Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
Environ Res. 2023 Nov 15;237(Pt 2):117091. doi: 10.1016/j.envres.2023.117091. Epub 2023 Sep 6.
Fine particulate matter (PM) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US.
We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time.
Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties.
This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.
细颗粒物（PM）暴露是许多不良健康后果的已知危险因素，其成分的具体影响估计也各不相同。健康状况受损的人群，如糖尿病患者，可能对空气污染暴露的健康影响更为敏感。之前的研究中，在主要为美洲印第安人（AI）居住的地区，PM 浓度呈逐渐下降趋势，与美国其他地区的下降趋势相比，这一趋势较为平缓。为了进一步研究这些发现的组成部分，我们比较了美国大陆 AI 与非 AI 人口县的六种 PM 成分浓度随时间的变化趋势（2000-2017 年）。
我们采用特定成分的线性混合模型，根据人口密度和家庭收入中位数进行调整，利用多标准方法将县分类为 AI 或非 AI 人口县，以评估硫酸盐、硝酸盐、铵、有机物、黑碳和矿物质粉尘等六种 PM 成分在 AI 与非 AI 人口县的年度县一级浓度差异。我们纳入了与日历年度的交互项，以估计 AI 与非 AI 人口县的浓度差异是否随时间变化。
我们的最终分析包括 3108 个县，其中 199 个（6.4%）被归类为 AI 人口县。在整个研究期间，AI 人口县所有六种 PM 成分的调整后浓度均明显低于非 AI 人口县。然而，AI 与非 AI 人口县的成分水平随时间变化而变化：硫酸盐和铵的水平在 2011 年之前在 AI 与非 AI 人口县中显著较低，但在 2011 年之后较高，而硝酸盐的水平在 AI 人口县中一直较低。
本研究表明，特定成分在 AI 人口县的时间趋势存在差异。值得注意的是，硫酸盐和铵的减少可能导致非 AI 与 AI 人口县的总 PM 下降更为陡峭。这些发现为监测和监管影响部落土地的关键排放源提供了潜在的指导。
