Scholarlink

<p>A thirty-year (1st January 1995–31st December 2025) assessment of PM<sub>2.5</sub> variability and meteorological controls over Bengaluru, India, was conducted using NASA MERRA-2 reanalysis data. Validation against CPCB observations, including a multi-station comparison across nine Continuous Ambient Air Quality Monitoring Stations (CAAQMS) during 2023–2024, demonstrated generally strong agreement between MERRA-2 and ground observations, supporting the suitability of the reanalysis product for long-term air-quality assessment. Mann–Kendall analysis revealed a highly significant increasing PM<sub>2.5</sub> trend (Sen’s slope =  + 0.317&#xa0;<i>μ</i>g&#xa0;m<sup>-3</sup>&#xa0;yr<sup>-1</sup>; <i>p</i> &lt; 0.0001), whereas key meteorological parameters exhibited no comparable long-term trends, indicating a dominant anthropogenic influence on the observed increase. Seasonally, PM<sub>2.5</sub> concentrations peaked during the pre-monsoon season (22.1&#xa0;<i>μ</i>g&#xa0;m<sup>-3</sup>) and reached minimum levels during the monsoon season (12.7&#xa0;<i>μ</i>g&#xa0;m<sup>-3</sup>) owing to enhanced atmospheric ventilation and wet scavenging. Analysis of planetary boundary layer height (PBLH) revealed that the apparent positive PM<sub>2.5</sub>–PBLH relationship was seasonally confounded, with elevated PBLH ultimately promoting pollutant dilution above approximately 800&#xa0;m. Extreme pollution episodes occurred predominantly during pre-monsoon and winter periods under low-moisture and weak-wind conditions. Random Forest analysis identified water vapour (27.1%) and wind speed (26.6%) as the dominant meteorological controls on PM<sub>2.5</sub> variability, whereas PBLH contributed only 12.7%. Overall, the study provides one of the first integrated long-term assessments of PM<sub>2.5</sub> variability over Bengaluru by combining satellite reanalysis, multi-station ground validation, trend analysis, nonlinear meteorological interpretation, and machine-learning attribution to distinguish anthropogenic forcing from meteorological variability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Thirty-Year Variability and Meteorological Controls of PM2.5 over Bengaluru, India (1995–2025): Evidence from MERRA-2 Reanalysis

  • 期刊论文

  • 发表日期：

    2026年7月26日

查看全文
  • D. Govindaraju,
  • K. E. Ganesh

摘要

A thirty-year (1st January 1995–31st December 2025) assessment of PM2.5 variability and meteorological controls over Bengaluru, India, was conducted using NASA MERRA-2 reanalysis data. Validation against CPCB observations, including a multi-station comparison across nine Continuous Ambient Air Quality Monitoring Stations (CAAQMS) during 2023–2024, demonstrated generally strong agreement between MERRA-2 and ground observations, supporting the suitability of the reanalysis product for long-term air-quality assessment. Mann–Kendall analysis revealed a highly significant increasing PM2.5 trend (Sen’s slope =  + 0.317 μg m-3 yr-1; p < 0.0001), whereas key meteorological parameters exhibited no comparable long-term trends, indicating a dominant anthropogenic influence on the observed increase. Seasonally, PM2.5 concentrations peaked during the pre-monsoon season (22.1 μg m-3) and reached minimum levels during the monsoon season (12.7 μg m-3) owing to enhanced atmospheric ventilation and wet scavenging. Analysis of planetary boundary layer height (PBLH) revealed that the apparent positive PM2.5–PBLH relationship was seasonally confounded, with elevated PBLH ultimately promoting pollutant dilution above approximately 800 m. Extreme pollution episodes occurred predominantly during pre-monsoon and winter periods under low-moisture and weak-wind conditions. Random Forest analysis identified water vapour (27.1%) and wind speed (26.6%) as the dominant meteorological controls on PM2.5 variability, whereas PBLH contributed only 12.7%. Overall, the study provides one of the first integrated long-term assessments of PM2.5 variability over Bengaluru by combining satellite reanalysis, multi-station ground validation, trend analysis, nonlinear meteorological interpretation, and machine-learning attribution to distinguish anthropogenic forcing from meteorological variability.