Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter

2020 ◽  
Author(s):  
Xiao Tang ◽  
Lei Kong ◽  
Jiang Zhu ◽  
Zifa Wang ◽  
Yuepeng Pan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
A Priori ◽  
Monitoring Network ◽  
Northwest China ◽  
Mitigation Strategies ◽  
Spatial And Temporal Patterns ◽  
Policy Makers ◽  
Air Quality Prediction ◽  
Essential Input

<p>Ammonia (NH<sub>3</sub>) emission inventories are an essential input in chemical transport models and are helpful for policy-makers to refine mitigation strategies. However, current estimates of Chinese NH<sub>3</sub> emissions still have large uncertainties. In this study, an improved inversion estimation of NH<sub>3</sub> emissions in China has been made using an ensemble Kalman filter and the Nested Air Quality Prediction Modeling System. By first assimilating the surface NH<sub>3</sub> observations from the Ammonia Monitoring Network in China at a high resolution of 15 km, our inversion results have provided new insights into the spatial and temporal patterns of Chinese NH<sub>3</sub> emissions. More enhanced NH<sub>3</sub> emission hotspots, likely associated with industrial or agricultural sources, were captured in northwest China, where the a posteriori NH<sub>3</sub> emissions were more than twice the a priori emissions. Monthly variations of NH<sub>3</sub> emissions were optimized in different regions of China and exhibited a more distinct seasonality, with the emissions in summer being twice those in winter. The inversion results were well-validated by several independent datasets that traced gaseous NH<sub>3</sub> and related atmospheric processes. These findings highlighted that the improved inversion estimation can be used to advance our understanding of NH<sub>3</sub> emissions in China and their environmental impacts.</p>

Developing High-resolution Air Quality Reanalysis Dataset over China for Years 2013-2018 Based on Ensemble Kalman Filter and Surface Observations from CNEMC

2020 ◽  
Author(s):  
Lei Kong ◽  
Xiao Tang ◽  
Jiang Zhu ◽  
Zifa Wang ◽  
Huangjian Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Air Quality ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Air Pollutants ◽  
Reanalysis Data ◽  
Training Data ◽  
Reanalysis Dataset ◽  
Air Quality Prediction ◽  
Surface Observations

<p>A six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) covering the period 2013-2018 has been developed in this study by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) based on the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This reanalysis provides the surface fields of six conventional air pollutants in China, namely PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO and O<sub>3</sub>, at high spatial (15km×15km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis fields of air pollutants in China. A twenty-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the air pollutants in China with the biases (normalized mean bias) of the reanalysis data about -2.6 (-4.9%) μg/m<sup>3</sup> for PM<sub>2.5</sub>, -6.8 (-7.6%) μg/m<sup>3</sup> for PM<sub>10</sub>, -2.0 (-8.5%) μg/m<sup>3</sup> for SO<sub>2</sub>, -2.3 (-6.9%) μg/m<sup>3</sup> for NO<sub>2</sub>, -0.06 (-6.1%) mg/m<sup>3</sup> for CO and -2.3 (-4.0%) μg/m<sup>3</sup> for O<sub>3</sub>. The interannual changes of the air quality in China were also well represented by the CAQRA in terms of the six air pollutants. Comparisons with previous datasets of daily PM<sub>2.5</sub>, SO<sub>2</sub> and NO<sub>2</sub> concentrations indicate that the CAQRA is more accurate with smaller RMSE values. We also compared our reanalysis dataset to the CAMSRA (The Copernicus Atmosphere Monitoring Service reanalysis) produced by ECMWF (European Centre for Medium-Range Weather Forecasts), which suggests that the CAQRA has higher accuracy in representing the surface air pollutants in China due to the assimilation of surface observations. This reanalysis dataset can provide us comprehensive pictures of the air quality in China from 2013 to 2018 with a complete spatial and temporal coverage, which can be used in the assessment of health impacts of air pollution, validation of model simulations and providing training data for the statistical or AI (Artificial Intelligence) based forecast.</p>

scholarly journals Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter

2019 ◽  
Vol 53 (21) ◽  
pp. 12529-12538 ◽  
Author(s):  
Lei Kong ◽  
Xiao Tang ◽  
Jiang Zhu ◽  
Zifa Wang ◽  
Yuepeng Pan ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Monitoring Network ◽  
Ammonia Emissions

Ensemble Kalman Filter for Out of Sequence Measurement Tracking

2012 ◽  
Vol 132 (10) ◽  
pp. 1617-1625
Author(s):  
Sirichai Pornsarayouth ◽  
Masaki Yamakita
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter

Data assimilation with the weighted ensemble Kalman filter

2010 ◽  
Author(s):  
Nicolas Papadakis ◽  
Etienne Mémin ◽  
Anne Cuzol ◽  
Nicolas Gengembre
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter

scholarly journals Promoting Renewable Energy to Cope with Climate Change—Policy Discourse in Israel

2021 ◽  
Vol 13 (6) ◽  
pp. 3170
Author(s):  
Avri Eitan
Keyword(s):  
Climate Change ◽  
Renewable Energy ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Climate Change Mitigation ◽  
Renewable Energies ◽  
Mitigation Strategies ◽  
Policy Discourse ◽  
Policy Makers ◽  
Change Mitigation

Evidence shows that global climate change is increasing over time, and requires the adoption of a variety of coping methods. As an alternative for conventional electricity systems, renewable energies are considered to be an important policy tool for reducing greenhouse gas emissions, and therefore, they play an important role in climate change mitigation strategies. Renewable energies, however, may also play a crucial role in climate change adaptation strategies because they can reduce the vulnerability of energy systems to extreme events. The paper examines whether policy-makers in Israel tend to focus on mitigation strategies or on adaptation strategies in renewable energy policy discourse. The results indicate that despite Israel’s minor impact on global greenhouse gas emissions, policy-makers focus more on promoting renewable energies as a climate change mitigation strategy rather than an adaptation strategy. These findings shed light on the important role of international influence—which tends to emphasize mitigation over adaptation—in motivating the domestic policy discourse on renewable energy as a coping method with climate change.

scholarly journals Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Jinming Yang ◽  
Chengzhi Li
Keyword(s):  
Remote Sensing ◽  
Kalman Filter ◽  
Water Resource ◽  
Ensemble Kalman Filter ◽  
Snow Depth ◽  
Resource Assessment ◽  
Measured Data ◽  
Hydrological Process ◽  
Depth Data ◽  
Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

scholarly journals Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter

2021 ◽  
Vol 11 (7) ◽  
pp. 2898
Author(s):  
Humberto C. Godinez ◽  
Esteban Rougier
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Failure Modes ◽  
Split Hopkinson Pressure Bar ◽  
Peak Stress ◽  
Discrete Element ◽  
Material Behavior ◽  
Model Parameters ◽  
Hopkinson Pressure Bar ◽  
Parameter Values

Simulation of fracture initiation, propagation, and arrest is a problem of interest for many applications in the scientific community. There are a number of numerical methods used for this purpose, and among the most widely accepted is the combined finite-discrete element method (FDEM). To model fracture with FDEM, material behavior is described by specifying a combination of elastic properties, strengths (in the normal and tangential directions), and energy dissipated in failure modes I and II, which are modeled by incorporating a parameterized softening curve defining a post-peak stress-displacement relationship unique to each material. In this work, we implement a data assimilation method to estimate key model parameter values with the objective of improving the calibration processes for FDEM fracture simulations. Specifically, we implement the ensemble Kalman filter assimilation method to the Hybrid Optimization Software Suite (HOSS), a FDEM-based code which was developed for the simulation of fracture and fragmentation behavior. We present a set of assimilation experiments to match the numerical results obtained for a Split Hopkinson Pressure Bar (SHPB) model with experimental observations for granite. We achieved this by calibrating a subset of model parameters. The results show a steady convergence of the assimilated parameter values towards observed time/stress curves from the SHPB observations. In particular, both tensile and shear strengths seem to be converging faster than the other parameters considered.

scholarly journals COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ye Emma Zohner ◽  
Jeffrey S. Morris
Keyword(s):  
Web Application ◽  
The United States ◽  
Mitigation Strategies ◽  
Time Lags ◽  
Policy Makers ◽  
Major Health ◽  
Dynamic Representation ◽  
Graphical Displays ◽  
The Usa ◽  
User Friendly

Abstract Background The COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available dynamic representation tool, COVID-TRACK, that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions. Findings COVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states in the USA and countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe. Conclusion The COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes over time at different locations around the USA and the globe.

scholarly journals Corruption in Construction Projects: Bibliometric Analysis of Global Research

2021 ◽  
Vol 13 (8) ◽  
pp. 4400
Author(s):  
Zhao Zhai ◽  
Ming Shan ◽  
Amos Darko ◽  
Albert P. C. Chan
Keyword(s):  
Construction Projects ◽  
Research Policy ◽  
Mitigation Strategies ◽  
Future Research ◽  
Policy And Practice ◽  
Policy Makers ◽  
Research Areas ◽  
Body Of Knowledge ◽  
Emerging Trends ◽  
Global Research

Corruption has been identified as a major problem in construction projects. It can jeopardize the success of these projects. Consequently, corruption has garnered significant attention in the construction industry over the past two decades, and several studies on corruption in construction projects (CICP) have been conducted. Previous efforts to analyze and review this body of knowledge have been manual, qualitative and subjective, thus prone to bias and limited in the number of reviewed studies. There remains a lack of inclusive, quantitative, objective and computational analysis of global CICP research to inform future research, policy and practice. This study aims to address this lack by providing the first inclusive bibliometric study exploring the state-of-the-art of global CICP research. To this end, a quantitative and objective technique aided by CiteSpace was used to systematically and computationally analyze a large corpus of 542 studies retrieved from the Web of Science and published from 2000 to 2020. The findings revealed major and influential CICP research journals, persons, institutions, countries, references and areas of focus, as well as revealing how these interact with each other in research networks. This study contributes to the in-depth understanding of global research on CICP. By highlighting the principal research areas, gaps, emerging trends and directions, as well as patterns in CICP research, the findings could help researchers, practitioners and policy makers position their future CICP research and/or mitigation strategies.

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