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Abstract China faces challenges in reaching its carbon neutrality goal by the year 2060 to meet the Paris Agreement and improving air quality simultaneously. Dramatic nitrogen emission reductions will be brought by this ambitious target, yet their impact on the natural ecosystem is not clear. Here, by combining two atmospheric chemistry models and two process-based terrestrial ecosystem models constrained using nationwide measurements, we show that atmospheric nitrogen deposition in China’s terrestrial land will decrease by 44–57% following two emission control scenarios including one aiming at carbon neutrality. They consequently result in a pronounced shrinkage in terrestrial net ecosystem production, by 11–20% depending on models and emission scenarios. Our results indicate that the nitrogen emission reductions accompanying decarbonization would undermine natural carbon sinks and in turn set back progress toward carbon neutrality. This unintended impact calls for great concern about the trade-offs between nitrogen management and carbon neutrality.

Abstract The hydroxyl radical (OH), as the central atmospheric oxidant, controls the removal rates of methane, a powerful greenhouse gas. It is being suggested that OH levels would decrease with reductions of nitrogen oxides and ozone levels by climate polices, but this remains unsettled. Here, we show that driven by the carbon neutrality pledge, the global-mean OH concentration, derived from multiple chemistry-climate model simulations, is projected to be significantly increasing with a trend of 0.071‒0.16% per year during 2015–2100. The leading cause of this OH enhancement is dramatic decreases in carbon monoxide and methane concentrations, which together reduce OH sinks. The OH increase shortens methane’s lifetime by 0.19‒1.1 years across models and subsequently diminishes methane’s radiative forcing. If following a largely unmitigated scenario, the global OH exhibits a significant decrease that would exacerbate methane’s radiative forcing. Thus, we highlight that targeted emission abatement strategies for sustained oxidation capacity can benefit climate change mitigation in the Anthropocene.

Abstract Background Previous studies have shown that an antioxidant diet is a protective factor against depression. However, the association between niacin, an important antioxidant consumed from the diet, and depression has received little attention. Therefore, we explored the association between niacin intake and depression through a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2016. Methods Depression was measured using the Patient Health Questionnaire (PHQ-9, score ≥ 10). Niacin intake was assessed through 24-h dietary recall interviews. The relationship of niacin intake with depression among adults in US was assessed by using a weighted multiple logistic regression model with subgroup analysis. Non-linear associations were explored using restricted cubic spline models. And we used a two-piece-wise logistic regression model with smoothing to explore the threshold for association between them. Results A total of 16,098 adults were included in this study. Compared with individuals with lowest niacin intake Q1 (≤ 15.96 mg/day), the adjusted OR values for dietary niacin intake and depression in Q2 (15.97–22.86 mg/day), Q3 (22.87–32.28 mg/day) and Q4 (≥ 32.29 mg/day), were 0.92 (95% CI: 0.70–1.20), 0.76 (95% CI: 0.56–0.99,) and 0.68 (95% CI: 0.48–0.98), respectively. The results were not modified by sex, by age and by BMI. Furthermore, the relationship between dietary niacin intake and depression exhibited a U-shaped curve (nonlinear, p

Recent vigorous controls in anthropogenic nitrogen oxide emissions in China cannot result in proportionate decreases in regional atmospheric nitrogen deposition. Enhanced atmospheric oxidizing capacity offsets those reductions of precursor emissions.

BackgroundIt has been shown in previous studies that botulinum toxin type A (BTX-A) can effectively relieve the motor symptoms of Meige syndrome. However, its effect on non-motor symptoms (NMS) and quality of life (QoL) has not been comprehensively studied. This study aimed to explore the effects of BTX-A on NMS and QoL and to clarify the relationship between changes in motor symptoms, NMS, and QoL after BTX-A.MethodsSeventy-five patients were recruited for the study. All patients were assessed by a series of clinical assessments before, one, and 3 months after BTX-A treatment. Dystonic symptoms, psychiatric disturbances, sleep disorders, and QoL were evaluated.ResultsAfter 1 and 3 months of BTX-A treatment, the scores of motor symptoms, anxiety, and depression were significantly decreased (P < 0.05). Except for general health, the scores of the other 36-item short-form health survey QoL subitems were significantly improved after BTX-A (P < 0.05). After 1 month of treatment, the changes in anxiety and depression were not correlated with changes in motor symptoms (P > 0.05). Still, they were negatively correlated with changes in physical functioning, role-physical and mental component summary QoL (P < 0.05).ConclusionsBTX-A effectively improved motor symptoms, anxiety, depression, and QoL. Anxiety and depression improvement did not correlate with motor symptom changes after BTX-A, and QoL improvements were strongly associated with psychiatric disturbances.

Abstract The atmospheric deposition of soluble (bioaccessible) iron enhances ocean primary productivity and subsequent atmospheric CO2 sequestration in iron-limited ocean basins, especially the Southern Ocean. While anthropogenic sources have been recently suggested to be important in some northern hemisphere oceans, the role in the Southern Ocean remains ambiguous. By comparing multiple model simulations with the new aircraft observations for anthropogenic iron, we show that anthropogenic soluble iron deposition flux to the Southern Ocean could be underestimated by more than a factor of ten in previous modeling estimates. Our improved estimate for the anthropogenic iron budget enhances its contribution on the soluble iron deposition in the Southern Ocean from about 10% to 60%, implying a dominant role of anthropogenic sources. We predict that anthropogenic soluble iron deposition in the Southern Ocean is reduced substantially (30‒90%) by the year 2100, and plays a major role in the future evolution of atmospheric soluble iron inputs to the Southern Ocean.

In order to increase agglomeration production and improve its quality while reducing energy consumption, a new intelligent blending method has been established at Ansteel to optimize its sinter blends. The statistical model of the ore blending results shows that (1) a blending ratio of 47.2% Ore A and 52.8% Ore C corresponded to the best sinter yield of 72.44%. (2) From the viewpoint of sinter reducibility, sinter basicity should not be less than 1.98 when the proportion of Ore A in the blend is more than 35%. Due to the low mixing efficiency of disc pelletizers, Ansteel has therefore gone through a massive technical transformation at Sinter Plant 3 to replace its 16 existing disc pelletizers with one drum granulator. Since the installation of the drum granulator, the standard deviation has decreased from 1.517 to 0.7332 for total Fe (T.Fe) and from 0.146 to 0.0956 for basicity. In the case of the drum granulator, the standard deviation for sinter T.Fe and basicity were 0.6926 and 0.05449, respectively, as compared to 0.8902 and 0.2033 for the disc pelletizers. In addition, a single lattice method is proposed to optimize the particle-size distribution of the coke breeze to further improve sinter quality and reduce fuel consumption. The lattice method indicated that the optimum coke breeze to achieve maximum sinter tumble strength should consist of approximately 57.20%: −1 mm, 25.63%: 1–3 mm, 11.17%: 3–5 mm, and 6.00%: >5 mm particles. Given the international trend of increasing bed depth, Ansteel has successfully achieved a bed height of 1050 mm or more under its blend conditions, which typically contain 75% concentrates. Finally, some new iron ore agglomerations research is discussed.

Temperature inversions inhibit the transfer of momentum, heat and moisture in the atmosphere and have led to severe air pollution in China. This study investigated the spatiotemporal variation in temperature inversions in China using sounding data for the past four decades. Surface-based inversion, elevated inversion, and both in one sounding dataset were analysed. Statistical analyses of inversion parameters included frequency, strength and depth. The annual frequency of total inversions showed no significant increasing or decreasing trend with mean values of 0.78, 0.33, 0.24, 0.28, 0.5 and 0.36 at six stations representing different climate zones—Beijing, Harbin, Haikou, Shaowu, Ruoqiang, and Xining, respectively. The annual inversion strength and depth showed downward trends. Monthly variation in inversion frequency and strength differed among stations. The weakest surface-based inversion was found in summer at Beijing and Harbin with mean values of 1 and 1.3 °C, respectively; the strongest surface-based inversion was found in winter with respective mean values of 3.5 and 3.6 °C. Higher surface temperature in summer and subsidence aloft in winter may explain the monthly variation in inversion depth with a minimum in summer, with mean values of 165, 334, 135, 267, 363 and 420 m, and a maximum in winter, with mean values of 250, 646, 140, 591, 806 and 664 m, at the six respective stations. Total inversion was least frequent in southwestern China (mean 0.15), surface-based inversion was most frequent in the north (mean 0.78), and elevated inversion was most frequent in the southeast (mean 0.42). The strongest, deepest surface-based inversion dominated in the north (mean 3.4 °C and 398 m). Elevated inversion strength did not significantly differ among regions (mean 2.5 °C). The deepest elevated inversion dominated in the southeast (mean 654 m). Future efforts should focus on the interactions between aerosols and temperature inversions and accurate model simulations of temperature inversions.

Background: The differentiation of microglia from M1 to M2 exerts a pivotal role in the aggression of intracerebral hemorrhage (ICH), and long non-coding RNAs (lncRNAs) are associated with the differentiation of microglia. However, the underlying mechanism had not been fully clarified.Methods: The expression profile of lncRNAs in thrombin-induced primary microglia was analyzed by RNA sequencing. Under thrombin treatment, the effect of lncRNA TCONS_00145741 on the differentiation of microglia was determined by immunofluorescence staining, quantitative real-time PCR, and Western blot. The potential mechanism and related signaling pathways of TCONS_00145741 in the M1 and M2 differentiation of microglia in ICH were assessed by Gene Ontology analysis, flow cytometry, RNA pull-down, RNA Immunoprecipitation, and RNA fluorescence in situ hybridization followed by immunofluorescence analysis.Results: LncRNA TCONS_00145741 expression was elevated in the thrombin-induced primary microglia, and the interference with TCONS_00145741 restrained the M1 differentiation of microglia and facilitated the M2 differentiation under thrombin treatment. The interference with TCONS_00145741 restrained the activation of the JNK pathway in microglia under thrombin treatment and repressed the JNK phosphorylation levels by enhancing the interaction between DUSP6 and JNK. In vivo experiments further illustrated that the interference with TCONS_00145741 alleviated ICH.Conclusion: LncRNA TCONS_00145741 knockdown prevented thrombin-induced M1 differentiation of microglia in ICH by enhancing the interaction between DUSP6 and JNK. This study might provide a promising target for the clinical treatment of ICH.

Delineation of agricultural fields is desirable for operational monitoring of agricultural production and is essential to support food security. Due to large within-class variance of pixel values and small inter-class difference, automated field delineation remains to be a challenging task. In this study, a strategy is proposed to effectively address this issue. Firstly, a framework was developed using the Canny operator connected with the Watershed segmentation algorithm (CW) to quickly label the training dataset, which minimizes the workload of dataset generation in comparison with the commonly used manual vectorization. Secondly, a CW-trained deep semantic segmentation network, recurrent residual U-Net, was selected to mine the low level and deep semantic features. Finally, a boundary connecting method (to integrate fragmented boundaries) was used to generate the agricultural field boundary. The proposed methods are tested over smallholder agricultural landscape in Heilongjiang province, China, using Sentinel-2 imagery. Compared with the U-Net (overall accuracy (OA) 82.18%), the residual U-Net (ResU-Net, with OA 85.78%), traditional object-based image analysis (OBIA, with OA about 82%), and the existing 10-m resolution global land cover map (FROM-GLC10), the proposed method shows an improved performance (OA 89.28%, and Kappa 0.85). The successful application of the proposed method suggests that the recurrent residual U-Net model has great universality in agricultural field boundary extraction, and the automated technique has the potential of being applied to other regions.

Timely and accurate mapping crops is essential for agricultural management, policy making and food security. The smallholder agricultural systems lead to large number of fragmented and heterogeneous landscape, making fine crop mapping a huge challenge. Feature and classifier selection are two important influencing factors in crop classification. However, there are few systematic tests to determine the specific features and classifiers needed for heterogeneous agricultural landscapes. In this study, 24 candidate spectral features, 8 spatial texture features from the red-edge (RE) and near-infrared (NIR) bands, and 4 supervised classifiers (i.e. random forest (RF), support vector machine (SVM), artificial neural network (ANN), and maximum likelihood classifier (MLC)) were used for crop mapping. 60 spatially heterogeneous landscapes in Heilongjiang Province, northeastern China were selected as the study areas, evaluated by compositional heterogeneity (homogeneity index, HOI) and configurational heterogeneity (splitting index, SPLIT). The results summarized a look-up table for searching the optimum classification features and classifiers in different landscape, providing a reference for future crop classifications and preventing the consumption of computing time. The results revealed that (1) an optimal feature-subset (with reduced data volume by 65%) can achieve high-accuracy crop mapping in heterogeneous regions. (2) The optimum type and number of features and classifiers are landscape sensitive. When a specific accuracy was required, homogenous regions need a smaller number of features and a simple MLC could meet the requirement. (3) The impact of configurational heterogeneity on textural features is more significant, while compositional heterogeneity performs better on spectral Vegetation Indices (VIs). Findings from this study provide a general guideline for crop mapping in plains or fragmented landscape and areas with single or complex planting structure.

East and South Asia are major hotspots of crop straw burning worldwide, with profound impacts on air quality and climate change. The Northeast China Plain (NECP) and Punjab, India, are two of the most fertile areas for crop production, which have large-scale agricultural fires during post-harvest seasons. Leveraging established fire-emission databases and satellite-retrieved agricultural fire spots, we show that, while the years 2018 and 2019 recorded low agricultural fire emissions in both the NECP and Punjab, probably due to the implementation of crop straw sustainable management, fire emissions markedly rebounded in 2020, reaching about 190% and 150% of 2019 levels, respectively. The COVID-19 lockdown measures somewhat disrupted eco-friendly crop straw management through restrictions on labor and transportation availability, such that farmers may have had to burn off crop wastes to clear up the land. We further demonstrate that the increased fire emissions in the NECP resulted in serious particulate matter pollution during the fire season in spring 2020, as opposed to considerable decreases in particles from fossil fuel emissions caused by the COVID-19 lockdown. This study suggests the unintended impacts of the COVID-19 pandemic on the agricultural sector and human health.

The decadal variations in emissions of high-reactivity biogenic volatile organics (BVOCs), as a result of land-cover changes, could significantly impact ozone (O3) production. In this study, the Weather Research and Forecasting/Chemistry (WRF/Chem) modelling system, coupled with dynamic vegetation data sets derived from Moderate Resolution Imaging Spectroradiometer (MODIS, 2001–2012) and Advanced Very High Resolution Radiometer (AVHRR, early 1990s) measurements, were used to investigate the impacts of land-cover changes on natural emissions, and consequently O3 production, in the Pearl River Delta (PRD) region of southern China over the past two decades. Model results indicate that BVOC emissions were highly dependent on forest area. The total BVOC emissions in the modelling domain increased by a factor of two due to afforestation since the early 1990s, declined slowly (−5.8% yr−1) until 2006 and then increased continuously (+9.1% yr−1) to 2012. The decadal variations in BVOC emissions have complex implications for summer O3 production in PRD, depending on the chemical regimes and prevailing winds. The impacts on O3 production were most sensitive in downwind areas, and it was found that the large increase in BVOC emissions during 2006–2012 tended to reduce surface O3 concentrations by 1.6–2.5 ppb in rural regions, but caused an increment of O3 peaks by up to 2.0–6.0 ppb in VOC-limited urban areas (e.g., Guangzhou, Foshan and Zhongshan). The opposite was true in the period 2001–2006, when the reduced BVOC emissions resulted in 1.3–4.0 ppb increases in daytime O3 concentrations over northern rural regions. Impact of the two-fold increase in BVOC emissions since the early 1990s to 2006 was a 0.9–4.6 ppb increment in surface O3 concentrations over the downwind areas. This study suggests that the potential impacts on ozone chemistry should be considered in long-term land-use planning and air-quality management.

Unveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MTFM network), to peer into the complex system dynamics. Through mapping the system features into different frequency-based layers and inferring interactions (edges) among different channels (nodes), the MTFM network allows efficiently integrating time, frequency and spatial information hidden in a multivariate time series. We employ two dynamic systems to illustrate the effectiveness of the MTFM network. We first apply the MTFM network to analyze the 48-channel measurements from industrial oil–water flows and reveal the complex dynamics ruling the transition of different flow patterns. The MTFM network is then utilized to analyze 30-channel fatigue driving electroencephalogram signals. The results demonstrate that MTFM network enables to quantitatively characterize brain behavior associated with fatigue driving. Our MTFM network enriches the multivariate time series analysis theory and helps to better understand the complicated dynamical behaviors underlying complex systems.

The sulfur cycle plays a key role in atmospheric air quality, climate, and ecosystems. In this study, we compare the spatial and temporal distribution of four sulfur-containing species, dimethyl sulfide (DMS), sulfur dioxide (SO2), particulate methanesulfonate (MSA), and particulate sulfate (SO4), that were measured during the airborne NASA Atmospheric Tomography (ATom) mission and simulated by five AeroCom-III models to analyze the budget of sulfur cycle from the models. This study focuses on remote regions over the Pacific, Atlantic, and Southern Oceans from near the ocean surface to ~12-km altitude range, and covers all four seasons. These regions provide us with highly heterogeneous natural and anthropogenic source environments, which is not usually the case for traditional continental studies. We examine the vertical and seasonal variations of these sulfur species over tropical, mid-, and high-latitude regions in both hemispheres. We identify their origins from land versus ocean and from anthropogenic versus natural sources with sensitivity studies by applying tagged tracers linking to emission types and regions. In general, the differences among model results can be greater than one-order of magnitude. Comparing with observations, simulated SO2 is generally low while SO4 is high, and the model-observation agreement is much better in ATom-4 (April-May, 2018). There are much larger DMS concentrations simulated close to the sea surface than observed, indicating that the DMS emissions may be too high from all models. Anthropogenic emissions are the dominant source (40-60% of the total amount) for atmospheric sulfate simulated at locations and times along the ATom flight tracks at almost every altitude, followed by volcanic emissions (18-32%) and oceanic sources (16-32%). Similar source contributions can also be derived at broad ocean basin and monthly scales, indicating that any reductions of anthropogenic sulfur emissions would have global impacts in modern times. [ABSTRACT FROM AUTHOR]