Assessing plant uptake of organic contaminants by food crops tomato, wheat, and corn through sap concentration factor.

This study investigated uptake of two organic compounds including hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and exogenous caffeine by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aestivum L.). The plants were grown in a growth chamber under recommended conditions and then were exposed to these compounds for 19 days. The uptake of the compounds was measured by sap concentration factor. The plant samples (stem transpiration stream) and solution in the exposure media were taken and analyzed by high performance liquid chromatography-tandem mass spectrometry. The plant stem samples were analyzed after a freeze-thaw centrifugation process. The average sap concentration factor for the RDX by tomato, wheat, and corn was 0.71, 0.67, and 0.65. The average sap concentration factor for the exogenous caffeine by tomato, wheat, and corn was 0.72, 0.50, and 0.34. These relatively high sap concentration factor values were expected as available predictive models offer high sap concentration factor values for moderately hydrophobic and hydrophilic compounds. The generated sap concentration factor values for the RDX and exogenous caffeine are important for improving the accuracy of previously developed machine learning models predicting the uptake and translocation of emerging contaminants.

The uptake of two organic compounds (RDX and exogenous caffeine) was examined in three crop plants (corn, wheat, and tomato). There have not been any uptake studies on exogenous caffeine and also we do not have good data for the uptake of RDX by these three crop plants. The estimated sap concentration factor from these experiments fills the gap in the data for developing predictive models for uptake of emerging contaminants. A novel rapid freeze­thaw/centrifugation extraction method followed by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was used to analyze the samples.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Deep learning models for predicting plant uptake of emerging contaminants by including the role of plant macromolecular compositions.

Deep learning models can predict uptake of emerging contaminants in plants with improved accuracy because they leverage advanced data-driven approaches to capture non-linear relationships that traditional models struggle to address. Traditional models suffer from low accuracy in predicting transpiration stream concentration factor (TSCF) and root concentration factor (RCF). This study applied deep neural networks (DNN), recurrent neural networks (RNN), and long short-term memory (LSTM) to enhance the accuracy of predictive models for TSCF and RCF. The three models used nine chemical properties and two plant root macromolecular compositions for predicting TSCF and RCF. The results indicated that deep learning models predict TSCF and RCF with improved accuracy compared to mechanistic models. The coefficient of determination (R2) for the DNN, RNN, and LSTM models in predicting TSCF was 0.62, 0.67, and 0.56, respectively. The corresponding mean squared error (MSE) on the test set for the models was 0.055, 0.035, and 0.060, respectively. The R2 for the DNN, RNN, and LSTM models in predicting RCF was 0.90, 0.91, and 0.84, respectively. The corresponding MSE for the models was 0.124, 0.071, and 0.126, respectively. The results of feature extraction using extreme gradient boosting underlined the importance of lipophilicity and root lipid fraction.

The effect of video training and intraoperative progress report on the anxiety of family caregivers waiting for relatives undergoing surgery.

Purpose: This study aimed to examine the effect of video training and intraoperative progress report on the anxiety of family caregivers awaiting relatives undergoing surgery. Methods: A three-armed randomized controlled design was used. One hundred and two participants were enrolled and randomly assigned to three groups: the video training group (n = 34), the intraoperative progress report group (n = 34), and the control group (n = 34). Interventions were performed when the relatives of the participants were undergoing surgery. The participants in the video training group received video training containing images of the operating room environment and animations related to the patient's surgical procedure, postoperative care, and possible complications from the surgery. In the intraoperative progress report group, information regarding the patient's general condition, the percentage of surgical progress, and the approximate time of the patient's transfer from the operating room were provided. The control group received routine care. A demographic data questionnaire and the Spielberger State-Trait Anxiety Inventory (STAI) was used for data collection. Results: It was found a statistically significant decrease in the state anxiety in the video training (p < 0.001) and intraoperative progress report (p < 0.001) group after the intervention when compared to before the intervention. It was found no significant difference among the study groups in terms of the level of state and trait anxiety after the intervention (p > 0.05). Conclusions: This study found that both video training and intraoperative progress report are effective in reducing the state anxiety of family caregivers awaiting relatives undergoing surgery.

Fate and transport of per- and polyfluoroalkyl substances (PFASs) in the vadose zone.

Per- and polyfluoroalkyl substances (PFASs) are a heterogeneous group of persistent organic pollutants that have been detected in various environmental compartments around the globe. Emerging research has revealed the preferential accumulation of PFASs in shallow soil horizons, particularly at sites impacted by firefighting activities, agricultural applications, and atmospheric deposition. Once in the vadose zone, PFASs can sorb to soil, accumulate at interfaces, become volatilized, be taken up in biota, or leach to the underlying aquifer. At the same time, polyfluorinated precursor species may transform into highly recalcitrant perfluoroalkyl acids, changing their chemical identity and thus transport behavior along the way. In this review, we critically discuss the current state of the knowledge and aim to interconnect the complex processes that control the fate and transport of PFASs in the vadose zone. Furthermore, we identify key challenges and future research needs. Consequently, this review may serve as an interdisciplinary guide for the risk assessment and management of PFAS-contaminated sites.

Investigating plant uptake of organic contaminants through transpiration stream concentration factor and neural network models.

Uptake of seven organic contaminants including bisphenol A, estriol, 2,4-dinitrotoluene, N,N-diethyl-meta-toluamide (DEET), carbamazepine, acetaminophen, and lincomycin by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aestivum L.) was measured. The plants were grown in a growth chamber under recommended conditions and dosed by these chemicals for 19 days. The plant samples (stem transpiration stream) and solution in the exposure media were taken to measure transpiration stream concentration factor (TSCF). The plant samples were analyzed by a freeze-thaw centrifugation technique followed by high performance liquid chromatography-tandem mass spectrometry detection. Measured average TSCF values were used to test a neural network (NN) model previously developed for predicting plant uptake based on physicochemical properties. The results indicated that moderately hydrophobic compounds including carbamazepine and lincomycin have average TSCF values of 0.43 and 0.79, respectively. The average uptake of DEET, estriol, acetaminophen, and bisphenol A was also measured as 0.34, 0.29, 0.22, and 0.1, respectively. The 2,4-dinitrotoluene was not detected in the stem transpiration stream and it was shown to degrade in the root zone. Based on these results together with plant physiology measurements, we concluded that physicochemical properties of the chemicals did predict uptake, however, the role of other factors should be considered in the prediction of TSCF. While NN model could predict TSCF based on physicochemical properties with acceptable accuracies (mean squared error less than 0.25), the results for 2,4-dinitrotoluene and other compounds confirm the needs for considering other parameters related to both chemicals (stability) and plant species (role of lipids, lignin, and cellulose).

Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security.

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log Kow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.

A deeper look at plant uptake of environmental contaminants using intelligent approaches.

Uptake of contaminants from the groundwater is one pathway of interest, and efforts have been made to relate root exposure to transloation throughout the plant, termed the transpiration stream concentration factor (TSCF). This work utilized machine learning techniques and statistcal analysis to improve the understanding of plant uptake and translocation of emerging contaminants. Neural network (NN) was used to develop a reliable model for predicting TSCF using physicochemical properties of compounds. Fuzzy logic was as a technique to examine the simultaneous impact of properties on TSCF, and interactions between compound properties. The significant and effective compound properties were determined using stepwise and forward regression as two widely used statiscal techniques. Clustering was used for detecting the hidden structures in the plant uptake data set. The NN predicted the TSCF with improved accuracy compared to mechanistic models. We also delivered new insight to compound properteis and their importance in transmembrane migration. The sensitivity analysis indicated that log Kow, molecular weight, hydrogen bond donor, and rotatable bonds are the most important properties. The results of fuzzy logic demonstrated that the relationship between molecular weight and log Kow with TSCF are both bell-shape and sigmoidal. The employed clustering algorithms all discovered two major distinct clusters in the data set.

Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants.

Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeO2NPs) are now used in a plethora of industrial products, while cadmium (Cd) is a great environmental concern because of its toxicity to animals and humans. Up to now, the interactions between heavy metals, nanoparticles and plants have not been extensively studied. The main objectives of this study were (i) to determine the synergistic effects of Cd and CeO2NPs on the physiological parameters of Brassica and their accumulation in plant tissues and (ii) to explore the underlying physiological/phenotypical effects that drive these specific changes in plant accumulation using Artificial Neural Network (ANN) as an alternative methodology to modeling and simulating plant uptake of Ce and Cd. The combinations of three cadmium levels (0 [control] and 0.25 and 1 mg/kg of dry soil) and two CeO2NPs concentrations (0 [control] and 500 mg/kg of dry soil) were investigated. The results showed high interactions of co-existing CeO2NPs and Cd on plant uptake of these metal elements and their interactive effects on plant physiology. ANN also identified key physiological factors affecting plant uptake of co-occurring Cd and CeO2NPs. Specifically, the results showed that root fresh weight and the net photosynthesis rate are parameters governing Ce uptake in plant leaves and roots while root fresh weight and Fv/Fm ratio are parameters affecting Cd uptake in leaves and roots. Overall, ANN is a capable approach to model plant uptake of co-occurring CeO2NPs and Cd.

Green Analysis: High Throughput Analysis of Emerging Pollutants in Plant Sap by Freeze-Thaw-Centrifugal Membrane Filtration Sample Preparation-HPLC-MS/MS Analysis.

Emerging and fugitive contaminants (EFCs) released to our biosphere have caused a legacy and continuing threat to human and ecological health, contaminating air, water, and soil. Polluted media are closely linked to food security through plants, especially agricultural crops. However, measuring EFCs in plant tissues remains difficult, and high-throughput screening is a greater challenge. A novel rapid freeze-thaw/centrifugation extraction followed by high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis was developed for high-throughput quantification of 11 EFCs with diverse chemical properties, including estriol, codeine, oxazepam, 2,4-dinitrotoluene, 1,3,5-trinitroperhydro-1,3,5-triazine, bisphenol A, triclosan, caffeine, carbamazepine, lincomycin, and DEET, in three representative crops, corn, tomato, and wheat. The internal aqueous solution, i.e., sap, is liberated via a freeze/thaw cycle, and separated from macromolecules utilizing molecular weight cutoff membrane centrifugal filtration. Detection limits ranged from 0.01 µg L-1 to 2.0 µg L-1. Recoveries of spiked analytes in three species ranged from 83.7% to 109%. Developed methods can rapidly screen EFCs in agriculture crops and can assess pollutant distribution at contaminated sites and gain insight on EFCs transport in plants to assess transmembrane migration in vascular organisms. The findings contribute significantly to environmental research, food security, and human health, as it assesses the first step of potential entry into the food chain, that being transmembrane migration and plant uptake, the primary barrier between polluted waters or soils and our food.

Critical review of fouling mitigation strategies in membrane bioreactors treating water and wastewater.

The current research was an effort to critically review all approaches used for membrane fouling control in the membrane bioreactors treating water and wastewater. The first generation of antifouling methods tried to optimize operational conditions, or used chemical agents to control membrane fouling. Despite their positive impacts on the fouling mitigation, these methods did not provide a sustainable solution for the problem. Moreover, chemical agents may affect microorganisms in bioreactors and has some environmental drawbacks. The improved knowledge of membrane fouling mechanism and effective factors has directed the attention of researchers to novel methods that focus on disrupting fouling mechanism through affecting fouling causing bacteria. Employing nanomaterials, cell entrapment, biologically- and electrically-based methods are the latest efforts. The results of this review indicate that sustainable control of membrane fouling requires employing more than one single approach. Large scale application of fouling mitigation strategies should be the focus of future studies.

A Survey on Mental Health Status of Adult Population Aged 15 and above in the Province of Qom, Iran.

INTRODUCTION: This research aims to determine the mental health status of population aged 15 and over in the province of Qom in 2015. METHODS: The statistical population of this cross-sectional field survey consisted of residents of urban and rural areas of Qom in Iran. An estimated sample size of 600 people was chosen using systematic random cluster sampling. The access was provided by the contribution of Geographical Post Office of Qom city. The General Health Questionnaire-28 (GHQ-28) was used as the screening tool for mental disorders. Data analysis in the current study was carried out using the SPSS-18 computer software. RESULTS: Using GHQ traditional scoring method, 16.2% of the subjects were shown to be at risk of mental disorders (19.7% of females and 12.6% of males). Urban areas (17%) were more at risk of mental disorders compared with rural residents (6.5%). Anxiety and somatization symptoms were more frequent than depression and social dysfunction among respondents. The obtained data revealed that the prevalence of mental disorders increased with age. Such disorders were more common in females, age group of 65 and above, people living in rural areas, divorced and widowed, illiterate, retired and unemployed individuals compared with the other groups. CONCLUSION: The results of this study showed that a sixth of the people in the province were suspected to have mental disorders. Therefore, it is mandatory for the provincial public health authorities to take the needed steps to ensure that necessary requirements encompassing prevention and promotion of mental health are implemented. .

Comparison of subthreshold diode laser micropulse therapy versus conventional photocoagulation laser therapy as primary treatment of diabetic macular edema.

PURPOSE: The aim of the present study was to investigate the effect of subthreshold diode laser micropulse (SDM) in comparison with conventional laser photocoagulation in the treatment of the diabetic macular edema (DME). METHODS: Sixty-eight eyes from 68 patients with clinically significant DME were divided randomly into two equal groups. In the first group, SDM photocoagulation was employed, while conventional laser photocoagulation was performed on the eyes of the second group. Central macular thickness (CMT), central macular volume (CMV), and best corrected visual acuity (BCVA) were measured before, 2, and 4 months after intervention, and the results were compared. RESULTS: The mean CMT was 357.3 and 354.8 microns before the treatment in Groups 1 and 2, respectively (P = 0.85), and decreased significantly to 344.3 and 349.8 after 4 months, respectively (P = 0.012 and P = 0.049). The changes in the central macular thickness was statistically higher in the first group (P = 0.001). The mean CMV significantly decreased in Group 1 (P = 0.003), but it was similar to pretreatment in Group 2 after 4 months (P = 0.31). The BCVA improved significantly in Group 1 (P < 0.001), but it remained unchanged in Group 2 (P = 0.38). CONCLUSIONS: In this study, SDM was more effective than conventional laser photocoagulation in reducing CMT and CMV and improving visual acuity in patients with DME.

Retraction Note: Estimation of flood environmental effects using flood zone mapping techniques in Halilrood Kerman, Iran.

[This retracts the article DOI: 10.1186/s40201-014-0153-z.].

Performance evaluation and modeling of a submerged membrane bioreactor treating combined municipal and industrial wastewater using radial basis function artificial neural networks.

Treatment process models are efficient tools to assure proper operation and better control of wastewater treatment systems. The current research was an effort to evaluate performance of a submerged membrane bioreactor (SMBR) treating combined municipal and industrial wastewater and to simulate effluent quality parameters of the SMBR using a radial basis function artificial neural network (RBFANN). The results showed that the treatment efficiencies increase and hydraulic retention time (HRT) decreases for combined wastewater compared with municipal and industrial wastewaters. The BOD, COD, [Formula: see text] and total phosphorous (TP) removal efficiencies for combined wastewater at HRT of 7 hours were 96.9%, 96%, 96.7% and 92%, respectively. As desirable criteria for treating wastewater, the TBOD/TP ratio increased, the BOD and COD concentrations decreased to 700 and 1000 mg/L, respectively and the BOD/COD ratio was about 0.5 for combined wastewater. The training procedures of the RBFANN models were successful for all predicted components. The train and test models showed an almost perfect match between the experimental and predicted values of effluent BOD, COD, [Formula: see text] and TP. The coefficient of determination (R(2)) values were higher than 0.98 and root mean squared error (RMSE) values did not exceed 7% for train and test models.

Estimation of flood environmental effects using flood zone mapping techniques in Halilrood Kerman, Iran.

High flood occurrences with large environmental damages have a growing trend in Iran. Dynamic movements of water during a flood cause different environmental damages in geographical areas with different characteristics such as topographic conditions. In general, environmental effects and damages caused by a flood in an area can be investigated from different points of view. The current essay is aiming at detecting environmental effects of flood occurrences in Halilrood catchment area of Kerman province in Iran using flood zone mapping techniques. The intended flood zone map was introduced in four steps. Steps 1 to 3 pave the way to calculate and estimate flood zone map in the understudy area while step 4 determines the estimation of environmental effects of flood occurrence. Based on our studies, wide range of accuracy for estimating the environmental effects of flood occurrence was introduced by using of flood zone mapping techniques. Moreover, it was identified that the existence of Jiroft dam in the study area can decrease flood zone from 260 hectares to 225 hectares and also it can decrease 20% of flood peak intensity. As a result, 14% of flood zone in the study area can be saved environmentally.