Phytoremediation of formaldehyde from indoor environment by ornamental plants: An approach to promote occupants health

Background: Formaldehyde is a common hazardous indoor air pollutant which recently raised public concerns due to its well-known carcinogenic effects on human. The aim of this study was to investigate a potted plant-soil system ability in formaldehyde removal from a poor ventilated indoor air to promote dwellers health. Methods: For this purpose, we used one of the common interior plants from the fern species (Nephrolepis obliterata), inside a Plexiglas chamber under controlled environment. Entire plant removal efficiency and potted soil/roots contribution were determined by continuously introducing different formaldehyde vapor concentrations to the chamber (0.6–11 mg/m3) each over a 48-h period. Sampling was conducted from inlet and outlet of the chamber every morning and evening over the study period, and the average of each stage was reported. Results: The results showed that the N. obliterata plant efficiently removed formaldehyde from the polluted air by 90%–100%, depending on the inlet concentrations, in a long time exposure. The contribution of the soil and roots for formaldehyde elimination was 26%. Evaluation of the plant growing characteristics showed that the fumigation did not affect the chlorophyll content, carotenoid, and average height of the plant; however, a decrease in the plant water content was observed. Conclusions: According to the results of this study, phytoremediation of volatile organic compound-contaminated indoor air by the ornamental potted plants is an effective method which can be economically applicable in buildings. The fern species tested here had high potential to improve interior environments where formaldehyde emission is a health concern

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

Abstract With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control

Maternal exposure to benzophenone derivatives and their impacts on offspring's birth outcomes in a Middle Eastern population

Abstract Widespread use of benzophenones (BPs), a group of environmental phenolic compounds, is suspected of interfering with human health. The association of prenatal exposure to benzophenone derivatives with birth outcomes including birth weight and length, head, arm and thoracic circumference, abnormalities, corpulence index and anterior fontanelle diameter (AFD) was investigated. Mother-infant pairs of 166 within PERSIAN cohort population in Isfahan, Iran, in the 1st and 3rd trimesters of pregnancy were assessed. Four common benzophenone metabolites including 2,4-dihydroxy benzophenone (BP-1), 2-hydroxy-4-methoxy benzophenone (BP-3), 4-hydroxy benzophenone (4-OH-BP) and 2,2′-dihydroxy-4-methoxy benzophenone (BP-8) were measured in maternal urine samples. The median concentration of 4-OH-BP, BP-3, BP-1 and BP-8 were 3.15, 16.98, 9.95 and 1.04 µg/g Cr, respectively. In the 1st trimester, 4-OH-BP showed a significant correlation with AFD in total infants, decreasing 0.034 cm AFD per a log unit increase of 4-OH-BP. Within the male neonates, 4-OH-BP in the 1st and BP-8 in the 3rd trimester were significantly associated with head circumference and AFD increase, respectively. Among female neonates in the 3rd trimester, increasing 4-OH-BP and BP-3 concentration was correlated with a decrease in birth weight and AFD, respectively. This study demonstrated that all the target BP derivatives can influence normal fetal growth at any age of the pregnancy, nevertheless, to support these findings further studies are needed in a large and different group population