First close insight into global daily gapless 1 km PM 2.5 pollution, variability, and health impact.
Jing WeiZhanqing LiAlexei LyapustinJun WangOleg DubovikJoel SchwartzLin SunChi LiSong LiuTong ZhuPublished in: Nature communications (2023)
Here we retrieve global daily 1 km gapless PM 2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, and public health studies. We find that 96%, 82%, and 53% of Earth's populated areas are exposed to unhealthy air for at least one day, one week, and one month in 2022, respectively. Strong disparities in exposure risks and duration are exhibited between developed and developing countries, urban and rural areas, and different parts of cities. Wave-like dramatic changes in air quality are clearly seen around the world before, during, and after the COVID-19 lockdowns, as is the mortality burden linked to fluctuating air pollution events. Encouragingly, only approximately one-third of all countries return to pre-pandemic pollution levels. Many nature-induced air pollution episodes are also revealed, such as biomass burning.
Keyphrases
- drug induced
- air pollution
- particulate matter
- big data
- public health
- machine learning
- climate change
- human health
- coronavirus disease
- sars cov
- artificial intelligence
- lung function
- risk assessment
- heavy metals
- physical activity
- healthcare
- cardiovascular events
- risk factors
- deep learning
- global health
- mental health
- case control
- respiratory syndrome coronavirus
- health risk assessment
- affordable care act
- wastewater treatment
- single cell
- diabetic rats
- clinical trial
- randomized controlled trial
- high glucose
- cystic fibrosis
- double blind
- coronary artery disease
- oxidative stress