Re-estimating China's lake CO2 flux considering spatiotemporal variability

Seasonal CO2 fluxes from alpine river influenced by freeze-thaw in the Qinghai Lake basin, northeastern of the Qinghai-Tibet Plateau

River CO2 emissions, which contribute 53 % of the basin's overall carbon emissions, are essential parts of the global and regional carbon cycles. Previous CO2 flux calculates are mostly based on single samples collected during ice-free periods; however, little is known about the effects of freeze-thaw cycles on the river CO2 flux (FCO2) of inland rivers in alpine regions. Based on one year-round monthly continuous field sampling, we quantified the FCO2 and determined their driving factors in typical rivers during different freeze-thaw periods in the Qinghai Lake Basin (QLB) using the thin boundary layer model (TBL) and the path analysis method. The findings indicated that (1) the average FCO2 in the typical rivers was 184.98 ± 329.12 mmol/m2/d, acting as a carbon source during different freeze-thaw periods, and showed a decreasing trend with completely thawed periods (CTP, 303.15 ± 376.56 mmol/m2/d) > unstable freezing periods (UFP, 189.44 ± 344.08 mmol/m2/d) > unstable thawing periods (UTP, 62.35 ± 266.71 mmol/m2/d); (2) pH, surface water temperature (Tw) and total alkalinity (TA) were the dominant controlling factors during different freeze-thaw periods. Interestingly, they significantly affected FCO2 more before completely frozen than after frozen, with Tw and TA changing from having promoting effects to having limiting effects; (3) in addition, dissolved carbon components indirectly affected FCO2, primarily through the indirect effects of pH and Tw in the UTP; wind speed (U) directly promoted FCO2 in the CTP; and Ca2+ and dissolved inorganic carbon (DIC) were susceptible to indirect effects, which promoted/limited the release of FCO2 in the UFP, respectively. Our results reveal the changes of FCO2 and the factors influencing it in inland rivers within alpine regions during different freeze-thaw periods, thereby offering valuable support for carbon emission-related studies in alpine regions.

Spatial distribution, pollution level and human health risk assessment of heavy metals in urban street dust at neighbourhood scale

Urban street dust (UStD) is a vital issue for human health and is crucial for urban sustainability. This study aims to enhance the creation of safe, affordable, and resilient cities by examining environmental contamination and health risks in urban residential areas. Specifically, it investigates the concentrations and spatial distribution of chromium (Cr), cadmium (Cd), nickel (Ni), copper (Cu), lead (Pb), and zinc (Zn) in UStD in Yenimahalle, Ankara. The mean concentrations of Zn, Cr, Pb, Cd, Ni, and Cu in UStD were 97.98, 66.88, 55.22, 52.45, 38.37, and 3.81 mg/kg, respectively. The geoaccumulation pollution index (Igeo) values for these elements were: Cd (5.12), Ni (1.61), Cr (1.21), Pb (1.13), Cu (0.78), and Zn (0.24). These indices indicate that the area is moderately polluted with Cr, Pb, and Ni, uncontaminated to moderately contaminated with Cu and Zn, and extremely polluted with Cd. The hazard index (HI) values for Cr, Cd, Ni, Cu, Pb, and Zn were below the non-carcinogenic risk threshold for adults, indicating no significant risk. However, for children, the HI values for Pb, Ni, Cd, and Zn were 3.37, 1.80, 1.25, and 1.25, respectively, suggesting a higher risk. Carcinogenic risk (RI) of Cd, Ni, and Pb was significant for both children and adults, indicating that exposure through ingestion, inhalation, and dermal contact is hazardous. The findings highlight the need for strategic mitigation measures for both natural and anthropogenic activities, providing essential insights for residents, policymakers, stakeholders, and urban planners.

Thermal structure regulates the dynamics of carbon dioxide flux in alpine saline lake on the Qinghai-Tibet Plateau, China

Thermal stratification and mixing play important roles in the physicochemical composition of lakes and affect the geochemical cycle. However, the regulation of lake carbon exchange at the water-air interface by seasonal thermal structures remains unclear, especially for alpine saline lake on the Qinghai-Tibet Plateau (QTP). Based on continuous field sampling, carbon dioxide flux (FCO2) at the water-air interface in Qinghai Lake during the ice-free period was quantitatively analyzed by thin boundary layer model, as well as the driving factors of the change in FCO2 at the water-air interface. The findings revealed that the FCO2 was -22.16 ± 11.73 mmol m-2d-1 during the stratification period, and - 45.32 ± 29.67 mmol m-2d-1 during the mixing period. We found that thermal stratification limits the matter-energy exchange between the upper and bottom water columns, and carbonate precipitation results in a higher FCO2 than during mixing stage. However, the mixing process reduces the limiting effect of thermal stratification. During the carbonate process, water with higher salinity and pH at the bottom of the water column enters the upper part of the water column, reducing the partial pressure of carbon dioxide (pCO2) in the water column and causing the absorption of CO2 by the lake. Thermal stratification affects the vertical material-energy exchange and atmospheric CO2 uptake of lake. The present study further explains the possible underlying regulation of CO2 uptake in saline lake on the QTP involving the varied thermal structure.

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.

Chemical characteristics and health risk evaluation of natural waters in the Du River Source National Nature Reserve: A case study in Zhushan County, Hubei Province, China

To study the distribution of trace elements in natural water of the Du River Source National Nature Reserve and to assess the water quality and health risks, Zhushan County in Hubei Province was selected as the study area. Element content in 361 natural water samples collected from Zhushan County were measured by ICP-MS, ICP-OES, and HG-AFS. The main anions and cations present in water samples from Zhushan County are Ca2+ and HCO3-. The water chemistry is predominantly influenced by the weathering of carbonate rocks. The water samples with high content of selenium (Se) (0∼82.9 μg/L, mean 4.6 μg/L) in natural water in Zhushan County are mainly distributed in the northern part of Zhushan. The strontium (Sr) content of 49.6% of the water samples (0.001-2.177 mg/L, mean 0.234 mg/L) reached the criteria of natural mineral water for drinking in China (Sr ≥ 0.2 mg/L), which is distributed throughout the county. The high content of metasilicic acid (H2SiO3) (0.026-35.910 mg/L, mean 12.598 mg/L) and zinc (Zn) (0∼407.218 μg/L, mean 12.406 μg/L) are concentrated in northern Zhushan County. 99.7% water samples were freshwater and 98.9% meet the criteria of "good" water quality. All of the natural water samples have low health risk and low heavy metal pollution. 6.1% water samples meet the criteria of Se-type mineral water, while 45.4% meet the criteria of Sr-type mineral water, and 4.4% water samples meet the criteria of "low sodium, high Se, and high Sr" mineral water. Zhushan County has the potential for Se-type mineral water and Sr-type mineral water development. The findings of this study hold immense significance for the public health implications of drinking water in Du River Source, thereby offering valuable insights for effective water resources management.

Ecological patterns of phytoplankton across lake cross-section: insights into co-evolution of physicochemical conditions in Chashma Lake on Indus River

Physicochemical properties of water influence planktonic diversity and distribution, which is essential in obtaining basic knowledge of aquatic biodiversity. Thus current study aims to investigate the spatiotemporal diversity, abundance ratio, and distribution of phytoplankton species and their association with water quality parameters of Chashma Lake, Pakistan. During the study period from 2018 to 2019, we measured 13 physicochemical parameters across three selected sampling sites (S1, S2, and S3) in Chashma Lake, revealing both spatial and temporal variability. Dissolved oxygen (DO) was higher in S3, while S1 exhibited higher alkalinity levels, carbon dioxide, phosphorus, and chloride levels. The study identified 77 phytoplankton species grouped into five taxonomic categories, with Cyanobacteria dominating (39.90%), followed by Chlorophyta (33.4%) and Bacillariophyta (24.88%). Euglenozoa and Ochrophyta were less abundant (1.3% and 0.41%, respectively). Spatial variations in phytoplankton distribution were noted, with Chlorophyta being more abundant at S2, Bacillariophyta and Cyanobacteria at S1, and Euglenozoa dominating at S3. Canonical Correspondence Analysis (CCA) revealed the influence of various physicochemical parameters on phytoplankton distribution. This comprehensive study provides valuable insights for the ecological assessment and monitoring of water bodies. It is recommended that continuous monitoring is required to capture long-term trends, further explore the specific environmental drivers impacting phytoplankton dynamics, and consider management strategies for maintaining water quality and biodiversity in Chashma Lake.

From lake to fisheries: Interactive effect of climate and landuse changes hit on lake fish catch?

Global warming and unpredictable nature possess a negative impact on fisheries and the daily activities of other habitats. GIS and remote sensing approach is an effective tool to determine the morphological characteristics of the lake. The present study addresses the interactive effect of climate and landuse changes hit on fish catch in lake fisheries. We used a combination of the landscape disturbance index, vulnerability index, and loss index to construct a complete ecological risk assessment framework based on the landscape structure of regional ecosystems. The results indicate an increase from around 45%-76% in the percentage of land susceptible to moderate to ecological severe risk in the landscape from 2004 to 2023. Since 1950, temperature changes have increased by 0.4%, precipitation has decreased by 6%, and water levels have decreased by 4.2%, based on the results. The results indicate that landuse, water temperature, precipitation, and water depth significantly impact the aquaculture system. The findings strongly suggest integrating possible consequences of environmental change on fish yield for governance modeling techniques to minimize their effects.

Enhancing nutrient absorption through the influence of mangrove ecosystem on flow rate and retention time in salt marshes

This study investigates the impact of pneumatophores (the aerial roots of Avicenna marina) on water flow rate, retention time, contact time, and consequently on nutrient absorption through the sediment in sub-tropical salt marshes. The goal is to realize how the density of mangroves in salt marshes influences the kinematic factors of streamflow at estuaries. To this end, a field experiment was carried out to assess nutrient and organic compound levels in the sediment and water samples, spanning six sampling stations along the Chabahar River discharging to the Chabahar Bay, Iran. Then, we delved into the influence of altering environmental parameters, such as density and geometry, on the kinematic features of the flow through statistical analysis and hydraulic modeling. The results showed that the aerial roots reduce the flow rate and increase both retention and contact times. The longest retention time was observed at station #5 due to increased vegetation density and decreased instream velocity. In addition, measurements of total organic matter, total organic carbon, and total nitrogen indicated that an extended contact time resulted in increased absorption flux to the stream by sediments. As a result, pneumatophores can serve as an effective sink for organic matter in ecotones in salt marshes.

Unraveling the ecological threads: How invasive alien plants influence soil carbon dynamics

Invasive alien plants (IAPs) pose significant threats to native ecosystems and biodiversity worldwide. However, the understanding of their precise impact on soil carbon (C) dynamics in invaded ecosystems remains a crucial area of research. This review comprehensively explores the mechanisms through which IAPs influence soil C pools, fluxes, and C budgets, shedding light on their effects and broader consequences. Key mechanisms identified include changes in litter inputs, rates of organic matter decomposition, alterations in soil microbial communities, and shifts in nutrient cycling, all driving the impact of IAPs on soil C dynamics. These mechanisms affect soil C storage, turnover rates, and ecosystem functioning. Moreover, IAPs tend to increase gross primary productivity and net primary productivity leading to the alterations in fluxes and C budgets. The implications of IAP-induced alterations in soil C dynamics are significant and extend to plant-soil interactions, ecosystem structure, and biodiversity. Additionally, they have profound consequences for C sequestration, potentially impacting climate change mitigation. Restoring native plant communities, promoting soil health, and implementing species-specific management are essential measures to significantly mitigate the impacts of IAPs on soil C dynamics. Overall, understanding and mitigating the effects of IAPs on soil C storage, nutrient cycling, and related processes will contribute to the conservation of native biodiversity and complement global C neutrality efforts.

Assessment of land use change and carbon emission: A Log Mean Divisa (LMDI) approach

Changes in land use have a notable influence on carbon emissions since they can affect the levels of carbon stored in both soil and vegetation. To effectively analyze the factors influencing carbon emissions from land use change, the Log Mean Divisa (LMDI) method is commonly employed. The LMDI method is a decomposition analysis that dissects changes in carbon emissions into different factors, including shifts in land use patterns, population growth, economic activity, and energy intensity. This approach enables the identification of specific drivers of carbon emission changes and the development of targeted policy interventions to address them. To explore the relationship between land use change, carbon emissions, and the LMDI method, a case study analysis can be conducted. This involves selecting a particular region or country experiencing land use change and examining the factors driving these transformations. Subsequently, the LMDI method can be applied to decompose the changes in carbon emissions within the selected region or country, thereby pinpointing the major contributors to these changes. In our study, we observed the necessity of regulating energy consumption and greenhouse gas emissions in urban communities through sustainable practices and technologies. The research highlighted variations in energy consumption, emissions, renewable energy utilization, and public transportation usage among selected cities in China. Moreover, the study demonstrated land use patterns and their associated carbon emissions, alongside the findings of the LMDI analysis, which explored carbon emissions based on different land use patterns. The study illuminates the importance of understanding the relationship between land use change and carbon emissions, employing the LMDI method as a valuable analytical tool. It underscores the significance of sustainable practices and technologies in mitigating carbon emissions in urban areas and provides insights into the role of land use patterns in shaping carbon emission outcomes.

Temporal variation of water quality parameters in the lacustrine of the Thrace Region, Northwest Türkiye

Thrace is a part of the Marmara Region northwest of Türkiye. This region hosts several lentic ecosystems used for irrigation and drinking water supply. The present study was conducted to analyze the temporal distributions of water quality parameters (WQPs) of lentic ecosystems (lacustrine habitats), including lakes (L1-L2), reservoirs (R1-R12), and ponds (P1-P19) of the Thrace Region. Thirty-three lacustrine habitats were identified in the region. Freshwaters were collected in the wet (end of winter) and dry (end of summer) seasons of 2021-2022 and tested for 12 WQPs. Data was evaluated for the water quality index (WQI) and nutrient pollution index (NPI) and their overall quality level. For the evaluation of non-carcinogenic health risk indices of WQPs, the chronic daily index (CDI), hazard quotient (HQ), and hazard index (HI) were applied. Cluster analysis (CA), Pearson correlation index (PCI), and principal component analysis (PCA) were used to classify the lacustrine habitats and identify the source of WQPs. The average values were as follows: 9.28 mg/L for dissolved oxygen (DO), 94.6% for oxygen (O2) saturation, 9.29 for pH, 613 μS/cm for electrical conductivity (EC), 3.96 NTU for turbidity, 358 mg/L for total dissolved solids (TDS), 3.17 mg/L for nitrate (NO3), 0.05 mg/L for nitrite (NO2), 1.01 mg/L for phosphate (PO4), 78.5 mg/L for sulfate (SO4), and 102 mg/L for chloride (Cl). Results showed a significant increase in WQPs, including NO3, NO2, and PO4, in the wet season, while the salinity decreased from the dry to wet season. Results revealed that HI values of water contaminants in lacustrine habitats were noted to be less than one. Based on determined WQPs, the present study recommends using lacustrine water habitats for irrigation, drinking, and other domestic and industrial purposes.