River width can reveal the water extent and water flow on the earth's surface. Remote sensing facilitates river width extraction at large scales in an automatic way. This letter proposes a new method named GrabRiver that implements whole-process automation from image preparation to river width calculation. It also, for the first time, develops river graph as the simultaneous river topology for width extraction, combining river planform morphology and network. Three major steps are proposed: 1) mapping and connecting rivers. The Multispectral Water Index (MuWI) is used to produce high-accuracy water maps from imagery, whereby specialized algorithms are used to reduce the impacts from nonriver water (lakes, reservoirs, wetlands) and on-channel objects (bridges, dams, ships) and to enforce river connectivity; 2) Constructing the river graph. A connected river map is skeletonized into the graph while maintaining georeferences as properties of edges and nodes in the river graph, and it is followed by river graph pruning to remove false and redundant river tributaries in the topologic structure; and 3) Measuring river widths. The cross-sectional measure is conducted on the river-reach (graph edge) basis, where orthogonals to centerlines are determined by the bounding geometry. In our experiments, the output results of GrabRiver are consistent with the reference river widths (R² = 0.98 in the mean width validation and R² = 0.91 in the transient width validation). Despite that GrabRiver is a promising method, random uncertainty in water mapping is identified as the major source of width measurement errors. The outputs of GrabRiver will be applicable in fluvial analysis and satellite-derived discharge estimates.

The Yangtze River Delta (YRD) is the most developed region in China. Influenced by intensive and complex anthropogenic activities, atmospheric pollution in this region is highly variable, and reports are sparse. In this study, a seven-year history of the atmospheric O₃ and NO_x mixing ratios over a typical city, Hangzhou, was presented to enrich the studies on air pollution in the YRD region. Our results revealed that the diurnal variation in NO_x corresponded to traffic rush hours, while O₃ was mainly impacted by photochemical reactions in the daytime. The weekend effect was significant for NO_x, but inapparent for O₃. Two O₃ peaks in May and September were caused by seasonal atmospheric stability and climatic conditions. The lower NO_x and higher O₃ levels observed suggested direct effects from traffic restrictions and large-scale industrial shutdowns during the COVID-19 lockdown in 2020 compared with those in the periods before and after lockdown. The model simulation results showed that O₃ mixing ratios were not only related to regional anthropogenic emissions but were impacted by air mass transportation from surrounding provinces and the China shelf seas. The NO_x mixing ratios showed a decreasing trend, while the O₃ mixing ratios showed the opposite trend from 2015 to 2021, which is indicative of the implementation of the Air Pollution Prevention and Control Acton Plan issued by the Chinese government in 2013.

As the most economically developed region in China, the Yangtze River Delta (YRD) region contributed to ~17% of the total anthropogenic CO₂ emissions from China. However, the studies of atmospheric CO₂ and CH₄ in this area are relatively sparse and unsystematic. Here, we analyze the changing characters of those gases in different development periods of China, based on the 11-year atmospheric CO₂ and CH₄ records (from 2010 to 2020) at one of the four Chinese sites participating in the World Meteorological Organization/Global Atmospheric Watch (WMO/GAW) program (Lin’an regional background station), located in the center of YRD region, China. The annual average atmospheric CO₂ and CH₄ mole fractions at LAN have been increasing continuously, with growth rates of 2.57 ± 0.14 ppm yr⁻¹ and 10.3 ± 1.3 ppb yr⁻¹, respectively. Due to the complex influence of regional sources and sinks, the characteristics of atmospheric CO₂ and CH₄ varied in different periods: (i) The diurnal and seasonal variations of both CO₂ and CH₄ in different periods were overall similar, but the amplitudes were different. (ii) The elevated mole fractions in all wind sectors tended to be uniform. (iii) The potential source regions of both gases expanded over time. (iv) The growth rate in recent years (2016–2020) changed significantly less than that in the earlier period (2010–2015). Our results indicated that the CO₂ and CH₄ mole fractions were mainly correlated to the regional economic development, despite the influence of special events such as the G20 Summit and COVID-19 lockdown.

A new meteorological station (DMS) was established at the Morning Glory summit in Zhejiang Province to provide regional background information on atmospheric composition in the Yangtze River Delta (YRD) region, China. This study investigated the first carbon monoxide (CO) records at DMS from September 2020 to January 2022. The annual average concentration of CO was 233.4 ± 3.8 ppb, which exceeded the measurements recorded at the other Asian background sites. The winter CO concentration remained elevated but peaked in March in the early spring due to the combined effect of regional emissions within the YRD and transportation impacts of North China and Southeast Asia sources. The diurnal cycle had a nocturnal peak and a morning valley but with a distinct afternoon climb, as the metropolis in the YRD contributed to a local concentration enhancement. The back trajectory analysis and the Weighted Potential Sources Contribution Function (WPSCF) maps highlighted emissions from Anhui, Jiangxi, Zhejiang, and Jiangsu provinces as significant sources. Due to well-mixed air conditions and fewer anthropogenic influences, DMS records closely aligned with the CO averages derived from the Copernicus Atmospheric Monitoring Service (CAMS) covering the YRD, confirming its representativeness for regional CO levels. This study underscored DMS as a valuable station for monitoring and understanding CO spatiotemporal characteristics in the YRD region.

Subtropical bamboo forests are important carbon sinks, yet long-term regional assessments of their net ecosystem production (NEP) and associated factor contributions remain scarce. Here, using approximately 646 km² of mapped Moso bamboo (Phyllostachys pubescens) in Anji County, China, we generated a monthly NEP reconstruction on a 500 m output grid for 2003–2022 by integrating eddy covariance observations, meteorological data, and multi-source remote sensing products within an automated machine learning framework. Bamboo NEP showed weak overall spatial heterogeneity but followed a clear unimodal elevational pattern, with the highest carbon sequestration at mid-elevations (342–550 m). Regional carbon uptake peaked in summer and reached a minimum in winter, consistent with bamboo phenology. Annual mean NEP was 0.43 Tg C yr⁻¹ and increased significantly over the study period (3.35 Gg C yr⁻², p < 0.001), with 95.8% of bamboo pixels exhibiting upward trends. Negative modeled anomalies coincided with drought years, whereas the biennial growth cycle of Moso bamboo had little effect on annual NEP at the regional scale. SHapley Additive exPlanations (SHAP) shows that long-term NEP enhancement was dominated by canopy vegetation indices and exhibited a descriptive transition around 2013, when the leading control changed from solar radiation to vegetation conditions. Structural equation modeling (SEM) further showed statistical pathways in which meteorological variables were associated with modeled NEP partly through vegetation indicators. These findings reveal a sustained strengthening of the regional bamboo carbon sink and identify shifting controls on its long-term variability, providing a scientific basis for carbon sink management in subtropical bamboo forests under climate change.