Spatiotemporal characteristics of NO₂, PM_2.5 and O₃ in a coastal region of southeastern China and their removal by green spaces

ABSTRACT

Understanding the spatio-temporal characteristics of air pollutants is essential to improving air quality. One aspect is the question of whether green spaces can reduce air pollutant concentrations. However, previous studies on this issue have reported mixed results. This study analyzed the spatio-temporal characteristics of NO₂, PM_2.5 and O₃ in Fujian Province, Southeast China in 2015. In order to reduce uncertainties in the conclusions drawn, the effects landscape metrics describing green spaces have on air pollutants have been analyzed using Pearson correlation analysis at six different spatial scales for the four seasons, considering the influence of meteorological conditions. The results show that PM_2.5 and O₃ are major pollutants whose relative importance varies with the seasons. Significant differences in pollutant concentrations were observed in suburban and urban areas, highlighting the importance of ensuring a reasonable spatial distribution of monitoring stations. Moreover, significant correlations between air pollutants and green space landscape patterns during the four seasons were found, revealing increased air pollutant concentrations with increasing landscape fragmentation and reduced connectivity and aggregation. This probably indicates that interconnected green spaces have the potential to improve air quality. Utilizing green space function regulations can alleviate NO₂ and PM_2.5 pollution effectively, but it is still difficult to reduce O₃ concentrations because green spaces are likely to not only serve as sinks for O_3, but can also promote O₃ formation.

KEYWORDS:

• Green space
• particulate matter
• ozone
• spatiotemporal variation
• landscape scale

Acknowledgments

We are grateful to the anonymous reviewers for their constructive suggestions.

Additional information

Funding

This work was supported by National Science Foundation of China [31670645, 31972951, 31470578, 31200363, 41807502 and 41801182]; National Social Science Fund [17ZDA058]; the National Key Research Program of China [2016YFC0502704]; Fujian Provincial Department of S&T Project [2016T3032, 2016T3037, 2016Y0083, 2018T3018, 2019J01136 and 2015Y0083]; the Strategic Priority Research Program of Chinese Academy of Sciences [XDA23020502]; Ningbo Municipal Department of Science and Technology [2009C10056]; Xiamen Municipal Department of Science and Technology [3502Z20130037 and 3502Z20142016]; Key Laboratory of Urban Environment and Health of CAS [KLUEH-C-201701]; Key Program of the Chinese Academy of Sciences [KFZDSW-324]; and Youth Innovation Promotion Association CAS [2014267].