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Chen F, Zhou B, Yang L, Zhuang J, Chen X. Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 356:124345. [PMID: 38852664 DOI: 10.1016/j.envpol.2024.124345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 05/12/2024] [Accepted: 06/06/2024] [Indexed: 06/11/2024]
Abstract
This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning algorithms with mechanism-based models (Phydrus). We initially review 28 published papers to gather data on E. coli's die-off and attachment characteristics in soil. Machine learning models, including deep learning and gradient boosting machine, are employed to predict key parameters such as the die-off rate of E. coli and first-order attachment coefficient in soil. Then, Phydrus was used to simulate E. coli transport and survival in 23692 subregions in China. The model considered regional differences in E. coli residual risk and transport, influenced by soil properties, soil depths, precipitation, seasonal variations, and regional disparities. The findings indicate higher residual risks in regions such as the Northeast China, Eastern Qinghai-Tibet Plateau, and pronounced transport risks in the fringe of the Sichuan Basin fringe, the Loess Plateau, the North China Plain, the Northeast Plain, the Shigatse Basin, and the Shangri-La region. The study also demonstrates a significant reduction in both residual and transport risks one month after manure application, highlighting the importance of timing manure application and implementing region-specific standards. This research contributes to the broader understanding of pathogen behavior in agricultural soils and offers practical guidelines for managing the risks associated with manure use. This study's comprehensive method offers a potentially valuable tool for evaluating microbial contaminants in agricultural soils across the globe.
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Affiliation(s)
- Fengxian Chen
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
| | - Bin Zhou
- Chair of model-based environmental exposure science, Faculty of Medicine, University of Augsburg, Augsburg 86159, Germany
| | - Liqiong Yang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
| | - Jie Zhuang
- Department of Biosystems Engineering and Soil Science, Institute for a Secure and Sustainable Environment, The University of Tennessee, Knoxville, TN 37996, United States
| | - Xijuan Chen
- Sino-Spain Joint Laboratory for Agricultural Environment Emerging Contaminants of Zhejiang Province, College of Environmental and Resource Sciences, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China.
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Li X, Tian Z, Kong Y, Cao X, Liu N, Zhang T, Xiao Z, Wang Z. The suspension stability of nanoplastics in aquatic environments revealed using meta-analysis and machine learning. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134426. [PMID: 38688220 DOI: 10.1016/j.jhazmat.2024.134426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/02/2024]
Abstract
Nanoplastics (NPs) aggregation determines their bioavailability and risks in natural aquatic environments, which is driven by multiple environmental and polymer factors. The back propagation artificial neural network (BP-ANN) model in machine learning (R2 = 0.814) can fit the complex NPs aggregation, and the feature importance was in the order of surface charge of NPs > dissolved organic matter (DOM) > functional group of NPs > ionic strength and pH > concentration of NPs. Meta-analysis results specified low surface charge (0 ≤ |ζ| < 10 mV) of NPs, low concentration (< 1 mg/L) and low molecular weight (< 10 kg/mol) of DOM, NPs with amino groups, high ionic strength (IS > 700 mM) and acidic solution, and high concentration (≥ 20 mg/L) of NPs with smaller size (< 100 nm) contribute to NPs aggregation, which is consistent with the prediction in machine learning. Feature interaction synergistically (e.g., DOM and pH) or antagonistically (e.g., DOM and cation potential) changed NPs aggregation. Therefore, NPs were predicted to aggregate in the dry period and estuary of Poyang Lake. Research on aggregation of NPs with different particle size,shapes, and functional groups, heteroaggregation of NPs with coexisting particles and aging effects should be strengthened in the future. This study supports better assessments of the NPs fate and risks in environments.
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Affiliation(s)
- Xiaona Li
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China
| | - Zheng Tian
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China
| | - Yu Kong
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China
| | - Xuesong Cao
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China
| | - Ning Liu
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China
| | - Tongze Zhang
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China
| | - Zhenggao Xiao
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi 214122, China; Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, Suzhou University of Science and Technology, Suzhou 215009, China.
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Gomez-Flores A, Jin S, Nam H, Cai L, Song S, Kim H. Attachment of various-shaped polystyrene microplastics to silica surfaces: Experimental validation of the equivalent Cassini oval extended DLVO model. JOURNAL OF HAZARDOUS MATERIALS 2024; 470:134146. [PMID: 38583206 DOI: 10.1016/j.jhazmat.2024.134146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024]
Abstract
Microplastics (MPs) vary in shape and surface characteristics in the environment. The attachment of MPs to surfaces can be studied using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. However, this theory does not account for the shape MPs. Therefore, we investigated the attachment of spherical, pear-shaped, and peanut-shaped polystyrene MPs to quartz sand in NaCl and CaCl2 solutions using batch tests. The attachment of MPs to quartz sand was quantified using the attachment efficiency (alpha). Subsequently, alpha behaviors were interpreted using energy barriers (EBs) and interaction minima obtained from extended DLVO calculations, which were performed using an equivalent sphere model (ESM) and a newly developed equivalent Cassini model (ECM) to account for the shape of the MPs. The ESM failed to interpret the alpha behavior of the three MP shapes because it predicted high EBs and shallow minima. The alpha values for spherical MPs (0.62-1.00 in NaCl and 0.48-0.96 in CaCl2) were higher than those for pear- and peanut-shaped MPs (0.01-0.63 in NaCl and 0.02-0.46 in CaCl2, and 0.01-0.59 in NaCl and 0.02-0.40 in CaCl2, respectively). Conversely, the ECM could interpret the alpha behavior of pear- and peanut-shaped MPs either by changes in EBs or interaction minima as a function of orientation angles and electrolyte ionic strength. Therefore, the particle shape must be considered to improve the attachment analyses.
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Affiliation(s)
- Allan Gomez-Flores
- Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Suheyon Jin
- Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyojeong Nam
- Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Li Cai
- College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China
| | - Shaoxian Song
- School of Resources and Environmental Engineering, Wuhan University of Technology, Wenzhi Street 34, Wuhan, Hubei 430070, China
| | - Hyunjung Kim
- Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea.
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Shah G, Bhatt U, Singh H, Kumar D, Sharma J, Strasser RJ, Soni V. Ecotoxicological assessment of cigarette butts on morphology and photosynthetic potential of Azolla pinnata. BMC PLANT BIOLOGY 2024; 24:300. [PMID: 38637728 PMCID: PMC11061998 DOI: 10.1186/s12870-024-04991-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 04/05/2024] [Indexed: 04/20/2024]
Abstract
Cigarette butts (CBs) have become the most ubiquitous form of anthropogenic litter globally. CBs contain various hazardous chemicals that persist in the environment for longer period. These substances are susceptible to leaching into the environment through waterways. The recent study was aimed to evaluate the effects of disposed CBs on the growth and development of Azolla pinnata, an aquatic plant. It was found that after a span of 6 days, the root length, surface area, number of fronds, and photosynthetic efficacy of plant were considerably diminished on the exposure of CBs (concentrations 0 to 40). The exposure of CBs led to a decrease in the FM, FV/F0, and φP0, in contrast, the φD0 increased in response to CBs concentration. Moreover, ABS/CSm, TR0/CSm, and ET0/CSm displayed a negative correlation with CB-induced chemical stress. The performance indices were also decreased (p-value ≤ 0.05) at the highest concentration of CBs. LD50 and LD90 represent the lethal dose, obtained value for LD50 is 20.30 CBs and LD90 is 35.26 CBs through probit analysis. Our results demonstrate that the CBs cause irreversible damage of photosynthetic machinery in plants and also reflect the efficacy of chlorophyll a fluorescence analysis and JIP test for assessing the toxicity of CBs in plants.
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Affiliation(s)
- Garishma Shah
- Plant Bioenergetics and Biochemistry Lab, Mohanlal Sukhadia University, Udaipur, Rajasthan, India, 313001
| | - Upma Bhatt
- Plant Bioenergetics and Biochemistry Lab, Mohanlal Sukhadia University, Udaipur, Rajasthan, India, 313001
| | - Hanwant Singh
- Plant Bioenergetics and Biochemistry Lab, Mohanlal Sukhadia University, Udaipur, Rajasthan, India, 313001
| | - Deepak Kumar
- Plant Bioenergetics and Biochemistry Lab, Mohanlal Sukhadia University, Udaipur, Rajasthan, India, 313001
| | - Jyotshana Sharma
- Plant Bioenergetics and Biochemistry Lab, Mohanlal Sukhadia University, Udaipur, Rajasthan, India, 313001
| | - Reto J Strasser
- Plant Bioenergetics Laboratory, University of Geneva, Jussy, 1254, Geneva, Switzerland
| | - Vineet Soni
- Plant Bioenergetics and Biochemistry Lab, Mohanlal Sukhadia University, Udaipur, Rajasthan, India, 313001.
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Hicks E, Rogers NMK, Hendren CO, Kuehn MJ, Wiesner MR. Extracellular Vesicles and Bacteriophages: New Directions in Environmental Biocolloid Research. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:16728-16742. [PMID: 37898880 DOI: 10.1021/acs.est.3c05041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
There is a long-standing appreciation among environmental engineers and scientists regarding the importance of biologically derived colloidal particles and their environmental fate. This interest has been recently renewed in considering bacteriophages and extracellular vesicles, which are each poised to offer engineers unique insights into fundamental aspects of environmental microbiology and novel approaches for engineering applications, including advances in wastewater treatment and sustainable agricultural practices. Challenges persist due to our limited understanding of interactions between these nanoscale particles with unique surface properties and their local environments. This review considers these biological particles through the lens of colloid science with attention given to their environmental impact and surface properties. We discuss methods developed for the study of inert (nonbiological) particle-particle interactions and the potential to use these to advance our understanding of the environmental fate and transport of extracellular vesicles and bacteriophages.
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Affiliation(s)
- Ethan Hicks
- Department of Civil & Environmental Engineering, Duke University, Durham, North Carolina 27708, United States
- Center for the Environmental Implications of Nanotechnology, Duke University, Durham, North Carolina 27708, United States
| | - Nicholas M K Rogers
- Department of Mechanical Engineering, Porter School of Earth and Environmental Studies, Tel Aviv University, Tel Aviv 69978, Israel
| | - Christine Ogilvie Hendren
- Center for the Environmental Implications of Nanotechnology, Duke University, Durham, North Carolina 27708, United States
- Research Institute for Environment, Energy and Economics, Appalachian State University, Boone, North Carolina 28608, United States
| | - Meta J Kuehn
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina 27710, United States
| | - Mark R Wiesner
- Department of Civil & Environmental Engineering, Duke University, Durham, North Carolina 27708, United States
- Center for the Environmental Implications of Nanotechnology, Duke University, Durham, North Carolina 27708, United States
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Gomez-Flores A, Bradford SA, Hong G, Kim H. Statistical analysis, machine learning modeling, and text analytics of aggregation attachment efficiency: Mono and binary particle systems. JOURNAL OF HAZARDOUS MATERIALS 2023; 454:131482. [PMID: 37119570 DOI: 10.1016/j.jhazmat.2023.131482] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/11/2023] [Accepted: 04/22/2023] [Indexed: 05/19/2023]
Abstract
The aggregation attachment efficiency (α) is the fraction of particle-particle collisions resulting in aggregation. Despite significant research, α predictions have not accounted for the full complexity of systems due to constraints imposed by particle types, dispersed matter, water chemistry, quantification methods, and modeling. Experimental α values are often case-specific, and simplified systems are used to rule out complexity. To address these challenges, statistical analysis was performed on α databases to identify gaps in current knowledge, and machine learning (ML) was used to predict α under various particle types and conditions. Moreover, text analytics was employed to support knowledge from statistics and ML, as well as gain insight into the ideas communicated by current literature. Most studies investigated α in mono-particle systems, but binary or higher systems require more investigation. Furthermore, our work highlights that numerous variables, interactions, and mechanisms influence α behavior, making its investigation complex and difficult for both experiments and modeling. Consequently, future research should incorporate more particle types, shapes, coatings, and surface heterogeneities, and aim to address overlooked variables and conditions. Therefore, building a comprehensive α database can enable the development of more accurate empirical models for prediction.
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Affiliation(s)
- Allan Gomez-Flores
- Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Scott A Bradford
- USDA, ARS, Sustainable Agricultural Water Systems Unit, 239 Hopkins Road, Davis, CA 95616, USA
| | - Gilsang Hong
- Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyunjung Kim
- Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea.
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