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Related Concept Videos

Aromatic Compounds: Overview01:25

Aromatic Compounds: Overview

15.0K
In general, the term ‘aromatic’ indicates a pleasant smell or fragrance from fresh flowers, freshly prepared coffee, etc. In the early history of organic chemistry, many benzene derivatives were isolated from the pleasant odor oils of the plants. For example, vanillin was isolated from the oil of vanilla, methyl salicylate from the oil of wintergreen, and cinnamaldehyde from the oil of cinnamon. They all had a pleasant odor; hence the name aromatic was given.
In 1825, Faraday isolated...
15.0K
Structure of Benzene: Kekulé Model01:07

Structure of Benzene: Kekulé Model

12.6K
In 1865, August Kekule suggested the structure of benzene according to the structural theory of organic chemistry based on the three assertions—formula of benzene is C6H6, all the hydrogens of benzene are equivalent, and each carbon must have four bonds due to its tetravalency.
He proposed that benzene has a cyclic structure of six carbon atoms attached to one hydrogen atom each, with three alternating pi bonds.
12.6K
Benzene to 1,4-Cyclohexadiene: Birch Reduction Mechanism01:18

Benzene to 1,4-Cyclohexadiene: Birch Reduction Mechanism

2.7K
Birch reduction uses solvated electrons as reducing agents. The reaction converts benzene to 1,4-cyclohexadiene. The reaction proceeds by the transfer of a single electron to the ring to form a benzene radical anion. This anion is highly basic—it abstracts a proton from the alcohol to form a cyclohexadienyl radical. Another single electron transfer gives the cyclohexadienyl anion. A proton transfer from the alcohol forms 1,4-cyclohexadiene. Since this reduction occurs via radical anion...
2.7K
NMR Spectroscopy of Benzene Derivatives01:37

NMR Spectroscopy of Benzene Derivatives

11.6K
Simple unsubstituted benzene has six aromatic protons, all chemically equivalent. Therefore, benzene exhibits only a singlet peak at δ 7.3 ppm in the 1H NMR spectrum. The observed shift is far downfield because the aromatic ring current strongly deshields the protons. Any substitution on the benzene ring makes the aromatic protons nonequivalent, and the protons split each other. The peak is, therefore, no longer a singlet and the splitting pattern and their associated coupling...
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Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids BPCA
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Uncovering Benzene Pollution Patterns Using an Interpretable, Setting-Aware Artificial Intelligence Approach.

Ivan Bešlić1, Timea Bezdan2, Gordana Jovanović3

  • 1Institute for Medical Research and Occupational Health, Ksaverska cesta 2, P.O. Box 291, 10001 Zagreb, Croatia.

Toxics
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Summary
This summary is machine-generated.

This study used artificial intelligence to identify distinct environmental settings influencing urban benzene levels. A few key settings explained most extreme benzene pollution, aiding air quality management.

Keywords:
air pollutionexplainable artificial intelligencegaseous pollutantsmachine learningmetaheuristics

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Artificial Intelligence

Background:

  • Urban air quality monitoring is crucial for public health.
  • Benzene variability is influenced by complex meteorological and emission factors.
  • Interpretable AI offers new approaches to understanding pollution dynamics.

Purpose of the Study:

  • To develop and apply an interpretable AI framework for analyzing urban benzene variability.
  • To identify distinct environmental settings governing benzene concentrations.
  • To provide a transferable model for air quality assessment and policy support.

Main Methods:

  • Analysis of a seven-year dataset of hourly pollutant concentrations (benzene, NO2, SO2, CO, O3) and meteorological variables in Zagreb, Croatia.
  • Development of multiple-ensemble decision tree models optimized with metaheuristic algorithms (Sine Cosine Algorithm).
  • Application of Shapley Additive Explanations (SHAP), PaCMAP embedding, and HDBSCAN clustering to identify environmental settings.

Main Results:

  • The optimized Extra Trees model achieved an R-squared of 0.87.
  • Seven distinct environmental settings (C0-C6) and a residual group were identified, characterizing pollution regimes.
  • Two settings (C6 and C4) were dominant for benzene extremes, linked to winter stagnation and synoptic stability, respectively.
  • Low-benzene settings (C0, C1, C3) correlated with better atmospheric mixing and oxidation.

Conclusions:

  • A limited number of environmental settings effectively explain extreme urban benzene pollution.
  • The developed AI framework is interpretable, transferable, and supports air quality policy.
  • Understanding these settings is key to targeted pollution control strategies.