Characteristics and Source Apportionment of Volatile Organic Compounds in a Coastal Industrial Area: A Case Study in the Yangtze River Delta of China.

In recent years, the coastal area in East China has experienced elevated volatile organic compounds (VOCs) levels during specific periods. VOCs have become one of the major atmospheric pollutants in these areas. In this study, 64 compounds including alkanes, alkenes, halohydrocarbons, aromatics, and oxygenated VOCs (OVOCs) were obtained by the TO-15 method through a 12-month campaign in industrial, urban and suburban areas in the Yangtze River Delta of China. The overall trends of total VOC (TVOC) concentrations at eight sampling sites were as follows: winter > autumn > spring > summer. The proportion of VOC categories was various at industrial sites, while OVOCs and halohydrocarbons had high proportions at urban sites and suburban sites, respectively. Coating, vehicle emission, petrochemical source, industrial source, and gasoline volatilization were identified as the major VOC emission sources by the positive matrix factorization model. Petrochemical and coating sources were the prime VOC sources at industrial sites. Aromatics contributed the most ozone formation potential at industrial sites, while OVOCs provided the main contributions at both urban and suburban sites during four seasons. According to the health risk assessment, a high probability of non-carcinogenic risk existed at three industrial sites. Special attention should be given to certain VOCs, such as acrolein and 1,2-dibromoethane in industrial areas.

Comparative physiological analyses and the genetic basis reveal heat stress responses mechanism among different Betula luminifera populations.

Betula luminifera is a subtropical fast-growing timber species with high economic value. However, along with global warming, heat stress become one of the main environmental variables that limit the productivity of B. luminifera, and the response of diverse geographic populations to high temperatures is still unclear. In order to offer a comprehensive understanding of the behavior of B. luminifera under heat stress, the physiological responses of six B. luminifera populations (across the core distribution area) were described in this work in an integrated viewpoint. The results showed that a multi-level physiological regulatory network may exist in B. luminifera, the first response was the activity of resistant enzymes [e.g., peroxidase (POD)] at a preliminary stage of 2 h heat stress, and then the proline (osmoregulation substance) content began to increase after 24 h of continuous high-temperature treatment. In addition, photosynthesis was stronlgly affected by heat stress, and the net photosynthetic rate (Pn ) showed a downward trend under heat treatment in all six B. luminifera populations. Interestingly, although the physiological change patterns of the six B. luminifera populations were relatively consistent for the same parameter, there were obvious differences among different populations. Comprehensive analysis revealed that the physiological response of Rongshui (RS) was the most stable, and this was the representative B. luminifera population. Illumina RNA-seq analysis was applied to reveal the specific biological process of B. luminifera under heat stress using the RS population, and a total of 116,484 unigenes were obtained. The differentially expressed genes (DEGs) between different time periods under heat stress were enriched in 34 KEGG pathways, and the limonene and pinene degradation pathway was commonly enriched in all pairwise comparisons. Moreover, transcription factors including bHLH (basic helix-loop-helix), MYB, WRKY, and NAC (NAM, ATAF1/2, and CUC2) were identified. In this study, the physiological response and tolerance mechanisms of B. luminifera under high temperature stress were revealed, which can conducive to the basis of B. luminifera selection and resistance assessment for cultivation and breeding.

A Physics-Based Analysis of Image Appearance Models.

Linear and multilinear models (PCA, 3DMM, AAM/ASM, and multilinear tensors) of object shape/appearance have been very popular in computer vision. In this paper, we analyze the applicability of these heuristic models from the fundamental physical laws of object motion and image formation. We prove that under suitable conditions, the image appearance space can be closely approximated to be multilinear, with the illumination and texture subspaces being trilinearly combined with the direct sum of the motion and deformation subspaces. This result provides a physics-based understanding of many of the successes and limitations of the linear and multilinear approaches existing in the computer vision literature, and also identifies some of the conditions under which they are valid. It provides an analytical representation of the image space in terms of different physical factors that affect the image formation process. Numerical analysis of the accuracy of the physics-based models is performed, and tracking results on real data are presented.

Inverse compositional estimation of 3D pose and lighting in dynamic scenes.

In this paper, we show how to estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D --> 3D --> 2D transformation. This also allows us to extend traditional two frame inverse compositional tracking to a sequence of frames, leading to even higher computational savings. We prove the theoretical convergence of this method and show that it leads to significant reduction in computational burden. Experimental analysis on multiple video sequences shows impressive speed-up over existing methods while retaining a high level of accuracy.

Integrating motion, illumination, and structure in video sequences with applications in illumination-invariant tracking.

In this paper, we present a theory for combining the effects of motion, illumination, 3D structure, albedo, and camera parameters in a sequence of images obtained by a perspective camera. We show that the set of all Lambertian reflectance functions of a moving object, at any position, illuminated by arbitrarily distant light sources, lies "close" to a bilinear subspace consisting of nine illumination variables and six motion variables. This result implies that, given an arbitrary video sequence, it is possible to recover the 3D structure, motion, and illumination conditions simultaneously using the bilinear subspace formulation. The derivation builds upon existing work on linear subspace representations of reflectance by generalizing it to moving objects. Lighting can change slowly or suddenly, locally or globally, and can originate from a combination of point and extended sources. We experimentally compare the results of our theory with ground truth data and also provide results on real data by using video sequences of a 3D face and the entire human body with various combinations of motion and illumination directions. We also show results of our theory in estimating 3D motion and illumination model parameters from a video sequence.