科研成果

The real part of the refractive index (RRI) of ambient aerosol, which is widely used in remote sensing and atmospheric models, is one of the key factors determining its particles' optical properties. The characteristics of ambient aerosol RRI in China have not yet been well studied owing to a lack of observations. For the first time, the properties of aerosol RRI were studied based on field measurements in China at four sites with different atmospheres. The results revealed that the measured ambient aerosol RRI varied significantly between 1.36 and 1.78, increasing with the mass ratio of organic components. The scattering coefficient and direct radiative effects of the aerosols were estimated to increase by factors of 2 and 3, respectively, when RRI increased from 1.36 to 1.78. Our results indicate that variation in ambient aerosol RRI should be considered in aerosol and climate models to achieve an accurate estimation of aerosol's radiative impacts. © 2021 Elsevier B.V.

<p id="p00015">Leaf is one of the important organs of plants that facilitates the exchange of water and air with the surrounding environment. The morphological variation of leaves directly affect the physiological and biochemical processes of plants, which also reflects the adaptive strategies of plants to obtain resources. By focusing on several leaf morphological traits, including leaf size, leaf shape, leaf margin (with or without teeth) and leaf type (i.e. single vs. compound leaf), here, we reviewed the relevant research progresses in this field. We summarized the ecological functions of leaf morphological traits, identified their geographical distribution patterns, and explored the underlying environmental drivers, potential ecological interactions, and their effects on ecosystem functioning. We found that the current studies exploring the distribution and determinants of leaf size and leaf margin states mainly focused on single or specific taxon in local regions. Studies have also explored the genetic mechanisms of leaf morphology development. Leaf traits trade off with other functional traits, and their spatial variation is driven by both temperature and water availability. Leaf morphological traits, especially leaf size, influence water and nutrient cycling, reflect the response of communities to climate change, and can be scaled up to predict ecosystem primary productivity. Further studies should pay attention to combine new approaches to obtain unbiased data with high coverage, to explore the long-term adaptive evolution of leaf morphology, and to generalize the scaling in leaf morphology and its effect on ecosystem functioning. Leaf provides an important perspective to understand how plants respond and adapt to environmental changes. Studying leaf morphological traits provides insight into species fitness, community dynamics and ecosystem functioning, and also improves our understanding of the research progresses made in related fields, including plant community ecology and functional biogeography.</p>

In this work, we develop a distributed least-square approximation (DLSA) method that is able to solve a large family of regression problems (e.g., linear regression, logistic regression, and Cox’s model) on a distributed system. By approximating the local objective function using a local quadratic form, we are able to obtain a combined estimator by taking a weighted average of local estimators. The resulting estimator is proved to be statistically as efficient as the global estimator. Moreover, it requires only one round of communication. We further conduct a shrinkage estimation based on the DLSA estimation using an adaptive Lasso approach. The solution can be easily obtained by using the LARS algorithm on the master node. It is theoretically shown that the resulting estimator possesses the oracle property and is selection consistent by using a newly designed distributed Bayesian information criterion. The finite sample performance and computational efficiency are further illustrated by an extensive numerical study and an airline dataset. The airline dataset is 52 GB in size. The entire methodology has been implemented in Python for a de-facto standard Spark system. The proposed DLSA algorithm on the Spark system takes 26 min to obtain a logistic regression estimator, which is more efficient and memory friendly than conventional methods. Supplementary materials for this article are available online.

Links between the optical properties and chemical compositions of brown carbon (BrC) are poorly understood because of the complexity of BrC chromophores. We conducted field studies simultaneously at both vehicle-influenced site and biomass burning-affected site in China in polluted winter. The chemical compositions and light absorption values of functionalized aromatic compounds, including phenyl aldehyde, phenyl acid, and nitroaromatic compounds, were measured. P-phthalic acid, nitrophenols and nitrocatechols were dominant BrC species, accounting for over 50% of the concentration of identified chromophores. Nitrophenols and nitrocatechols contributed more than 50% of the identified BrC absorbance between 300 and 400 nm. Oxidation of biomass burning-related products (e.g., pyrocatechol and methylcatechols) and anthropogenic volatile organic compounds (e.g., benzene and toluene) generated similar BrC chromophores, implying that these functionalized aromatic compounds play an important role in both environments. Compared with the biomass burning-affected site (22%), functionalized aromatic compounds at vehicle-influenced site accounted for a higher percentage of BrC absorption (25%). This research improves our understanding of the links between optical properties and composition of BrC, and the difference between BrC chromophores from BB-influenced area and vehicle-affected area under polluted atmospheric conditions. © 2021 Elsevier B.V.

Background Air pollutants, particularly fine particulate matters (PM2.5) have been associated with mental disorder such as depression. Clean air policy (CAP, i.e., a series of emission-control actions) has been shown to reduce the public health burden of air pollutions. There were few studies on the health effects of CAP on mental health, particularly, in low-income and middle-income countries (LMICs). We investigated the association between a stringent CAP and depressive symptoms among general adults in China. Methods We used three waves (2011, 2013 and 2015) of the China Health and Retirement Longitudinal Study (CHARLS), a prospective nationwide cohort of the middle-aged and older population in China. We assessed exposure to PM2.5 through a satellite-retrieved dataset. We implemented a difference-in-differences (DID) approach, under the quasi-experimental framework of the temporal contrast between 2011 (before the CAP) and 2015 (after the CAP), to evaluate the effect of CAP on depressive symptoms. The association was further explored using a mixed-effects model of the three waves. To increase the interpretability, the estimated impact of PM2.5 was compared to that of aging, an established risk factor for depression. Findings Our analysis included 15,954 participants. In the DID model, we found a 10-µg/m3 reduction of PM2.5 concentration was associated with a 4.14% (95% CI: 0.41–8.00%) decrement in the depressive score. The estimate was similar to that from the mixed-effects model (3.63% [95% CI, 2.00–5.27%]). We also found improved air quality during 2011–2015 offset the negative impact from 5-years’ aging. Interpretation The findings suggest that implementing CAP may improve mental wellbeing of adults in China and other LMICs.

A set-pair Lovász extension is established to construct equivalent continuous optimization problems for graph k-cut problems.