Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods

Global patterns of daily CO2 emissions reductions in the first year of COVID-19

Day-to-day changes in CO2 emissions from human activities, in particular fossil-fuel combustion and cement production, reflect a complex balance of influences from seasonality, working days, weather and, most recently, the COVID-19 pandemic. Here, we provide a daily CO2 emissions dataset for the whole year of 2020, calculated from inventory and near-real-time activity data. We find a global reduction of 6.3% (2,232 MtCO2) in CO2 emissions compared with 2019. The drop in daily emissions during the first part of the year resulted from reduced global economic activity due to the pandemic lockdowns, including a large decrease in emissions from the transportation sector. However, daily CO2 emissions gradually recovered towards 2019 levels from late April with the partial reopening of economic activity. Subsequent waves of lockdowns in late 2020 continued to cause smaller CO2 reductions, primarily in western countries. The extraordinary fall in emissions during 2020 is similar in magnitude to the sustained annual emissions reductions necessary to limit global warming at 1.5°C. This underscores the magnitude and speed at which the energy transition needs to advance

Pose evaluation based on bayesian classification error

Pose evaluation is a fundamental issue in image processing and computer vision. In this paper, we propose a new method called BCE for pose evaluation based on Bayesian classification error. Various image cues are incorporated to depict an object including object shape, side region statistics and temporal information. Then a PEF (Pose Evaluation Function) is constructed based on Bayesian classification error, and an efficient algorithm to calculate it is developed. We test our new method with real outdoor image sequences, and use two criteria to compare it with two other representative ones. It is shown that our new method leads to better performance with respect to localization accuracy and robustness against general clutter and occlusion.

An Illumination Invariant Change Detection Algorithm �

In this paper, a homomorphic filtering based change detection algorithm is proposed to detect moving objects from light-varing monocular image sequences. In our approach, a background model is first constructed, and background subtraction is applied to classify image pixels into background or foreground. We utilize illumination invariant local components to model the background, which are obtained using homomorphic filtering. Threshold for every pixel in the image can be selected automatically to accommodate the change of lighting. In addition, the connectivity information is integrated into the background-foreground classification process by Bayesian estimation. Experimental results show that the presented approach works well in the presence of heavy moving shadows and illumination variance. 1

The PARK2 Mutation Associated with Parkinson’s Disease Enhances the Vulnerability of Peripheral Blood Lymphocytes to Paraquat

Parkinson’s disease (PD) is the second most common neurodegenerative disease in middle-aged and elderly people. However, the etiology and pathogenesis of PD are still unclear and there is a lack of reliable biomarkers for early molecular diagnosis. Parkin (encoded by PARK2) is a ubiquitin E3 ligase that participates in mitochondrial homeostasis, the ubiquitin-proteasome pathway, oxidative stress response, and cell death pathways, which are involved in the pathogenesis of PD. However, Parkin is also expressed in peripheral blood lymphocytes (PBLs). In this study, permanent lymphocyte lines were established from the peripheral blood of sporadic PD (sPD) patients, PARK2 mutation carriers, and healthy controls. Reactive oxygen species (ROS), function of the mitochondrial respiratory chain complex I, and apoptosis were analyzed in the PBLs. There was no significant difference in ROS, mitochondrial respiratory chain complex I, and apoptosis between the experimental groups and the control group without paraquat treatment. Compared with the control group of healthy subjects, we found an increase of ROS (control 100±0, sPD 275.53±79.11, and C441R 340±99.67) and apoptosis, as well as a decline in the function of mitochondrial respiratory chain complex I in PBLs of PARK2 mutation carriers and sPD after the treatment of paraquat (control 0.65±0.08, sPD 0.44±0.08, and C441R 0.32±0.08). Moreover, overexpression of the wild-type (WT) PARK2 in HeLa cells and immortalized PBLs could rescue mitochondrial function and partially inhibit apoptosis following paraquat treatment, while the C441R mutation could not. Thus, ROS levels, activity of mitochondrial respiratory chain complex I, and apoptosis of PBLs are potential diagnostic biomarkers of PD