环境空气污染和农药混合物对农业社区呼吸道炎症指标的影响。

Environmental research, health : ERH Pub Date : 2024-09-01 Epub Date: 2024-06-25 DOI:10.1088/2752-5309/ad52ba
Matthew L Hughes, Grace Kuiper, Lauren Hoskovec, Sherry WeMott, Bonnie N Young, Wande Benka-Coker, Casey Quinn, Grant Erlandson, Nayamin Martinez, Jesus Mendoza, Greg Dooley, Sheryl Magzamen
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引用次数: 0

摘要

接触空气污染与不良的呼吸系统健康后果有关。职业和社区研究的证据也表明,农用杀虫剂对呼吸系统健康有负面影响。虽然人们会同时暴露于多种吸入性危害,但对多领域混合物(如不同类别的环境和化学污染物)的研究却很少。我们研究了环境空气污染-杀虫剂暴露混合物与尿液白三烯 E4(LTE4)(一种呼吸道炎症生物标志物)之间的关系,研究对象是加利福尼亚中部四个社区的 75 名参与者,研究历时两个季节。暴露包括通过社区多尺度空气质量模型估算的三种标准空气污染物(细颗粒物、臭氧和二氧化氮)以及有机磷农药的尿液代谢物(总二烷基磷酸盐 (DAP)、总二乙基磷酸盐 (DE) 和总二甲基磷酸盐 (DM))。我们采用多元线性回归模型来检验单一污染物模型中的相关性,并对年龄、性别、哮喘状况、职业状况、家庭成员职业状况、温度和相对湿度进行了调整,还评估了相关性是否随季节而变化。然后,我们采用贝叶斯核机器回归(BKMR)分析了作为混合物的这些标准空气污染物、DE 和 DM。我们的多元线性回归模型表明,DAPs总量的四分位数间距(IQR)增加与冬季尿液中LTE4的增加有关(β:0.04,95% CI:[0.01,0.07])。同样,总 DM 的 IQR 增加与冬季尿液中 LTE4 的增加相关(β:0.03,95% CI:[0.004,0.06])。所有标准空气污染物效应估计值的置信区间均包括空值。BKMR 分析显示,在我们的空气污染-杀虫剂混合物中,各暴露因子之间可能存在非线性相互作用,但所有置信区间均包含空值。我们的分析表明,在哮喘发病率较低的人群中,OP 农药代谢物与尿液中的 LTE4 之间存在正相关关系,这为有关环境空气污染和农药混合物对呼吸系统健康的共同影响的有限研究增添了新的内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (β: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (β:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

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