Research Background
In global change research, inland water bodies such as rivers and lakes are not merely conduits for the transport of carbon from land to the ocean; they also serve as critical interfaces for carbon transformation and the release of greenhouse gases. Organic carbon entering these water bodies is partly retained and buried within them, while another portion is converted into CO₂ and CH₄ through microbial decomposition, anaerobic methanogenesis, and gas diffusion, ultimately being released into the atmosphere.
In rapidly urbanizing regions, land-use changes, domestic wastewater discharge, river network modifications, and nutrient enrichment can further alter the physicochemical conditions of water bodies and their carbon cycling processes. However, the differences in greenhouse gas emissions among various types of urban water bodies, as well as the underlying driving mechanisms, remain to be elucidated.
On this issue, the research team led by Professor Cheng Junxiang at the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, has published its findings in the international journal Science of the Total Environment. The study focused on the highly urbanized Suzhou River–lake system, conducting in situ monitoring of CH₄ and CO₂ fluxes while simultaneously measuring the physicochemical parameters of the water bodies. It systematically elucidated the spatiotemporal patterns of greenhouse gas emissions from different types of urban water bodies and identified their key driving factors, thereby providing empirical evidence for assessing carbon cycling and managing emissions in urban aquatic environments.
Figure 1. Land cover map of Suzhou, showing the sampling points for rivers and lakes. Based on the dominant land cover types within each watershed, these 42 sampling points are classified into four categories: rivers and lakes located within urban (U), agricultural (A), and mixed (M) areas.
Research Methodology
Study area:
Suzhou, a core city in the Yangtze River Delta, has an urbanization rate of 77% and a water area share of 34.6%.
Sampling periods: May, July, and September 2021 (winter sampling was canceled due to the pandemic);
Sample point settings:
A total of 42 cross-sections: 27 rivers + 5 lakes;
Classified according to watershed landscape types: urban rivers (U), agricultural rivers (A), mixed-type rivers (M), and lakes (L);
Data Collection and Analysis:
Monitoring parameters: CO₂ and CH₄ fluxes, water temperature, pH, DO, TDS, NH₃-N, TN, TP, COD, chlorophyll a, etc.;
Statistical analyses: Pearson correlation analysis, stepwise multiple linear regression, and hotspot analysis (Getis–Ord Gi*).
This study utilized the PS-3010 Automatic Soil CO₂/CH₄ Flux System (Beijing Lijia United Technology Limited), combined with a miniature portable greenhouse gas analyzer and a waterborne buoyant device, to monitor CH₄ and CO₂ fluxes at the water–air interface in real time. During measurement, the waterborne buoyant device was placed on the water surface and connected to a sealed chamber lid, an analyzer, and a control system; a water filling process via a groove was employed to ensure airtightness. Once the system had stabilized, data were collected, and valid measurements were selected based on the linear slope of the gas concentration versus time curve.
Figure 2. Schematic diagram of a portable greenhouse gas flux collection device for water–air interface measurements.
Table 1. Average greenhouse gas emission rates and their CO₂-equivalent fluxes (mean ± standard deviation) for different types of rivers and lakes within the Suzhou metropolitan water network. U: urban rivers; A: agricultural rivers; L: lakes; M: mixed rivers.
Figure 3. Spatial patterns of CH₄ fluxes (a, c, e) and emission hotspots (b, d, f) across different months.
Figure 4. Spatial patterns of monthly CO₂ fluxes (a, c, and e) and CO₂ emission hotspots (b, d, and f).
Figure 5. For different types of rivers and lakes in the Suzhou region, a multiple stepwise regression analysis was conducted, with (a) CH₄ and (b) CO₂ fluxes as dependent variables and environmental factors as independent variables.
Research Results
(1) The Suzhou River and its associated water bodies as a whole exhibit net emissions of CH₄ and CO₂;
(2) The average CH₄ flux follows the pattern: agricultural rivers < urban rivers < mixed-landscape rivers < lakes.
(3) The average CO₂ flux exhibits the following pattern: agricultural rivers < mixed-landscape rivers < urban rivers < lakes, with seasonal variations:
(4) Seasonally, CH₄ and CO₂ fluxes are generally higher in summer, primarily due to elevated temperatures that stimulate microbial metabolism, organic matter decomposition, and gas diffusion; terrestrial organic matter and nutrients delivered by rainfall may also enhance emissions.
(5) Spatially, CH₄ hotspots are primarily distributed in lakes and their surrounding areas; CO₂ hotspots are largely concentrated along urban rivers and rivers with mixed land uses, with a more pronounced pattern near the Yangtze River. Agricultural rivers may also exhibit locally elevated emission levels in September.
(6) In terms of driving factors, CH₄ flux is primarily influenced by TDS, NH₃‑N, chlorophyll a, and water temperature; CO₂ flux is closely related to TDS, water transparency, and TP. The effect of NH₃‑N on CH₄ differs between lakes and mixed‑landscape rivers, suggesting that nitrogen‑regulation mechanisms vary across water body types.
Conclusion
The study demonstrates that rivers and lakes in highly urbanized areas both serve as significant sources of CH₄ and CO₂ emissions, with lakes and river segments strongly influenced by urbanization exhibiting even greater emission potential. The fluxes of greenhouse gases from aquatic environments are jointly regulated by water temperature, nutrient concentrations, dissolved oxygen, and other physicochemical parameters, resulting in pronounced seasonal patterns and spatial heterogeneity. Moving forward, urban river and lake management should not focus solely on improving water quality; it should also integrate the monitoring of greenhouse gas fluxes into its aquatic ecological assessment framework. By conducting long-term, continuous, multi‑parameter, coordinated observations, we can further enhance the accuracy of carbon cycle assessments for urban water bodies, thereby providing robust scientific support for urban water‑environment governance and emission‑reduction strategies.
Journal published in: Science of the Total Environment [Impact Factor: 8.0]
Research institutions: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences; University of Chinese Academy of Sciences; École Polytechnique Fédérale de Lausanne, among others. Research site: Suzhou Urban Water Network.
Equipment used: PS-3010 Automatic Soil CO₂/CH₄ Flux System
DOI: https://doi.org/10.1016/j.scitotenv.2024.170689