Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery

Intensifying methane emissions in Chinese Ponds: The interplay of warming, eutrophication, and depth changes

Ponds are significant contributors to global methane (CH4) emissions. However, accurately estimating their historical or future CH4 emissions remains challenging, particularly under dynamic environmental changes such as eutrophication, sedimentation-driven shallowing, and global warming. We synthesized 674 observations of CH₄ emission rates to identify key drivers and develop a process-based predictive model. We present a framework for spatially explicit estimation of pond CH₄ emissions in China from 1960 to 2020, accounting for factors such as temperature dependence, depth, nutrient levels, and pond area. Our findings show that pond CH₄ emissions are strongly temperature-dependent, characterized by a high average activation energy (0.834 eV). Notably, ebullitive emissions exhibit greater temperature sensitivity than diffusive emissions. Nitrogen concentrations and water column depth emerged as critical predictors of total CH₄ fluxes. Over the past six decades, CH₄ emissions from Chinese ponds increased approximately 9-fold, from 0.16 Tg CH₄ yr-1 in 1960 to 1.53 Tg CH₄ yr⁻¹ by 2020, emphasizing their growing role in global methane emissions. Notably, half of these emissions occur during summer, with ebullition accounting for 66 % of the total CH₄ flux. This increase was primarily driven by the interactions of warming, nutrient enrichment, declining water depth, and pond expansion. Our results underscore the growing role of ponds in CH₄ emissions and highlight the urgent need for mitigation measures, such as reducing nutrient loading and implementing periodic dredging management. This study provides a robust foundation for improving CH₄ emission estimates and developing sustainable management practices for ponds in the context of global environmental change.

Satellite-Based Estimation of Nitrous Oxide Concentration and Emission in a Large Estuary

Estuaries are nitrous oxide (N2O) emission hotspots and play an important role in the global N2O budget. However, the large spatiotemporal variability of emission in complex estuary environments is challenging for large-scale monitoring and budget quantification. This study retrieved water environmental variables associated with N2O cycling based on satellite imagery and developed a machine learning model for N2O concentration estimations. The model was adopted in China's Pearl River Estuary to assess spatiotemporal N2O dynamics as well as annual total diffusive emissions between 2003 and 2022. Results showed significant variability in spatiotemporal N2O concentrations and emissions. The annual total diffusive emission ranged from 0.76 to 1.09 Gg (0.95 Gg average) over the past two decades. Additionally, results showed significant seasonal variability with the highest contribution during spring (31 ± 3%) and lowest contribution during autumn (21 ± 1%). Meanwhile, emissions peaked at river outlets and decreased in an outward direction. Spatial hotspots contributed 43% of the total emission while covering 20% of the total area. Finally, SHapley Additive exPlanations (SHAP) was adopted, which showed that temperature and salinity, followed by dissolved inorganic nitrogen, were key input features influencing estuarine N2O estimations. This study demonstrates the potential of remote sensing for the estimation of estuarine emission estimations.

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure-property-effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

Utilization patterns strongly dominated the dynamics of CO2 and CH4 emissions from small artificial lakes

Small lakes are significant sources of CO2 and CH4 emissions to atmosphere. The dynamics and controls of CO2 and CH4 emissions from human-dominated small lakes with diverse functions remain poorly understood. We investigated the spatiotemporal dynamics of CO2 and CH4 concentrations and fluxes in 33 small lakes around the urban area with different landscape properties and utilization patterns, to clarify the impact of human-dominated functional shift on their greenhouse gas emissions. Meanwhile, we used microcosm cultivation methods to assess the CO2 and CH4 production rates of sediments in these lakes. The results indicated that the utilization ways significantly influence the CO2 and CH4 emissions in these lakes, with urban landscape lakes and aquaculture lakes showing significantly higher emissions compared to irrigation water-supplying lakes and drinking-water lakes. Extensive urbanization and aquaculture practices could increase the risk of that small lakes turn into hotspots of CO2 and CH4 emissions, and further complicate their spatial heterogeneity. Meanwhile, the production potential of CO2 and CH4 in sediments, as well as gas fluxes in small lakes, exhibited consistent functional differentiation across different utilization patterns. They were mainly driven by changes in sediment organic carbon and microbial carbon. Additionally, the difference of organic carbon and nitrogen loads were another drives for the variability in CO2 and CH4 emissions. We highlighted that the continuous accumulation of nutrient loads in water and sediments in human-dominated small lakes has greatly enhanced the potential for carbon gas emissions. We also found that utilization ways can significantly affect the key controls of CO2 and CH4 emission from small lakes, and also influence the reliability of carbon emission prediction models based on water chemistry parameters. To accurately estimate the contribution of small lakes to the global greenhouse gas inventory, it is essential to establish adaptive predictive models that consider the uncertainties in lake carbon emissions resulting from human utilization patterns.

Controls on spatial variation in porewater methane concentrations across United States tidal wetlands

Tidal wetlands can be a substantial sink of greenhouse gases, which can be offset by variable methane (CH4) emissions under certain environmental conditions and anthropogenic interventions. Land managers and policymakers need maps of tidal wetland CH4 properties to make restoration decisions and inventory greenhouse gases. However, there is a mismatch in spatial scale between point-based sampling of porewater CH4 concentration and its predictors, and the coarser resolution mapping products used to upscale these data. We sampled porewater CH4 concentrations, salinity, sulfate (SO42-), ammonium (NH4+), and total Fe using a spatially stratified sampling at 27 tidal wetlands in the United States. We measured porewater CH4 concentrations across four orders of magnitude (0.05 to 852.9 μM). The relative contribution of spatial scale to variance in CH4 was highest between- and within-sites. Porewater CH4 concentration was best explained by SO42- concentration with segmented linear regression (p < 0.01, R2 = 0.54) indicating lesser sensitivity of CH4 to SO42- below 0.62 mM SO42-. Salinity was a significant proxy for CH4 concentration, because it was highly correlated with SO42- (p < 0.01, R2 = 0.909). However, salinity was less predictive of CH4 with segmented linear regression (p < 0.01, R2 = 0.319) relative to SO42-. Neither NH4+, total Fe, nor relative tidal elevation correlated significantly with porewater CH4; however, NH4+ was positively and significantly correlated with SO42- after detrending CH4 for its relationship with SO42- (p < 0.01, R2 = 0.194). Future sampling should focus on within- and between-site environmental gradients to accurately map CH4 variation. Mapping salinity at sub-watershed scales has some potential for mapping SO42-, and by proxy, constraining spatial variation in porewater CH4 concentrations. Additional work is needed to explain site-level deviations from the salinity-sulfate relationship and elucidate other predictors of methanogenesis. This work demonstrates a unique approach to remote team science and the potential to strengthen collaborative research networks.

Methane dynamics altered by reservoir operations in a typical tributary of the Three Gorges Reservoir

Substantial nutrient inputs from reservoir impoundment typically increase sedimentation rate and primary production. This can greatly enhance methane (CH4) production, making reservoirs potentially significant sources of atmospheric CH4. Consequently, elucidating CH4 emissions from reservoirs is crucial for assessing their role in the global methane budget. Reservoir operations can also influence hydrodynamic and biogeochemical processes, potentially leading to pronounced spatiotemporal heterogeneity, especially in reservoirs with complex tributaries, such as the Three Gorges Reservoir (TGR). Although several studies have investigated the spatial and temporal variations in CH4 emissions in the TGR and its tributaries, considerable uncertainties remain regarding the impact of reservoir operations on CH4 dynamics. These uncertainties primarily arise from the limited spatial and temporal resolutions of previous measurements and the complex underlying mechanisms of CH4 dynamics in reservoirs. In this study, we employed a fast-response automated gas equilibrator to measure the spatial distribution and seasonal variations of dissolved CH4 concentrations in XXB, a representative area significantly impacted by TGR operations and known for severe algal blooms. Additionally, we measured CH4 production rates in sediments and diffusive CH4 flux in the surface water. Our multiple campaigns suggest substantial spatial and temporal variability in CH4 concentrations across XXB. Specifically, dissolved CH4 concentrations were generally higher upstream than downstream and exhibited a vertical stratification, with greater concentrations in bottom water compared to surface water. The peak dissolved CH4 concentration was observed in May during the drained period. Our results suggest that the interplay between aquatic organic matter, which promotes CH4 production, and the dilution process caused by intrusion flows from the mainstream primarily drives this spatiotemporal variability. Importantly, our study indicates the feasibility of using strategic reservoir operations to regulate these factors and mitigate CH4 emissions. This eco-environmental approach could also be a pivotal management strategy to reduce greenhouse gas emissions from other reservoirs.

Ecological water diversion activity changes the fate of carbon in a eutrophic lake

Lake eutrophication mitigation measures have been implemented by ecological water diversion, however, the responses of carbon cycle to the human-derived hydrologic process still remains unclear. With a famous river-to-lake water diversion activity at eutrophic Lake Taihu, we attempted to fill the knowledge gap with integrative field measurements (2011-2017) of gas carbon (CO2 and CH4) flux, including CO2-equivalent, and dissolved carbon (DOC and DIC) at water-receiving zone and reference zone. Overall, results showed the artificial water diversion activity increased gas carbon emissions. At water-receiving zone, total gas carbon (expressed as CO2-equivalent) emissions increased significantly due to the occurring of water diversion, with CO2 flux increasing from 9.31 ± 16.28 to 18.16 ± 12.96 mmol C m-2 d-1. Meanwhile, CH4 emissions at water-receiving zone (0.06 ± 0.05 mmol C m-2 d-1) was double of that at reference zone. Water diversion decreased DOC but increased DIC especially at inflowing river mouth. Temporal variability of carbon emissions and dissolved carbon were linked to water temperature, chlorophyll a, and nutrient, but less or negligible dependency on these environment variables were found with diversion occurring. Water diversion may increase gas carbon production via stimulating DOC mineralization with nutrient enrichment, which potentially contribute to increasing carbon emissions and decreasing DOC at the same time and the significant correlation between CO2 flux and CH4 flux. Our study provided new insights into carbon biogeochemical processes, which may help to predict carbon fate under hydrologic changes of lakes.