Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China

The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.

Space borne tropospheric nitrogen dioxide (NO₂) observations from 2005–2020 over the Yangtze River Delta (YRD), China: variabilities, implications, and drivers

Nitrogen dioxide (NO2) is mainly affected by local emission and meteorology rather than long-range transport. Accurate acknowledge of its long-term variabilities and drivers are significant for understanding the evolutions of economic and social development, anthropogenic emission, and the effectiveness of pollution control measures on regional scale. In this study, we quantity the long-term variabilities and the underlying drivers of NO2 from 2005 to 2020 over the Yangtze River Delta (YRD), one of the most densely populated and highly industrialized city clusters in China, using OMI space borne observations and the multiple linear regression (MLR) model. We have compared the space borne tropospheric results to the surface in-situ data, yielding correlation coefficients of 0.8 to 0.9 over all megacities within the YRD. As a result, the tropospheric NO2 column measurements can be used as representatives of near-surface conditions, and we thus only use ground-level meteorological data for MLR regression. The inter-annual variabilities of tropospheric NO2 vertical column densities (VCDs) from 2005 to 2020 over the YRD can be divided into two stages. The first stage was from 2005 to 2011, which showed overall increasing trends with a wide range of (1.91 ± 1.50) to (6.70 ± 0.10) × 1014 molecules/cm2·yr−1 (p < 0.01) over the YRD. The second stage was from 2011 to 2020, which showed over all decreasing trends of (−6.31 ± 0.71) to (−11.01 ± 0.90) × 1014 molecules/cm2·yr−1 (p < 0.01) over each of the megacities. The seasonal cycles of tropospheric NO2 VCDs over the YRD are mainly driven by meteorology (81.01 % – 83.91 %) except during winter when anthropogenic emission contributions are pronounced (16.09 % – 18.99 %). The inter annual variabilities of tropospheric NO2 VCDs are mainly driven by anthropogenic emission (69.18 % – 81.34 %) except for a few years such as 2018 which are partly attributed to meteorology anomalies (39.07 % – 91.51 %). The increasing trends in tropospheric NO2 VCDs from 2005 to 2011 over the YRD are mainly attributed to high energy consumption associated with rapid economic growth which cause significant increases in anthropogenic NO2 emissions. The decreasing trends in tropospheric NO2 VCDs from 2011 to 2020 over the YRD are mainly attributed to the stringent clean air measures which either adjust high energy industrial structure toward low energy industrial structure or directly reduce pollutant emissions from different industrial sectors.

Investigating Ecosystem Service Trade-Offs/Synergies and Their Influencing Factors in the Yangtze River Delta Region, China

A comprehensive understanding of the ecosystem services (ESs) trade-off/synergy relationships has become increasingly important for ecological management and sustainable development. This study employed the Yangtze River Delta (YRD) region in China as the study area and investigated the spatiotemporal changes in three ESs, namely, carbon storage (CS), water purification (WP), and habitat quality (HQ). A trade-off/synergy degree (TSD) indicator was developed that allowed for the quantification of the trade-off/synergy intensity, and the spatial pattern of the TSD between ESs in the YRD region to be analyzed. Furthermore, a geographically weighted regression (GWR) model was used to analyze the relationship between the influencing factors and trade-offs/synergies. The results revealed that CS, WP, and HQ decreased by 0.28%, 2.49%, and 3.38%, respectively, from 2005 to 2015. The TSD indicator showed that the trade-off/synergy relationships and their magnitudes were spatially heterogeneous throughout the YRD region. The coefficients of the natural and socioeconomic factors obtained from the GWR indicated that their impacts on the trade-offs/synergies vary spatiotemporally. The impact factors had both positive and negative effects on the trade-offs/synergies. The findings of this study could improve the understanding of the spatiotemporal dynamics of trade-offs/synergies and their spatially heterogeneous correlations with related factors.