Rivers and streams are recognized as potential greenhouse gas (GHGs: CO₂, CH₄, and N₂O) sources, contributing to global warming. However, GHG emissions from rivers and streams have received insufficient attention compared to other ecosystems (forests, grasslands, wetlands, etc.). In this study, dissolved GHG concentrations were measured in the Qingyijiang River, the longest tributary in the lower reaches of the Yangtze River, during two campaigns in September 2020 and April 2021. Our results showed that the Qingyijiang River was oversaturated with dissolved GHGs. The dissolved GHG concentration in the surface river water ranged from 8.70 to 67.38 μM CO₂, 0.03 to 2.06 μM CH₄, and 12.30 to 32.22 nM N₂O. The average diffusive GHG emission rates were 31.89 ± 22.23 mmol CO₂ m⁻² d⁻¹, 697.22 ± 939.82 μmol CH₄ m⁻² d⁻¹, and 18.12 ± 7.73 μmol N₂O m⁻² d⁻¹. The total emissions (CO₂-e) were CO₂ (58%) dominated, while CH₄ (38%) played a moderate role in total emissions. Temporally, average GHG concentrations and fluxes from the studied river in April were higher than those in September. The concentration and flux of CH₄ exhibited high spatial variability, similar to those in most rivers. In contrast, we found that there was no obvious spatial variability in CO₂ and N₂O concentrations but a significant difference among reaches in N₂O fluxes. We found that water temperature and flow velocity were the potential drivers for the regulating spatial variability in GHGs. However, no other observed limnological parameters were found in governing the spatial patterns of GHGs, suggesting a complex combination of factors governing GHG fluxes; thus, these inconspicuous mechanisms underscore the need for further research. Overall, our study suggests that this river acts as a minor source of GHGs relative to other rivers, and CH₄ cannot be ignored when considering aquatic carbon emissions.

Riverine heavy metal (HM) pollution, a critical global environmental issue, severely affects water quality, ecosystem health, and human well-being. The Huaihe River, once among China’s most polluted, has seen water quality improvements due to strict pollution controls, yet the extent of HM pollution reduction remains uncertain. Here, we investigated the distribution, sources, and potential ecological and health risks of nine typical HMs (Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, and Hg) in surface water and sediment in the Anhui section of the river. Seasonal variations in HM concentrations were observed, with most values below drinking water safety limits, except for Mn and Cd at specific sites and seasons. Indices including the HPI, HEI, HQ, and HI showed low contamination and health risks, yet children are more vulnerable to non-carcinogenic hazards, notably from Cd and As. Sediment HMs trends decreased as Mn > Zn > Cr > Pb > Ni > Cu > As > Cd > Hg, with moderate pollution from Cd, Mn, and Pb based on CF, EF, and I_geo assessments. PLI and NPI suggested moderate ecological risks in midstream areas due to HM accumulation. The correlation analysis and PCA revealed that HMs in uncontaminated sediments were mainly of geogenic origin, while contaminated sediments were largely influenced by anthropogenic activities, including agricultural runoff, industrial waste, and domestic sewage discharge. Overall, our findings highlight that control of anthropogenic activities within the Huaihe River basin is essential for reducing HM pollution in the river.

Rivers emit substantial amounts of carbon dioxide (CO₂) to the atmosphere, yet its response to heavy rainfall remains unclear with intensive anthropogenic disturbances. To fill the knowledge gap, this study investigated the dynamic variability of CO₂ partial pressure (pCO₂) and CO₂ emissions flux at the Chaohu Lake Basin, a watershed under intensive anthropogenic perturbations, based on field campaigns across diverse river systems during dry season, normal season, and post-rainfall periods. Results demonstrated marked differences in aquatic pCO₂ across river types, with urban rivers (3949 µatm) exhibiting significantly higher levels than non-urban counterparts (1423 µatm). Rainfall events elevated riverine pCO₂, but the effect size varied between river types (urban river versus non-urban river). In non-urban rivers, pCO₂ following heavy rainfall (2461 μatm) was significantly higher (p < 0.05) than those observed during both dry season (1096 μatm) and normal season (712 μatm). In contrast, urban rivers demonstrated only marginal pCO₂ elevation after rainfall (20–30%). Statistical analysis revealed that discharge, total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH₄⁺-N) showed significantly positive correlations with pCO₂, while dissolved oxygen (DO) and pH exhibited significantly negative correlations with pCO₂. Overall, rivers in the Chaohu Lake Basin act as significant sources of atmospheric CO₂, with an annual mean CO₂ emission flux of 297.84 mmol·m⁻²·d⁻¹, and the heavy rainfall events amplify riverine CO₂ emissions (629.91 mmol·m⁻²·d⁻¹), with observed enhancement effects exceeding 300% compared to baseline conditions. To accurately estimate the CO₂ emissions from human-dominated rivers, future research should emphasize the impacts of extreme or heavy rainfall events.

The substrate-induced respiration-inhibition (SIRIN) method has been used to estimate fungi-derived N₂O emissions, but its contribution to soil N₂O emissions remains unclear. There is a need to quantify the fungal fraction of N₂O production more precisely. Here, using isotopocule analysis, we assessed the relative contribution of fungi to soil N₂O production potential under denitrifying conditions, where key limiting factors of denitrification (soil moisture, soil NO₃⁻, and electron donor) were removed. The result showed that the ratio of fungi-derived N₂O emissions (R_F) was 0.83~4.28% in paddy soils, 13.80~23.21% in vineyard soils, and 15.34~65.94% in vegetable field soils, respectively. This indicated that the bacteria were the dominator of soil N₂O production potential in most cases, but fungal pathways could be significant in vegetable field soils. The experiment with bactericide addition showed that inhibitors could affect non-target microorganisms in the SIRIN method. Our further analyses suggest that it is worth to explore the effect of soil organic carbon and microbial synergies on fungi-derived N₂O emissions.

Land-use carbon emissions are typically analyzed within administrative or economic regions; however, natural temperature gradients may modulate how socioeconomic and policy drivers translate into emissions. Using China’s land-use, energy, and meteorological data (1990–2022), we applied a coupled nature–economy–policy modeling framework across six temperature zones to test whether temperature acts as a nonlinear moderator rather than a direct determinant. The warm temperate zone accounted for >55% of national emissions and, together with the mid-temperate zone, this share reached ~80%, while the Qinghai–Tibet Plateau remained a net sink. Driver contributions varied systematically: economic factors dominated tropical and subtropical zones (74.3%), natural constraints dominated the plateau (41.7%), and policy interactions dominated mid- and warm-temperate zones (58.4%). Temperature acted nonlinearly, with emission effects rising from 0.277 to 1.212 t CO₂ per °C above a 26.65 °C summer mean (a 4.4-fold amplification). A generalized additive model confirms this significant nonlinearity. Scenario simulations suggest that the business-as-usual scenario peaks in 2035–2040, whereas the ecological protection and low-carbon scenarios peak around 2030. Under RCP4.5, the model projects a potential shift of the plateau from sink to source, but this result is conditional on simplified climate–carbon feedbacks and should be interpreted as a plausible risk rather than a prediction. These findings indicate that temperature zones offer a complementary natural gradient—not a replacement for administrative divisions—for understanding how similar human activities produce different carbon outcomes under contrasting thermal conditions. Given the use of partly uniform emission factors, the results represent macro-scale differentiation; governance implications are therefore conditional on scenario assumptions and require local feasibility assessment.