High resolution spatiotemporal modeling of long term anthropogenic nutrient discharge in China.

High-resolution integration of large-scale and long-term anthropogenic nutrient discharge data is crucial for understanding the spatiotemporal evolution of pollution and identifying intervention points for pollution mitigation. Here, we establish the MEANS-ST1.0 dataset, which has a high spatiotemporal resolution and encompasses anthropogenic nutrient discharge data collected in China from 1980 to 2020. The dataset includes five components, namely, urban residential, rural residential, industrial, crop farming, and livestock farming, with a spatial resolution of 1 km and a temporal resolution of monthly. The data are available in three formats, namely, GeoTIFF, NetCDF and Excel, catering to GIS users, researchers and policymakers in various application scenarios, such as visualization and modelling. Additionally, rigorous quality control was performed on the dataset, and its reliability was confirmed through cross-scale validation and literature comparisons at the national and regional levels. These data offer valuable insights for further modelling the interactions between humans and the environment and the construction of a digital Earth.

Decoding China's anthropogenic typical pollutant discharge patterns: Long-term dynamics and hotspot transitions driven by population, diet, and sanitation.

Human activities have led to an alarming increase in pollution, resulting in widespread water contamination. A comprehensive understanding of the quantitative relationship between anthropogenic pollutant discharges and the escalating anthropogenic disturbances and environmental efforts is crucial for effective water quality management. Here we establish a Model for Estimating Anthropogenic pollutaNts diScharges (MEANS) and simulate the long-term dynamics of various types of anthropogenic discharges in China based on an unprecedented spatio-temporal dynamic parameter dataset. Our findings reveal that from 1980 to 2020, anthropogenic discharges exhibited an overall trend of initially increasing and subsequently decreasing, with the peak occurring around 2005. During this period, the dominant pollution sources in China shifted from urban to rural areas, thereby driving the transition of hotspot pollutants from nitrogen to phosphorus in the eastern regions. The most significant drivers of anthropogenic pollutant discharges gradually shifted from population size and dietary structure to wastewater treatment and agricultural factors. Furthermore, we observed that a significant portion of China's regions still exceed the safety thresholds for pollutant discharges, with excessive levels of total phosphorus (TP) being particularly severe. These findings highlight the need for flexible management strategies in the future to address specific pollution levels and hotspots in different regions. Our study underscores the importance of considering the complex interplay between anthropogenic disturbances, environmental efforts, and long-term anthropogenic pollutant discharges for effective water pollution control.

Estimating Yangtze River basin's riverine N2O emissions through hybrid modeling of land-river-atmosphere nitrogen flows.

Riverine ecosystems are a significant source of nitrous oxide (N2O) worldwide, but how they respond to human and natural changes remains unknown. In this study, we developed a compound model chain that integrates mechanism-based modeling and machine learning to understand N2O transfer patterns within land, rivers, and the atmosphere. The findings reveal a decrease in N2O emissions in the Yangtze River basin from 4.7 Gg yr-1 in 2000 to 2.8 Gg yr-1 in 2019, with riverine emissions accounting for 0.28% of anthropogenic nitrogen discharges from land. This unexpected reduction is primarily attributed to improved water quality from human-driven nitrogen control, while natural factors contributed to a 0.23 Gg yr-1 increase. Notably, urban rivers exhibited a more rapid N2O efflux ( [Formula: see text] ), with upstream levels nearly 3.1 times higher than rural areas. We also observed nonlinear increases in [Formula: see text] with nitrogen discharge intensity, with urban areas showing a gradual and broader range of increase compared to rural areas, which exhibited a sharper but narrower increase. These nonlinearities imply that nitrogen control measures in urban areas lead to stable reductions in N2O emissions, while rural areas require innovative nitrogen source management solutions for greater benefits. Our assessment offers fresh insights into interpreting riverine N2O emissions and the potential for driving regionally differentiated emission reductions.

Characterizing legacy nitrogen-induced time lags in riverine nitrogen reduction for the Songhuajiang River Basin: Source analysis, spatio-seasonal patterns, and impacts on future water quality improvement.

Legacy nitrogen (N) originating from net N inputs (NNI) may pose ongoing threats to riverine water quality worldwide and even cause serious time-lags between water quality restoration and NNI declines. A better understanding of legacy N effects on riverine N pollutions in different seasons is essential to improve riverine water quality. Here, we investigated contributions of legacy N on riverine dissolved inorganic N (DIN) changes in different seasons and quantified spatio-seasonal time-lags in the Songhuajiang River basin (SRB), a hotspot of NNI with four distinct seasons, by exploring long-term (1978-2020) NNI-DIN relationships. Results firstly showed a significant seasonal difference in NNI, with the highest value observed in spring (average, 2184.1 kg/km2), 1.2, 5.0, and 4.6 times higher than that in summer, autumn, and winter, respectively. Cumulative legacy N had dominated riverine DIN changes, with a relative contribution of approximately 64% in 2011-2020, causing time-lags of 11-29 years across the SRB. The longest seasonal lags existed in spring (average, 23 years) owing to greater impacts of legacy N to riverine DIN changes in this season. Mulch film application, soil organic matter accumulation, N inputs, and snow cover were identified as the key factors that strengthened seasonal time-lags by collaboratively enhancing legacy N retentions in soils. Furthermore, a machine learning-based model system suggested that timescales for water quality improvement (DIN, ≤1.5 mg/L) varied considerably (from 0 to >29 years, Improved N Management-Combined scenario) across the SRB, with greater lag effects contributing to slower recovery. These findings can provide a more comprehensive insight into sustainable basin N management in the future.

Towards the carbon neutrality of sludge treatment and disposal in China: A nationwide analysis based on life cycle assessment and scenario discovery.

Motivated by the carbon neutrality target, strategic planning for a low-carbon transition of sludge treatment and disposal in China is challenging due to the unpredictability of technical, regional, socioeconomic, and political factors affecting greenhouse gas (GHG) emissions. This study combines the use of a Life Cycle Assessment and the Patient Rule Induction Method, accounting for possibilities that could achieve net-zero carbon emissions by exploring multiple plausible future profiles of sludge treatment and disposal. Results show that reducing sludge landfill and increasing anaerobic digestion are effective methods to facilitate GHG reduction. Achieving carbon neutrality is closely linked to developing a cleaner electricity mix. Based on a cascaded scenario analysis considering regional differences for 31 Chinese provinces, results demonstrated a maximum cumulative reduction potential of 371 Mt CO2 equivalents from 2020 to 2050, equal to 59.84% of the business-as-usual scenario. Together with GHG reductions, terrestrial acidification and ecotoxicity as well as freshwater ecotoxicity are synergistically reduced. However, the shifting environmental burden results in freshwater eutrophication, human toxicity, marine ecotoxicity, marine eutrophication, and photochemical oxidant formation. This study presents a novel method for systematically identifying possible future development paths toward carbon neutrality. The findings may support policy designs for achieving target carbon reduction effects for sludge disposal.

Anthropogenic pollution discharges, hotspot pollutants and targeted strategies for urban and rural areas in the context of population migration: Numerical modeling of the Minjiang River basin.

Unprecedented urbanization-induced population migration in China severely affects the scale and geographic distribution of anthropogenic pollutant discharge. Understanding how pollutant discharge patterns respond to population migration can help guide future efforts to maintain water sustainability. Here, based on a new calculation framework with 18 dynamic parameters designed for anthropogenic discharges, we finely tracked and visualized the effects of population migration on the spatial and temporal changes in anthropogenic discharge from 1980 to 2019 in the Minjiang River basin. The results indicate that the increasing effect of population migration on anthropogenic discharges peaked in 2002 and started to contribute to pollutant reduction from 2010 onward. The direct impact of population migration only contributes to the shift of anthropogenic discharges from rural to urban areas, while the migration bonus is the key factor leading to the reduction in anthropogenic discharges. Population migration is highly beneficial for chemical oxygen demand (COD) reduction, which has contributed to a shift from COD to NH4+-N and total phosphorus (TP) as hotspot pollutants in the whole basin (NH4+-N in urban areas and TP in rural areas). Moreover, pollution reduction resulting from the demographic bonus phenomenon has remained limited only to urban areas. Since approximately 2010, the per capita amount and total amount of anthropogenic pollutant discharges in rural areas have exceeded those in urban areas; in particular, the per capita COD and TP discharges in rural areas reached four times those in urban areas. This suggests that future pollution control strategies should give more attention to rural areas and focus on the differentiation and targeting of urban and rural areas.

Self-enhanced and efficient removal of As(III) from water using Fe-Cu-Mn composite oxide under visible-light irradiation: Synergistic oxidation and mechanisms.

Here, we prepared a novel nanostructured Fe-Cu-Mn composite oxide (FCMOx) adsorbent using an ultrasonic coprecipitation method. The maximum adsorption capacity of As(III) and As(V) reached 158.5 and 115.2 mg/g under neutral conditions, respectively. The effects of several environmental factors (coexisting ions, solution pH, etc.) on the removal of inorganic arsenic using FCMOx were studied through batch experiments. The results showed that except for PO43- and high initial pH, it was not significantly affected by ionic strength and other existing anions, implying a higher selectivity and adaptability. Combined with EPR, FTIR, and XPS analysis, we concluded that the Cu component and the reactive oxygen species (ROS) it generates played a decisive role in maintaining the stability of the redox cycle between Mn(IV)/Mn(III)/Mn(II) and enhancing the oxidation efficiency of As(III). Meanwhile, the adsorption mechanism of As(V) was mainly through the replacement of the FCMOx surface -OH to form stable inner-sphere arsenic complexes, while the removal mechanism of As(III) may involve the process of synergistic oxidation and chemisorption coupling. Additionally, the effective removal of As from the simulated As-contaminated water and its satisfactory reuse performance make FCMOx adsorbents favorable candidates for the removal of As-contaminated water in the future.

Synthesis of a novel magnetic nano-scale biosorbent using extracellular polymeric substances from Klebsiella sp. J1 for tetracycline adsorption.

The magnetic nano-scale biosorbent (Fe3O4/MFX) was synthesized by the chelation and cross-linking procedure with extracellular polymeric substances (MFX) and Fe3O4. Fe3O4/MFX possessed the porous structure and numerous functional groups (i.e., amino, carboxyl, and hydroxyl), and its core region had a typical size of ∼11nm. The maximum adsorption capacity was 56.04mgg-1 at pH 6.0, 10mgL-1 of tetracycline, and 160mgL-1 of Fe3O4/MFX. The data is properly fitted by the Langmuir, Freundlich, and pseudo-second-order kinetics models. As elucidated by the model parameters and FTIR analysis, chemical ion exchange and COOH could mainly contribute to the adsorption. Meanwhile, the desorption and regeneration experiments implied the adsorption efficiency decreased by only 3.37-8.37% after five adsorption-desorption cycles, and the detection of iron leaching by ICP-OES showed a fine stability of Fe3O4/MFX. Therefore, this technically facile, easily recyclable, and environmentally friendly biosorbent has potential for practical applications in antibiotic removal.