Cải thiện mô hình ước tính PM2.5 từ dữ liệu đa nguồn - Phạm Văn Hà

VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY PHAM VAN HA IMPROVEMENT OF PM2.5 ESTIMATION MODEL USING MULTI-SOURCE AND MULTI-RESOLUTION DATA DOCTOR OF PHILOSOPHY IN INFORMATION SYSTEMS DISSERTATION Hanoi - 2024 VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY PHAM VAN HA IMPROVEMENT OF PM2.5 ESTIMATION MODEL USING MULTI-SOURCE AND MULTI-RESOLUTION DATA Major: Information Systems Code: 9480104.01 DOCTOR OF PHILOSOPHY IN INFORMATION SYSTEMS DISSERTATION SUPERVISOR: 1. Nguyen Thi Nhat Thanh Hanoi, 2024 ACKNOWLEDGEMENTS Firstly, I would like to express my sincere gratitude to my advisors, Prof. Dominique Laffly and Assoc. Nguyen Thi Nhat Thanh, for their continuous support of my dissertation and related research, their patience, motivation, and immense knowledge.

Their guidance helped me throughout my research, and I couldn't have imagined having better advisors and mentors for my dissertation. During my doctoral thesis, I received enthusiastic support and guidance from teachers and scientists at the Department of Information Systems, Faculty of Information Technology (VNU University of Engineering and Technology), Ecole Doctorale TESC (Temps, Espaces, Sociétés, Cultures), University of Toulouse Jean Jaurès. I would also like to express my gratitude to Agence Universitaire de la Francophonie (AUF) for their scholarship, which allowed me to continue my research. I want to express my deep gratitude to the reviewers and the members of the thesis evaluation committee who agreed to review and provided valuable comments.

I also extend my special thanks to all the members of the GEOI research group, FIMO Center, Laboratoire de Recherche en Architecture - ENSA Toulouse, CY Tech – Pau, Faculty of Computer Science (Phenikaa University) for their friendship, good advice, and collaboration. In addition, I would like to acknowledge Honorary Prof. Nguyen Thanh Thuy and Assoc. Astrid Jourdan and Assoc.

Yannick Le Nir for their insightful comments and encouragement. Without their precious support, this research would not have been possible. Finally, with all my love, I would like to thank my family for their love and encouragement. Thank you! DECLARATION I hereby declare that I carried out the research presented in this doctoral dissertation under the guidance and supervision of Prof.

Dominique Laffly and Assoc. Nguyen Thi Nhat Thanh, for the Doctor of Philosophy degree. I confirm that the scientific results presented in this dissertation are the results of my research during my PhD and have not appeared in the publications of other authors who are not part of our research group. The results obtained are accurate, truthful and do not overlap with previously published results.

Research results shared with other authors in scientific articles are agreed to be used before being included in this dissertation. Hanoi, April 2024 Author Pham Van Ha TABLE OF CONTENTS ACKNOWLEDGEMENTS .ii TABLE OF CONTENTS. vi LIST OF FIGURES. viii LIST OF TABLES.

Background and motivation of the study. Research subjects and scope. Particulate matter overview. Impact of PM2.5 estimation using numerical model.

Numerical model overview. Literature review of numerical model .5 estimation using a statistical model. Statistical model overview. Literature review of statistical model.

Challenges and solutions of this dissertation. The quality of the numerical model. The quality of the statistical model. The performance of numerical and statistical model.

Summary of Chapter 1. IMPROVEMENT OF PM2.5 ESTIMATION PROCESS USING NUMERICAL MODEL. Study area and data. WRF-Chem simulation (STEP3).

Seasonal and monsoon analysis (STEP5). Fire hotspot assessment (STEP6). Experimental results and discussion. Seasonal and monsoon analysis.

Fire hotspot analysis. Summary of Chapter 2. IMPROVEMENT OF PM2.5 ESTIMATION METHOD USING STATISTICAL MODEL. Data and study area.

Data fusion method. Results and discussions. Data fusion results. Summary of Chapter 3.

PERFORMANCE IMPROVEMENT OF PM2. WRF-Chem model optimization. Experimental data and configuration. WRF-Chem configuration.

WRF-Chem model optimization. WRF-Chem model optimization. Summary of Chapter 4 .142 CONCLUSION AND FUTURE WORKS. 143 LIST OF PUBLICATIONS RELATED TO DISSERTATION.

149 OTHER PUBLICATIONS OF THE AUTHOR. 165 ABBREVIATIONS AD Activity data AERONET Aerosol Robotic Network ANN Artificial Neural Network AOD Aerosol Optical Depth CALIOP Cloud-Aerosol Lidar with Orthogonal Polarization Cloud-Aerosol Lidar and Infrared Pathfinder Satellite CALIPSO Observations CAM-Chem Community Atmosphere Model with Chemistry CAMx Comprehensive Air Quality Model with extensions COP21 Conference of the Parties CTM Chemical Transport Model CV Cross-validation Evaluating the Climate and Air Quality Impact of Short-Lived ECLIPSE Pollutants ECMWF European Centre for Medium-Range Weather Forecasts EDGAR Emissions Database for Global Atmospheric Research EF Emission factor EI Emission inventory ERA ECMWF Reanalysis of the Atmospheric FINN Fire INventory from NCAR GADM Global Administrative Area GAINS Greenhouse Gas - Air Pollution Interactions and Synergies GCP Ground control point GDAL Geospatial Data Abstraction Library GDAS National Centers for Environmental Prediction GFS Global Forecasting System GOES Geostationary Operational Environmental Satellites GWR Geographically Weighted Regression HPC High-performance computing HTAP_v2 Hemispheric Transport of Air Pollution version 2 LSTM Long Short-Term Memory LUR Land Use Regression MAE Mean Absolute Error MB Mean Bias ME Mean Error MEGAN Model of Emissions of Gases and Aerosols from Nature Modern-Era Retrospective analysis for Research and MERRA-2 Applications, Version 2 MFB Mean Fractional Bias MFE Mean Fractional Error MLE Maximum Likelihood Estimation MLP MultiLayer Perceptron MLR Multivariate Linear Regression MODEL-3/CMAQ Models-3/Community Multi-scale Air Quality MODIS Moderate Resolution Imaging Spectroradiometer MPE Mean percentage error NCAR National Center for Atmospheric Research NCEM Northern Center for Environmental Monitoring NMB Normalized Mean Bias NME Normalized Mean Error PBLH Planetary Boundary Layer Height PM Particulate Matter REAS Regional Emissions Inventory in Asia RMSE Root Mean Square Error RPC Representative Concentration Pathway SVM Support Vector Machine TAPM-CTM The Air Pollution Model - Chemical Transport Model TPS Thin Plate Spline UCAR University Corporation for Atmospheric Research USGS United States Geological Survey VIIRS Visible Infrared Imaging Radiometer Suite WPS WRF Preprocessing System WRF Weather Research & Forecasting Model LIST OF FIGURES Figure 0.1 Position error of MODIS Terra Reflectance image (gray and black) compared to Vietnam's administrative boundary (Red Line). The coverage of MODIS Terra over the Vietnam region (January 1, 2016) 6 Figure 0. WRF-Chem simulation time for Vietnam (24-hour simulation) corresponds to different number of cores .5 levels at some monitoring stations in Vietnam [34].

Number of Deaths Attributable to PM2. Number of PM2.5 monitoring station in Vietnam (2010-2021). Summary of PM2.5 modeling methods using statistical and numerical models. Description of georeferencing method using Ground Control Points.

Patches and Tiles decomposition mechanism of the WRF-Chem model [154]. Distribution of Ground Control Point in each VIIRS AOD image. Three nested domain of WRF-Chem simulation (a) and location of PM2. The process of estimating PM2.5 concentration using the WRF-Chem model.

The process of the Emission adjustment method. The process of input data preprocessing for the WRF-Chem model. Simulation execution steps in the WRF-Chem model. Analysis of seasonal variations and monsoon influences on PM2.

Analysis process of fire hotspot and PM2. Validation of WRF-Chem model output using HTAP_v2 and REASv3. Validation of WRF-Chem model output using Baseline and Adjusted emission datasets. Comparison of daily PM2.5 from measurements and WRF-Chem model 72 Figure 2.

Variation of monthly PM2.5 in dry season (January 2019) and rainy seasons (June 2019). Temporal variation of daily PM2.5 concentrations in Hanoi, Phu Tho and Quang Ninh during the monsoon period (January 2019). spatial variation of daily PM2.5 concentration during the monsoon period (from January 15 to January 25). Daily and weekly correlation of PM2.5 concentration and number of fire points in North and Vietnam (Jan – June 2019).

Distribution of fire point and monthly PM2.5 concentrations in the Northern region (Jan - June, 2019). Study area and location of PM2.5 monitoring station in Vietnam. Statistic of available data at selected monitoring stations (2012-2020). Overview of PM2.5 estimation process using the statistical model.

The process of implementing and evaluating the georeferencing methods 94 Figure 3. The process of implementing and evaluating the data fusion methods. The process of implementing and evaluating the PM2. Comparison between GDAL P1, GDAL P2 and TPS results.

The correlation of MODIS Terra/Aqua, VIIRS NPP and AERONET AOD using TPS and Polynomial function (2012 – 2016). Correlation of selected MODIS and VIIRS NPP AOD georeferenced images. The coverage of MODIS Terra/Aqua, VIIRS NPP AOD and combined AOD images over the Vietnam region (January 1, 2016). Monthly coverage comparison of original (MODIS Aqua/Terra, VIIRS NPP) and fused images from 2012 to 2016.

The spatial distribution of monthly modeled PM2. Role of Performance Optimization in the improvement of PM2.5 estimation model using statistical and numerical models. Performance optimization in PM2. WRF-Chem experimental simulation domain, software library and chemistry option.

Effecting factors of WRF-Chem performance optimization. Distribution of training and testing VIIRS GCP points using regular and random sampling method. The proportion of computation, communication (MPI), and I/O time. WRF-Chem simulation time for Vietnam (24-hour simulation) corresponds to different numbers of cores (4 – 76 cores).

The ratio of Compute, MPI, and IO time corresponds to the number of cores. Relationship of GCPs and VIIRS Execution time. Relationship of training sample size and georeferencing error .141 LIST OF TABLES Table 0. Common global and regional anthropogenic emission dataset.

Comparison of MODIS Terra/Aqua and VIIRS NPP satellite AOD with AERONET ground measurement AOD data [PVH04, PVH05]. Georeferencing time for each MODIS and VIIRS AOD image over the Vietnam region using Polynomial and TPS function. Characteristics of the WRF-Chem model input datasets. Physics and chemistry parameters for WRF-Chem model running.

WRF-Chem model quality assessment indicators [167]. WRF-Chem model validation scenarios. Experimental dataset and the number of available days. Comparison of statistical indicators assessing PM2.5 simulation quality of WRF-Chem model in 2014 and 2019.

The detailed description of PM2.5 estimation using statistical problems (objective, input, and output). Experimental datasets for PM2.5 estimation using the statistical model. Quality assessment of original images (MODIS Aqua/Terra, VIIRS NPP) and fused images( (MLE, Terra Regression, GWR). Model validation and cross-validation results of PM2.

Comparison between PM2.5 maps using numerical model (WRF-Chem) and statistical model (MEM). Detail hardware resource of HPC server. Detail hardware resource for georeferencing experimental. Sampling size and training, testing datasets.

Simulation time and data quality of different chemistry options. Background and motivation of the study The Paris Agreement, signed by 195 countries at the twenty-first Conference of the Parties (COP21), intends to limit global temperature rise to no more than 2 degrees Celsius above pre-Industrial Revolution levels. During the recent COP26, Vietnam's Prime Minister vowed to reduce net carbon emissions to zero by 2050. Climate change and air pollution are inextricably linked, with fossil fuel extraction and combustion contributing to both CO2 emissions and air pollutants such as methane and black carbon.

To meet the COP26 pledge, CO2 emissions, and other air pollutants must be reduced rapidly. Air pollution was the fifth biggest risk factor for death globally in 2017, according to the 2019 State of Global Air report, and Vietnam fares poorly on the Environmental Efficiency Index (EPI), with air quality ranked 130th. Particulate Matter (PM) is an atmospheric mixture of solid particles and liquid droplets.5 is one of numerous forms of particulate matter that is measured and regulated as an air pollutant. High levels of PM2.5 exposure can raise the risk of respiratory and cardiovascular illnesses, including lung cancer.

Aside from health problems, PM2.5 can have environmental consequences, such as limiting visibility and contributing to climate change.5 monitoring relies on diverse ground-based measuring networks in many parts of the world. However, due to the high cost of installation, Vietnam has a limited number of automatic monitoring stations. Low-cost sensor networks are becoming increasingly popular; however, due to data quality constraints, they are mostly employed for trend analysis. These shortcomings underline the importance of developing a model for calculating PM2.5 maps in Vietnam.