Greenhouse gas dynamics in an urbanized river system: influence of water quality and land use

Raising wastewater collection and discharge standards will reduce greenhouse gas emissions from metropolitan rivers

Urban rivers are increasingly recognized as significant sources for greenhouse gas (GHG) emissions. Few studies, however, quantify emissions of all three GHG from rehabilitating urban rivers that receive treated wastewater. This study analyzed carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) concentrations and diffusive fluxes from the Suzhou Creek in Shanghai that were investigated across the four seasons in 2021. Our results show that Suzhou Creek behaves as a source of atmospheric GHG emissions. The mean concentrations of CO2 CH4 and N2O, in the main and tributaries were 80.62 ± 37.81 versus 82.07 ± 50.77 μmol L-1, 0.38 ± 0.31 versus 0.73 ± 0.87 μmol L-1, and 37.33 ± 17.70 versus 51.26 ± 35.84 nmol L-1, respectively. The corresponding fluxes of CO2, CH4, and N2O were 3.04 ± 2.36 versus 2.78 ± 2.25 mmol m-2 h-1, 17.82 ± 18.91 versus 35.35 ± 49.51 μmol m-2 h-1, and 1.44 ± 1.25 versus 2.2 ± 2.95 μmol m-2 h-1, respectively. GHG emissions from Suzhou Creek are lower than global urban rivers. N2O generation in the nitrate-rich mainstem may primarily be attributed to denitrification and nitrification, and ammonium-rich tributaries may mainly associate with nitrification and coupling nitrification-denitrification. Tributaries are more suitable for CH4 generation. CO2 in the basin comes mainly from heterotrophic respiration of organic matter, and the high nutrient load and Chlorophyll a concentration in tributaries support photosynthesis. Although wastewater treatment plants and sewage treatment stations provide direct inputs of GHG and nutrient substrates, respectively, their input load (including GHG and nutrient substrates) is lower than that of other urban rivers. The study highlights that with the improvement of sewage collection capacity and treatment discharge standards in large cities, the input load and water pollution situation in urbanized areas will be greatly improved, thus reducing GHG emissions from urban rivers.

How do varying nitrogen fertilization rates affect crop yields and riverine N2O emissions? A hybrid modeling study

Headwater streams in agricultural areas constitute significant sources of nitrous oxide (N2O) due to nutrient enrichment; however, their emissions are often overlooked in current environmental impact assessments. This scarcity highlights the importance of developing advanced decision tools to evaluate these contributions and create effective mitigation strategies. Our study establishes the first integrated modeling framework that combines a process-based model SWAT+ with a linear mixed model (LMM) to predict N2O emissions from a headwater agricultural river system in Belgium under diverse climate change and fertilization scenarios. In particular, the calibrated and validated SWAT+ model was used to simulate streamflow, nutrient transport, and crop yields under these scenarios, from which, together with biochemical data collected from sampling campaigns, riverine N2O emissions were predicted via LMM. Our results revealed hydrologically driven patterns in riverine N2O emissions, with peak emissions in winter and spring, driven by precipitations enhancing shallow subsurface flows, carrying leached nutrients from fields to the river, and fueling N2O emissions. These phenomena were intensified under climate change scenarios, especially during combined wetter and hotter winters and springs, which elevated headwater N2O emissions by 40 %. Moreover, when coupling these conditions with a 20 % increase in fertilizer rates, riverine N2O emissions would be boosted by 83 %. These findings underscore the importance of integrating land-surface and river processes, to effectively quantify the feedback loop between river nutrient enrichment and climate change under the influence of agricultural practices, and to support comprehensive mitigation strategies under the warming climate.

The role of riverbed substrates in N2O and CH4 emission: Insights from metagenomic analysis of epilithic biofilms

Riverbed substrates are critical in N2O and CH4 emission with functional microbes adhering to them. However, the role of substrates remains to be fully understood. This study monitors N2O and CH4 emission and collects epilithic biofilms on riverbed substrates with various diameters and size heterogeneity from 10 sections along a mountain river. Compared with the global range, moderate water-air exchange rates of N2O (-2.34-29.2 μg/m2/h) and rapid CH4 emission (-2.58-35.2 mg/m2/h) are observed. Based on metagenomic analysis, the abundances of nirS and fmdA genes, which encode catalysts in the denitrification and the hydrogenotrophic methanogenesis process, are found to be significantly higher in the medium diameter group (2-100 mm), implying higher N2O and CH4 emission. Meanwhile, the abundance of nirS and nirK genes, which are key to N2O production, is significantly higher in the low size heterogeneity group, promoting N2O release. In contrast, the abundance of ftr, pta,ackA and ACS genes critical in the methanogenesis processes are significantly lower in the low size heterogeneity group, inhibiting CH4 emission. For N2O production, the nitrification process is found to be dominated by species of Nocardioides and Planctomycetales, denitrification process by species of Tabrizicola and Rhodobacteraceae, and dissimilatory nitrate reduction to ammonium process by Leptospiraceae species. In contrast, CH4 is mainly generated by species of Pirellula and Proteobacteria through hydrogenotrophic, acetoclastic and methylotrophic methanogenesis respectively. A structural equation model indicated that substrate physical properties are equally or even more important as/than the aquatic nutrients concentration for N2O or CH4 emission in mountain rivers.

Land use and urbanization indirectly control riverine CH4 and CO2 emissions by altering nutrient input

Urban rivers are recognized as significant sources of methane (CH4) and carbon dioxide (CO2) emissions. Despite this, the influence of land use and urbanization on carbon emissions across rural-urban rivers at the watershed scale has been insufficiently explored. This study utilized in-situ surveys of the Liao River in northern China to investigate the spatial and temporal variations of CH4 and CO2 emissions and their relationship with urbanization and its potential controlling factors. The findings revealed that CH4 emissions peaked in fall, whereas CO2 emissions were highest in summer. The average fluxes of CH4 and CO2 at the water-gas interface were 1387.22 ± 2474.98 µmol·m-2·d-1 and 52.78 ± 54.44 mmol·m-2·d-1, respectively. Water quality parameters accounted for 80.49 % of the total variation in CH4 and CO2 concentrations and fluxes. Structural equation modeling indicated that TN, TP, DTC, and conductivity had direct effects on riverine CH4 and CO2 emissions, with standardized direct effects of 0.50 and 0.49, respectively. Nutrient input emerged as the primary driver, increasing CH4 and CO2 concentrations and fluxes, particularly in urban-adjacent river sections likely receiving higher nutrient loads. This study underscores that land use and urbanization indirectly influence riverine CH4 and CO2 emissions by modifying nutrient inputs. Effective land use management and nutrient input control are recommended strategies to mitigate riverine CH4 and CO2 emissions.

Regulating greenhouse gas dynamics in tidal wetlands: Impacts of salinity gradients and water pollution

Tidal wetlands play a critical role in emitting greenhouse gases (GHGs) into the atmosphere; our understanding of the intricate interplay between natural processes and human activities shaping their biogeochemistry and GHG emissions remains lacking. In this study, we delve into the spatiotemporal dynamics and key drivers of the GHG emissions from five tidal wetlands in the Scheldt Estuary by focusing on the interactive impacts of salinity and water pollution, two factors exhibiting contrasting gradients in this estuarine system: pollution escalates as salinity declines. Our findings reveal a marked escalation in GHG emissions when moving upstream, primarily attributed to increased concentrations of organic matter and nutrients, coupled with reduced levels of dissolved oxygen and pH. These low water quality conditions not only promote methanogenesis and denitrification to produce CH4 and N2O, respectively, but also shift the carbonate equilibria towards releasing more CO2. As a result, the most upstream freshwater wetland was the largest GHG emitter with a global warming potential around 35 to 70 times higher than the other wetlands. When moving seaward along a gradient of decreasing urbanization and increasing salinity, wetlands become less polluted and are characterized by lower concentrations of NO3-, TN and TOC, which induces stronger negative impact of elevated salinity on the GHG emissions from the saline wetlands. Consequently, these meso-to polyhaline wetlands released considerably smaller amounts of GHGs. These findings emphasize the importance of integrating management strategies, such as wetland restoration and pollution prevention, that address both natural salinity gradients and human-induced water pollution to effectively mitigate GHG emissions from tidal wetlands.

Divergent drivers of the spatial variation in greenhouse gas concentrations and fluxes along the Rhine River and the Mittelland Canal in Germany

Lotic ecosystems are sources of greenhouse gases (GHGs) to the atmosphere, but their emissions are uncertain due to longitudinal GHG heterogeneities associated with point source pollution from anthropogenic activities. In this study, we quantified summer concentrations and fluxes of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and dinitrogen (N2), as well as several water quality parameters along the Rhine River and the Mittelland Canal, two critical inland waterways in Germany. Our main objectives were to compare GHG concentrations and fluxes along the two ecosystems and to determine the main driving factors responsible for their longitudinal GHG heterogeneities. The results indicated that the two ecosystems were sources of GHG fluxes to the atmosphere, with the Mittelland Canal being a hotspot for CH4 and N2O fluxes. We also found significant longitudinal GHG flux discontinuities along the mainstems of both ecosystems, which were mainly driven by divergent drivers. Along the Mittelland Canal, peak CO2 and CH4 fluxes coincided with point pollution sources such as a joining river tributary or the presence of harbors, while harbors and in-situ biogeochemical processes such as methanogenesis and respiration mainly explained CH4 and CO2 hotspots along the Rhine River. In contrast to CO2 and CH4 fluxes, N2O longitudinal trends along the two lotic ecosystems were better predicted by in-situ parameters such as chlorophyll-a concentrations and N2 fluxes. Based on a positive relationship with N2 fluxes, we hypothesized that in-situ denitrification was driving N2O hotspots in the Canal, while a negative relationship with N2 in the Rhine River suggested that coupled biological N2 fixation and nitrification accounted for N2O hotspots. These findings stress the need to include N2 flux estimates in GHG studies, as it can potentially improve our understanding of whether nitrogen is fixed through N2 fixation or lost through denitrification.

Urbanization and weather dynamics co-dominated the spatial-temporal variation in pCO2 and CO2 fluxes in small montanic rivers draining diverse landscapes

Rivers have been widely reported as important CO2 emitters to the atmosphere. Rapid urbanization has a profound impact on the carbon biogeochemical cycle of rivers, leading to enhanced riverine CO2 evasions. However, it is still unclear whether the spatial-temporal patterns of CO2 emissions in the rivers draining diverse landscapes dominated by urbanization were stable, especially in mountainous areas. This study carried out a two-year investigation of water environmental hydrochemistry in three small mountainous rivers draining urban, suburban and rural landscapes in southwestern China, and CO2 partial pressure (pCO2) and fluxes (fCO2) in surface water were measured using headspace equilibrium method and classical thin boundary layer model. The average pCO2 and fCO2 in the highly urbanized river were of 4783.6 μatm and 700.0 mmol m-2 d-1, conspicuously higher than those in the rural river (1525.9 μatm and 123.2 mmol m-2 d-1), and the suburban river presented a moderate level (3114.2 μatm and 261.2 mmol m-2 d-1). It provided even clearer evidence that watershed urbanization could remarkably enhance riverine CO2 emissions. More importantly, the three rivers presented different longitudinal variations in pCO2, implying diversified spatial patterns of riverine CO2 emissions as a result of urbanization. The urban land can explain 49.6-69.1% of the total spatial variation in pCO2 at the reach scale, indicating that urban land distribution indirectly dominated the longitudinal pattern of riverine pCO2 and fCO2. pCO2 and fCO2 in the three rivers showed similar temporal variability with higher warm-rainy seasons and lower dry seasons, which are significantly controlled by weather dynamics, including monthly temperature and precipitation, but seem to be impervious to watershed urbanization. High temperature-stimulated microorganisms metabolism and riched-CO2 runoff input lead much higher pCO2 in warm-rainy seasons. However, it showed more sensitivity of pCO2 to monthly weather dynamics in urbanized rivers than that in rural rivers, and warm-rainy seasons showed hot moments of CO2 evasion for urban rivers. TOC, DOC, TN, pH and DO were the main controls on pCO2 in the urban and suburban rivers, while only pH and DO were connected with pCO2 in the rural rivers. This indicated differential controls and regulatory processes of pCO2 in the rivers draining diverse landscapes. Furthermore, it suggested that pCO2 calculated by the pH-total alkalinity method would obviously overestimate pCO2 in urban polluted rivers due to the inevitable influence of non-carbonate alkalinity, and thus, a relatively conservative headspace method should be recommended. We highlighted that urbanization and weather dynamics co-dominated the multiformity and uncertainty in spatial-temporal patterns of riverine CO2 evasions, which should be considered when modeling CO2 dynamics in urbanized rivers.

Impact of salinity gradient, water pollution and land use types on greenhouse gas emissions from an urbanized estuary

Estuaries have been recognized as one of the major sources of greenhouse gases (GHGs) in aquatic systems; yet we still lack insights into the impact of both anthropogenic and natural factors on the dynamics of GHG emissions. Here, we assessed the spatiotemporal dynamics and underlying drivers of the GHG emissions from the Scheldt Estuary with a focus on the effects of salinity gradient, water pollution, and land use types, together with their interaction. Overall, we found a negative impact of salinity on carbon dioxide (CO2) and nitrous oxide (N2O) emissions which can be due to the decrease of both salinity and water quality when moving upstream. Stronger impact of water pollution on the GHG emissions was found at the freshwater sites upstream compared to saline sites downstream. In particular, when water quality of the sites reduced from good, mainly located in the mouth and surrounded by arable sites, to polluted, mainly located in the upstream and surrounded by urban sites, CO2 emissions from the sites doubled while N2O emissions tripled. Similarly, the effects of water pollution on methane (CH4) emissions became much stronger in the freshwater sites compared to the saline sites. These decreasing effects from upstream to the mouth were associated with the increase in urbanization as sites surrounded by urban areas released on average almost two times more CO2 and N2O than sites surrounded by nature and industry areas. Applied machine learning methods also revealed that, in addition to salinity effects, nutrient and organic enrichment stimulated the GHG emissions from the Scheldt Estuary. These findings highlight the importance of the interaction between salinity, water pollution, and land use in order to understand their influences on GHG emissions from dynamic estuarine systems.

Unravelling spatiotemporal N2O dynamics in an urbanized estuary system using natural abundance isotopes

Estuaries are strong sources of N2O to the atmosphere; yet we still lack insights into the impact of their biogeochemical dynamics on the emissions of this powerful greenhouse gas. Here, we investigated the spatiotemporal dynamics of the N cycle in an estuary with a focus on the emission mechanisms and pathways of N2O. By coupling N2O isotopocule analysis and substrate NO3- isotope analysis, we found that nutrient availability, oxygen level, salinity gradient and temperature variation were major drivers of the N2O emissions from the Scheldt Estuary. In winter, lower temperature and higher O2 concentration diminished denitrification rates and reduction of N2O to N2, while both were enhanced in warmer summer, causing higher fraction of reduced N2O. As a result, we found comparable N2O fluxes and dissolved concentrations between the two seasons. Decrease in salinity level and increase in NO3- concentration accelerated N2O production when moving upstream of the estuary where more urbanization and higher NO3- from wastewater discharges were found. However, these drivers had no significant effect on the fraction of N2O derived by either denitrification or nitrification and/or fungal denitrification since the fractional proportion of these pathways showed no spatiotemporal variations, remaining around 89 % and 11 %, respectively. These findings challenge the conventional notion that N2O fluxes are generally higher in summer because of higher denitrification rates while confirming that denitrification is the most important pathway of N2O production in the estuaries. Furthermore, our study highlight the importance of combining various isotope analyses to gain in-depth understanding about N2O emission pathways and N cycling in dynamic systems like estuaries.

Impact of microplastics on riverine greenhouse gas emissions: a view point

In recent decades, microplastics (MPs < 5 mm) are ubiquitous and considered a serious emerging environmental problem. However, due to the limited recovery and long-lasting durability MPs, debris is frequently accumulating in riverine ecosystems, thereby impacting microbial activity and its communities. The presence of MPs may alter the microbial richness, variety, and population, thereby impacting the transformation of biogeochemical cycles. The occurrence, fate, and transport of MPs in marine and terrestrial ecosystems and their impact on biogeochemical or nutrient cycling are reported in the scientific fraternity. Yet, the global scientific community is conspicuously devoid of research on impact of MPs on riverine greenhouse gas (GHG) emissions. The presented view point provides a novel idea about the fate of MPs in the riverine system and its impact on GHG emissions potential. Literature reveals that DO and nutrients (organic carbon, NH4+, NO3-) concentrations play an important role in potential of GHG emission in riverine ecosystems. The proposed mechanism and research gaps provided will be highly helpful to the hydrologist, environmentalist, biotechnologist, and policymakers to think about the strategic mitigation measure to resolve the future climatic risk.

Comparison of machine learning algorithms to predict dissolved oxygen in an urban stream

Water quality monitoring for urban watersheds is critical to identify the negative urbanization impacts. This study sought to identify a successful predictive machine learning model with minimal parameters from easy-to-deploy, low-cost sensors to create a monitoring system for the urban stream network, Hunnicutt Creek, in Clemson, SC, USA. A multiple linear regression model was compared to machine learning algorithms k-nearest neighbor, decision tree, random forest, and gradient boosting. These algorithms were evaluated to understand which best predicted dissolved oxygen (DO) from water temperature, conductivity, turbidity, and water level change at four locations along the urban stream. The random forest algorithm had the highest performance in predicting DO for all four sites, with Nash-Sutcliffe model efficiency coefficient (NSE) scores > 0.9 at three sites and > 0.598 at the fourth site. The random forest model was further examined using explainable artificial intelligence (XAI) and found that temperature influenced the DO predictions for three of the four sites, but there were different water quality interactions depending on site location. Calculating the land cover type in each site's sub-watershed revealed that different amounts of impervious surface and vegetation influenced water quality and the resulting DO predictions. Overall, machine learning combined with land cover data helps decision-makers better understand the nuances of urban watersheds and the relationships between urban land cover and water quality.

Watershed urbanization dominated the spatiotemporal pattern of riverine methane emissions: Evidence from montanic streams that drain different landscapes in Southwest China

Methane (CH₄) emissions from streams are an important component of the global carbon budget of freshwater ecosystems, but these emissions are highly variable and uncertain at the temporal and spatial scales associated with watershed urbanization. In this study, we conducted investigations of dissolved CH₄ concentrations and fluxes and related environmental parameters at high spatiotemporal resolution in three montanic streams that drain different landscapes in Southwest China. We found that the average CH₄ concentrations and fluxes in the highly urbanized stream (2049 ± 2164 nmol L^-1 and 11.95 ± 11.75 mmol·m^-2·d^-1) were much higher than those in the suburban stream (1021 ± 1183 nmol L^-1 and 3.29 ± 3.66 mmol·m^-2·d^-1) and were approximately 12.3 and 27.8 times those in the rural stream, respectively. It provides powerful evidence that watershed urbanization strongly enhances riverine CH₄ emission potential. Temporal patterns of CH₄ concentrations and fluxes and their controls were not consistent among the three streams. Seasonal CH₄ concentrations in the urbanized streams had negative exponential relationships with monthly precipitation and demonstrated greater sensitivity to rainfall dilution than to the temperature priming effect. Additionally, the CH₄ concentrations in the urban and semiurban streams showed strong, but opposite, longitudinal patterns, which were closely related to urban distribution patterns and the HAILS (human activity intensity of the land surface) within the watersheds. High carbon and nitrogen loads from sewage discharge in urban areas and the spatial arrangement of the sewage drainage contributed to the different spatial patterns of the CH₄ emissions in different urbanized streams. Moreover, CH₄ concentrations in the rural stream were mainly controlled by pH and inorganic nitrogen (NH₄⁺ and NO₃^-), while urban and semiurban streams were dominated by total organic carbon and nitrogen. We highlighted that rapid urban expansion in montanic small catchments will substantially enhance riverine CH₄ concentrations and fluxes and dominate their spatiotemporal pattern and regulatory mechanisms. Future work should consider the spatiotemporal patterns of such urban-disturbed riverine CH₄ emissions and focus on the relationship between urban activities with aquatic carbon emissions.

Urbanization promotes specific bacteria in freshwater microbiomes including potential pathogens

Freshwater ecosystems are characterized by complex and highly dynamic microbial communities that are strongly structured by their local environment and biota. Accelerating urbanization and growing city populations detrimentally alter freshwater environments. To determine differences in freshwater microbial communities associated with urbanization, full-length 16S rRNA gene PacBio sequencing was performed in a case study from surface waters and sediments from a wastewater treatment plant, urban and rural lakes in the Berlin-Brandenburg region, Northeast Germany. Water samples exhibited highly habitat specific bacterial communities with multiple genera showing clear urban signatures. We identified potentially harmful bacterial groups associated with environmental parameters specific to urban habitats such as Alistipes, Escherichia/Shigella, Rickettsia and Streptococcus. We demonstrate that urbanization alters natural microbial communities in lakes and, via simultaneous warming and eutrophication and creates favourable conditions that promote specific bacterial genera including potential pathogens. Our findings are evidence to suggest an increased potential for long-term health risk in urbanized waterbodies, at a time of rapidly expanding global urbanization. The results highlight the urgency for undertaking mitigation measures such as targeted lake restoration projects and sustainable water management efforts.