Articles | Volume 13, issue 3
https://doi.org/10.5194/amt-13-1213-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-1213-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Mar 2020
Research article |
| 11 Mar 2020
| 11 Mar 2020
Filling the gaps of in situ hourly PM2.5 concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles
Kaixu Bai
Key Laboratory of Geographic Information Science (Ministry of
Education), East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai
200241, China
Institute of Eco-Chongming, Chongming, Shanghai 202162, China
Ke Li
School of Geographic Sciences, East China Normal University, Shanghai
200241, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Yuanjian Yang
School of Atmospheric Physics, Nanjing University of Information
Science & Technology, Nanjing 210044, China
Institute of Environment, Energy and Sustainability, The Chinese
University of Hong Kong, Hong Kong SAR, China
Ni-Bin Chang
Department of Civil, Environmental, and Construction Engineering,
University of Central Florida, Orlando, FL 32816, USA
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Cited
20 citations as recorded by crossref.
- Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment K. Li et al. 10.1016/j.jenvman.2021.114121
- A homogenized daily in situ PM<sub>2.5</sub> concentration dataset from the national air quality monitoring network in China K. Bai et al. 10.5194/essd-12-3067-2020
- Do More Frequent Temperature Inversions Aggravate Haze Pollution in China? K. Bai et al. 10.1029/2021GL096458
- Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China Y. Chen et al. 10.3390/atmos15010028
- Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM S. Tan et al. 10.1088/1748-9326/ac52c9
- LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion K. Bai et al. 10.5194/essd-14-907-2022
- Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. 10.1016/j.earscirev.2023.104461
- Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde H. Yin et al. 10.1016/j.scitotenv.2022.158271
- Application of Empirical Orthogonal Function Interpolation to Reconstruct Hourly Fine Particulate Matter Concentration Data in Tianjin, China H. Zhou et al. 10.1155/2020/9724367
- Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? K. Bai et al. 10.1016/j.isprsjprs.2021.12.002
- Synergistic data fusion of satellite observations and in-situ measurements for hourly PM2.5 estimation based on hierarchical geospatial long short-term memory X. Yu et al. 10.1016/j.atmosenv.2022.119257
- NOx Emissions Reduction and Rebound in China Due to the COVID‐19 Crisis J. Ding et al. 10.1029/2020GL089912
- A Comparative Assessment of Multisensor Data Merging and Fusion Algorithms for High-Resolution Surface Reflectance Data X. Wei et al. 10.1109/JSTARS.2020.3008746
- Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States C. Wu et al. 10.3390/rs12223813
- Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning M. Ma et al. 10.1117/1.JRS.18.012004
- A Comparative Study of Several EOF Based Imputation Methods for Long Gap Missing Values in a Single-Site Temporal Time Dependent (SSTTD) Air Quality (PM10) Dataset S. Ghazali et al. 10.47836/pjst.29.4.21
- Improving machine-learned surface NO2 concentration mapping models with domain knowledge from data science perspective M. Hu et al. 10.1016/j.atmosenv.2024.120372
- Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
- Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data S. Zhu et al. 10.1139/er-2022-0125
- Missing Value Imputation for PM10 Concentration in Sabah using Nearest Neighbour Method (NNM) and Expectation-Maximization (EM) Algorithm M. Rumaling et al. 10.5572/ajae.2020.14.1.062
19 citations as recorded by crossref.
- Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment K. Li et al. 10.1016/j.jenvman.2021.114121
- A homogenized daily in situ PM<sub>2.5</sub> concentration dataset from the national air quality monitoring network in China K. Bai et al. 10.5194/essd-12-3067-2020
- Do More Frequent Temperature Inversions Aggravate Haze Pollution in China? K. Bai et al. 10.1029/2021GL096458
- Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China Y. Chen et al. 10.3390/atmos15010028
- Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM S. Tan et al. 10.1088/1748-9326/ac52c9
- LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion K. Bai et al. 10.5194/essd-14-907-2022
- Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives K. Bai et al. 10.1016/j.earscirev.2023.104461
- Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde H. Yin et al. 10.1016/j.scitotenv.2022.158271
- Application of Empirical Orthogonal Function Interpolation to Reconstruct Hourly Fine Particulate Matter Concentration Data in Tianjin, China H. Zhou et al. 10.1155/2020/9724367
- Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy? K. Bai et al. 10.1016/j.isprsjprs.2021.12.002
- Synergistic data fusion of satellite observations and in-situ measurements for hourly PM2.5 estimation based on hierarchical geospatial long short-term memory X. Yu et al. 10.1016/j.atmosenv.2022.119257
- NOx Emissions Reduction and Rebound in China Due to the COVID‐19 Crisis J. Ding et al. 10.1029/2020GL089912
- A Comparative Assessment of Multisensor Data Merging and Fusion Algorithms for High-Resolution Surface Reflectance Data X. Wei et al. 10.1109/JSTARS.2020.3008746
- Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States C. Wu et al. 10.3390/rs12223813
- Resolving contributions of NO2 and SO2 to PM2.5 and O3 pollutions in the North China Plain via multi-task learning M. Ma et al. 10.1117/1.JRS.18.012004
- A Comparative Study of Several EOF Based Imputation Methods for Long Gap Missing Values in a Single-Site Temporal Time Dependent (SSTTD) Air Quality (PM10) Dataset S. Ghazali et al. 10.47836/pjst.29.4.21
- Improving machine-learned surface NO2 concentration mapping models with domain knowledge from data science perspective M. Hu et al. 10.1016/j.atmosenv.2024.120372
- Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
- Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data S. Zhu et al. 10.1139/er-2022-0125
Latest update: 23 Apr 2024
Short summary
A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function (DCCEOF) is proposed. Cross validation indicates that this method gives high accuracy in predicting missing values in daily PM2.5 time series by accounting for the local diurnal phases, especially by reconstructing daily extrema that cannot be accurately restored by other approaches. The DCCEOF method can be easily applied to other data sets because of its self-consistent capability.
A novel gap-filling method called the diurnal-cycle-constrained empirical orthogonal function...
