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Background <p>Previous studies have associated pesticide exposure among people living near fields with adverse health outcomes. However, the factors influencing residential contamination by agricultural pesticides remain unclear.</p> Objective <p>This study aims to assess the effect of the local environment, home characteristics, and occupant behaviors on residential contamination.</p> Methods <p>In 2021, wipe samples from outdoor and indoor surfaces were collected in 28 homes in Bordeaux vineyard region during peak pesticide spraying. Eight fungicides were analyzed by LC-MS/MS or GC-MS/MS. Environmental, residential, and occupant-related data were gathered through questionnaires and databases. Using multilevel structural equation modeling, we simultaneously examined the effects of multiple factors on pesticide contamination outdoors (Model 1, <i>n</i> = 227 samples) and indoors (Model 2, <i>n</i> = 543 samples), reporting standardized β coefficients.</p> Results <p>Outdoor pesticide contamination was positively associated with local vineyard areas (<i>β</i> = 0.79, <i>p</i> = &lt;0.001) and the probability of a recent pesticide application (<i>β</i> = 0.34, <i>p</i> = 0.09). In contrast, daily wind speed (<i>β</i> = −0.54, <i>p</i> = 0.001), cumulative rainfall over the past month (<i>β</i> = −0.32, <i>p</i> = 0.03), and cleaning (<i>β</i> = −0.37, <i>p</i> &lt; 0.001) were negatively associated with outdoor contamination. Indoor contamination was significantly associated with the annual local purchases of pesticides (<i>β</i> = 0.79, <i>p</i> &lt; 0.001) and pesticide track-in by occupants (<i>β</i> = 0.16, <i>p</i> &lt; 0.001). Conversely, households with active adults and children (<i>β</i> = −0.49, <i>p</i> &lt; 0.01), cleaning (<i>β</i> = −0.40, <i>p</i> &lt; 0.001), and surface contact frequency (<i>β</i> = −0.30, <i>p</i> &lt; 0.001) showed significant negative associations. Air exchange exhibited only a weak suggestive association (<i>β</i> = 0.07, <i>p</i> = 0.09). Both models demonstrated good fit indices.</p> Significance <p>These results improve our understanding of residential pesticide contamination and could help inform the design of strategies to reduce exposure in rural populations.</p> <p></p> Impact statement <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Residential pesticide contamination from agricultural field drift may represent an important exposure pathway in rural communities. Our study identifies key factors driving this contamination through comprehensive surface sampling both inside and outside homes near vineyards, using advanced statistical methods, such as structural equation modeling. We found that the main contributors include outdoor pesticide applications, meteorological conditions, track-in, and air exchange, while factors, such as cleaning practices and frequent contact with surfaces help mitigate contamination. These findings provide useful insights for designing targeted interventions to reduce exposure and inform public health strategies in agricultural regions.</p> </ItemContent> </UnorderedList></p>

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Determinants of residential pesticide contamination in vineyard regions: a structural equation modeling approach

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Raphaëlle Teysseire,
  • Cécile Proust-Lima,
  • Rémi Béranger,
  • Audrey Roudil,
  • Marie-Hélène Devier,
  • Emmanuelle Barron,
  • Hélène Budzinski,
  • Carole Bedos,
  • Isabelle Baldi,
  • Fleur Delva

摘要

Background

Previous studies have associated pesticide exposure among people living near fields with adverse health outcomes. However, the factors influencing residential contamination by agricultural pesticides remain unclear.

Objective

This study aims to assess the effect of the local environment, home characteristics, and occupant behaviors on residential contamination.

Methods

In 2021, wipe samples from outdoor and indoor surfaces were collected in 28 homes in Bordeaux vineyard region during peak pesticide spraying. Eight fungicides were analyzed by LC-MS/MS or GC-MS/MS. Environmental, residential, and occupant-related data were gathered through questionnaires and databases. Using multilevel structural equation modeling, we simultaneously examined the effects of multiple factors on pesticide contamination outdoors (Model 1, n = 227 samples) and indoors (Model 2, n = 543 samples), reporting standardized β coefficients.

Results

Outdoor pesticide contamination was positively associated with local vineyard areas (β = 0.79, p = <0.001) and the probability of a recent pesticide application (β = 0.34, p = 0.09). In contrast, daily wind speed (β = −0.54, p = 0.001), cumulative rainfall over the past month (β = −0.32, p = 0.03), and cleaning (β = −0.37, p < 0.001) were negatively associated with outdoor contamination. Indoor contamination was significantly associated with the annual local purchases of pesticides (β = 0.79, p < 0.001) and pesticide track-in by occupants (β = 0.16, p < 0.001). Conversely, households with active adults and children (β = −0.49, p < 0.01), cleaning (β = −0.40, p < 0.001), and surface contact frequency (β = −0.30, p < 0.001) showed significant negative associations. Air exchange exhibited only a weak suggestive association (β = 0.07, p = 0.09). Both models demonstrated good fit indices.

Significance

These results improve our understanding of residential pesticide contamination and could help inform the design of strategies to reduce exposure in rural populations.

Impact statement

Residential pesticide contamination from agricultural field drift may represent an important exposure pathway in rural communities. Our study identifies key factors driving this contamination through comprehensive surface sampling both inside and outside homes near vineyards, using advanced statistical methods, such as structural equation modeling. We found that the main contributors include outdoor pesticide applications, meteorological conditions, track-in, and air exchange, while factors, such as cleaning practices and frequent contact with surfaces help mitigate contamination. These findings provide useful insights for designing targeted interventions to reduce exposure and inform public health strategies in agricultural regions.