Abstract
Abstract
In this study, we assessed air quality (AQ) and urban climate during the mobility restrictions implemented in the Greater Tokyo Area, Japan, the world’s most populated region, in response to the COVID-19 pandemic. Observations from dense surface networks were analyzed using an interpretable machine learning approach. In parallel with a ∼50% reduction in mobility and an altered lifestyle of the population, we found limited reductions in nitrogen dioxide; decreases in fine particulate matter not entirely driven by local mobility; minor variations in ozone, with a positive (negative) tendency in areas with high (low) emissions; a decrease in air temperature consistent with mobility; and pollution levels and air temperature changes with well-defined, common spatiotemporal patterns. Specifically, cooling mainly occurred in urbanized areas with an improved AQ. Overall, although reductions in mobility were moderately effective in improving the typical indicators of urban AQ, including those known to negatively impact human health, the reductions in waste heat had a stronger impact on Tokyo’s urban heat island, suggestive of a strategy to minimize exposure to heat stress. These findings can help guide urban planning strategies and policies aimed at addressing climate change.
Funder
Virtual Laboratory (VL) project by the Ministry of Education, Culture, Sports, Science and Technology
Comisión Nacional de Investigación Científica y Tecnológica
JAXA 3rd research announcement on the Earth Observations
Environment Research and Technology Development Fund
JSPS KAKENHI
Climate Change Adaptation Research Program of NIES
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献