The deteriorating air quality in the Yangtze delta region is attracting growing public concern. In this paper, seasonal estimation models of the surface particulate matter (PM) were established by using aerosol optical thickness (AOT) retrievals from the moderate resolution imaging spectro-radiometer (MODIS) on board NASA’s Terra satellite. The change of the regional distribution of the atmospheric mixed layer, relative humidity and meteorological elements have been taken into account in these models. We also used PM mass concentrations of ground measurements to evaluate the estimation accuracy of those models. The results show that model estimation of PM_2.5 and PM₁₀ mass concentrations were in good agreement with the ground-based observation of PM mass concentrations (p < 0.01, the R² value of the PM_2.5 concentrations experimental model for four seasons are 0.48, 0.62, 0.61 and 0.52 respectively. The R² value of PM₁₀ concentrations experimental model for four seasons are 0.57, 0.56, 0.64 and 0.68 respectively). At the same time, spatial and temporal variations of PM_2.5 and PM₁₀ mass concentrations were analysed over the Yangtze delta region from 2000 to 2013. The results show that PM_2.5 and PM₁₀ show a trend of increase in the Yangtze delta region from 2000 to 2013 and change periodically. The maximum mass concentration of PM_2.5 and PM₁₀ was in January–February, and the minimum was in July–August. The highest values of PM_2.5 and PM₁₀ mass concentration are in the region of urban agglomeration which is grouped to a delta-shaped region by Shanghai, Hangzhou and Nanjing, while the low values are in the forest far away from the city. PM mass concentration over main cities and rural areas increased gradually year by year, and were increasing more quickly in urban areas than in rural areas.

<!-- *** Custom HTML *** --> We comprehensively considered the spatial resolution and seasonal differences in NO₂ column concentration data observed by different satellites to reconstruct an NO₂ column concentration dataset in China from 1997 to 2018. On this basis, the spatial and temporal variations in NO₂ column concentrations were analyzed over China from 1997 to 2018. The results show that the reconstructed NO₂ column concentration dataset with high spatial structure characteristics in China from 1997 to 2018 effectively compensates for the lack of long-term high-resolution monitoring of NO₂ pollution in China by a single satellite. (The accuracy of the reconstructed dataset was verified by season: correlation coefficient between GOME and SCIAMACHY, R > 0.87, P < 0.01; correlation coefficient between GOME2A and SCIAMACHY, R > 0.91, P < 0.01.) The analysis of the spatial and temporal variations showed that the NO₂ column concentrations in China from 1997 to 2018 generally showed high values in the east and low values in the west. The high concentrations of NO₂ are mainly concentrated in the northern and eastern coastal areas and the Pearl River Delta. From 1997 to 2010, the NO₂ column concentrations continued to increase with a growth rate of ( 0.180 ± 0.011 ) × 10¹⁵ molec / ( cm² a ) over China. From 2011 to 2018, the NO₂ column concentrations showed a downward trend, with a decreasing rate of ( 0.144 ± 0.037 ) × 10¹⁵ molec / ( cm² a ) over China. After 2011, NO₂ pollution in high pollution regions such as Beijing, Tianjin, Hebei, Shandong, the Yangtze River Delta, and the Pearl River Delta was alleviated. Energy saving and emission reduction measures can reduce nitrogen dioxide pollution while ensuring sustained gross domestic product (GDP) growth.

Over the past three decades, the Yangtze River Delta has undergone a rapid urbanization phenomenon, resulting in pronounced urban sprawl that has significantly impacted regional sustainable development and air quality. This study constructs an urban sprawl index based on nighttime light data spanning 2000–2020 and employs exploratory spatio-temporal analysis, panel data models, and spatial econometric models to examine the evolution of urban sprawl and its effects on PM_2.5 concentrations. The results reveal four key findings: (1) Urban sprawl is spatially heterogeneous, exhibiting a ‘high in the centre-east, low in the north-west’ pattern, with high-intensity sprawl expanding from the central region towards the north-west and south-west; (2) The dominant growth pattern is characterized by relatively rapid expansion. The global Moran’s I index fluctuates between 0.428 and 0.214, indicating a gradual decline in the global clustering effect of urban sprawl. Meanwhile, the share of local high–high agglomeration zones decreases to 21.9%, whereas low–low zones increase to 24.3%; (3) Spatio-temporal transitions of urban sprawl show strong spatial dependence while overall relocation exhibits inertia; (4) Before the implementation of the Ten Key Measures for Air Pollution Prevention and Control in 2013, urban sprawl significantly intensified PM_2.5 pollution. Following the policy, this relationship notably reversed, with sprawl exhibiting pollution-mitigating effects in certain regions. The spatial diffusion of pollution is evident, as urban sprawl influences air quality through both local development and inter-regional interactions. This study provides an in-depth analysis of the spatio-temporal evolution of urban sprawl and establishes a framework to examine the interactive mechanisms between urban expansion and air pollution, thereby broadening perspectives on atmospheric pollution research and offering scientific and policy guidance for sustainable land use and air quality management in the Yangtze River Delta.