Assessing the temporal-spatial dynamic reduction in ecosystem services caused by air pollution: A near-real-time data perspective

Air pollution is impacting ecosystem services (ES). This paper proposes an emergy-based method framework to assess the dynamic impacts of near-real-time air pollution on ES at different temporal-spatial scales. The paper presents the cases of Shenzhen, Shanghai, Beijing and Baoding, China, investigates the impacts of air pollution on the ES in 2020. In particular, we compared the reduction in ES evaluated using either average (R1) or near-real-time (R2) air pollution data. The results indicate that the differences between R1 and R2 range from about 9% (for Baoding) to almost 45% (for Shenzhen), thus implying the underestimation of the impacts of air pollution on ES based on average data. The ratios of the reduction in ES to the average ES per hour are near-real-time dynamic, with the values of 0-231.62%, 0-59.42%, 0-50.51%, and 0-35.26% in Shanghai, Baoding, Beijing and Shenzhen respectively. Although the air quality in Shanghai is aggregately better than that in Baoding in 2020, the reduction in ES caused by air pollution in Shanghai is more than twice as that of in Baoding, emphasizing the necessity of investigating the impacts of near-real-time air pollution to properly reveal and assess the potential ecological risks. The ratio of the annual total reduction in ES to the annual total ES, near-real-time reduction in ES to the average ES per hour, and near-real-time reduction in ES to the near-real-time ES in Beijing are 7.22%, 0-50.51% and 0-219.27% respectively, highlighting the significance of near-real-time monitoring of air pollution and ES to reveal the potential or hidden impacts of air pollution on ES, and further to facilitate more high time-efficient and fine ecosystem management and conservation.