The shape of reactive nitrogen losses from intensive farmland in China.

Reactive nitrogen (Nr) pollution has changed radically accompanied by severe intensive farming. This pollution further contributes to ecological degradation and climate warming. Despite this recognition, little is known about the spatial pattern of various Nr loss from croplands and corresponding environmental costs. Here, we identified the major pathway of Nr loss based on provincial estimates in 2008 and 2018, and validated by synchronous observation of ammonia volatilization, N runoff and N leaching using historical literature synthesis. We also evaluated environmental costs at provincial scale and detected the influence factors that dominating the pollution swapping among different Nr forms. Our results show that the total Nr loss was 6.28 ± 1.81 and 5.56 ± 2.30 Tg N yr-1 for Chinese croplands in 2008 and 2018. Ammonia volatilization, which accounted for more than half of the total Nr at the national scale, was proven to be the major Nr loss for two-thirds of the provinces and 80 % of the field observations. The contribution of runoff, which is dominant by precipitation, soil clay content and CEC, was gradually smaller than that of leaching from southeast to northwest. Ammonia and nitrous oxide contributed of 59.3 % âˆ¼ 65.4 % of TNr but 80.9 % âˆ¼ 81.5 % of total environmental damage caused by Nr in China. The use of nitrification inhibitors and straw return indicated pollution swapping among various Nr forms. This study emphasizes that the future practices to reduce total Nr loss need to account for local environmental conditions and have pollution swapping in sights.

Groundwater pollution source identification and health risk assessment in the North Anhui Plain, eastern China: Insights from positive matrix factorization and Monte Carlo simulation.

Groundwater contaminants from natural and anthropogenic sources pose a serious threat to the ecological environment and public health. In this study, 30 groundwater samples were collected from shallow wells at a large central water source in the North Anhui Plain, eastern China. Hydrogeochemical methods, positive matrix factorization (PMF) model, and Monte Carlo simulation were used to determine the characteristics, sources, and human health risks of inorganic and organic analytes in groundwater. The groundwater was weakly alkaline with high total hardness and was dominated by HCO3-Mg·Ca, HCO3-Ca·Mg, and HCO3-Ca·Mg·Na hydrochemical facies. The concentration of naphthalene was at a safe level, while the concentrations of F-, NO3- and Mn in 16.7 %, 26.7 % and 40 % of the samples, respectively, exceeded threshold risk-based values based on Chinese groundwater quality standards. Hydrogeochemical methods revealed that water-rock interactions (including weathering of silicate minerals, dissolution of carbonates, and cation exchange), acidity, and runoff conditions control the migration and enrichment of these analytes in groundwater. The PMF model indicated that local geogenic processes, hydrogeochemical evolution, agricultural activities, and petroleum-related industrial sources were the main factors affecting groundwater quality, with contributions of 38.2 %, 33.7 %, 17.8 %, and 10.3 %, respectively. A health risk evaluation model based on Monte Carlo simulation indicated that 77.9 % of children were exposed to a total noncarcinogenic risk above safe thresholds, about 3.4 times higher than the risk to adults. The main contributor to human health risk was F- originating from geogenic processes; thus, F- was identified as a priority for control. This study demonstrates the feasibility and reliability of combining source apportionment techniques and health risk assessment to evaluate groundwater quality.

Improving lake chlorophyll-a interpreting accuracy by combining spectral and texture features of remote sensing.

Cyanobacterial blooms in lakes fueled by increasing eutrophication have garnered global attention, and high-precision remote sensing retrieval of chlorophyll-a (Chla) is essential for monitoring eutrophication. Previous studies have focused on the spectral features extracted from remote sensing images and their relationship with chlorophyll-a concentrations in water bodies, ignoring the texture features in remote sensing images which is beneficial to improve interpreting accuracy. This study explores the texture features in remote-sensing images. It proposes a retrieval method for estimating lake Chla concentration by combining spectral and texture features of remote sensing images. Remote sensing images from Landsat 5 TM and 8 OLI were used to extract spectral bands combination. The gray-level co-occurrence matrix (GLCM) of remote sensing images was used to obtain a total of 8 texture features; then, three texture indices were calculated using texture features. Finally, a random forest regression was used to establish a retrieval model of in situ Chla concentration from texture and spectral index. Results showed that texture features are significantly correlated with lake Chla concentration, and they can reflect the temporal and spatial distribution change of Chla. The retrieval model combining spectral and texture indices performs better (MAE = 15.22 µg·L-1, bias = 9.69%, MAPE = 47.09%) than the model without texture features (MAE = 15.76 µg·L-1, bias = 13.58%, MAPE = 49.44%). The proposed model performance varies in different Chla concentration ranges and is excellent in predicting higher concentrations. This study evaluates the potential of incorporating texture features of remote sensing images in lake water quality estimation and provides a novel remote sensing method to better estimate lake Chla concentration.

The influence of nutrients on the composition and quantity of buried organic carbon in a eutrophic plateau lake, Southwest China.

The regulation of lacustrine organic carbon (OC) burial by nutrient is an outstanding knowledge gap in the current understanding of lake carbon cycles. In this study, we determined how nutrients quantitatively correspond with OC burial using the parallel factor analysis (PARAFAC) method in Dianchi Lake, southwest China. Factors were classified into three types according to their historical sedimentation characteristics: the background factor (BF), response factor (RF), and contingency factor (CF). The BF represented the original OC input combination in the lake and was insensitive to nutrient changes. The RF represented the OC input combination that was induced or promoted by nutrient changes in the lake. The CF represented short-term discontinuous factors in sedimentary history, which may be related to unique historical events. The results indicate that changes in the total nitrogen (TN) to total phosphorus (TP) ratio correlated with changes in the BF contribution; whereas the quantity of OC was mainly correlated with TN. The >90% of OC buried in sediment was quantitatively simulated by BF and RF; the driving effect of RF on OC burial was approximately 13 times higher than that of BF. It was observed that a 1 mg kg-1 increase in TN led to approximately 2.2 units increase in RF contribution in Dianchi Lake, while the BF was insensitive to changes in TN. Thus, changes in lake nutrients may effectively change the composition and quantity of OC buried in lake sediment.

Application of a semi-automatic, intensive follow-up for improving efficacy and adherence of Helicobacter pylori eradication therapy: A randomized controlled trial.

A complete understanding and good adherence are crucial for successful Helicobacter pylori eradication. Proper frequency of reminders might be helpful to both doctors and patients to maintain adherence during treatment. The study was to evaluate the influence of an intensive follow-up system based on a clinical database on H. pylori eradication therapy. A total of 196 eligible patients were equally and randomly divided into an intensive follow-up group and a control group. Both groups were administered bismuth-containing quadruple therapy for 14 days. Patients in the intensive follow-up group were informed of pre-treatment, including the duration and potential adverse events. Subsequently, they received telephone follow-ups on days 3 and 14 and 3 days before the urea breath test (UBT). The time points were automatically reminded by a follow-up system in the established clinical database. The control group was only informed of pre-treatment information. UBT was performed 4 weeks after treatment in both groups to assess the presence of H. pylori. The eradication rate, patient compliance, and adverse events were calculated and compared. The H. pylori eradication rates of the intensive follow-up and control groups were 94.7% (90/95, 95% CI: 90%-99%) and 92.9% (78/84, 95% CI: 87%-98%), respectively, by PP analysis (p = 0.601), and 91.8% (90/98, 95% CI: 86%-97%) and 81.6% (80/98, 95% CI: 74%-89%) by ITT analysis (p = 0.035). Adverse events occurred in 9 intensive follow-up group patients and 12 in the control group. Adherence was 96.9% (95/98) in the intensive follow-up group and 85.7% (84/98) in the control group. Semi-automatic intensive follow-up contributed to a higher eradication rate and adherence to H. pylori treatment.

River algal blooms are well predicted by antecedent environmental conditions.

River algal blooms have become a challenging environmental problem worldwide due to strong interference of human activities and megaprojects (e.g., big dams and large-scale water transfer projects). Previous studies on algal blooms were mainly focused on relatively static water bodies (i.e., lakes and reservoirs), but less on the large rivers. As the largest tributary of the Yangtze River of China and the main freshwater source of the South-to-North Water Diversion Project (SNWDP), the Han River has experienced frequent algal blooms in recent decades. Here we investigated the algal blooms during a decade (2003-2014) in the Han River by two gradient boosting machine (GBM) models with k-fold cross validation, which used explanatory variables from current 10-day (GBMc model) or previous 10-day period (GBMp model). Our results advocate the use of GBMp due to its higher accuracy (median Kappa = 0.9) and practical predictability (using antecedent observations) compared to GBMc. We also revealed that the algal blooms in the Han River were significantly modulated by antecedent water levels in the Han River and the Yangtze River and water level variation in the Han River, whereas the nutrient concentrations in the Han River were usually above thresholds and not limiting algal blooms. This machine-learning-based study potentially provides scientific guidance for preemptive warning and risk management of river algal blooms through comprehensive regulation of water levels during the dry season by making use of water conservancy measures in large rivers.

Multi-factor identification and modelling analyses for managing large river algal blooms.

River algal blooms have become a newly emerging global environmental issue in recent decades. Compared with water eutrophication in lakes and reservoirs, algal blooms in large river systems can cause more severe consequences to watershed ecosystems at the watershed scale. However, reveal the causes of river algal blooms remains challenging in the interdisciplinary of hydrological-ecological-environmental research, due to its complex interaction mechanisms impacted by multiple factors. In addition, there were still considerable uncertainties on the characteristics, impacts, driving factors, as well as the applicable water system models for river algal blooms. In this paper, we reviewed existing literature to elaborate the definition and negative effects of river algal blooms. We analyzed sensitive factors including nutrient, hydrological and climatic elements. We also discussed the application of ecohydrological models under complicated hydrological conditions. Finally, we explored the essence of the river algal bloom by the interaction effects of physical and biogeochemical process impacted by of climate change and human activities. The model-data integration accounting for multi-factor effects was expected to provide scientific guidance for the prevent and control of algal blooms in large river systems.

Improved Jayaweera-Mikkelsen model to quantify ammonia volatilization from rice paddy fields in China.

Current estimates of China's ammonia (NH3) volatilization from paddy rice differ by more than twofold, mainly due to inappropriate application of chamber-based measurements and improper assumptions within process-based models. Here, we improved the Jayaweera-Mikkelsen (JM) model through multiplying the concentration of aqueous NH3 in ponded water by an activity coefficient that was determined based on high-frequency flux observations at Jingzhou station in Central China. We found that the improved JM model could reproduce the dynamics of observed NH3 flux (R2 = 0.83, n = 228, P < 0.001), while the original JM model without the consideration of activity of aqueous NH3 overstated NH3 flux by 54% during the periods of fertilization and pesticide application. The validity of the improved JM model was supported by a mass-balance-based indirect estimate at Jingzhou station and the independent flux observations from the other five stations across China. The NH3 volatilization losses that were further simulated by the improved JM model forced by actual wind speed were in general a half less than previous chamber-based estimates at six stations. Difference in wind speed between the inside and outside of the chamber and insufficient sampling frequency were identified as the primary and secondary causes for the overestimation in chamber-based estimations, respectively. Together, our findings suggest that an in-depth understanding of NH3 transfer process and its robust representation in models are critical for developing regional emission inventories and practical mitigation strategies of NH3.

Detection and attribution of nitrogen runoff trend in China's croplands.

Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990-2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China.

A new high-resolution N2O emission inventory for China in 2008.

The amount and geographic distribution of N2O emissions over China remain largely uncertain. In this study, county-level and 0.1° × 0.1° gridded anthropogenic N2O emission inventories for China (PKU-N2O) in 2008 are developed based on high-resolution activity data and regional emission factors (EFs) and parameters. These new estimates are compared with previous inventories, and with two sensitivity tests: one that uses high-resolution activity data but the default IPCC methodology (S1) and the other that uses regional EFs and parameters but starts from coarser-resolution activity data. The total N2O emissions are 2150 GgN2O/yr (interquartile range from 1174 to 2787 GgN2O/yr). Agriculture contributes 64% of the total, followed by energy (17%), indirect emissions (12%), wastes (5%), industry (2.8%), and wildfires (0.2%). Our national emission total is 17% greater than that of the EDGAR v4.2 global product sampled over China and is also greater than the GAINS-China, NDRC, and S1 estimates by 10%, 50%, and 17%, respectively. We also found that using uniform EFs and parameters or starting from national/provincial data causes systematic spatial biases compared to PKU-N2O. Spatial analysis shows nonlinear relationships between N2O emission intensities and urbanization. Per-capita and per-GDP N2O emissions increase gradually with an increase in the urban population fraction from 0.3 to 0.9 among 2884 counties, and N2O emission density increases with urban expansion.

[County-scale N2O emission inventory of China's manure management system].

Manure is one of the two largest contributors to China's N2O emission. By using the county-scale activity data and the regional emission factors and related parameters with spatial differentiation in China in 2008, this paper assessed the N2O emission loading, sources profile, spatial pattern, and uncertainty, aimed to establish a high-resolution N2O emission inventory of China's manure management system in 2008. As compared with the research results based on the IPCC, EDGAR, and other works, the proposed emission inventory was more reliable and comprehensive. The total China' s N2O emission from manure in 2008 was estimated as 572 Gg, among which, the emission from the manure except pasture/range/paddock was 322 Gg (56.3%), from the manure in pasture/range/paddock was 180 Gg (31.5%), and the indirect emission from atmospheric volatilized N deposition and leaching/runoff was 45.8 Gg (8.0%) and 1.23 Gg (0.2%), respectively. The spatial pattern of China's N2O emission from manure was more centralized, and mainly concentrated in Jilin, Shandong, Sichuan, Hunan, Henan, Heilongjiang, and Liaoning provinces, contributing 52.4% of the total emission, and more than 25% being from 84 counties (only < 3% of the whole counties). The proposed emission inventory had a higher spatial resolution and accuracy. Different with this inventory, the IPCC underestimated the direct emission while overestimated the indirect emission, with the regions of higher emission rate being underestimated by -1.5%-6.0% and those of lower emission rate being overestimated by 1.6%-13%. As for the EDGAR, the regions of higher emission rate were underestimated by -18. 8--50.0%, and those of lower emission rate were mostly overestimated by 25%-54.1%.