Occurrence of arsenic in brown rice and its relationship to soil properties from Hainan Island, China

The acquaintance of arsenic concentrations in rice grain is vital in risk assessment. In this study, we determined the concentration of arsenic in 282 brown rice grains sampled from Hainan Island, China, and discussed its possible relationships to the considered soil properties. Arsenic concentrations in the rice grain from Hainan Island varied from 5 to 309 μg/kg, with a mean (92 μg/kg) lower than most published data from other countries/regions and the maximum contaminant level (MCL) for As_i in rice. The result of correlation analysis between grain and soil properties showed that grain As concentrations correlated significantly to soil arsenic speciation, organic matter and soil P contents and could be best predicted by humic acid bound and Fe-Mn oxides bound As fractions. Grain arsenic rises steeply at soil As concentrations lower than 3.6 mg/kg and gently at higher concentrations.     

Metabolomics identifying biomarkers of PM2.5 exposure for vulnerable population: based on a prospective cohort study

Long-term exposure to particular matter (PM), especially fine PM (< 2.5 μm in the aerodynamic diameter, PM_2.5), is associated with increased risk of cardiovascular disorders. This study aimed to evaluate the association between long-term exposure to PM_2.5/PM₁₀ and the metabolic change in the plasma. Specifically, using metabolomics, we sought to identify the biomarkers for the vulnerable subgroup to PM_2.5 exposure. A total of 78 college student volunteers were recruited into this prospective cohort study. All participants received 8 rounds of physical examinations at twice quarterly. Air purifiers were placed in 40 of 78 participants’ dormitories for 14 days. Before and after intervention, physical examinations were performed and the peripheral blood was collected. Plasma metabolomics was determined by ultra-performance liquid chromatography-mass spectrometry. During the follow-up, the average concentrations of PM_2.5 and PM₁₀ were 53 μg/m³ and 93 μg/m³, respectively. Totally, 42 and 120 differential metabolic features were detected for PM₁₀ and PM_2.5 exposure, respectively. In total, 25 differential metabolites were identified for PM_2.5 exposure, most of which were phospholipids. No distinctive metabolites were found for PM₁₀ exposure. A total of 6 differential metabolites (lysoPC (P-20:0), lysoPC (P-18:1(9z)), lysoPC (20:1), lysoPC (O-16:0), choline, and found 1,3-diphenylprop-2-en-1-one) were characterized and confirmed for sensitive individuals. Importantly, we found LysoPC (P-20:0) and LysoPC (P-18:1(9z)) changed significantly before and after air purifier intervention. Our results indicated that the phospholipid catabolism was involved in long-term PM_2.5 exposure. LysoPC (P-20:0) and LysoPC (P-18:1(9z)) may be the biomarkers of PM_2.5 exposure.