Trial (PREDICT 1) study, whose detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements were available. We found many significant associations between microbes and specific nutrients, foods, food groups and general dietary indices, which were driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across external publicly available cohorts and in agreement with circulating blood metabolites that are indicators of cardiovascular disease risk. While some microbes, such as Prevotella copri and Blastocystis spp., were indicators of favorable postprandial glucose metabolism, overall microbiome composition was predictive for a large panel of cardiometabolic blood markers including fasting and postprandial glycemic, lipemic and inflammatory indices. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favorable cardiometabolic and postprandial markers, indicating that our large-scale resource can potentially stratify the gut microbiome into generalizable health levels in individuals without clinically manifest disease