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Biomass burning serves as important sources of volatile organic compounds (VOCs), yet our understanding of the molecular characteristics of oxygenated volatile organic compounds (OVOCs) from fresh emissions remains limited. In this study, gaseous organic compounds in fresh smokes from burning typical Chinese crop straws and woods are measured using an iodide time‐of‐flight chemical ionization mass spectrometer. Approximately 750 molecular formulas are identified, with CHO compounds containing carbon, hydrogen and oxygen atoms accounting for 77%–95% of the total. C1–C3 organic acids and dicarboxylic acids dominate the total CHO signal intensities by 27%–48%, while well‐known molecular markers of biomass burning, such as monosaccharide, guaiacol and syringol derivatives, contribute 7%–17%. Notably, crop straw and wood burning emit a higher abundance of guaiacol than syringol derivatives by a factor of 5. Additionally, a variety of nitrogen‐containing compounds (mainly in the CHON group) is identified, including isocyanate, amide, amino acids, and pyridine. The mass spectral profiles of organic compounds are largely similar between crop straw and wood burning fuels, although wood burning produces higher contributions of compounds with carbon atoms numbers >10. The saturation concentrations of organic compounds are estimated using molecular formula‐based volatility parameterization, revealing that semi‐volatile and intermediate VOCs (S/intermediate volatility organic compounds (IVOCs)) predominate smoke releases by 35%–60% and 20%–43%, respectively, with only a small fraction of low‐volatility compounds. Given the widespread nature of biomass burning in winter China, our results may have significant implications for interpreting secondary organic aerosol formation through gas‐particle partitioning or aqueous‐phase reactions. Plain Language Summary: Biomass burning is a significant source of volatile organic compounds (VOCs), with oxygenated volatile organic compounds (OVOCs) being particularly important due to their high reactivity and their contribution to the radical budget and secondary products formation. However, previous research has primarily focused on a limited number of OVOCs species from pine, spruce, and other biofuels, leading to insufficient studies on the full mass spectra of OVOCs from biomass smokes. Our study has addressed this gap by identifying approximately 750 OVOCs from burning typical Chinese crop straws and woods. We found that C1–C3 organic acids and dicarboxylic acids are the predominant contributors to total signal intensities. This expands on previous studies that have focused on a limited well‐known molecular markers of biomass burning, such as monosaccharide, guaiacol and syringol derivatives, and reveals the presence of nitrogen‐containing compounds, including isocyanate, amide, amino acids, and pyridine. The saturation concentrations of organic compounds are estimated, highlighting the importance of semi‐volatile and intermediate VOCs (S/IVOCs) in fresh smoke releases. Our study contributes to a better understanding of complex chemistry of biomass burning and its impact on air quality. It will serve as a crucial basis for accurately assessing biomass burning related‐secondary organic aerosol formation. Key Points: C1–C3 organic acids and dicarboxylic acids dominate the total signal intensities of fresh crop straw and wood burning emissionsThe mass spectral profiles are largely similar across different fuels, with wood burning produces higher contributions of large compoundsSemi‐volatile and intermediate volatile organic compounds predominate fresh smoke emissions, with only a small fraction of low‐volatility compounds [ABSTRACT FROM AUTHOR]

The vast rural areas often experience more severe haze pollution than megacities during wintertime in North China Plain (NCP), yet the sources and evolution processes of aerosol particles, particularly organic aerosol (OA) remain poorly understood. Here we conducted real‐time measurements of submicron aerosol (PM1) species using a high‐resolution aerosol mass spectrometer at a representative rural site in NCP in winter 2019. Our results showed the dominance of organics (36%) and nitrate in PM1 (22%) in 2019. Positive matrix factorization analysis illustrated similarly important primary sources from traffic emissions, coal combustion, and biomass burning, yet secondary OA (SOA) from photochemical and aqueous‐phase related processing exceeded primary OA (53% vs. 47%). Substantial decreases in primary species and considerable increases in nitrate and sulphate were observed since winter 2018, demonstrating enhanced secondary formation in winter 2019. OA composition changed significantly from clean period to fog events with the contribution of aqueous phase‐related oxygenated OA increasing from 6% to 44%, while the photochemical SOA decreased correspondingly from 51% to 19%. The size distributions of aerosol species also changed by shifting toward large sizes during fog events. Elemental analysis of OA and the Van Krevelen diagram (H/C vs. O/C) illustrated the different roles of photochemical and aqueous‐phase processing during daytime and nighttime, respectively, and aqueous‐phase processing is subject to the formation of organic compounds with high H/C and O/C ratios. The large differences in fog processing of submicron aerosol species between 2018 and 2019 due to different temperatures were also elucidated. Plain Language Summary: Air quality in megacities in North China Plain (NCP) has been improved significantly during the last decade, however, severe haze pollution events occur frequently in vast rural areas during wintertime. Recent studies have demonstrated stronger health impacts of aerosol particles in rural area than megacities, yet the composition, sources and evolution processes of aerosol particles, particularly organic aerosol (OA) in rural area in NCP remain poorly characterized. Here we have a comprehensive characterization of submicron aerosol species at a rural site in winter in NCP. We observed substantial decreases in primary species and considerable increases in nitrate and sulphate from 2018 to 2019 due to enhanced secondary formation and the decreases in primary emissions. We found similarly important primary sources from traffic emissions, coal combustion, and biomass burning in rural area. However, secondary OA (SOA) from photochemical and aqueous‐phase related processing exceeded primary OA. The aqueous‐phase related SOA increased significantly by up to 44% during fog events, while the photochemical SOA decreased correspondingly from 51% to 19%. The size distributions, elemental composition of OA, and Van Krevelen diagram further illustrated the different roles of photochemical and aqueous‐phase processing in secondary aerosol formation in rural area in NCP. Key Points: Primary organic aerosol (OA) was primarily from coal combustion, biomass burning and traffic emissions in rural North China Plain in winterAqueous phase‐related OA showed large increases during fog events, while photochemical secondary OA was important during non‐fog periodsAerosol composition showed significant changes from 2018 to 2019 due to enhanced secondary formation and changes in primary emissions [ABSTRACT FROM AUTHOR]

The utility of elevational gradients as tools to test either ecological hypotheses and delineate elevation‐associated environmental factors that explain the species diversity patterns is critical for moss species conservation. We examined the elevational patterns of species richness and evaluated the effects of spatial and environmental factors on moss species predicted a priori by alternative hypotheses, including mid‐domain effect (MDE), habitat complexity, energy, and environment proposed to explain the variation of diversity. Last, we assessed the contribution of elevation toward explaining the heterogeneity among sampling sites. We observed the hump‐shaped distribution pattern of species richness along elevational gradient. The MDE and the habitat complexity hypothesis were supported with MDE being the primary driver for richness patterns, whereas little support was found for the energy and the environmental factors. [ABSTRACT FROM AUTHOR]

Drip fertigation is an advanced technology combining micro-irrigation with fertilization, which is conducive to deliver water and nutrients to crops or fruit trees synchronously. To optimize drip fertigation scheduling for wine grape in North China, a three-year field experiment was conducted to study the effects of irrigation and fertilization quota on yield, berry quality, water use efficiency (WUE) and partial factor productivity of fertilizer (PFP F). Nine treatments were set up based on the combinations of three irrigation levels and three fertilization rates. The three irrigation levels were 100 % (I H), 80 % (I M) and 60 % (I L) of the designed irrigation quota, i.e., 25, 20 and 15 mm per irrigation event. The three fertilization rates were 100 % (F H), 80 % (F M) and 60 % (F L) of the compound water-soluble fertilizer amount locally recommended, i.e., 630, 504 and 378 kg ha−1 yr−1. Both irrigation and fertilization had a significant effect on yield. Irrigation also significantly affected titratable acid and sugar-acid ratio. Averaged across the years and fertilization rates, compared with I H treatment, I M treatment decreased titratable acid by 3.3 % and increased sugar-acid ratio by 12.2 %; I L treatment decreased titratable acid by 10.7 % and increased sugar-acid ratio by 20.0 %. F M treatment presented the richest anthocyanins and condensed tannins among the three fertilizer rates. IWUE was significantly increased with the decrease of irrigation amounts; WUE and PFP F reached the maximum in I M treatment. The decrease of fertilizer rates significantly increased PFP F but was not beneficial for WUE. Interaction effects between irrigation and fertilization were only significant on PFP F. Assessed by gray relational analysis (GRA), technique for order preference by similarity to ideal solution (TOPSIS) and combinational evaluation method (CEM), the combinational evaluation value (Q i) of I M F M treatment (moderate water and fertilizer, 20 mm per irrigation event combined with 504 kg ha−1 yr−1 compound water-soluble fertilizer) was the highest on the whole, indicating that I M F M treatment was the optimal treatment which could balance yield, quality and water-fertilizer use efficiency. These results may provide a benchmark for wine grape cultivation under drip fertigation in North China and other regions with similar environmental conditions. • Deficit irrigation decreased titratable acid and increased sugar-acid ratio. • Optimal water and fertilizer strategy was identified by comprehensive evaluation. • Moderate water and fertilizer supply improved comprehensive performance of grape. [ABSTRACT FROM AUTHOR]

Landslide disasters have occurred frequently in the Chinese Loess Plateau (CLP) due to increased agricultural irrigation. To explore the spatiotemporal relationship between landslides and mechanisms at the Heifangtai terrace, the HFT irrigated area was selected as a typical case study to investigate the fundamental mechanism of the irrigation-induced landslide in the CLP. Multi-temporal remote sensing images, topographic maps, and unmanned aerial vehicle (UAV) photogrammetry data were used to investigate the evolution progress of landslides. Moreover, the evolution mechanism was discussed through topographic analysis, field monitoring, and laboratory testing. The results showed that erosion, collapse, and sliding had occurred at different scales and at different locations in the past 50 years. With an average retrogressive speed of 9.6 × 103 m2 per year, the tableland decreased by 4.9 × 105 m2 from 1967 to 2018, accounting for about 4.5% of its total area. Over 20 landslides and collapses were extracted in the Dangchuan section in the past four years. More than 5.48 × 105 m3 of loess slipped with an average volume of 381 m3 per day. The evolutionary process of the irrigation-induced landslide, which features retrogression, lateral extension, and clustering, began with local failure and ended in a series of slidings. The increase of groundwater level was a slow process, which is the reason for the lagged occurrence of the landslide. The influence of rainfall and irrigation on slope stability was greater than that of the periodic change of the groundwater level. The triggering effect of irrigation and rainfall on the landslide had a time lag due to slow loess infiltration, and the time response among irrigation, rainfall, and groundwater level was 4–6 months. Our findings provide guidance, concerning the planning and controlling of landslide disasters, which is of critical value for human and construction safety. [ABSTRACT FROM AUTHOR]

• Aerosol composition in central China plain was dominated by nitrate and organics. • Air pollution in central China plain (CCP) was relatively homogeneously distributed. • The effect of local emission controls in mitigating air pollution in CCP is limited. Extensive studies on aerosol chemistry have been carried out in megacities in China, however, aerosol characterization in Central China Plain (CCP) is limited. Here we conducted real-time measurements of fine particle composition with a time-of-flight aerosol chemical speciation monitor in Kaifeng, Henan province in October 2019. Our results showed that nitrate and organics constituted the major fraction of non-refractory PM 2.5 for the entire study, on average accounting for 34% and 33%, respectively. However, aerosol composition was substantially different among four periods due to different meteorological conditions and chemical processing. For instance, nitrate presented the lowest contribution during the first period due to evaporative loss associated with high temperature (T), and then rapidly increased during polluted periods as a function of relative humidity (RH). Positive matrix factorization analysis showed the dominance of secondary organic aerosol (SOA) in OA, and also the changes in OA composition under different T and RH levels. In addition, this study is unique with two periods of local emission controls. Back trajectory and coefficient of divergence analysis showed that air pollution in CCP was overall homogeneously distributed. As a result, the effectiveness of local emission controls in this region was strongly affected by meteorological conditions and regional transport. We found that one of the periods with emission control even showed the highest concentrations for the entire study. Our results point towards the limited effect of local emission controls in mitigating air pollution in CCP, and highlight the importance of joint emission controls under unfavorable meteorological conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

Air quality has been improved significantly from 2017 to 2020 in Fenhe Plain, one of the most polluted regions in China. However, severe haze episodes from days to weeks were still frequently observed in cold seasons, and the sources and formation mechanisms especially organic aerosols are poorly understood due to the limited highly time-resolved measurements. Here we have a comprehensive characterization of chemical composition, variations and sources of fine particles in fall and winter in Fenhe Plain mainly using a PM 2.5 Aerosol Chemical Speciation Monitor. Our results showed that organics and nitrate were two major species of PM 2.5 , on average accounting for 35.4% and 27.0%, respectively, and they played consistently important roles across different pollution levels. Positive matrix factorization revealed three primary sources from traffic, biomass burning and coal combustion emissions, and two secondary factors that are associated with photochemical and aqueous-phase processing. While secondary OA (SOA) dominated OA for the entire study (63%), large increases in primary OA from 19% to 41% were observed during heating period. Particularly, we found a high contribution of photochemical OOA (45%) during severely polluted periods in heating season demonstrating the presence of strong photochemical production in winter. Most aerosol species presented similarly pronounced diurnal cycles with low concentrations during daytime indicating the major influences from planetary boundary layer and primary emissions. The bivariate polar plots further illustrated the different sources of primary and secondary species, i.e., local emissions versus regional transport from the south and southeast. By analyzing four case studies, we found three major formation mechanisms of severe haze episodes that were driven by photochemical processing, aqueous-phase production, and local emissions, respectively. Therefore, different mitigating strategies are suggested in Fenhe Plain depending on the types of severe haze episodes. [Display omitted] • Organics and nitrate dominated particle composition in cold season in Fenhe plain. • POA were mainly from coal combustion, biomass burning, and traffic emissions. • Strong photochemical production of SOA was observed during winter severe pollution. [ABSTRACT FROM AUTHOR]

Air quality in China has been continuously improved since clean air action in 2013, yet the visibility was not improved simultaneously. Here we employed a new method by integrating highly time-resolved aerosol compositions with particle light extinction (b ext) into positive matrix factorization to quantify the different contributors to visibility degradation during four seasons in Beijing. Our results show that ammonium nitrate-related factor contributed dominantly to b ext during all seasons (31–48%) and played more significant roles during low-visibility periods. Secondary organic aerosol (SOA) was an important contributor of b ext (27–35%) in autumn and spring while primary OA related sources were more important in winter (37%). An increase in aerosol mass extinction efficiency and similarly important roles of ammonium nitrate and SOA in visibility degradation were also observed during the COVID-19 lockdown period. Our results point towards a future challenge in improving visibility in China due to the increased contributions of nitrate and SOA in PM 2.5. Future emission controls with a priority to decrease nitrate would benefit both air quality and visibility. [Display omitted] • A new method to quantify the source contributions to particle light extinction. • Dominant contributions of ammonium nitrate and SOA to light extinction in Beijing. • Increased aerosol mass extinction efficiency during the COVID-19 period. [ABSTRACT FROM AUTHOR]

Hydroxymethanesulfonate (HMS), a key marker species of aqueous-phase processing, plays a significant role in sulfur budget in atmosphere. Here we have a comprehensive characterization of HMS at urban and rural sites in North China Plain (NCP) by using the real-time measurements from a high-resolution aerosol mass spectrometer (AMS) and a single-particle AMS together with offline filter analysis. Our results showed much higher winter concentration of HMS at the rural site (average±1σ: 2.58 ± 2.56 μg m -3 ) than that (1.70 ± 2.68 μg m -3 ) in Beijing due to the more frequent fog events, low particle acidity and high concentration of precursors. The HMS on average contributed 6.3% and 5.2% to organic aerosol (OA), and 16% and 12% to the total particulate sulfur, at the rural and urban sites, respectively. HMS was highly correlated with aqueous-phase secondary OA and sulfate, and its contribution to the total particulate sulfur increased significantly as a function of relative humidity demonstrating the effective HMS production from aqueous-phase processing. Single-particle analysis showed that HMS-containing particles were mainly mixed with amine-related compounds. In addition, we found that organosulfur compounds (OS) estimated from sulfur-containing fragments of AMS correlated well with HMS at both urban and rural sites. While OS at the rural site was dominated by HMS, other types of OS were also important in urban area. The high HMS also affected the estimation of particle acidity using the AMS measured and predicted ammonium, particularly during severe haze episodes. Overall, our results demonstrated the importance of HMS in winter in NCP, and it could be more important in total particulate sulfur budget as the continuous decrease in sulfate in the future., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Context: The previous studies showed that hypogonadotropic hypogonadism (HH) occurred commonly in men with type 2 diabetes. However, since all the cohorts tested were from American and European studies, the occurrence of HH/nongonadal illness (NGI) in Chinese populations is unclear., Objective: The study aimed to explore the occurrence of HH/NGI in Chinese men with type 2 diabetes. Furthermore, the correlative factors and predictors of hypogonadism were investigated., Design: We conducted a cross-sectional study of 637 Chinese men with type 2 diabetes aged 20-75 years in our clinic. The prevalence of HH/NGI was investigated by measuring serum total testosterone (TT), sex hormone-binding globulin (SHBG), luteinizing hormone (LH) and follicle-stimulating hormone (FSH) in the enrolled subjects. Free testosterone (FT) was calculated by using SHBG and TT levels and hypogonadism was defined as TT lower than 10.4 nmol/L and calculated FT (cFT) lower than 0.225 nmol/L. The LH cut-off value for defining HH/NGI was 9.4 mIU/ml., Results: The results suggested that 31.9% of male Chinese type 2 diabetes patients had hypogonadism and 26.5% of subjects in our cohort were determined as HH/NGI. The occurrence of hypogonadism was markedly correlated with body mass index (BMI). There was a significant association between TT, cFT and SHBG levels with BMI. TT levels are inversely correlated with BMI and homeostasis model assessment-estimated insulin resistance (HOMA-IR) while positively related with SHBG. The cFT levels were inversely correlated with age, LH, FSH, BMI and HOMA-IR. Multiple regression analysis suggested that SHBG, BMI and HOMA-IR were significant predictors of TT and cFT., Conclusion: Our present study offered the first evidence that the occurrence of HH/NGI in Chinese male type 2 diabetes was 26.5%. TT and cFT were significantly correlated with BMI, SHBG and HOMA-IR in Chinese men with type 2 diabetes., (© 2022 John Wiley & Sons Ltd.)

Motivation: Warfarin is a widely used oral anticoagulant, but it is challenging to select the optimal maintenance dose due to its narrow therapeutic window and complex individual factor relationships. In recent years, machine learning techniques have been widely applied for warfarin dose prediction. However, the model performance always meets the upper limit due to the ignoration of exploring the variable interactions sufficiently. More importantly, there is no efficient way to resolve missing values when predicting the optimal warfarin maintenance dose., Methods: Using an observational cohort from the Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine, we propose a novel method for warfarin maintenance dose prediction, which is capable of assessing variable interactions and dealing with missing values naturally. Specifically, we examine single variables by univariate analysis initially, and only statistically significant variables are included. We then propose a novel feature engineering method on them to generate the cross-over variables automatically. Their impacts are evaluated by stepwise regression, and only the significant ones are selected. Lastly, we implement an ensemble learning based approach, LightGBM, to learn from incomplete data directly on the selected single and cross-over variables for dosing prediction., Results: 377 unique patients with eligible and time-independent 1173 warfarin order events are included in this study. Through the comprehensive experimental results in 5-fold cross-validation, our proposed method demonstrates the efficiency of exploring the variable interactions and modeling on incomplete data. The R 2 can achieve 75.0% on average. Moreover, the subgroup analysis results reveal that our method performs much better than other baseline methods, especially in the medium-dose and high-dose subgroups. Lastly, the IWPC dosing prediction model is used for further comparison, and our approach outperforms it by a significant margin., Conclusion: In summary, our proposed method is capable of exploring the variable interactions and learning from incomplete data directly for warfarin maintenance dose prediction, which has a great premise and is worthy of further research., (Copyright © 2021. Published by Elsevier Ltd.)

Objective: Risk prediction models are widely used to inform evidence-based clinical decision making. However, few models developed from single cohorts can perform consistently well at population level where diverse prognoses exist (such as the SARS-CoV-2 [severe acute respiratory syndrome coronavirus 2] pandemic). This study aims at tackling this challenge by synergizing prediction models from the literature using ensemble learning., Materials and Methods: In this study, we selected and reimplemented 7 prediction models for COVID-19 (coronavirus disease 2019) that were derived from diverse cohorts and used different implementation techniques. A novel ensemble learning framework was proposed to synergize them for realizing personalized predictions for individual patients. Four diverse international cohorts (2 from the United Kingdom and 2 from China; N = 5394) were used to validate all 8 models on discrimination, calibration, and clinical usefulness., Results: Results showed that individual prediction models could perform well on some cohorts while poorly on others. Conversely, the ensemble model achieved the best performances consistently on all metrics quantifying discrimination, calibration, and clinical usefulness. Performance disparities were observed in cohorts from the 2 countries: all models achieved better performances on the China cohorts., Discussion: When individual models were learned from complementary cohorts, the synergized model had the potential to achieve better performances than any individual model. Results indicate that blood parameters and physiological measurements might have better predictive powers when collected early, which remains to be confirmed by further studies., Conclusions: Combining a diverse set of individual prediction models, the ensemble method can synergize a robust and well-performing model by choosing the most competent ones for individual patients., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Severe haze episodes in cold season in Beijing have been mitigated greatly during the last decade. However, the changes in aerosol chemistry as responses to the large reductions in gaseous precursors during the two phases of clean air action, i.e., phase Ⅰ (2013-2017) and phase Ⅱ (2018-2020), are less understood. Here we characterized such changes in cold season (January-March) by using five-year real-time aerosol particle composition measurements. Our results showed consistently large reductions for all chemical species from 2013 to 2020 with the largest decreases being chloride (95%) and organics (74%) followed by sulfate (69%), while the decreases in nitrate were comparatively small (44%). However, the contributions of sulfate were fairly stable despite the increased nitrate contributions from 18% in 2013 to 30% in 2020. Organic aerosol (OA) composition also changed significantly since 2018 with large increases in the contributions of secondary OA and corresponding decreases in primary OA from fossil fuel combustion and cooking emissions. The changes in aerosol chemistry were closely related to the different reductions in gaseous precursors, e.g., SO 2 vs. NO 2 , and the enhanced secondary processes, e.g., the increases in O 3 , sulfur and nitrogen oxidation efficiency. Further, we found that the changes in aerosol chemistry in cold season during the phase Ⅱ of clean air action (2018-2020) started to slow down with relatively small changes in PM 2.5 and secondary inorganic species. Our results point towards a future challenge in mitigating air pollution in cold season, and the need of more stringent and scientific strategies to control secondary aerosol pollution in an environment with enhanced oxidation capacity and high precursors., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The rapidly spread coronavirus disease (COVID-19) has limited people's outdoor activities and hence caused substantial reductions in anthropogenic emissions around the world. However, the air quality in some megacities has not been improved as expected due to the complex responses of aerosol chemistry to the changes in precursors and meteorology. Here we demonstrate the responses of primary and secondary aerosol species to the changes in anthropogenic emissions during the COVID-19 outbreak in Beijing, China along with the Chinese New Year (CNY) holiday effects on air pollution by using six-year aerosol particle composition measurements. Our results showed large reductions in primary aerosol species associated with traffic, cooking and coal combustion emissions by 30-50% on average during the CNY, while the decreases in secondary aerosol species were much small (5-12%). These results point towards a future challenge in mitigating secondary air pollution because the reduced gaseous precursors may not suppress secondary aerosol formation efficiently under stagnant meteorological conditions. By analyzing the long-term measurements from 2012 to 2020, we found considerable increases in the ratios of nitrate to sulfate, secondary to primary OA, and sulfur and nitrogen oxidation capacity despite the overall decreasing trends in mass concentrations of most aerosol species, suggesting that the decreases in anthropogenic emissions have facilitated secondary formation processes during the last decade. Therefore, a better understanding of the mechanisms driving the chemical responses of secondary aerosol to the changes in anthropogenic emissions under complex meteorological environment is essential for future mitigation of air pollution in China., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Freeze-thaw (FT) in northern China is a common event in spring and autumn, and the release of sulfur dioxide from coal-burning in winter is apt to trigger acid precipitation. Both these stresses can aggravate the wintering ability of white clover (Trifolium repens L.). Acid precipitation and FT simulation experiments were carried out in the field and an indoor alternation refrigerator, respectively. The contents of soluble protein, soluble sugar, malondialdehyde (MDA), proline and antioxidant activity were tested under acid precipitation and FT stress. The results showed that under acid precipitation stress, the content of MDA, peroxidase and superoxide dismutase increased in both leaves and stolons, whereas soluble protein and soluble sugar content declined compared with the control groups. During the freezing period, the content of antioxidant enzyme activity, soluble protein and proline increased at first and then dropped, whereas MDA and soluble sugar content both increased. As a conclusion, the stolon of white clover is more sensitive than the leaf to short-term stress, either as the single FT stress or the combined stress of FT and acid precipitation, suggesting that maintaining more leaves can contribute to the resistance of white clover to these stresses.