Soil organic matter and total nitrogen as key driving factors promoting the assessment of acid-base buffering characteristics in a tea (Camellia sinensis) plantation habitat.

The issue of soil acidification in tea plantations has become a critical concern due to its potential impact on tea quality and plant health. Understanding the factors contributing to soil acidification is essential for implementing effective soil management strategies in tea-growing regions. In this study, a field study was conducted to investigate the effects of tea plantations on soil acidification and the associated acid-base buffering capacity (pHBC). We assessed acidification, pHBC, nutrient concentrations, and cation contents in the top 0-20 cm layer of soil across forty tea gardens of varying stand ages (0-5, 5-10, 10-20, and 20-40 years old) in Anji County, Zhejiang Province, China. The results revealed evident soil acidification due to tea plantation activities, with the lowest soil pH observed in tea gardens aged 10-20 and 20-40 years. Higher levels of soil organic matter (SOM), total nitrogen (TN), Olsen phosphorus (Olsen-P), available iron (Fe), and exchangeable hydrogen (H+) were notably recorded in 10-20 and 20-40 years old tea garden soils, suggesting an increased risk of soil acidification with prolonged tea cultivation. Furthermore, prolonged tea cultivation correlated with increased pHBC, which amplified with tea stand ages. The investigation of the relationship between soil pHBC and various parameters highlighted significant influences from soil pH, SOM, cation exchange capacity, TN, available potassium, Olsen-P, exchangeable acids (including H+ and aluminum), available Fe, and available zinc. Consequently, these findings underscore a substantial risk of soil acidification in tea gardens within the monitored region, with SOM and TN content being key driving factors influencing pHBC.

Influence of climate and land use on watershed anthropogenic phosphorus inputs and riverine phosphorus export dynamics: A global analysis.

Many studies have found predictive relationships between riverine phosphorus (P) export and net anthropogenic P inputs (NAPI) at the watershed scale, but the global or regional extent of these relationships has not been empirically quantified. Herein, we present a data-driven global assessment of the response of riverine total P (TP) fluxes to NAPI based on 358 watersheds. NAPI exhibited high spatial heterogeneity (2-12,085 kg P km-2 yr-1) and was well correlated with riverine TP fluxes. Riverine TP export fractions of NAPI were primarily regulated by NAPI components, hydroclimate factors, and land-use as determined through a random-forest meta-analysis. In watersheds dominated by disturbed land-use (e.g., agricultural and developed lands), runoff emerged as pivotal climate-related factors influencing riverine export fractions of NAPI. In watersheds dominated by natural land-use, runoff, precipitation and temperature were identified as the most critical factors. We developed a mixed-effects meta-regression model (R2 = 0.63-0.70, RMSE = 19-78 %, n = 87-202) to examine the quantitative relationship between riverine TP fluxes and NAPI, which avoids subjectivity in selecting influencing factors and regression forms. The model estimated that legacy P contributed 14-17 % of annual riverine TP fluxes in Chinese watersheds, 25 % in North American watersheds and 11-27 % in European watersheds. Annual NAPI contributions to annual riverine TP flux were 83-86 % in China, 75 % in North America and 73-89 % in Europe. The model forecasted 52-67 %, 69-71 % and 74-77 % reductions in riverine TP fluxes across Chinese, North American, and European watersheds by 2050 under five shared socio-economic pathway scenarios compared to 2010 baseline conditions, respectively. This study provides a straightforward and reliable method for quantifying anthropogenic P input and riverine P export dynamics within an acceptable error range. It provides guidance for developing phosphorus pollution control strategies to counter potential increases in phosphorus inputs due to expected changes in climate and land use.

Tracing fossil-based plastics, chemicals and fertilizers production in China.

Phasing down fossil fuels is crucial for climate mitigation. Even though 80-90% of fossil fuels are used to provide energy, their use as feedstock to produce plastics, fertilizers, and chemicals, is associated with substantial CO2 emissions. However, our understanding of hard-to-abate chemical production remains limited. Here we developed a chemical process-based material flow model to investigate the non-energy use of fossil fuels and CO2 emissions in China. Results show in 2017, the chemical industry used 0.18 Gt of coal, 88.8 Mt of crude oil, and 12.9 Mt of natural gas as feedstock, constituting 5%, 15%, and 7% of China's respective total use. Coal-fed production of methanol, ammonia, and PVCs contributes to 0.27 Gt CO2 emissions ( ~ 3% of China's emissions). As China seeks to balance high CO2 emissions of coal-fed production with import dependence on oil and gas, improving energy efficiency and coupling green hydrogen emerges as attractive alternatives for decarbonization.

[Scale Effects of Landscape Pattern on Impacts of River Water Quality： A Meta-analysis].

The landscape pattern determines water pollution source and sink processes and plays an important role in regulating river water quality. Due to scale effects, studies on the relationship between landscape pattern and river water quality showed variance at different scales. However, there is still a lack of integrated study on the scale effect of landscape pattern and river water quality dynamics. This study collected 4 041 data from results of previous publications to address the characteristics of landscape pattern and river water quality dynamics at different scales and to identify the key temporal and spatial scales as well as landscape pattern indices for regulating river water quality. The results indicated that, compared to precipitation events, base flow periods, and interannual scales, the high-flow period was the key temporal scale for linking landscape pattern on river water quality. Compared to the watershed scale, the landscape pattern of buffer zones had a greater impact on river water quality. The high-flow period-buffer zone scale was the key spatiotemporal coupling scale for linking landscape pattern and river water quality. Compared to croplands, water bodies, grasslands, and the overall landscape of the watershed, the landscape pattern of forests and urban areas had a greater impact on river water quality. Fragmentation degree was the most important landscape pattern factor regulating river water quality. In river water quality management, it is important to focus on the landscape configuration of buffer zones, increase forest area, reduce patch density of forests and water bodies, and decrease the aggregation degree of urban areas.

[Nitrate Pollution Characteristics and Its Quantitative Source Identification of Major River Systems in China].

Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρ（NO3--N） in the seven major river systems was （4.54±3.99） mg·L-1， with 9.6% of river ρ（NO3--N） exceeding 10 mg·L-1. The riverine ρ（NO3--N） in eastern China were higher than that in western China， and the highest concentration was observed in the Haihe River system. Additionally， tributaries experienced more serious NO3--N pollution than that in the main stream. The ρ（NO3--N） in most river systems in the dry season was higher than that in the wet season， except in the Yellow River system. There was significant nitrification in the Pearl River system， the middle and lower reaches of the Yellow River system， the middle reaches of the Liaohe River system， the Songhua River system， and the Haihe River system， whereas there was significant denitrification in the Yangtze River system， the Huaihe River system， and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR model， the major NO3--N source was sewage/manure （ > 50%） in the Yangtze River system， Haihe River system， Liaohe River system， and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system （56.4%）， and the contribution of fertilizer nitrogen， soil nitrogen， and sewage/manure to NO3--N pollution in the Pearl River system， Huai River system， and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main stream， whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogen， fertilizer nitrogen， and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry season， whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Therefore， point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River system， the Yangtze River system， the Liaohe River system， the tributaries and the downstream main stream areas of Yellow River system， and the downstream area of the Pearl River system， whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River system， the Songhua River system， the middle reaches of the main stream area of the Yellow River system， and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.

Regulating electronic structure of Fe single-atom site by S/N dual-coordination for efficient Fenton-like catalysis.

The activity of single-atom catalysts in peroxymonosulfate activation process is bound up with the local electronic state of metal center. However, the large electronegativity of N atoms in Metal-N4 restricts the electron transfer between center metal atom and peroxymonosulfate. Herein, we constructed Fe-SN-C catalyst by incorporating S atom in the first coordination sphere of Fe single-atom site (Fe-S1N3) for Fenton-like catalysis. The Fe-SN-C with a low valent Fe is found to exhibit excellent catalytic activity for bisphenol A degradation, and the corresponding rate constant reaches 0.405 min-1, 11.9-fold higher than the original Fe-N-C. Besides, the Fe-SN-C/PMS system exhibits ideal catalytic stability under the effect of wide pH range and background substrates by the fast generation of high-valent Fe species. Experimental results and theoretical calculations reveal that the dual coordination of S and N atoms notably increases the local electron density of Fe atoms and electron filling in eg orbital, causing a d band center shifting close to the fermi level and thereby optimizes the activation energy for peroxymonosulfate decomposition via Fe 3d-O 2p orbital interaction. This work provides further development of promising SACs for the efficient activation of peroxymonosulfate based on direct regulation of the coordination environment of active center metal atoms.

Decreasing resilience of China's coupled nitrogen-phosphorus cycling network requires urgent action.

The coupled nature of the nitrogen (N) and phosphorus (P) cycling networks is of critical importance for sustainable food systems. Here we use material flow and ecological network analysis methods to map the N-P-coupled cycling network in China and evaluate its resilience. Results show a drop in resilience between 1980 and 2020, with further decreases expected by 2060 across different socio-economic pathways. Under a clean energy scenario with additional N and P demand, the resilience of the N-P-coupled cycling network would suffer considerably, especially in the N layer. China's socio-economic system may also see greater N emissions to the environment, thus disturbing the N cycle and amplifying the conflict between energy and food systems given the scarcity of P. Our findings on scenario-specific synergies and trade-offs can aid the management of N- and P-cycling networks in China by reducing chemical fertilizer use and food waste, for example.

Benzotriazole Ultraviolet Stabilizers (BUVSs) as Potential Protein Kinase Antagonists in Rice.

The ubiquitous occurrence of benzotriazole ultraviolet stabilizers (BUVSs) in the environment and organisms has warned of their potential ecological and health risks. Studies showed that some BUVSs exerted immune and chronic toxicities to animals by disturbing signaling transduction, yet limited research has investigated the toxic effects on crop plants and the underlying mechanisms of signaling regulation. Herein, a laboratory-controlled hydroponic experiment was conducted on rice to explore the phytotoxicity of BUVSs by integrating conventional biochemical experiments, transcriptomic analysis, competitive sorption assays, and computational studies. The results showed that BUVSs inhibited the growth of rice by 6.30-20.4% by excessively opening the leaf stomas, resulting in increased transpiration. BUVSs interrupted the transduction of abscisic acid (ABA) signal through competitively binding to Ca2+-dependent protein kinase (CDPK), weakening the CDPK phosphorylation and further inhibiting the downstream signaling. As structural analogues of ATP, BUVSs acted as potential ABA signaling antagonists, leading to physiological dysfunction in mediating stomatal closure under stresses. This is the first comprehensive study elucidating the effects of BUVSs on the function of key proteins and the associated signaling transduction in plants and providing insightful information for the risk evaluation and control of BUVSs.

Evaluation of three prevalent global riverine nutrient transport models.

Global riverine nitrogen (N) and phosphorus (P) transport models offer important insights into basin nutrient cycling. However, appropriate model selection for a given research objective remains ambiguous. This study conducted a meta-analysis to evaluate the performance and applicability of three prevalent global riverine nutrient transport models: Global NEWS, IMAGE-GNM, and WorldQual. According to performance criteria (satisfactory: R2 > 0.50 and NSE > 0.50), the Global NEWS model performs satisfactorily in simulating dissolved organic nitrogen (DON; n = 101, R2 = 0.58, NSE = 0.57) and dissolved organic phosphorus loads (DOP; n = 80, R2 = 0.59, NSE = 0.59). The model falls short in simulating dissolved inorganic nitrogen (DIN; n = 644, R2 = 0.56, NSE = - 0.80) and dissolved inorganic phosphorus loads (DIP; n = 450, R2 = 0.33, NSE = - 0.12). The IMAGE-GNM model shows satisfactory accuracies in simulating riverine total nitrogen (TN; n = 831, R2 = 0.56, NSE = 0.53) and total phosphorus (TP; n = 902, R2 = 0.59, NSE = 0.48) concentrations, particularly in European basins. The WorldQual model presented unsatisfactory performance in simulating riverine TN (n = 11, R2 = 0.76, NSE = 0.34) and TP (n = 13, R2 = 0.71, NSE = - 0.25) concentrations. Using a two-segment linear model, we recommend the Global NEWS model for basins larger than 2.2 × 104 km2 for DIN and 3.2 × 104 km2 for DIP. The IMAGE-GNM model is best suited for basins with long-term datasets and high latitudes (TN > 21 years and > 53.8 °N; TP > 22 years and > 54.5 °N). For model improvements, both the Global NEWS and WorldQual models could benefit from enhanced in-stream nutrient retention/release modules. The Global NEWS model could be further improved with a better chemical weathering module. For the IMAGE-GNM model, refining the soil erosion module is warranted to enhance model performance. Addressing legacy nutrient effects is crucial for all three models. This study provides valuable guidance for selecting and improving nutrient transport models based on specific research needs.

Activation of peroxymonosulfate by Cu-Ni-Fe layered double oxides for degradation of butyl 4-hydroxybenzoate: Synergistic effect of oxygen vacancy and Cu(I).

In this study, Cu hybridization coupling oxygen defect engineering was adopted to synthesis of CuNiFe layered double oxides (CuNiFe-LDOs) in peroxymonosulfate (PMS) activation for degradation of methyl 4-hydroxybenzoate. The morphology and crystal structure of CuNiFe-LDOs was characterized in detail, which exhibited regular layered-structure at a Cu:Ni doping ratio of 1:1 and annealing temperature of 400 °C, and presented the crystal of CuxO@Fe3O4-NiO. Besides, the X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR) results demonstrated that abundant oxygen vacancies (OVs) and low oxidation state Cu species were composed in CuNiFe-LDOs400. The Cu1·5Ni1·5Fe1-LDOs400/PMS system showed excellent catalytic performance toward the degradation of butyl 4-hydroxybenzoate (BuP), and resistant to the effect of pH value and background inorganic anions. Based on quenching experiments and EPR measurements, singlet oxygen (1O2) was identified as the dominant active species during the heterogeneous catalytic process, which was generated by the synergistic interaction between OVs-Cu(I) site and PMS. In this process, the electron-drawing property of OVs promoted the adsorption of PMS molecule on Cu(I) site, followed by the accumulation of electron and cleavage of O-O bond to generate intermediate oxygen radical species, which donated one electron to eventually generate singlet oxygen.

Biochar carbon sequestration potential rectification in soils: Synthesis effects of biochar on soil CO2, CH4 and N2O emissions.

Biochar production and its soil sequestration are promising ways to mitigate global warming. Effects of biochar on soil CO2, CH4 and N2O release have been studied extensively. In contrast, few studies have comprehensively quantified and synthesized the effect of biochar on soil greenhouse gas (GHG) emission and coupled it to the calculation of carbon sequestration potential. This study obtained the influence coefficient of biochar on soil GHG release relative to biochar carbon storage potential in soils under different environmental conditions, by literature statistics and data transformations. Our results showed that the overall average effect of biochar on soil CO2, CH4, N2O and CO2e release observed in our databases would compensate the potential of biochar soil carbon storage by -2.1 ± 3.3 %, 13.1 ± 9.8 %, -1.6 ± 8.6 % and 5.3 ± 11.4 %, respectively. By combining biochar induced soil GHG emission reduction mechanism and results from our literature statistics, some specific application environmental scenarios (such as biochar with high pyrolysis temperature of 500-600 °C, application in flooded soils, application in straw-return scenarios, etc.) were recommended, which could increase the actual carbon sequestration potential of biochar by an average of about 43.3 ± 30.2 % relative the amount of carbon buried. Our findings provide a scientific basis for developing a precise application strategy towards large scale adoption of biochar as a soil amendment for climate change mitigation.

[Estimation of Cropland Nitrogen Runoff Loss Loads in the Yangtze River Basin Based on the Machine Learning Approaches].

A quantitative understanding of cropland nitrogen (N) runoff loss is critical for developing efficient N pollution control strategies. Using correlation analysis, a structural equation model, variance decomposition, and machine learning methods, this study identified the primary influencing factors of total N (TN) runoff loss from uplands (n=570) and paddy (n=434) fields in the Yangtze River Basin (YRB) and then developed a machine learning-based prediction model to quantify cropland N runoff loss load. The results indicated that runoff depth, soil N content, and fertilizer addition rate were the major influencing factors of TN runoff loss from uplands, whereas TN runoff loss rate from paddy fields was mainly regulated by runoff depth and fertilizer addition rate. Among the four used machine learning methods, the prediction models based on the random forest algorithm presented the highest accuracy (R2=0.65-0.94) for predicting upland and paddy field TN runoff loss rates. The random forest algorithm based model estimated a total cropland TN loss load in the YRB of 0.47 Tg·a-1 (upland:0.25 Tg·a-1; paddy field:0.22 Tg·a-1) in 2013, with 58% of TN runoff loss load derived from the midstream and downstream regions. The models predicted that TN runoff loss loads from croplands in YRB would decrease by 2.4%-9.3% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. To mitigate cropland N nonpoint source pollution in YRB, it is essential to integrate efficient water, fertilizer, and soil nutrient managements as well as to consider the midstream and downstream regions as the high priority area. The machine learning-based modeling method developed in this study overcame the difficulty of identifying the functional relationships between cropland TN loss rate and multiple influencing factors in developing relevant prediction models, providing a reliable method for estimating regional and watershed cropland TN loss load.

Quantifying groundwater phosphorus flux to rivers in a typical agricultural watershed in eastern China.

Increasing evidence indicates that groundwater can contain high dissolved phosphorus (P) concentrations, thereby contributing as a potential pollution source for surface waters. However, limited quantitative knowledge is available concerning groundwater P fluxes to rivers. Based on monthly hydrochemical monitoring data for rivers and groundwater in 2017-2020, this study combined baseflow separation methods and a load apportionment model (LAM) to quantify contributions from point sources, surface runoff, and groundwater/subsurface runoff to riverine P pollution in a typical agricultural watershed of eastern China. In the studied Shuanggang River, most total P (TP) and dissolved P (DP) concentrations exceeded targeted water quality standards (i.e., TP ≤ 0.2 mg P L-1, DP ≤ 0.05 mg P L-1), with DP (76 ± 20%) being the major riverine P form. Observed DP concentrations in groundwater were generally higher than those of river waters. There was a strong correlation between river and groundwater P concentrations, implying that groundwater might be a considerable P pollution source to rivers. The nonlinear reservoir algorithm estimated that baseflow/groundwater contributed 66-68% of monthly riverine water discharge on average, which was consistent with results estimated by an isotope-based sine-wave fitting method. The LAM incorporating point sources, surface runoff, and groundwater effectively predicted daily riverine TP [calibration: coefficient of determination (R2) = 0.76-0.82, Nash-Sutcliffe Efficiency (NSE) = 0.61-0.77; validation: R2 = 0.88-0.98, NSE = 0.54-0.64] and DP loads (calibration: R2 = 0.73-0.84, NSE = 0.67-0.72; validation: R2 = 0.88-0.97, NSE = 0.56-0.83). The LAM estimated point source, surface runoff, and groundwater contributions to riverine loads were 15-18%, 14-35%, and 46-70% for TP loads and 7-9%, 10-32%, and 59-82% for DP loads, respectively. Groundwater was the dominant riverine P source due to long-term accumulation of P from excess fertilizer and farmyard manure applications. The developed methodology provides an alternative method for quantifying P pollution loads from point sources, surface runoff, and groundwater to rivers. This study highlights the importance of controlling groundwater P pollution from agricultural lands to address riverine water quality objectives and further implies that decreasing fertilizer P application rates and utilizing legacy soil P for crop uptake are required to reduce groundwater P loads to rivers.

Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China.

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes ([Formula: see text] and [Formula: see text]) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine [Formula: see text] in the Cao'e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

[Estimation of Nitrous Oxide Emission from River System Based on Water Discharge and Dissolved Nitrous Oxide Concentration].

Due to increasing active nitrogen pollution loads, river systems have become an important source of nitrous oxide (N2O) in many areas. Due to the lack of monitoring data in many studies as well as the difficulty in estimating intermediate parameters and expressing temporal-spatial variability in current methods, a high level of uncertainty remains in the estimates of riverine N2O emission quantity. Based on the monthly monitoring efforts conducted for 10 sampling sites across the Yonganxi River system in Zhejiang Province from June 2016 to July 2019, the temporal and spatial dynamics of riverine N2O dissolved concentrations ρ(N2O), N2O fluxes, and their influencing factors were addressed. A multiple regression model was then developed for predicating riverine N2O emission flux to estimate annual N2O emission quantity for the entire river system. The results indicated that observed riverine ρ(N2O) (0.03-2.14 µg·L-1) and the N2O fluxes[1.32-82.79 µg·(m2·h)-1] varied by 1-2 orders of magnitude of temporal-spatial variability. The temporal and spatial variability of ρ(N2O) were mainly influenced by the concentrations of nitrate, ammonia, and dissolved organic carbon, whereas the N2O emission fluxes were mainly affected by river water discharges and ρ(N2O). A multiple regression model that incorporates variables of river water discharge and ρ(N2O) could explain 90% of the variability in riverine N2O emission fluxes and has high accuracy. The model estimated N2O emission quantity from the entire Yonganxi River system of 3.67 t·a-1, with 29% from the main stream and 71% from the tributaries. The IPCC default emission factor method might greatly overestimate and underestimate N2O emission quantities for rivers impacted by low and high pressures of human activities, respectively. This study advances our quantitative understanding of N2O emission for the entire river system and provides a reference method for estimating riverine N2O emission with more accuracy.

Modeling Riverine N2O Sources, Fates, and Emission Factors in a Typical River Network of Eastern China.

Estimates of riverine N2O emission contain great uncertainty because of the lack of quantitative knowledge concerning riverine N2O sources and fates. Using a 3.5-year record of monthly N2O measurements from the Yongan River network of eastern China, we developed a mass-balance model to address the riverine N2O source and sink processes. We achieved reasonable model efficacies (R2 = 0.44-0.84, Nash-Sutcliffe coefficients = 0.40-0.80) across three tributaries and the entire river system. Estimated riverine N2O loads originated from groundwater (38-88%), surface runoff (3-26%), and in-stream production (4-48%). Estimated in-stream losses via atmospheric release + complete denitrification accounted for 76, 95, 25, and 89% of riverine N2O fate for the agricultural, residential, forest, and entire river system, respectively. Considering limited complete denitrification, the model estimated an upper-bound riverine N2O emission rate of 2.65 ton N2O-N km-2 year-1 for the entire river system. Riverine N2O emission estimates were of comparable magnitude to those estimated with a power-law scaling model. Riverine N2O emissions using the IPCC default emission factor (0.26%) overestimated emissions by 3-15 times, whereas the dissolved N2O concentration-based emission factor overestimated or underestimated emissions. This study highlights the importance of combining comprehensive information on N2O sources and fates to achieve accurate riverine N2O emission estimates.

National estimates of environmental thresholds for upland soil phosphorus in China based on a meta-analysis.

The environmental threshold for upland soil phosphorus (P) content (ETSP, i.e., inflection point of soil P content leading to enhanced P loss) provides an important metric for guiding agricultural nonpoint source P pollution control. This study achieved the first meta-analysis to determine ETSP values for upland soils in China. The estimated national-level ETSP based on 472 field experimental observations of Olsen-P content and P loss rate was 30.1 ± 4.0 mg P kg-1, which was lower than the average ETSP value (52.1 ± 5.0 mg P kg-1) but higher than the average agronomic threshold values (16.0 ± 6.4 mg P kg-1) previously reported. Lower upland ETSP values occurred in acidic soils and soils having higher organic matter content (SOM), precipitation and slope (ETSP: 30.5 for pH < 7.0 versus 46.1 for pH ≥ 7.0; >56.4 for SOM < 2%, 49.9 for SOM = 2%-3%, and <3 for SOM > 3%; 33 for precipitation < 1000 mm yr-1, 27.5 for precipitation = 1000-1200 mm yr-1 and <5 for precipitation > 1200 mm yr-1; and 39.8 for slopes < 5° versus <9 for slopes ≥ 5°). A multiple regression model that incorporates SOM, pH, precipitation and slope was developed to predict upland ETSP values (R2 = 0.73, p < 0.01). The model estimated national upland ETSP values ranging from ~0 to 100 mg P kg-1 with an areal-weighted average of 60.6 mg P kg-1 and 15% of national upland soils having ETSP values <30 mg P kg-1. Upland soil P contents in Guangdong, Fujian and Zhejiang provinces largely exceeded their corresponding ETSP values by 1-22 mg P kg-1, indicating high P loss risks. Controlling upland P loss requires integrated management of soil P content, SOM, pH and erosion control. This study provides the first national estimate of upland soil ETSP, providing critical quantitative information for designing management practices to attenuate agricultural nonpoint source P pollution.

Estimation of nitrogen runoff loss from croplands in the Yangtze River Basin: A meta-analysis.

Nitrogen (N) runoff loss from croplands due to excessive anthropogenic N additions is a principal cause of non-point source water pollution worldwide. Quantitative knowledge of regional-scale N runoff loss from croplands is essential for developing sustainable agricultural N management and efficient water N pollution control strategies. This meta-analysis quantifies N runoff loss rates and identifies the primary factors regulating N runoff loss from uplands (n = 570) and paddy (n = 434) fields in the Yangtze River Basin (YRB). Results indicated that total N (TN) runoff loss rates from uplands and paddy fields consistently increased from upstream to downstream regions. Runoff depth, soil N content and fertilizer addition rate (chemical fertilizer + manure) were the major factors regulating variability of TN runoff loss from uplands, while runoff depth and fertilizer addition rate were the main controls for paddy fields. Multiple regression models incorporating these influencing factors effectively predicted TN runoff loss rates from uplands (calibration: R2 = 0.60, n = 242; validation: R2 = 0.55, n = 104) and paddy fields (calibration: R2 = 0.70, n = 189; validation: R2 = 0.85, n = 82). Models estimated total cropland TN runoff loss load in YRB of 0.54 (95% Cl: 0.23-1.33) Tg, with 0.30 (95% Cl: 0.15-0.56) Tg from uplands and 0.24 (95% Cl: 0.08-0.77) Tg from paddy fields in 2017. Guangxi, Jiangxi, Fujian, Hunan and Henan provinces within the YRB were identified as cropland TN runoff loss hotspots. Models predicted that TN runoff loss loads from croplands in YRB would decrease by 0.8-13.7% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. Reducing upland TN runoff loss should focus primarily on soil N utilization and runoff management, while reducing N fertilizer addition and runoff provided the most sensitive strategies for paddy fields. Integrated management of water, soil and fertilizer is required to effectively reduce cropland N runoff loss.

Improving Subnational Input-Output Analyses Using Regional Trade Data: A Case-Study and Comparison.

Environmentally extended input-output analysis (EE-IO) is widely used for evaluating environmental performance (i.e., footprint) at a national level. Many studies have extended their analyses to the subnational level to guide regional policies. One promising method is to embed nationally disaggregated input-output tables, e.g., nesting a provincial level table, into a global multiregional input-output table. However, a widely used approach to environmental assessment generally disaggregates the trade structure at the national level to the provincial level using the same proportions (proportionality assumption). This means that the subnational spatial heterogeneities on international trade are not fully captured. By calculating the Chinese provincial material footprint (MF) based on two approaches-the proportionality assumption and the actual customs statistics-in the same framework, we evaluate the quantitative differences when the proportionality assumption is addressed. By computing MF for 23 aggregated resources across 30 Chinese provinces, our results show for countries with large material flows like China, estimating subnational-level international trade by proportionality assumption may lead to significant differences in material flows at both the disaggregated and aggregated levels. An important follow-up question is whether these differences are also relevant for other footprints.

Estimation and analysis of municipal food waste and resource utilization potential in China.

With rapid urbanization, municipal food waste (MFW), which is an important part of municipal solid waste, has attracted considerable attention owing to its environmental impact and polluting nature. There has been little research on the quantity and distribution of food waste (FW) produced in China. This study focused on a systematic estimation and analysis of MFW produced in administrative divisions at the prefecture-level and above in China for the first time. From the national level to the prefectural level, with the shrinking of the research units, more intuitive support was obtained for relevant decisions. On the basis of the estimated results, suggestions are provided for proper FW treatment technologies and operational scale of the facilities, and the resource utilization potential has also been estimated. The distribution results indicated that FW characteristics have great variability in the different economic regions of China. Furthermore, it was found that the available FW has a resource utilization potential that is equivalent to 4669.1 million m3 of biogas, 3.6 million tons of biodiesel, and 1.5 million tons of organic fertilizer (dry weight). It is worth mentioning that this amount of biogas can replace 7.5 million tons of standard coal. However, only a small part of the generated MFW can be treated in the existing treatment plants in China. Finally, current key bottlenecks of FW treatment in China have been discussed, and detailed suggestions are presented for further improvement of MFW management.