Scholarlink

<p>Airborne particulate matter (PM) may originate from exposed agricultural fields and activities during dry periods. A preliminary study assessed sources of airborne PM in 2023, starting two weeks before soybean harvest. Three passive samplers were placed alongside a 101—ha field in Mississippi, USA. Collection substrates were in each sampler for two-week intervals during pre-harvest; harvest; post-harvest disking; and post-harvest hipping. Substrates were analyzed by scanning electron microscopy-energy dispersive X-ray spectroscopy automated single particle analysis combined with machine learning to identify particle types and estimate concentrations in PM<sub>80-10</sub> and PM<sub>10-2.5</sub>. At pre-harvest, 56 wt. % of particulates (PM<sub>10-2.5</sub>) were biogenic/organic, while 41 wt.% were minerals. During and after harvest, 36 wt.% of particulates were biogenic/organic and 61 wt.% were minerals. High concentrations of phosphorus, primarily biogenic and mineral, were noted. Results demonstrate the need for inclusion of airborne PM investigations to assess the potential impacts of intensive agricultural activities.</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

Characterizing Airborne Particulate Matter During Late-Season Soybean Production Using SEM/EDX Automated Single Particle Analyses and Machine Learning: A Preliminary Study in the Mississippi Delta

  • 期刊论文

  • 发表日期：

    2026年5月18日

查看全文
  • Matthew T. Moore,
  • Lucas J. Heintzman,
  • Juanita Rausch,
  • David Jaramillo-Vogel,
  • Martin A. Locke

摘要

Airborne particulate matter (PM) may originate from exposed agricultural fields and activities during dry periods. A preliminary study assessed sources of airborne PM in 2023, starting two weeks before soybean harvest. Three passive samplers were placed alongside a 101—ha field in Mississippi, USA. Collection substrates were in each sampler for two-week intervals during pre-harvest; harvest; post-harvest disking; and post-harvest hipping. Substrates were analyzed by scanning electron microscopy-energy dispersive X-ray spectroscopy automated single particle analysis combined with machine learning to identify particle types and estimate concentrations in PM80-10 and PM10-2.5. At pre-harvest, 56 wt. % of particulates (PM10-2.5) were biogenic/organic, while 41 wt.% were minerals. During and after harvest, 36 wt.% of particulates were biogenic/organic and 61 wt.% were minerals. High concentrations of phosphorus, primarily biogenic and mineral, were noted. Results demonstrate the need for inclusion of airborne PM investigations to assess the potential impacts of intensive agricultural activities.