Pesticide residues degradation strategies in soil and water: a review

Singh, Drishtant; Kumari, Arpna; Sharma, G.; Rajput, Sneh; Arora, Saroj; Kaur, Rajinder

doi:10.1007/s13762-021-03696-2

Cited by 29 publications

(8 citation statements)

References 205 publications

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“…The specificity of pesticide effects on the bacterial and fungal abundance within each soil could possibly be associated with differences in soil physicochemical properties. These properties are known to influence both components in this interaction, the soil microbiota 68 and pesticide behavior 69 . Amongst the soil tested, the Dijon soil exhibited the highest pH, organic carbon content, and clay content.…”

Section: Discussionmentioning

confidence: 99%

The effect of natural products used as pesticides on the soil microbiota: OECD 216 nitrogen transformation test fails to identify effects that were detected viaq‐PCR microbial abundance measurement

Pedrinho,

Karas,

Kanellopoulos

et al. 2024

Pest Management Science

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BACKGROUNDNatural products present an environmentally attractive alternative to synthetic pesticides which have been implicated in the off‐target effect. Currently, the assessment of pesticide toxicity on soil microorganisms relies on the OECD 216 N transformation assay (OECD stands for the Organisation Economic Co‐operation and Development, which is a key international standard‐setting organisation). We tested the hypotheses that (i) the OECD 216 assay fails to identify unacceptable effects of pesticides on soil microbiota compared to more advanced molecular and standardized tests, and (ii) the natural products tested (dihydrochalcone, isoflavone, aliphatic phenol, and spinosad) are less toxic to soil microbiota compared to a synthetic pesticide compound (3,5‐dichloraniline). We determined the following in three different soils: (i) ammonium (NH4+) and nitrate (NO3−) soil concentrations, as dictated by the OECD 216 test, and (ii) the abundance of phylogenetically (bacteria and fungi) and functionally distinct microbial groups [ammonia‐oxidizing archaea (AOA) and bacteria (AOB)] using quantitative polymerase chain reaction (q‐PCR).RESULTSAll pesticides tested exhibited limited persistence, with spinosad demonstrating the highest persistence. None of the pesticides tested showed clear dose‐dependent effects on NH4+ and NO3− levels and the observed effects were <25% of the control, suggesting no unacceptable impacts on soil microorganisms. In contrast, q‐PCR measurements revealed (i) distinct negative effects on the abundance of total bacteria and fungi, which were though limited to one of the studied soils, and (ii) a significant reduction in the abundance of both AOA and AOB across soils. This reduction was attributed to both natural products and 3,5‐dichloraniline.CONCLUSIONOur findings strongly advocate for a revision of the current regulatory framework regarding the toxicity of pesticides to soil microbiota, which should integrate advanced and well‐standardized tools. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Section: Discussionmentioning

confidence: 99%

The effect of natural products used as pesticides on the soil microbiota: OECD 216 nitrogen transformation test fails to identify effects that were detected viaq‐PCR microbial abundance measurement

Pedrinho,

Karas,

Kanellopoulos

et al. 2024

Pest Management Science

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“…Variations of SVM, for example, have been used alone and in conjunction with other algorithms-like PCA and fast Fourier transforms (FFT) [116] and variations of WTs [117][118][119] -to confirm the diagnosis of neuromuscular disorders, making automated detection of neuromuscular disorders through EMG analysis a very real possibility. [120][121][122] Similarly, many clever implementations of deep learning have been employed for the diagnosis of patients with neuromuscular disorders, including wavelet neural networks [123] -a modification of a standard neural network where the activation function is itself a wavelet [124] -PCA and FFT in conjunction with standard MLPs, [116] and modified feed-forward neural networks (FNN). [123] However, for the purposes of this review, our focus is on applications that allow for the identification of specific muscle activity or their intended movement.…”

Section: Electromyographymentioning

confidence: 99%

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

Putcha

Nguyen

Smith

et al. 2023

Advanced Intelligent Systems

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Advanced technologies for muscle tracking provide easy access to identify and track muscle activity, often for the purposes of therapeutic interventions. The necessity of muscle trackers arises from the acute and chronic sources that disrupt neuromuscular control, resulting in an impaired ability to perform daily activities without assistance. In the context of human–machine interfaces, muscle trackers can serve as the “sensory” component, providing real‐time information to machines, such as exoskeletons and prosthetics, that can act upon such information for therapeutic and functional aid. Recently developed devices for muscle tracking rely on combinations of sensor modalities and algorithms to extract information from biosignals track muscle activity, or even extrapolate kinematic information, including gestures. However, a number of obstacles remain to be overcome to further facilitate the practical implementation of muscle‐tracking technologies, the most notable being real‐time analysis of biosignal data and extracting kinematic information from complex movements. This review attempts to cover the mechanisms behind various sensor modalities and algorithms commonly used for muscle tracking, as well as establish the current state of applications within the field. Given its multidisciplinary nature and ability to free users from rehabilitation constraints, the field of muscle tracking holds significant promise for future study.

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“…1 Nevertheless, the pesticides used in agriculture can easily move from farms to drinking water sources and ecosystems, which causes water pollution and has direct negative impacts on human health. 2 Therefore, a rapid and sensitive method for the detection of mixed pesticide residues is essential to be developed. Nowadays, there are many analytical methods dedicated to achieving the aforementioned purposes, including mass spectroscopy, fluorescence spectroscopy, gas chromatography, high-performance liquid chromatography, 3,4 etc.…”

Section: Introductionmentioning

confidence: 99%

“…At present, large amounts of chemical pesticides are widely used worldwide to improve the yields and quality of crops in agricultural activities . Nevertheless, the pesticides used in agriculture can easily move from farms to drinking water sources and ecosystems, which causes water pollution and has direct negative impacts on human health . Therefore, a rapid and sensitive method for the detection of mixed pesticide residues is essential to be developed.…”

Section: Introductionmentioning

confidence: 99%

MoS₂/Au/Ag Nanostructures for Ratiometric Surface-Enhanced Raman Scattering Determination of Pesticide Residues

Sun

et al. 2022

ACS Appl. Nano Mater.

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Surface-enhanced Raman scattering (SERS) is a promising method for rapid and sensitive detection of pesticide residues. Herein, a three-dimensional MoS2/Au/Ag nanostructure was developed by employing composite systems containing semiconductors, noble metal nanoparticles, and internal standard. The MoS2 nanoflowers provide abundant anchors for the loading of plasmonic nanoparticles so as to generate intense electromagnetic “hot spots,” and the Au-MBN/Ag nanostructures can increase the accuracy of SERS detection with the calibration ability of the internal standard molecules (4-mercaptobenzonitrile, 4-MBN). Therefore, the MoS2/Au/Ag nanostructures integrate the advantages of high-enhancement SERS substrates and high-accuracy ratiometric Raman detection. Moreover, the nanostructures were fabricated via a facile procedure and showed high sensitivity, accuracy, and reproducibility in SERS detection. The nanostructures also showed good linear fitting between the ratios of Raman intensity and logarithmic concentrations of analytes in ratiometric SERS analysis. In addition, thiabendazole (TBZ) in apple juice was successfully detected by using the as-prepared SERS substrates with a limit of detection down to 0.1 ppm in practical applications. To conclude, the MoS2/Au/Ag nanostructures showed excellent SERS activity in ratiometric analysis and the substrate is expected to have great potential for the quantitative detection of low-concentration pesticide residues.

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Pesticide residues degradation strategies in soil and water: a review

Cited by 29 publications

References 205 publications

The effect of natural products used as pesticides on the soil microbiota: OECD 216 nitrogen transformation test fails to identify effects that were detected viaq‐PCR microbial abundance measurement

The effect of natural products used as pesticides on the soil microbiota: OECD 216 nitrogen transformation test fails to identify effects that were detected viaq‐PCR microbial abundance measurement

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

MoS₂/Au/Ag Nanostructures for Ratiometric Surface-Enhanced Raman Scattering Determination of Pesticide Residues

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Pesticide residues degradation strategies in soil and water: a review

Cited by 29 publications

References 205 publications

The effect of natural products used as pesticides on the soil microbiota: OECD 216 nitrogen transformation test fails to identify effects that were detected viaq‐PCR microbial abundance measurement

The effect of natural products used as pesticides on the soil microbiota: OECD 216 nitrogen transformation test fails to identify effects that were detected viaq‐PCR microbial abundance measurement

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

MoS2/Au/Ag Nanostructures for Ratiometric Surface-Enhanced Raman Scattering Determination of Pesticide Residues

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Product

Resources

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MoS₂/Au/Ag Nanostructures for Ratiometric Surface-Enhanced Raman Scattering Determination of Pesticide Residues