Details

Autor(en) / Beteiligte

• Liu, Kai
• Li, Qingqing
• Andrady, Anthony L.
• Wang, Xiaohui
• He, Yinan
• Li, Daoji

Titel

Underestimated activity-based microplastic intake under scenario-specific exposures

Ist Teil von

• Environmental science and ecotechnology, 2024-03, Vol.18, p.100316-100316, Article 100316

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Despite increasing alarms over the health impacts of microplastics (MPs) due to their detection in human organs and feces, precise exposure evaluations remain scarce. To comprehend their risks, there is a distinct need to prioritize quantitive estimates in MP exposome, particularly at the environmentally-realistic level. Here we used a method rooted in real-world MP measurements and activity patterns to determine the daily intake of MPs through inhalation and from ground dust/soil ingestion. We found that nearly 80% of this intake comes from residential sectors, with activity intensity and behavioral types significantly affecting the human MP burden. The data showed a peak in MP exposure for those aged 18–64. When compared to dietary MP intake sources like seafood, salt, and water, we identified a previously underestimated exposure from inhalation and dust/soil ingestion, emphasizing the need for more realistic evaluations that incorporate activity factors. This discovery raises questions about the accuracy of past studies and underscores MP's potential health risks. Moreover, our time-based simulations revealed increased MP intake during the COVID-19 lockdown due to more surface dust ingestion, shedding light on how global health crises may inadvertently elevate MP exposure risks. [Display omitted] •We developed a detailed inventory using real-world microplastic (MP) measurements.•We introduce a refined method to assess the human MP burden realistically.•Up to 1.26 × 105 and 8.38 × 104 MPs are predicted to be inhaled or ingested daily.•Our findings advocate for integrating activity factors into exposure predictions.