PDF(Pubmed)
Abstract：
This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced greatest rising O3 concentrations. Further, day-to-day exposure variations decreased for NO2 and O3. The disparity in NO2 exposure decreased, while it persisted for O3. In addition, PM2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.
摘要：
这项研究通过研究弱势社区的暴露动态来弥合空气污染研究中的空白。利用尖端的机器学习和海量数据处理，我们为二氧化氮（NO2）制作了高分辨率（100米）每日空气污染图，细颗粒物（PM2.5），以及2012-2019年加州各地的臭氧(O3)。我们的发现揭示了NO2和PM2.5与O3相反的空间格局。我们还确定了一致的，从2012年到2019年，弱势社区的污染物暴露量较高，尽管最弱势社区的NO2和PM2.5下降幅度最大，而优势社区的O3浓度上升幅度最大。Further,NO2和O3的日常暴露变化减少。NO2暴露的差异减小，而它坚持O3。此外,PM2.5显示，由于野火频率和强度的增加，所有社区的日常变化都在增加。特别是影响优势郊区和农村社区。
