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<p>Temperature inversions, by limiting mixing height and inhibiting vertical dispersion of pollutants, are one of the most important drivers of increased PM2.5 concentrations in urban areas. Despite existing studies, quantitative understanding of inversion impacts in humid subtropical climates like the southeastern U.S. remains limited. This research aims to systematically analyse temporal patterns of inversions and examine their causal relationships with PM2.5 fluctuations over the 2020–2024 period in Birmingham, Alabama a city with valley topography, high humidity, and a complex mix of industrial and traffic sources in order to fill this knowledge gap. The study developed an enhanced method integrating high-resolution surface data with radiosonde profiles to accurately detect shallow surface-based inversions (SIs) below 200&#xa0;m, improving inversion type differentiation (Surface/SI, Elevated/EI, Low-tropospheric/LTI). Results reveal distinct seasonal dynamics: summer months exhibit peak total inversion frequency driven by elevated layers (EI depth &gt; 6,200&#xa0;m, LTI strength &gt; 200&#xa0;K), while winter experiences maximum SI frequency (38.9%) and depth. Despite higher average summer PM2.5 (14.66&#xa0;µg/m³ in July), type-specific correlation analysis shows contrasting relationships: SI frequency negatively correlates with PM2.5 (<i>r</i> = -0.797 at 12Z), while EI and LTI frequencies positively correlate (<i>r</i> = 0.612 and 0.605, respectively). This indicates that elevated inversions, rather than surface inversions, drive sustained pollution accumulation during summer peaks. The strong positive correlation between total inversion frequency and PM2.5 (<i>r</i> &gt; 0.83) emerges from LTI dominance (47–63% frequency year-round) despite the counteracting negative SI correlation. Case studies demonstrate how multi-layer inversions synergize with emissions to produce extreme episodes (peaking at 179.6&#xa0;µg/m³). These findings highlight the need for inversion-type-specific forecasting in air quality management, particularly for elevated inversions during summer pollution events in humid subtropical regions.</p> Graphical Abstract <p>This graphical abstract provides a quick summary of the research, designed to give readers an immediate understanding of the study’s core contributions. It visually narrates the complete research pipeline in three logical stages. The first panel illustrates the central problem by showing how temperature inversions act as atmospheric “lids” trapping PM₂.₅ pollution near the ground. The study area shows locations marked for EPA monitoring sites, weather stations, and upper-air sounding stations in Alabama. The second panel details the innovative methodology, displaying the integration of surface observations with traditional radiosonde profiles to enhance detection of critical shallow inversion layers, while demonstrating how Python coding was employed to analyze both inversion characteristics and PM₂.₅ data. The final panel summarizes key quantitative findings through multiple visualization methods, including the significant increase in inversion detection, substantial reduction in mixing height, strong PM₂.₅ correlations, and Skew-T Log-P diagrams that visually confirm inversion presence and strength. By incorporating these technical elements and analytical approaches, this graphical abstract effectively translates complex atmospheric processes into an accessible format, underscoring the study’s importance for improving air quality forecasting accuracy and supporting evidence-based public health guidance.</p>

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The Impact of Temporal Fluctuations in Temperature Inversions on PM2.5 Levels in Urban Alabama

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Amira Moustafa,
  • Gamal El Afandi,
  • Salem Ibrahim,
  • Santosh Sapkota,
  • Reda Algendy,
  • Muhammad Irfan

摘要

Temperature inversions, by limiting mixing height and inhibiting vertical dispersion of pollutants, are one of the most important drivers of increased PM2.5 concentrations in urban areas. Despite existing studies, quantitative understanding of inversion impacts in humid subtropical climates like the southeastern U.S. remains limited. This research aims to systematically analyse temporal patterns of inversions and examine their causal relationships with PM2.5 fluctuations over the 2020–2024 period in Birmingham, Alabama a city with valley topography, high humidity, and a complex mix of industrial and traffic sources in order to fill this knowledge gap. The study developed an enhanced method integrating high-resolution surface data with radiosonde profiles to accurately detect shallow surface-based inversions (SIs) below 200 m, improving inversion type differentiation (Surface/SI, Elevated/EI, Low-tropospheric/LTI). Results reveal distinct seasonal dynamics: summer months exhibit peak total inversion frequency driven by elevated layers (EI depth > 6,200 m, LTI strength > 200 K), while winter experiences maximum SI frequency (38.9%) and depth. Despite higher average summer PM2.5 (14.66 µg/m³ in July), type-specific correlation analysis shows contrasting relationships: SI frequency negatively correlates with PM2.5 (r = -0.797 at 12Z), while EI and LTI frequencies positively correlate (r = 0.612 and 0.605, respectively). This indicates that elevated inversions, rather than surface inversions, drive sustained pollution accumulation during summer peaks. The strong positive correlation between total inversion frequency and PM2.5 (r > 0.83) emerges from LTI dominance (47–63% frequency year-round) despite the counteracting negative SI correlation. Case studies demonstrate how multi-layer inversions synergize with emissions to produce extreme episodes (peaking at 179.6 µg/m³). These findings highlight the need for inversion-type-specific forecasting in air quality management, particularly for elevated inversions during summer pollution events in humid subtropical regions.

Graphical Abstract

This graphical abstract provides a quick summary of the research, designed to give readers an immediate understanding of the study’s core contributions. It visually narrates the complete research pipeline in three logical stages. The first panel illustrates the central problem by showing how temperature inversions act as atmospheric “lids” trapping PM₂.₅ pollution near the ground. The study area shows locations marked for EPA monitoring sites, weather stations, and upper-air sounding stations in Alabama. The second panel details the innovative methodology, displaying the integration of surface observations with traditional radiosonde profiles to enhance detection of critical shallow inversion layers, while demonstrating how Python coding was employed to analyze both inversion characteristics and PM₂.₅ data. The final panel summarizes key quantitative findings through multiple visualization methods, including the significant increase in inversion detection, substantial reduction in mixing height, strong PM₂.₅ correlations, and Skew-T Log-P diagrams that visually confirm inversion presence and strength. By incorporating these technical elements and analytical approaches, this graphical abstract effectively translates complex atmospheric processes into an accessible format, underscoring the study’s importance for improving air quality forecasting accuracy and supporting evidence-based public health guidance.