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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 the blooms. 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, and 8 were used to extract NIR-Red, GREEN-BLUE, MNDWI, and KIVU bands ratio. 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-site 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 has better performance (R2 = 0.801, RMSE = 16.0 µg·L− 1) than the model without texture features(R2 = 0.746, RMSE = 16.2 µg·L− 1). 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.

Changes in soil nitrogen mineralization can impact nutrient availability, and further affect plant growth. It is unclear, however, how temperature elevation in alpine grassland will affect soil net N mineralization rate (Nmin) across altitudes. At six altitudes (3200–4200 m with an interval of 200 m) along a slope in Lenglong mountain in the northern Qinghai-Tibetan Plateau, we performed an in situ soil incubation experiment by using the resin-core method to assess altitudinal variations of Nmin. Meanwhile, we evaluated the effects of temperature elevation on Nmin and its temperature sensitivity (Q10) through a soil downward transplantation experiment based on three reference baseline altitudes (3800, 4000 and 4200 m). The results showed that high altitudes generally led to low values of Nmin. Structural equation modeling analysis revealed that Nmin along the altitude was mainly controlled by soil temperature. Increased temperature caused by the altitude transplantation significantly elevated Nmin for all of the three reference altitudes. The value of Q10 was 3.4 for soil samples transplanted from the reference altitude of 4200 m, which was about twice that of the lower reference altitudes of 4000 and 3800 m.

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. The90% 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

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., Semi‐automatic intensive follow‐up contributed to a higher eradication rate and adherence to Helicobacter pylori treatment. 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).

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.

Plant communities and their environments are a vital part of synecology research. In this study, we investigated the characteristics of three typical medicinal perennial herbs, Glycyrrhiza uralensis, Rheum altaicum, and Ferula sinkiangensis, and examined their response to local soil properties in Altay Prefecture, China. A total of 29 species belonging to 15 families and 25 genera were recorded. In terms of the target medicinal plants, the importance value was low and community structures were exceedingly simple. Quantitative classification based on WARD cluster analysis suggested that each medicinal plant can be classified into one of three types: annual herbaceous, perennial herbaceous, and subshrub or shrub. Interestingly, the plants gradually transformed from exhibiting nonresistance to xerophytes or halophytes. An investigation of the soil properties beneath the medicinal plants showed that nutrients in black soil were much more abundant than those in sandy and saline soil. Except for leaching phenomenon, the nutrient contents in the soil surface layer (0&ndash, 10 cm) were higher than those in deep layers (10&ndash, 20 cm and 20&ndash, 40 cm), and to some extent, the nutrient contents were inversely proportional to the depth of the soil. The redundancy analysis (RDA) results of the medicinal plants and their relationship with soil properties indicated that the main soil variables impacting the distribution of G. uralensis were available potassium (AK), aspect (ASP), soil moisture (SM), total nitrogen (TN), and total phosphorus (TP), SM, electric conductivity (EC), elevation (ELE), ASP, pH value (pH), available nitrogen (AN), soil organic content (SOC), and the carbon/nitrogen (C/N) ratio had positive effects on the distribution of R. altaicum. F. sinkiangensis grew better in habitats with high contents of SM, EC, TN, AN, available phosphorus (AP), total potassium (TK), and AK. This study aimed at providing effective suggestions for artificial cultivation, conservation, and resilience of Chinese medicinal plants.

Fertilizer additions (Nrate) have increased surface runoff of nitrogen (RTN) from croplands, which is eventually transferred to rivers. This pollution further contributes to ecological degradation and and health risks of drinking waters. Despite this recognition, little is known about the spatial pattern of RTN from croplands and the drivers of its variation. On the basis of 210 site-years of measurements at 41 sites in Chinese paddy fields, we examined the nonlinear response of RTN to Nrate and the effects of environmental factors on RTN, RTN per unit nitrogen fertilizer additions (RR), and background N runoff (R0). The results show that (i) RTN-Nrate relationship deviates from linearity and the parameters vary by climate and soil attributes; (ii) Observed variation of RTN is better explained by precipitation and clay content (48%) than Nrate and its interaction (17%); (iii) The RTN is 1.09 ± 0.36 Tg N yr−1 for Chinese paddy fields in 2008, to which R0 contributes more than 50%, and the corresponding average RR is 6.8 ± 1.7% (1 − σ), 30% lower than the linear model. This study therefore suggests that the future policies for agricultural N runoff need to account for local environmental conditions rather than solely attempting to reduce N fertilizer applications.

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

In an agricultural county like China, agricultural fertilizers are the source of ammonia (NH3) emissions. However, the spatial variability in NH3 emissions is large, and the associated uncertainties affect the reliability of total NH3 emission estimates. In this study, an inventory is presented for NH3 emissions from China's agricultural fertilizer application at the city-level, and on a 1 × 1 km grid for croplands in 2010. We present NH3 emissions by source, the temporal and spatial patterns, and the associated uncertainties. The inventory is based on high-resolution activity data, regional emission factors (EFs) and related parameters that are derived from local studies. We compare our emissions estimates with previous inventories from EDGAR and other studies. The total NH3 emissions from China's agricultural fertilizer is 10.7 (8.9–12.3) TgNH3·yr−1. Livestock manure spreading contributes 47.5% to the total emissions, and synthetic fertilizer use by 41.9%. Rural excrement (5.0%) and cake fertilizer (5.5%) are relatively small sources. The spatial pattern of NH3 emissions from China's agricultural fertilizer were primarily concentrated in the North China Plain, the Songliao Plain, the Huaihe River Basin, the Middle-Lower Yangtze Plain, the Pearl River Delta Plain, the Sichuan Basin, the Tarim basin and the Leizhou Peninsula. Approximately 50% of the emissions are from only 76 cities. Our temporal analysis reveals a clear seasonal pattern in NH3 emissions: highest and lowest emissions are calculated for summer and winter, accounting for 42% and 14% of the total emissions, respectively. Peak emissions are calculated for July (1.7 TgNH3·yr−1) and lowest emissions for January (0.5 TgNH3·yr−1). The emissions are correlated with temperature, planting time and cultivation practices.

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.