Input Of Pollutants Research Articles

Temporal and Spatial Dynamics of Carbon, Nitrogen, and Phosphorus in a Subtropical Urban River (Tamanduateí River, São Paulo, Brazil)

Water quality in urban streams often reflects the broader environmental challenges posed by dense population centers, where pollution from untreated sewage and runoff can significantly degrade ecosystems. This study examines the spatial and temporal variations of carbon, nitrogen, and phosphorus concentrations in the Tamanduateí River, which runs through the Metropolitan Region of São Paulo, Brazil. Data were sourced from the annual reports of the Environmental Company of the State of São Paulo (CETESB) covering the period from 2011 to 2022. Between 2011 and 2017, carbon and phosphorus concentrations declined, likely due to sanitation improvements. However, since 2017, these concentrations have been rising again, indicating renewed pollution inputs, primarily from untreated sewage. Nitrogen levels remained consistently high, with elevated concentrations observed upstream, linked especially to domestic effluent discharges. The recent increase in phosphorus levels is also of concern. The absence of spatial variation in phosphorus suggests diffuse pollution from urban areas, while nitrogen decreases downstream, possibly due to biological assimilation. The study underscores the pressing need for enhanced sewage management. Drawing from the successful revitalization of the Cheonggyecheon stream in Seoul, implementing nature-based solutions and regular maintenance could effectively reduce nutrient pollution and improve water quality, facilitating the restoration of the Tamanduateí River.

Analysis of Nitrogen and Phosphorus Pollution in Shenzhen Bay over the Past 40 Years

Based on 40 years of routine water quality monitoring data from Shenzhen Bay and special intensive monitoring since 2021, we analysed the spatiotemporal variations in nitrogen and phosphorus pollution. The results indicate serious levels of nitrogen and phosphorus pollution in Shenzhen Bay. Although recent trends show a decline in concentrations of inorganic nitrogen and reactive phosphate—with the annual average concentration of inorganic nitrogen dropping below 1.0 mg/L since 2020 and reactive phosphate levels near the bay mouth decreasing to approximately 0.020 mg/L—the overall water quality still does not meet China’s Class IV seawater quality standards. Additionally, water quality is poorer during the rainy season, with the average summer concentration of inorganic nitrogen being over 25% higher than in spring and autumn. The water quality is primarily influenced by land-sourced pollutants and tidal effects, generally, with the concentration of DIN and DIP decreased from the estuary of the Shenzhen River, the main seagoing river, to the bay mouth. Near the river’s mouth, concentrations of inorganic nitrogen and reactive phosphate are approximately six and four times higher, respectively, than those near the bay mouth. Due to current emission standards and sewage disposal methods, the proportion of nitrate nitrogen in DIN has significantly increased, exceeding 80% since 2020. To improve the water quality of Shenzhen Bay and to reduce pollution inputs, coordinated land and marine development activities are recommended.

Chemical profiling of organic contaminants in rural surface waters combining target and non-target LC-HRMS/MS analysis

The pollution of natural waters by contaminants of emerging concern (CECs) is one of the pressing problems due to their global distribution and potential negative effects on the environment and human health. In rural areas with lower population density and limited industrial development, less contamination is expected. However, the lack of wastewater treatment plants (WWTPs) or their poor removal efficiency can lead to significant input of pollutants. In this context, 11 streams of rural areas in the Guadalquivir River basin, southeast of Spain, were studied over two years to obtain an overview of the origin and distribution of contaminants. A target method using solid-phase extraction and liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) was developed for the analysis of 316 compounds in surface waters. A total of 78 target analytes were detected, comprising pesticides, pharmaceuticals, personal care products (PCPs), transformation products (TPs), and industrial chemicals. The flame retardant tributyl phosphate (16–3572 ng L−1) was detected in all samples, followed by caffeine (30–8090 ng L−1) and the analgesic tramadol (3–1493 ng L−1). The target approach was combined with a non-target analysis (NTA) strategy to obtain an overall perspective of the chemical profile of unexpected or unknown compounds in the samples. Up to 79 contaminants were tentatively identified, and 12 of them were finally confirmed with standards. Most of the contaminants determined by NTA were pharmaceuticals and their TPs. The results indicated that most of CECs have an urban origin despite traditional agriculture is the main economic activity in this region. Moreover, the absence of WWTPs in small towns is significant, as contamination levels at these sites were comparable to or higher than those in larger populations with sewage treatments.

Composition and Distribution of Bacteria, Pathogens, and Antibiotic Resistance Genes at Shanghai Port, China

As one of the biggest ports, the aquatic ecology of Shanghai Port has a significant impact on the health of surrounding residents and aquatic animals. Therefore, this study explored the composition and distribution of bacteria, pathogens, and ARGs at Shanghai Ports by high-throughput sequencing. The results showed that the bacterial community was dominated by the Proteobacteria at the phylum level. With the refinement of classification level, the main composition of bacteria in different samples shows significant differences. In contrast, there was no significant difference in the composition and distribution of pathogens among the samples. It indicates that pathogens and ARGs have steady community composition at Shanghai Port. Pseudomonas aeruginosa and Salmonella enterica could be regarded as candidate indicator pathogens. macB, tetA (58), and evgS were the main ARGs, and they showed a close relationship with Mycobacterium tuberculosis, Magnaporthe oryzae, Streptococcus pneumonia, Xanthomonas oryzae, and Vibrio cholerae in different sites and layers. This may lead to the transfer of ARGs between different pathogens, resulting in more threatening pathogens. The results showed that the pathogens and ARGs present posed a risk of transmission and were potentially harmful to residents in the environment. They can spread to any other ports in the world through the transmission of ballast water, posing a threat to humans and local ecology. In order to keep human health, it is necessary to pay more attention to the water environment of ports and increase the monitoring. Furthermore, it is essential to strengthen the supervision of external source pollution input due to the special features of the port.

Removing multiple refractory organic pollutants in wastewater by cell bioaugmentation and genetic bioaugmentation with Rhodococcus sp. strain p52

Bioaugmentation is an appealing remediation strategy to treat refractory organic pollutants in contaminated environments. In this study, activated sludge was bioaugmented with Rhodococcus sp. strain p52 to treat synthetic wastewater containing multiple refractory organic pollutants (dibenzofuran, dibenzo-p-dioxin, and phenanthrene). Bioaugmentation of activated sludge reactors with strain p52 alleviated the adverse effects of refractory pollutants on the chemical oxygen demand and ammonia nitrogen removal capacity of the reactors and enhanced the removal of the refractory organic pollutants, especially highly refractory compound dibenzo-p-dioxin. Toxicity of the refractory pollutants to activated sludge bacteria was attenuated due to contaminant degradation by strain p52. Genetic bioaugmentation mediated by catabolic plasmid transfer from strain p52 to activated sludge bacteria and cell bioaugmentation occurred along with successful colonization and proliferation of strain p52. In addition, a potential metabolically mutualistic relationship existed between strain p52 and activated sludge bacteria for refractory pollutants degradation. Both refractory organic pollutants input and strain p52 bioaugmentation profoundly influenced the structure of the activated sludge bacterial community and drove bacterial community shifts in different directions. This study provides insights into the activities of bioaugmented bacteria and bioaugmentation technology for environmental contaminant remediation.

Assessment of carbon flux gradients and dominant processes in a subtropical highly urbanized coastal ecosystem

Highly urbanized coastal ecosystems are vital in the global carbon budget. However, there are limited researches on carbon flux gradients in these nearshore areas, considering both natural and anthropogenic influences. Through on-site measurements and field samplings during wet-to-dry season in 2023, this study investigated spatial variations and factors affecting carbon fluxes, focusing on the impacts of salinity and eutrophic status in five geographically connected coastal waters of the Guangdong-Hong Kong-Macau Greater Bay Area (GBA). By estimating carbon exchange at land-sea-air interface, dominant processes in carbon dynamics were identified as well. Results showed that partial pressure of CO2 (pCO2) varied from 391 to 2290 μatm, and sea-air CO2 exchange fluxes (FCO2) ranged from -3.07 to 70.07 mmol m–2 d–1, indicating significant geographical distinctions among five coastal waters of the GBA. The total carbon transport from rivers to these nearshore waters was approximated at 6.44 Tg C yr-1, with the Pearl River (PR) contributing 99.7%, primarily in dissolved forms. Atmospheric CO2 release was calculated at 0.29 Tg C yr-1 for studied five coastal waters, primarily as carbon sources, except for Dapeng Bay (DPB) as a sink. CO2 emissions inversely correlated with salinity, yet positively with eutrophication status, particularly in river-dominated estuaries. Moreover, CO2 flux decreased 23 times as eco-status shift from eutrophic to non-eutrophic. River plumes, terrestrial pollutant inputs, and economic structure were underlying drivers, influencing carbon species concentrations and fluxes. Elevated CO2 concentrations in eutrophic coastal waters were mainly attributed to terrestrial carbon and nutrients inputs, supporting active biological respiration and microbial decomposition. Conversely, carbon dynamics potentially depend on the balance of respiration and photosynthesis in non-eutrophic coastal waters. This study offers high geographic precision and specificity of carbon species, and provides land-sea integration insight to understand carbon dynamic mechanisms, promoting advancements in water quality management and climate mitigation.

Environmental impact assessment in the Caspian region

Background. The current state and pressures associated with the anthropogenic impact on the Caspian region environment are examined. Surrounded by five coastal countries, including the Republic of Azerbaijan, the Russian Federation, the Republic of Kazakhstan, Turkmenistan, and the Islamic Republic of Iran, the Caspian Sea is the world’s largest endorheic water body. The isolation of the Caspian Sea, which is characterized by special climatic conditions and salinity gradients, has led to the formation of a unique ecosystem.Aim. Environmental impact assessment of the Caspian region and the associated anthropogenetic load.Materials and methods. The anthropogenic processes affecting the environment of the Caspian Sea and the surrounding area were assessed using the DPSIR concept. This concept includes the components of driving forces, pressures, state, impacts, and response in order to inclusively determine the connections between human activities and the environment. This concept was used as by the European Environmental Agency as the basis for integrated environmental analysis.Results. The conducted study determined the scale and geography of greenhouse gas degassing, the volume of pollutant emissions into the atmosphere by components, the structure of greenhouse gas emissions, and the dynamics of emissions of carbon dioxide, methane, and nitrogen oxide generated from stationary sources in the Caspian region. Anthropogenic emissions of greenhouse gases that pollute the atmosphere and sea waters, generated from stationary sources, are formed as a result of the production activities of a number of industries: mining; electricity, gas, and steam generation; air conditioning; manufacturing; water supply; wastewater treatment; construction industry; agriculture and forestry; transport storage and communications; repair of vehicles and motorcycles; as well as other activities.Conclusion. The share of enterprises in the fuel and energy complex that emit greenhouse gases is about 60%. The structure of greenhouse gas emissions and the dynamics of CO2, CH4, and N2O emissions generated from stationary sources in the Caspian region are shown. The Caspian Sea is one of the most important endorheic basins in the world. Being surrounded by land on all sides, the Caspian Sea lacks flows that would maintain its self-purification. Pollutants that enter the Sea remain there due to the lack of mechanisms for their removal. Complete information about the level of pollutant inputs is crucial for selecting the most effective means of environmental compensation or pollution reduction. The largest polluting sectors are agriculture, industry, including oil and gas, and urbanization.

Effect-directed analysis of endocrine and neurotoxic effects in stormwater depending discharges

The investigation of pollutant inputs via stormwater runoff and subsequent effects in receiving waters is becoming increasingly urgent in view of climate change with accompanying extreme weather situations such as heavy rainfall events. In this study, two sampling areas, one urban and one rural but dominated by a highway, were investigated using effect-directed analysis to identify endocrine and neurotoxic effects and potentially responsible substances in stormwater structures and receiving waters. For this purpose, a transgenic yeast cell assay for the simultaneous detection of estrogenic, androgenic, and progestogenic effects (YMEES) was performed directly on high-performance thin-layer chromatography (HPTLC) plates. Concomitantly, estrogens were analyzed by GC–MS/MS and other micropollutants typical for wastewater and stormwater by LC-MS/MS. Discharges from the combined sewer overflow (CSO) contribute a large portion of the endocrine load to the studied water body, even surpassing the load from a nearby wastewater treatment plant (WWTP). An effect pattern similar to the CSO sample was shown in the receiving water after the CSO with lower intensities, consisting of an estrogenic, androgenic, and progestogenic effect. In contrast, after the WWTP, only one estrogenic effect with a lower intensity was detected. Concentrations of E1, 17α-E2, 17β-E2, EE2, and E3 in the CSO sample were 2000, 410, 1100, 560, and 2700 pg/L, respectively. HPTLC-YMEES and GC–MS/MS complement each other very well and help to elucidate endocrine stresses. An Acetylcholinesterase (AChE) inhibitory effect could not be assigned to a causative compound by suspect and non-target analysis using LC-HRMS. However, the workflow showed how information from HPTLC separation, effect-based methods, and other meta-information on the sampling area and substance properties can contribute to an identification of effect-responsible substances. Overall, the study demonstrated that effect-based methods in combination with HPTLC and instrumental analysis can be implemented to investigate pollution by stormwater run-off particularly regarding heavy rain events due to climate change.

Multi-habitat distribution and coalescence of resistomes at the watershed scale based on metagenomics

The characteristics of the resistome distribution in rivers have been extensively studied. However, the distribution patterns of resistomes in multiple habitats and contributions of upstream habitats to the resistome profile in water bodies remains unclear. The current study explored the distribution and coalescence of antibiotic resistance genes (ARGs), metal resistance genes (MRGs), and mobile genetic elements (MGEs) in four habitats (including water bodies, sediments, biofilms, and riparian soils) within the Shichuan River watershed. The results revealed significant variations in the abundances and diversity of resistomes across the four habitats and two seasons. Assembly processes of resistomes were predominated by stochastic processes in summer but deterministic processes in winter. The main source of the resistome in summer water bodies was the movement of genes from upstream water bodies. However, the main sources of resistome in downstream water bodies in winter were the movement of resistomes in upstream sediments and the input of external pollution. The physicochemical properties of winter water bodies significantly influenced the movement of the resistomes across habitats. The current study elucidated the multi-habitat distribution pattern and migration mechanism of the resistome in the river system, providing new insights for effectively monitoring and controlling bacterial resistance.

Spatial distribution and risk assessment of microplastics in surface waters of the St. Lawrence Estuary

Development of effective prevention and mitigation strategies for marine plastic pollution requires a better understanding of the pathways and transport mechanisms of plastic waste. Yet the role of estuaries as a key interface between riverine inputs of plastic pollution and delivery to receiving marine environments remains poorly understood. This study quantified the concentration and distribution of microplastics (MPs) (50–3200 μm) in surface waters of the St. Lawrence Estuary (SLE) in eastern Canada. Microplastics were identified and enumerated based on particle morphology, colour, and size class. Fourier Transform Infrared (FTIR) spectroscopy was used on a subset of particles to identify polymers. Generalized linear models (Gamma distribution with log-link) examined the relationship between MP concentrations and oceanographic variables and anthropogenic sources. Finally, a risk assessment model, using MP concentrations and chemical hazards based on polymer types, estimated the MP pollution risk to ecosystem health. Mean surface MP concentration in the SLE was 120 ± 42 SD particles m−3; MP concentrations were highest in the fluvial section and lowest in the Northwest Gulf of St. Lawrence. However, MP concentrations exhibited high heterogeneity along the length and width of the SLE. Microplastics were elevated at stations located closer to wastewater treatment plant outflows and downstream sites with more agricultural land. Black, blue, and transparent fibers and fragments ≤250 μm were most commonly encountered. Predominant polymer types included polyethylene terephthalate, regenerated cellulose, polyethylene, and alkyds. While the overall risk to ecosystem health in the entire estuary was considered low, several stations, particularly near urban centres were at high or very high risk. This study provides new insights into the quantification and distribution of MPs and first estimates of the risk of MP pollution to ecosystem health in one of the world's largest estuaries.

Rainfall seasonality shapes microbial assembly and niche characteristics in Yunnan Plateau lakes, China

Microorganisms are crucial components of freshwater ecosystems. Understanding the microbial community assembly processes and niche characteristics in freshwater ecosystems, which are poorly understood, is crucial for evaluating microbial ecological roles. The Yunnan Plateau lakes in China represent a freshwater ecosystem that is experiencing eutrophication due to anthropogenic activities. Here, variation in the assembly and niche characteristics of both prokaryotic and microeukaryotic communities was explored in Yunnan Plateau lakes across two seasons (dry season and rainy season) to determine the impacts of rainfall and environmental conditions on the microbial community and niche. The results showed that the environmental heterogeneity of the lakes decreased in the rainy season compared to the dry season. The microbial (bacterial and microeukaryotic) α-diversity significantly decreased during the rainy season. Deterministic processes were found to dominate microbial community assembly in both seasons. β-Diversity decomposition analysis revealed that microbial community compositional dissimilarities were dominated by species replacement processes. The co-occurrence networks indicated reduced species complexity for microbes and a destabilized network for prokaryotes prior to rainfall, while the opposite was found for microeukaryotes following rainfall. Microbial niche breadth decreased significantly in the rainy season. In addition, lower prokaryotic niche overlap, but greater microeukaryotic niche overlap, was observed after rainfall. Rainfall and environmental conditions significantly affected the microbial community assembly and niche characteristics. It can be concluded that rainfall and external pollutant input during the seasonal transition alter the lake environment, thereby regulating the microbial community and niche in these lakes. Our findings offer new insight into microbiota assembly and niche patterns in plateau lakes, further deepening the understanding of freshwater ecosystem functioning.

Improving river water quality prediction with hybrid machine learning and temporal analysis

River systems provide multiple ecosystem services to society globally, but these are already degraded or threatened in many areas of the world due to water quality issues linked to diffuse and point-source pollutant inputs. Water quality evaluation is essential to develop remediation and management strategies. Computational tools such as machine learning based predictive models have been developed to improve monitoring network capabilities. The model's performance is reduced when datasets composed of reductant information are used for training, on the other hand, the selection of most representative and variable water quality scenarios could result in higher precision. This study analyzed historical water quality behavior in the Santiago River, Mexico, to identify the most variable and representative data available to train machine learning models (Adaptive Neuro Fuzzy Inference System – ANFIS, Artificial Neural Network – ANN, and Support Vector Machine - SVM). Thirteen monitoring sites were clustered according to their water quality variability from 2009 to 2022. Subsequently, a Time Series Analysis (TSA) was used to select the most representative monitoring station from each cluster. Data for 6/13 monitoring sites were retained for the Best Training Subset (BTS) used to train restricted models that performed with similar (ANN and SMV) or higher (ANFIS) prediction accuracy (in terms of RMSE, MAE, MSE and R2) for both training and testing. This study provides evidence of water quality data containing redundant information that is not useful to improve machine learning model performance, in turn leading to overtraining. Combined analytical approaches can maximize the representativeness and variability of data selected for machine learning applications, leading to improved prediction.

A multi-evidence approach in an Amazonian river based on land use, water quality, histopathological effects and habitat selection behavior in fish

The Amazon rivers constitute the largest river basin in the world, with a high level of biodiversity. The Tocantins River is one of the most important rivers in this region, which has been impacted by different land uses. The objective of this study was to carry out a multi-evidence analysis focusing on the water quality of the Tocantins River, close to the municipality of Marabá-PA. We analyzed forest cover and water quality and, using the model organism Danio rerio, performed toxicity tests for histopathological effects, as well as the habitat selection approach by exposing fish to different river water samples in a multi-compartment device. The results showed that the studied area has already lost almost 30% of its forests in recent decades. Regarding water quality, the upstream (C1) and downstream (C5) points are the least impacted. On the other hand, the other points (C2–C4), closer to the city, greater input of pollutants was detected. Fish exposed to water samples from the most impacted sites showed several oedemas and hyperplastic cells in the gills. Regarding habitat selection behavior, there was a marked avoidance by samples with the highest contamination load. The results of this study lead to the understanding of the potential negative effects of human activities on local Amazonian biodiversity, since the potential toxicity of the environment, in conjunction with changes in the habitat selection process, could lead to a decline in populations of aquatic organisms, altering the environmental balance.

Antibiotic resistance of heterotrophic bacteria from the sediments of adjoining high Arctic fjords, Svalbard.

Antibiotic resistance bacteria (ARB) and antibiotic resistance genes (ARGs) are now considered major global threats. The Kongsfjorden and Krossfjorden are the interlinked fjords in the Arctic that are currently experiencing the effects of climate change and receiving input of pollutants from distant and regional sources. The present study focused on understanding the prevalence of antibiotic resistance of retrievable heterotrophic bacteria from the sediments of adjacent Arctic fjords Kongsfjorden and Krossfjorden. A total of 237 bacterial isolates were tested against 16 different antibiotics. The higher resistance observed towards Extended Spectrum β-lactam antibiotic (ESBL) includes ceftazidime (45.56%) followed by trimethoprim (27%) and sulphamethizole (24.05%). The extent of resistance was meagre against tetracycline (2.53%) and gentamycin (2.95%). The 16S rRNA sequencing analysis identified that Proteobacteria (56%) were the dominant antibiotic resistant phyla, followed by Firmicutes (35%), Actinobacteria (8%) and Bacteroidetes. The dominant resistant bacterial isolates are Bacillus cereus (10%), followed by Alcaligenes faecalis (6.47%), Cytobacillus firmus (5.75%) Salinibacterium sp. (5%) and Marinobacter antarcticus (5%). Our study reveals the prevalence of antibiotic resistance showed significant differences in both the inner and outer fjords of Kongsfjorden and Krossfjorden (p < 0.05). This may be the input of antibiotic resistance bacteria released into the fjords from the preserved permafrost due to the melting of glaciers, horizontal gene transfer, and human influence in the Arctic region act as a selection pressure for the development and dissemination of more antibiotic resistant bacteria in Arctic fjords.

Hydrochemical fingerprints of karst underground river systems impacted by urbanization in Guiyang, Southwest China

Karst groundwater plays an irreplaceable role in the formation and development of urban areas, and land-use and land-cover change (LUCC) and the input of pollutants during the urbanization process would pose potential environmental risks to underground rivers. We analysed the relationship between urbanization processes and underground river hydrochemistry over nearly 35 years in Guiyang city, southwest of China, it was found that concentrations of various cations and anions, as well as total dissolved solids (TDS), gradually increased with the urbanization process, with significant fluctuations during the rapid urbanization periods. The Hydrochemical Facies Evolution Diagram (HFED) clearly showed the influence of urbanization on the hydrochemistry of the underground rivers. The ion ratios of γMg2+/γCa2+—γHCO3−, γNa+/γCl−, Ca2+/Mg2+—Ca2+ or Mg2+/Σ cations, HCO3−/SO42−—HCO3− or SO42−/Σ anions revealed two distinct phases in the hydrochemical evolution of the underground river system, highly consistent with the urbanization process. Before the rapid urbanization, acid deposition and agricultural activities affected the hydrochemistry, with HCO3-Ca·Mg and HCO3·SO4-Ca·Mg as the dominant types controlled by limestone and dolomite dissolution in water-rock interactions. As acid deposition diminished, the input of SO42− from urban sewage compensated for the reduced impact, but the increased impermeable surfaces reduced the infiltration of atmospheric precipitation, leading to a reduced dissolution of dolomite minerals in water-rock interactions, resulting in a decrease in Mg2+ and a change in the hydrochemical type. The hydrochemical type evolved from a single HCO3·SO4-Ca·Mg type and HCO3-Ca·Mg type to multiple types, such as HCO3·Cl-Ca, HCO3·SO4-Ca, HCO3-Ca, and HCO3·SO4-Ca·Mg, and was highly unstable. With changes in land use, the proportions of various cations and anions in the hydrochemistry changed, especially NH4+, NO3−, SO42−, Na+, and Cl−, which were more sensitive to human activities. This study indicated the impact of urbanization on the hydrochemistry of the underground river system, with the input of SO42− from human activities and the increase in paved surfaces due to urbanization collectively altering the hydrochemical types of the underground river system. The rapid response of karst underground river system hydrochemistry indicates a potential impact on groundwater system by urbanization that should not be ignored.

Chronic increasing nitrogen and endogenous phosphorus release from sediment threaten to the water quality in a semi-humid region reservoir

The water quality in the drinking water reservoir directly affects people's quality of life and health. When external pollution input is effectively controlled, endogenous release is considered the main cause of water quality deterioration. As the major nitrogen (N) and phosphorus (P) sources in reservoirs, sediment plays a vital role in affecting the water quality. To understand the spatial and temporal variation of N and P in the sediment, this study analyzed the current characteristics and cumulative effects of a semi-humid reservoir, Yuqiao Reservoir, in North China. The N and P concentrations in the reservoir sediment were decreased along the flow direction, while the minimum values were recorded at the central sediment profile. External input and algal deposition were the main factors leading to higher sediment concentrations in the east (Re-E) and west (Re-W) areas of reservoir sediment profiles. According to the long-term datasets, the peaks of both sediment total nitrogen content and deposition rate were observed in the 2010s, which has increased about three times and six times than in the1990s, respectively. Therefore, the increase in phosphorus concentration may be the main reason for eutrophication in water in recent years. The mineralization of organic matter has a significant promoting effect on releasing N and P from sediments, which will intensify eutrophication in water dominated by P and bring huge challenges to water environment management. This study highlights that the current imbalance in N and P inputs into reservoirs and the endogenous P release from sediment will have a significant impact on water quality.

An Outsized Contribution of Rivers to Carbon Emissions From Interconnected Urban River‐Lake Networks Within Plains

AbstractUrban aquatic ecosystems in plains are often subject to extensive anthropogenic pollutant inputs and have prolonged times for pollutant degradation, potentially leading to diverse carbon emission patterns. This study explored carbon emission patterns and underlying mechanisms in Ge Lake and its tributaries, located in an urban area within a plain in China. The results revealed that carbon emissions from rivers were significantly higher than those from the downstream lake. Spatial interpolation analysis further revealed that CO2‐eq emissions from a 1‐km2 river area can be equivalent to those from an area as large as 86‐km2 of the downstream lake. Rivers are the gateway for the entry of organic compounds, often carrying substances that are readily biodegradable. As the river water moves slowly, these compounds accumulate and undergo degradation in rivers before they reach downstream lakes. The findings may benefit the estimates of carbon emissions in these regions with greater precision.

MAX-DOAS observations of pollutant distribution and transboundary transport in typical regions of China

Studying the spatiotemporal distribution and transboundary transport of aerosols, NO2, SO2, and HCHO in typical regions is crucial for understanding regional pollution causes. In a 2-year study using multi-axis differential optical absorption spectroscopy in Qingdao, Shanghai, Xi'an, and Kunming, we investigated pollutant distribution and transport across Eastern China-Ocean, Tibetan Plateau-Central and Eastern China, and China-Southeast Asia interfaces. First, pollutant distribution was analyzed. Kunming, frequently clouded and misty, exhibited consistently high aerosol optical depth throughout the year. In Qingdao and Shanghai, NO2 and SO2, as well as SO2 in Xi'an, increased in winter. Elevated HCHO in summer in Shanghai and Xi'an, especially Xi'an, suggests potential ozone pollution issues. Subsequently, pollutant transportation across interfaces was studied. At the Eastern China-Ocean interface, the gas transport flux was the largest among other interfaces, with the outflux exceeding the influx, especially in winter and spring. The input of pollutants from the Tibetan Plateau to central-eastern China was larger than the output in winter and spring, with SO2 having the highest transport flux in winter. The pollution input from Southeast Asia to China significantly exceeded the output, with spring and winter inputs being 3.22 and 3.03 times the output, respectively. Lastly, the transportation characteristics of a pollution event at Kunming were studied. During this period, pollutants were transported from west to east, with the maximum SO2 transport flux at an altitude of 2.87 km equaling 27.74 µg/(m2·s). It is speculated that this pollution was caused by the transport from Southeast Asian countries to Kunming.

Internal loading regulates the phosphorus concentration in a diversion input shallow lake in the semi-humid region of North China: Differentiated processes in littoral wetland and open water areas

Diversion input lakes usually have a low catchment area/lake area ratio and pulsing pollution input. Various pollutants might accumulate in the lake continuously owing to the concentration effect under high evaporation but low precipitation over the entire area, typically for sedimentary cyclic elements such as phosphorus (P). However, the detailed transportation, sedimentation, and internal release mechanisms of P in the diversion input lakes remain unclear. This study conducted a year-long investigation of the littoral wetlands and open water areas of the shallow Lake Hengshui in the semi-humid region of North China. Results revealed that the average total P concentrations in the water and surficial sediment reached as high as 0.202 mg L−1 and 878.21 mg kg−1 in summer. The high water P levels in the lake were mainly regulated by the high internal P loading during summer and autumn, with the internal P loading being approximately nine times the external P loading. The littoral wetland area serves as a higher sedimentation sink and release source of P than the open water area. The concentrated P was continuously transported to the littoral wetland area through detritus burial, coprecipitation, and deposition of suspended particles. The release of P was mainly controlled by the dissolution of redox-sensitive Fe-P and Org-P at high temperatures and organic matter mineralization in the sediment, accompanied by the potential release capacity of apatite P (Ca-P). Future management of eutrophication and P levels in similar diversion input lakes should pay more attention to the high internal P loading in the sediment and the differentiated sedimentation and release processes in the littoral wetland and open water areas.

Evolutionary trends and analysis of the driving factors of Ulva prolifera green tides: A study based on the random forest algorithm and multisource remote sensing images

Understanding the prolonged spatiotemporal evolution and identifying the underlying causes of Ulva prolifera green tides play pivotal roles in managing such occurrences, restoring water ecology, and fostering sustainable development in marine ecosystems. Satellite remote sensing represents the primary choice for monitoring Ulva prolifera green tides due to its capability for extensive, long-term ocean monitoring. Based on multi-source remote sensing images, ecological and environmental datasets, and machine learning algorithms, therefore, this study focused on "remote sensing modelling - evolution history - change trends - mechanism analysis" to elucidate both the remote sensing monitoring models and the underlying driving factors governing the spatiotemporal evolution of Ulva prolifera green tides in the highly impacted South Yellow Sea of China. With the use of GOCI Ⅰ/Ⅱ images, an hybrid remote sensing extraction model merging the robustness of the random forest (RF) model and the optical algae cloud index (ACI) was established to map Ulva prolifera distribution patterns. The ACI-RF method exhibited exceptional performance, with an F1 score surpassing 0.95, outperforming alternative methods such as the support vector machine (SVM) and K-nearest neighbour (KNN) methods. On the basis, we analysed the evolutionary trends and the driving factors determining these distribution patterns using meteorological data, runoff data, and data on various water quality parameters (SST, ocean current speed, wind speed, precipitation, DO, PAR, Si, NO3-, PO43-and N/P). Over the period from 2011 to 2022, excluding 2021, there was a notable decline in the area of Ulva prolifera green tides, varying between 397 and 2689.9 km2, with an average annual reduction rate of 3%. The maximum annual biomass varied between 0.12 and 15.9 kt. Notably, more than 75% of the area of Ulva prolifera green tides exhibited northward drift, which was significantly influenced by northern currents and wind fields. The analysis of driving factors indicates that factors such as average sea surface temperature, eastward wind speed, northward wind speed, precipitation, PO43- and N/P/Si significantly influence the biological growth rate of Ulva prolifera. Furthermore, coastal land use change and surface runoff, particularly surface runoff in June, significantly impacted the growth rate of Ulva prolifera, with Pearson correlation coefficients of 0.74 and 0.67, respectively. Against the background of global warming and severe deterioration in the marine environment, Ulva prolifera blooms persist. Consequently, two distinct management strategies were proposed based on the distribution patterns and cause analysis results for addressing Ulva prolifera green tides: establishing a continuous protection framework for rivers, lakes, and nearshore areas to mitigate pollutant inputs and implementing precise environmental monitoring measures in urban expansion areas and farmlands to combat overgrowth-induced green tides. This methodology could be applied in other regions affected by marine ecological disasters, and the criteria for selecting influencing factors offer a valuable reference for designing tailored and proactive measures aimed at controlling Ulva prolifera green tides.