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Результаты 1611-1620 из 7,995
Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models Полный текст
2021
Bhagat, Suraj Kumar | Tiyasha, Tiyasha | Awadh, Salih Muhammad | Tung, Tran Minh | Jawad, Ali H. | Yaseen, Zaher Mundher
Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9–5 parameters without losing their learned information over the models’ training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost–SVM, XGBoost–ANN, XGBoost–Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost–Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.
Показать больше [+] Меньше [-]Hexafluoropropylene oxide dimer acid (HFPO-DA) induced developmental cardiotoxicity and hepatotoxicity in hatchling chickens: Roles of peroxisome proliferator activated receptor alpha Полный текст
2021
Xu, Xiaohui | Ni, Hao | Guo, Yajie | Lin, Yongfeng | Ji, Jing | Jin, Congying | Yuan, Fuchong | Feng, Mengxiao | Ji, Na | Zheng, Yuxin | Jiang, Qixiao
Hexafluoropropylene oxide dimer acid (HFPO-DA) is a perfluorooctanoic acid (PFOA) substitute. In the current study, potential developmental cardiotoxicity and hepatotoxicity following HFPO-DA exposure in chicken embryo has been investigated, focusing on the roles of peroxisome proliferator activated receptor alpha (PPARα), the major molecular target in PFOA-induced toxicities. HFPO-DA was exposed to fertile chicken eggs via air cell injection, morphology and function of the target organs (heart and liver) in hatchlings were investigated with histopathology and electrocardiography, and the serum levels of HFPO-DA had been measured with quadrupole-time of flight liquid chromatograph-mass spectrometer (Q-TOF LC/MS). Additionally, lentivirus-mediated in ovo PPARα silencing was used to assess the roles of PPARα in HFPO-DA induced developmental toxicities. The results indicated that developmental exposure to HFPO-DA induced developmental cardiotoxicity, including thinned right ventricular wall and elevated heart rates, similar to those observed with PFOA exposure, as well as developmental hepatotoxicity in the form of steatosis. Silencing of PPARα alleviated such effects, suggesting participation of PPARα in HFPO-DA induced developmental toxicities in chicken embryo. Moreover, enhanced expression of PPARα downstream genes, cluster of differentiation 36 (CD36) and enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (EHHADH), were observed in HFPO-DA exposed animal heart tissues, which can be abolished by PPARα silencing. On the other hand, liver-type fatty acid binding protein (L-FABP) and CD36 expression were effectively enhanced in exposed liver tissues, but not EHHADH, suggesting differential mechanism of toxicity in heart and liver tissues. In summary, developmental exposure to HFPO-DA induced developmental cardiotoxicity and hepatotoxicity in hatchling chickens similar to PFOA, and PPARα still participates in such toxicities, with some differential downstream gene regulations in different organs. Further investigation on HFPO-DA-induced developmental toxicities is guaranteed.
Показать больше [+] Меньше [-]Efficient utilization of Iris pseudacorus biomass for nitrogen removal in constructed wetlands: Combining alkali treatment Полный текст
2021
Gu, Xushun | He, Shengbing | Huang, Jungchen
Aquatic plant biomass like Iris pseudacorus can be used as electron donor to improve denitrification performance in subsurface constructed wetlands. However, the phenomenon that the nitrogen removal rate declined in the terminal stage restricted the utilization of litters. In terms of this problem, this study investigated the performance of the used biomass through alkali treatment on nitrogen removal and analyzed the effect of alkali treatment on the component and structure of biomass and microbial community. The results showed that the alkali-treated biomass could further enhance the nitrogen removal by nearly 15% compared with used ones. The significant damage of cell walls and compact fibers containing cellulose and lignin through alkali treatment mainly resulted in the improvement of carbon release and nitrogen removal. With the addition of alkali-treated biomass, the richness index of microbes was higher compared with other biomass materials. Furthermore, the abundance of denitrification related genera increased and the abundance of genera for nitrification was maintained. Based on these finds, a mode of a more efficient Iris pseudacorus self-consumed subsurface flow constructed wetlands was designed. In this mode, the effluent total nitrogen could be stabilized below 5 mg L⁻¹ for nine months and the weight of litters could be further cut down by 75%. These findings would contribute to efficient utilization of plant biomass for nitrogen removal enhancement and final residue reduction in the wetlands.
Показать больше [+] Меньше [-]Electrocatalytic inactivation of antibiotic resistant bacteria and control of antibiotic resistance dissemination risk Полный текст
2021
Liu, Haiyang | Hua, Xiuyi | Zhang, Ya-nan | Zhang, Tingting | Qu, Jiao | Nolte, Tom M. | Chen, Guangchao | Dong, Deming
Antibiotic resistance in environmental matrices becomes urgently significant for public health and has been considered as an emerging environmental contaminant. In this work, the ampicillin-resistant Escherichia coli (AR E. coli) and corresponding resistance genes (blaTEM₋₁) were effectively eliminated by the electrocatalytic process, and the dissemination risk of antibiotic resistance was also investigated. All the AR E. coli (∼8 log) was inactivated and 8.17 log blaTEM₋₁ was degraded by the carbon nanotubes/agarose/titanium (CNTs/AG/Ti) electrode within 30 min. AR E. coli was inactivated mainly attributing to the damage of cell membrane, which was attacked by reactive oxygen species and subsequent leakage of intracellular cytoplasm. The blaTEM₋₁ was degraded owing to the strand breaking in the process of electrocatalytic degradation. Furthermore, the dissemination risk of antibiotic resistance was effectively controlled after being electrocatalytic treatment. This study provided an effective electrocatalytic technology for the inactivation of antibiotic resistant bacteria and control of antibiotic resistance dissemination risk in the aqueous environment.
Показать больше [+] Меньше [-]Two novelty learning models developed based on deep cascade forest to address the environmental imbalanced issues: A case study of drinking water quality prediction Полный текст
2021
Chen, Xingguo | Liu, Houtao | Liu, Fengrui | Huang, Tian | Shen, Ruqin | Deng, Yongfeng | Chen, Da
Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction. Although some traditional models were improved by introducing the cost matrix, little is known about whether cost matrix could enhance the prediction performance of DCF. Additionally, feature extraction is also an important way to potentially improve the model's ability to predict the imbalanced data. Here, we developed two novelty learning models based on DCF: cost-sensitive DCF (CS-DCF) and DCF that combines unsupervised learning models and greedy methods (USM-DCF-G). Subsequently, CS-DCF and USM-DCF-G were successfully verified by an imbalanced drinking water quality data set. Our data presented both CS-DCF and USM-DCF-G show better prediction performance than that of DCF alone did. In particular, USM-DCF-G shows the best performance with the highest F1-score (95.12 ± 2.56%), after feature extraction and selection by using unsupervised learning models and greedy methods. Thus, the two learning models, especially USM-DCF-G, were promising learning models to address environmental imbalanced issues and accurately predict environmental quality.
Показать больше [+] Меньше [-]Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach Полный текст
2021
Chen, Chu-Chih | Wang, Yin-Ru | Yeh, Hung-Yi | Lin, Tang-Huang | Huang, Chun-Sheng | Wu, Chang-Fu
Fine particulate matter (PM₂.₅) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM₂.₅ measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM₂.₅ estimates in a study area. In this study, we used AOD estimates of surface PM₂.₅ together with meteorological and land use variables to estimate monthly PM₂.₅ concentrations at a spatial resolution of 3 km² over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R² of 0.98 with a root mean square error (RMSE) of 1.40 μg/m3. The leave-one-out cross-validation (LOOCV) R² with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m3, whereas the R² and RMSE obtained by using the pure random forest approach produced R² and RMSE values of 0.74 and 4.60 μg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM₂.₅ estimates over the entire island, which has complex terrain in terms of land use and topography.
Показать больше [+] Меньше [-]Effects of the technical ingredient clomazone and its two formulated products on aquatic macrophytes Полный текст
2021
Stevanović, Marija | Brkić, Dragica | Tomić, Tanja | Mihajlović, Varja | Đorđević, Tijana | Gašić, Slavica
One active ingredient can be a component of different types of formulations of pesticides, while the toxicity of its formulations may vary depending on various constituents used in the mixture. The present study focuses on evaluating the effects of the active ingredient clomazone and its formulations (Rampa® EC and GAT Cenit 36 CS, both containing 360 g a.i./l of clomazone) on non-target aquatic macrophytes. The two formulation types differ in their active ingredient release and presumed environmental impact. In order to cover different ecological traits, two species of aquatic macrophytes – the floating monocot Lemna minor and the rooted dicot Myriophyllum aquaticum, were used as test models. The results of this study revealed differences in the sensitivity of tested plants to clomazone. Based on the most sensitive parameters, M. aquaticum proved to be more sensitive than L. minor to the technical ingredient and both formulations. The species sensitivity distribution (SSD) approach that was tried out in an attempt to create a higher tier step of risk assessment of clomazone for primary producers indicates that tests on rooted macrophytes can add value in risk assessment of plant protection products. The capsule formulation of clomazone was less toxic than the emulsion for L. minor, but more toxic for M. aquaticum. The most toxic for L. minor was the emulsifiable concentrate formulation Rampa® EC, followed by technical clomazone (EC₅₀ 33.3 and 54.0 mg a.i./l, respectively), while the aqueous capsule suspension formulation GAT Cenit 36 CS did not cause adverse effects. On the other hand, the most toxic for M. aquaticum was the formulation GAT Cenit 36 CS, followed by technical clomazone and the formulation Rampa® EC, demonstrating a greater effect of the capsule formulation.
Показать больше [+] Меньше [-]A community-based study on associations between PM2.5 and PM1 exposure and heart rate variability using wearable low-cost sensing devices Полный текст
2021
Tsou, Ming-Chien Mark | Lung, Shih-Chun Candice | Shen, Yu-Sheng | Liu, Chun-Hu | Hsieh, Yu-Hui | Chen, Nathan | Hwang, Jing-Shiang
Few studies have investigated the effect of personal PM₂.₅ and PM₁ exposures on heart rate variability (HRV) for a community-based population, especially in Asia. This study evaluates the effects of personal PM₂.₅ and PM₁ exposure on HRV during two seasons for 35 healthy adults living in an urban community in Taiwan. The low-cost sensing (LCS) devices were used to monitor the PM levels and HRV, respectively, for two consecutive days. The mean PM₂.₅ and PM₁ concentrations were 13.7 ± 11.4 and 12.7 ± 10.5 μg/m³ (mean ± standard deviation), respectively. Incense burning was the source that contributed most to the PM₂.₅ and PM₁ concentrations, around 9.2 μg/m³, while environmental tobacco smoke exposure had the greatest impacts on HRV indices, being associated with the highest decrease of 20.2% for high-frequency power (HF). The results indicate that an increase in PM₂.₅ concentrations of one interquartile range (8.7 μg/m³) was associated with a change of −1.92% in HF and 1.60% in ratio of LF to HF power (LF/HF). Impacts on HRV for PM₁ were similar to those for PM₂.₅. An increase in PM₁ concentrations of one interquartile range (8.7 μg/m³) was associated with a change of −0.645% in SDNN, −1.82% in HF and 1.54% in LF/HF. Stronger immediate and lag effects of PM₂.₅ exposure on HRV were observed in overweight/obese subjects (body mass index (BMI) ≥24 kg/m²) compared to the normal-weight group (BMI <24 kg/m²). These results indicate that even low-level PM concentrations can still cause changes in HRV, especially for the overweight/obese population.
Показать больше [+] Меньше [-]Arsenic removal by iron-oxidizing bacteria in a fixed-bed coconut husk column: Experimental study and numerical modeling Полный текст
2021
Abdur Razzak, | Shafiquzzaman, Md | Haider, Husnain | Alresheedi, Mohammad
Groundwater in several parts of the world, particularly in developing countries, has been contaminated with Arsenic (As). In search of low-cost As removal methods, the biological oxidation of As(III) and Fe(II) followed by co-precipitation requires detailed investigation for the practical implementation of this technology. The present study investigated the biological oxidation of As(III) and Fe(II) through a combination of laboratory experiments and reactive transport modeling. Batch experiments were conducted to evaluate the As(III) oxidation by Fe-oxidizing bacteria, mainly Leptothrix spp. A fixed-bed down-flow biological column containing inexpensive and readily available coconut husk support media was used to evaluate the combined removal of As(III) and Fe(II) from synthetic groundwater. Oxidation and co-precipitation processes effectively reduced the concentration of As(III) from 500 μg/L to < 10 μg/L with a hydraulic retention time of 120 min. A one-dimensional reactive transport model was developed based on the microbially mediated biochemical reactions of As(III) and Fe(II). The model successfully reproduced the observed As(III) and Fe(II) removal trends in the column experiments. The modeling results showed that the top 20 cm aerobic layer of the column played a primary role in the microbial oxidation of Fe(II) and As(III). The model calibration identified the hydraulic residence time as the most significant process parameter for the removal of Fe and As in the column. The developed model can effectively predict As concentrations in the effluent and provide design guidelines for the biological treatment of As. The model would also be useful for understanding the biogeochemical behavior of Fe and As under aerobic conditions.
Показать больше [+] Меньше [-]Oxidative stress, metallomics and blood toxicity after subacute low-level lead exposure in Wistar rats: Benchmark dose analyses Полный текст
2021
Javorac, Dragana | Antonijević, Biljana | Anđelković, Milena | Repić, Aleksandra | Bulat, Petar | Djordjevic, Aleksandra Buha | Baralić, Katarina | Đukić-Ćosić, Danijela | Antonić, Tamara | Bulat, Zorica
Exposure to lead (Pb) is still rising concern worldwide, having in mind that even low-dose exposure can induce various harmful effects. Thus, in-depth knowledge of the targets of Pb toxicity and corresponding mechanisms is essential. In the presented study, the six groups (male Wistar rats, n = 6) received 0.1; 0.5; 1; 3; 7; 15 mg Pb/kg body weight/day for 28 days, each day by oral gavage, while the control group received distilled water only. All animals were sacrificed 24 h after the treatment, and blood was collected for the analysis of hematological, biochemical, oxidative status and essential elements levels. An external and internal dose-response relationship was performed using PROASTweb 70.1 software. The results showed that low doses of Pb affect hematological parameters and lipid profile after 28 days. The possible mechanisms at examined Pb dose levels were a decrease in SOD, O₂•⁻ and Cu and an increase in Zn levels. The dose-dependent nature of changes in cholesterol, HDL cholesterol, O₂.⁻, SOD, AOPP in serum and hemoglobin, Fe, Zn, Cu in blood were obtained in this study. The most sensitive parameters that were alerted are Cu blood levels (BMDL₅: 1.4 ng/kg b.w./day) and SOD activity (BMDL₅: 0.5 μg/kg b.w./day). The presented results provide information that may be useful in further assessing the health risks of low-level Pb exposure.
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