Association of air pollution and greenness with carotid plaque: A prospective cohort study in China.

Previous studies indicated that exposure to air pollution was associated with the progress of atherosclerosis, but evidence is very limited in China and even in the world. This study aims to assess the associations of long-term exposures to air pollution and greenness with the occurrence of carotid plaque. Participants of this cohort study were urban residents and office workers who visited Hebei General Hospital for routine physical examination annually from September 2016 through to December 2018.

Data assimilation of ambient concentrations of multiple air pollutants using an emission-concentration response modeling framework.

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure and related health impacts, especially in China that experiences both PM and O pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants, and thus cannot support simultaneous assimilation for both PM and O. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation).

Image Stitching Based on Nonrigid Warping for Urban Scene.

Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper.

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage.

Background: The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development.

Methods: In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato.

Estimating hourly full-coverage PM over China based on TOA reflectance data from the Fengyun-4A satellite.

It is challenging to retrieve hourly ground-level PM on a national scale in China due to the sparse site measurements and the limited coverage of Low Earth Orbit (LEO) satellite observations. The new geostationary meteorological satellite of China, Fengyun-4A (FY-4A), provides a unique opportunity to fill this gap. In this study, the Random Forest (RF) algorithm was applied to retrieve hourly PM of China directly from FY-4A Top-of-Atmosphere (TOA) reflectance data.

The quest for improved air quality may push China to continue its CO reduction beyond the Paris Commitment.

China is challenged with the simultaneous goals of improving air quality and mitigating climate change. The "Beautiful China" strategy, launched by the Chinese government in 2020, requires that all cities in China attain 35 μg/m or below for annual mean concentration of PM (particulate matter with aerodynamic diameter less than 2.5 μm) by 2035.

A physiology-based Earth observation model indicates stagnation in the global gross primary production during recent decades.

Earth observation-based estimates of global gross primary production (GPP) are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. In this study, we attempt an ecosystem-level physiological approach of estimating GPP using an asymptotic light response function (LRF) between GPP and incoming photosynthetically active radiation (PAR) that better represents the response observed at high spatiotemporal resolutions than the conventional light use efficiency approach. Modelled GPP is thereafter constrained with meteorological and hydrological variables.

Contamination assessment, health risk evaluation, and source identification of heavy metals in the soil-rice system of typical agricultural regions on the southeast coast of China.

To quantitatively assess heavy metal accumulation and potential ecological and human health risks as well as analyze the sources of metals in a typical soil-rice system located on the southeast coast of China, 120 topsoil samples and corresponding rice grain samples were collected across the study area. The concentrations of As, Cd, Pb, Cr, Hg, Zn, Cu, and Ni were analyzed. The results revealed that Hg, Cd, and Cu were the main pollutants in soils.

A reclaimed piezoelectric catalyst of MoS@TNr composites as high-performance anode materials for supercapacitors.

A piezoelectric catalyst of the MoS@TNr composite (MoS nanosheets composited with TiO nanorods) was synthesized by a two-step hydrothermal method, and can be recycled and reused as an advanced anode material for supercapacitors. In the dark, the MoS@TNr composite exhibited ultra-fast piezoelectric catalytic performance and good cycle stability on dye degradation; within 10 min, nearly all rhodamine B (50 mL, 20 ppm) was removed from the solution with the assistance of magnetic stirring. After the 5 cycle degradation reaction, the catalyst was reclaimed and applied to electrochemical testing, which showed better supercapacitor capacitance properties than the fresh catalyst due to the introduction of oxygen vacancies generated from the piezoelectric degradation process.

High-Precision CO Column Length Analysis on the Basis of a 1.57-μm Dual-Wavelength IPDA Lidar.

For high-precision measurements of the CO column concentration in the atmosphere with airborne integrated path differential absorption (IPDA) Lidar, the exact distance of the Lidar beam to the scattering surface, that is, the length of the column, must be measured accurately. For the high-precision inversion of the column length, we propose a set of methods on the basis of the actual conditions, including autocorrelation detection, adaptive filtering, Gaussian decomposition, and optimized Levenberg-Marquardt fitting based on the generalized Gaussian distribution. Then, based on the information of a pair of laser pulses, we use the direct adjustment method of unequal precision to eliminate the error in the distance measurement.

Automatic Changes Detection between Outdated Building Maps and New VHR Images Based on Pre-Trained Fully Convolutional Feature Maps.

Detecting changes between the existing building basemaps and newly acquired high spatial resolution remotely sensed (HRS) images is a time-consuming task. This is mainly because of the data labeling and poor performance of hand-crafted features. In this paper, for efficient feature extraction, we propose a fully convolutional feature extractor that is reconstructed from the deep convolutional neural network (DCNN) and pre-trained on the Pascal VOC dataset.

Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan.

Wuhan encountered a serious attack in the first round of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in a public health social impact, including public mental health. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.

A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency.

Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem.

A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence.

Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem for traditional CD methods, as they can only detect areas with highly changed radiometric and geometric information.

Direct Georeferencing for the Images in an Airborne LiDAR System by Automatic Boresight Misalignments Calibration.

Airborne Light Detection and Ranging (LiDAR) system and digital camera are usually integrated on a flight platform to obtain multi-source data. However, the photogrammetric system calibration is often independent of the LiDAR system and performed by the aerial triangulation method, which needs a test field with ground control points. In this paper, we present a method for the direct georeferencing of images collected by a digital camera integrated in an airborne LiDAR system by automatic boresight misalignments calibration with the auxiliary of point cloud.

Contamination assessment and source apportionment of heavy metals in agricultural soil through the synthesis of PMF and GeogDetector models.

Heavy metal pollution in soils has attracted great attention worldwide in recent decades. Selecting Hangzhou as a case study location, this research proposed the synthesis application of positive matrix factorization (PMF) and GeogDetector models for quantitative analysis of pollution sources, which is the basis for subsequent soil pollution prevention and remediation. In total, 2150 surface soil samples were collected across the study area.

Response of major air pollutants to COVID-19 lockdowns in China.

COVID-19 suddenly struck Wuhan at the end of 2019 and soon spread to the whole country and the rest of world in 2020. To mitigate the pandemic, China authority has taken unprecedentedly strict measures across the country. That provides a precious window to study how the air quality response to quick decline of anthropogenic emissions in terms of national scale, which would be critical basis to make atmospheric governance policies in the future.

A residual-based deep learning approach for ghost imaging.

Ghost imaging using deep learning (GIDL) is a kind of computational quantum imaging method devised to improve the imaging efficiency. However, among most proposals of GIDL so far, the same set of random patterns were used in both the training and test set, leading to a decrease of the generalization ability of networks. Thus, the GIDL technique can only reconstruct the profile of the image of the object, losing most of the details.

A Novel Multi-Focus Image Fusion Network with U-Shape Structure.

Multi-focus image fusion has become a very practical image processing task. It uses multiple images focused on various depth planes to create an all-in-focus image. Although extensive studies have been produced, the performance of existing methods is still limited by the inaccurate detection of the focus regions for fusion.

Association Between Changes in Timing of Spring Onset and Asthma Hospitalization in Maryland.

Importance: Ongoing climate change is affecting the health of communities across the globe. While direct consequences, including morbidity and mortality tied to increases in the frequency of extreme weather events, have received significant attention, indirect health effects, particularly those associated with climate change-driven disruptions in ecosystems, are less understood.

Objective: To investigate how ongoing changes in the timing of spring onset related to climate change are associated with rates of asthma hospitalization in Maryland.

Detecting Matching Blunders of Multi-Source Remote Sensing Images via Graph Theory.

Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunder detection methods cannot work effectively. To address this problem, we propose two matching blunder detection methods based on graph theory.

[PM Inversion Using Remote Sensing Data in Eastern China Based on Deep Learning].

PM, which is a major source of air pollution, has a considerable impact on human health. In this study, a multi-element joint PM inversion method based on a deep learning model is proposed. With PM concentration as the ground truth, 10 elements including the Himawari-AOD daily data products, temperature, relative humidity, and pressure, were introduced as inversion elements.

High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images.

Detailed and precise urban land-cover maps are crucial for urban-related studies. However, there are limited ways of mapping high-resolution urban land cover over large areas. In this paper, we propose an operational framework to map urban land cover on the basis of Ziyuan-3 satellite images.

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM).