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

Research on composite resonator network with onstant—current output for current—fed ICPT systems

In order to solve problem of constant current wireless power supply of different sensors used in coal mine safety monitoring and control system, a composite resonator compensation network for wireless power supply were studied based on current—fed ICPT system，the necessary conditions of the compensation network for realizing constant output were derived, and influences of the implementation mode of soft switch and the system parameters on output characteristics and input impedance angle of the system were analyzed. The network can obtain different output gain through reconfiguration and parameter configuration of compensation element without redesigning loosely coupled transformer, which simplifies hardware circuit. The experiment results show that the proposed composite resonator compensation network can achieve load independence of the primary coil current and output current, and satisfy soft turn off of switching tube. The resonant compensation networks also can be used to realize the constant current power supply of different sensors used in coal mine safety monitoring and control system, which is beneficial to improve reliability of coal mine safety monitoring and control system and adaptability of different loads

Nanohybridization of Ni-Co-S Nanosheets with ZnCo2O4 Nanowires as Supercapacitor Electrodes with Long Cycling Stabilities

Hybrid structured electrode materials for supercapacitors have attracted enormous interest. However, the rational design of electrode materials with high conductivity and energy density is still a challenge for energy storage devices. Herein, ZnCo2O4@Ni-Co-S composites are prepared through a simple electrochemical process. Due to the high structural stability, the products show excellent specific capacitance. Furthermore, the device delivers an energy density of 53.1 Wh kg(-1) at a power density of 3375 W kg(-1) and demonstrates excellent cycle stability

Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants and the precursors of acid rain, tropospheric ozone, and atmospheric aerosols. However, due to the poor quality of source data and the computing power of the models, current ground-level NO2 concentration data lack either high-resolution coverage or full nation-wide coverage. This study estimates the ground-level NO2 concentration in China with national coverage at relatively high spatiotemporal resolution (0.25°; daily intervals) over the newest past 6 years (2013–2018). We developed an advanced model, named Random Forest model integrated K-means (RF-K), for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, and anthropogenic emission inventories parameters, we also innovatively introduce socioeconomic parameters to assess the impact of human activities. Our results show that: (1) the RF-K model developed by us shows better prediction performance than others. (2) the annual average NO2 concentration of China showed a weak declining trend (-0.013±0.217 μgm−3yr−1) from 2013 to 2018, indicating that pollutant controlling targets had been achieved in China overall. By mapping daily nationwide ground-level NO2 concentrations, this study provides high-quality timely, and detailed data for air quality management and epidemiological analyses for China. The RF-K model can be used easily for other pollutants (e.g. SO2 and O3) considering that their ground-level concentrations can be estimated depending on the similar emitting sources and influence factors, and our model's input data sources also cover information on other pollutants

Near-real-time global gridded daily CO2 emissions

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Near-real-time global gridded daily CO2 emissions 2021

We present a near-real-time global gridded daily CO 2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO 2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO 2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO 2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO 2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level twosigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly

Near-real-time global gridded daily CO2 emissions 2021

We present a near-real-time global gridded daily CO 2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO 2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO 2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO 2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO 2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level twosigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly

Near-real-time global gridded daily CO2 emissions 2021

Abstract We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from the near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly