The Canadian wildfires in June 2023 significantly impacted the northeastern United States, particularly in terms of worsened air pollution and environmental justice concerns. While advancements have been made in low-cost sensor deployments and satellite observations of atmospheric composition, integrating dynamic human mobility with wildfire PM exposure to fully understand the environmental justice implications remains underinvestigated. This study aims to enhance the accuracy of estimating ground-level fine particulate matter (PM) concentrations by fusing chemical transport model outputs with empirical observations, estimating exposures using human mobility data, and evaluating the impact of environmental justice. Employing a novel data fusion technique, the study combines the Weather Research and Forecasting model with Chemistry (WRF-Chem) outputs and surface PM measurements, providing a more accurate estimation of PM distribution. The study addresses the gap in traditional exposure assessments by incorporating human mobility data and further investigates the spatial correlation of PM levels with various environmental and demographic factors from the US Environmental Protection Agency (EPA) Environmental Justice Screening and Mapping Tool (EJScreen). Results reveal that despite reduced mobility during high PM levels from wildfire smoke, exposure for both residents and individuals on the move remains high. Regions already burdened with high environmental pollution levels face amplified PM effects from wildfire smoke. Furthermore, we observed mixed correlations between PM concentrations and various demographic and socioeconomic factors, indicating complex exposure patterns across communities. Urban areas, in particular, experience persistent high exposure, while significant correlations in rural areas with EJScreen factors highlight the unique vulnerabilities of these populations to smoke exposure. These results advocate for a comprehensive approach to environmental health that leverages advanced models, integrates human mobility data, and addresses socio-demographic disparities, contributing to the development of equitable strategies against the growing threat of wildfires.

中文翻译：

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评估 2023 年加拿大野火烟雾对美国东北部的影响：空气质量、暴露和环境正义

2023 年 6 月的加拿大山火对美国东北部产生了重大影响，特别是空气污染恶化和环境正义问题。尽管在低成本传感器部署和大气成分卫星观测方面取得了进展，但将动态人类流动性与野火 PM 暴露相结合以充分了解环境正义的影响仍然没有得到充分研究。本研究旨在通过将化学迁移模型输出与实证观察相融合、利用人类流动数据估算暴露量以及评估环境正义的影响，提高估算地面细颗粒物 (PM) 浓度的准确性。该研究采用新颖的数据融合技术，将天气研究和预报模型与化学 (WRF-Chem) 输出和表面 PM 测量相结合，提供对 PM 分布的更准确估计。该研究通过纳入人类流动数据来解决传统暴露评估中的差距，并进一步研究美国环境保护署 (EPA) 环境正义筛查和绘图工具 (EJScreen) 中 PM 水平与各种环境和人口因素的空间相关性。结果显示，尽管野火烟雾导致PM2.5浓度高时流动性降低，但居民和移动中的个人的暴露程度仍然很高。环境污染水平已经很高的地区面临着野火烟雾对PM2.5的放大影响。此外，我们观察到 PM 浓度与各种人口和社会经济因素之间的混合相关性，表明各个社区的复杂暴露模式。尤其是城市地区，暴露程度持续较高，而农村地区与 EJScreen 因素的显着相关性凸显了这些人群对烟雾暴露的独特脆弱性。这些结果提倡对环境健康采取全面的方法，利用先进的模型，整合人员流动数据，并解决社会人口差异，有助于制定公平的战略来应对日益严重的野火威胁。