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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 34236

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Special Issue Editors

Prof. Myong-In Lee

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Guest Editor

Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, South Korea
Interests: air quality forecasts; aerosol data assimilation; air quality modeling

Dr. Daisuke Goto

E-Mail Website
Guest Editor

National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-0053, Japan
Interests: global-to-regional air quality modeling

Dr. Dan Chen

E-Mail Website
Guest Editor

Institute of Urban Meteorology, China Meteorological Administration, Beijing 100886, China
Interests: aeorosol data assimilation; aerosol-meteorology interaction

Special Issue Information

Dear Colleagues,

Air quality prediction using numerical models exhibits large forecast errors with systematic model biases. There are major uncertainties in the representations of meteorological and chemical processes in models along with inaccurate anthropogenic emissions and initial and boundary conditions used for model simulations. Recent advances in data assimilation techniques, which effectively imbed observations into numerical model predictions, provide unprecedented opportunities to significantly improve forecast capability. In particular, observations from geostationary satellites, as well as polar-orbiting satellites cover wide areas and fill the spatial gap in the existing ground-based observation networks.

This Special Issue proposes to document recent advances and improvements in air quality modeling and forecasting techniques and the development of aerosol data assimilation methods for utilizing surface and satellite observations for gases and aerosols.

Potential topics for this Special Issue include but are not limited to the following:

Monitoring and data acquisition for gases and various air pollutants using in-situ and/or remotely-sensed observations, or intensive observations from field campaigns;
Data assimilation techniques based on sequential, variational, or ensemble-based techniques;
Optimization problems for air quality data assimilation;
Observation system experiments (OSEs) and observation system simulation experiments (OSSEs) to evaluate the impact of data assimilation on air quality forecast;
Improvements of short-, and/or medium-range forecasting skills by employing data assimilation;
Application of artificial intelligence and machine learning algorithms for statistical or dynamical forecasting;
Emission inventory and its optimizations;
Improved chemistry and/or aerosol schemes to be embedded in large-scale atmospheric chemical transport models

Prof. Myong-In Lee
Dr. Daisuke Goto
Dr. Dan Chen
Guest Editors

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Keywords

Aerosol data assimilation
Air quality forecasts
Air pollutions
Satellite data
Artificial intelligence
Machine learning
Chemical transport models
Aerosol-radiation feedback
Emission
Meteorology
Ozone forecasts

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Published Papers (13 papers)

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Research

28 pages, 15933 KiB

Open AccessArticle

Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data

by Miranti Indri Hastuti, Ki-Hong Min and Ji-Won Lee

Remote Sens. 2023, 15(11), 2760; https://doi.org/10.3390/rs15112760 - 25 May 2023

Cited by 1 | Viewed by 2154

Abstract

Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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21 pages, 6366 KiB

Open AccessArticle

Nonlinear Bias Correction of the FY-4A AGRI Infrared Radiance Data Based on the Random Forest

by Xuewei Zhang, Dongmei Xu, Xin Li and Feifei Shen

Remote Sens. 2023, 15(7), 1809; https://doi.org/10.3390/rs15071809 - 28 Mar 2023

Cited by 5 | Viewed by 1771

Abstract

Bias correction is a key prerequisite for radiance data assimilation. Directly assimilating the radiance observations generally involves large systematic biases affecting the numerical prediction accuracy. In this study, a nonlinear bias correction scheme with Random Forest (RF) technology is firstly proposed based on the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) channels 9–10 observations in the Weather Research and Forecasting Data Assimilation (WRFDA) system. Two different settings of the predictors are additionally designed and evaluated based on the performance of the RF model. It seems that an apparent scene temperature-dependent bias could be effectively resolved by the RF scheme when applying the RF method with newly added predictors. Results suggest that the proposed nonlinear scheme of RF performs better than the linear scheme does in terms of reducing the systematic biases. A more idealized error distribution of observation minus background (OMB) is found in the RF-based experiments that measure the nonlinear relationship between the OMB biases and the predictors when using the Gaussian distribution as the reference. Furthermore, the RF scheme shows a consistent improvement in bias correction with the potential to ameliorate the atmospheric variables of analyses. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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16 pages, 9790 KiB

Open AccessArticle

A Data Assimilation Method Combined with Machine Learning and Its Application to Anthropogenic Emission Adjustment in CMAQ

by Congwu Huang, Tao Niu, Hao Wu, Yawei Qu, Tijian Wang, Mengmeng Li, Rong Li and Hongli Liu

Remote Sens. 2023, 15(6), 1711; https://doi.org/10.3390/rs15061711 - 22 Mar 2023

Cited by 3 | Viewed by 2067

Abstract

Anthropogenic emissions play an important role in air quality forecasting. To improve the forecasting accuracy, the use of nudging as the data assimilation method, combined with extremely randomized trees (ExRT) as the machine learning method, was developed and applied to adjust the anthropogenic emissions in the Community Multiscale Air Quality modeling system (CMAQ). This nudging–ExRT method can iterate with the forecast and is suitable for linear and nonlinear emissions. For example, an episode between 15 and 30 January 2019 was simulated for China’s Beijing–Tianjin–Hebei (BTH) region. For PM_2.5, the correlation coefficient of the site averaged concentration (Ra) increased from 0.85 to 0.94, and the root mean square error (RMSEa) decreased from 24.41 to 9.97 µg/m³. For O₃, the Ra increased from 0.75 to 0.81, and the RMSEa decreased from 13.91 to 12.07 µg/m³. These results showed that nudging–ExRT can significantly improve forecasting skills and can be applied to routine air quality forecasting in the future. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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22 pages, 13693 KiB

Open AccessArticle

Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations

by Farouk Lemmouchi, Juan Cuesta, Mathieu Lachatre, Julien Brajard, Adriana Coman, Matthias Beekmann and Claude Derognat

Remote Sens. 2023, 15(6), 1510; https://doi.org/10.3390/rs15061510 - 9 Mar 2023

Cited by 6 | Viewed by 3096

Abstract

We present a supervised machine learning (ML) approach to improve the accuracy of the regional horizontal distribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry transport model over North Africa and the Arabian Peninsula using Moderate Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. Our method produces daily AOD maps with enhanced precision and full spatial domain coverage, which is particularly relevant for regions with a high aerosol abundance, such as the Sahara Desert, where there is a dramatic lack of ground-based measurements for validating chemistry transport simulations. We use satellite observations and some geophysical variables to train four popular regression models, namely multiple linear regression (MLR), random forests (RF), gradient boosting (XGB), and artificial neural networks (NN). We evaluate their performances against satellite and independent ground-based AOD observations. The results indicate that all models perform similarly, with RF exhibiting fewer spatial artifacts. While the regression slightly overcorrects extreme AODs, it remarkably reduces biases and absolute errors and significantly improves linear correlations with respect to the independent observations. We analyze a case study to illustrate the importance of the geophysical input variables and demonstrate the regional significance of some of them. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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13 pages, 3510 KiB

Open AccessArticle

Nocturnal Boundary Layer Height Uncertainty in Particulate Matter Simulations during the KORUS-AQ Campaign

by Hyo-Jung Lee, Hyun-Young Jo, Jong-Min Kim, Juseon Bak, Moon-Soo Park, Jung-Kwon Kim, Yu-Jin Jo and Cheol-Hee Kim

Remote Sens. 2023, 15(2), 300; https://doi.org/10.3390/rs15020300 - 4 Jan 2023

Cited by 5 | Viewed by 2085

Abstract

Vertical mixing in the planetary boundary layer (PBL) is an important factor in the prediction of particulate matter (PM) concentrations; however, PBL height (PBLH) in the stable atmosphere remains poorly understood. In particular, the assessment of uncertainties related to nocturnal PBLH (nPBLH) is challenging due to the absence of stable atmosphere observations. In this study, we explored nPBLH–PM_2.5 interactions by comparing model results and observations during the Korea–United States Air Quality Study (KORUS-AQ) campaign (1–31 May 2016). Remote sensing measurements (e.g., aerosol and wind Doppler lidar) and on-line WRF-Chem modeling results were used by applying three different PBL parameterizations: Yonsei University (YSU), Mellor–Yamada–Janjic (MYJ), and Asymmetrical Convective Model v2 (ACM2). Our results indicated that the uncertainties of PBLH–PM interactions were not large in daytime, whereas the uncertainties of nPBLH–PM_2.5 interactions were significant. All WRF-Chem experiments showed a clear tendency to underestimate nighttime nPBLH by a factor of ~3 compared with observations, and shallow nPBLH clearly led to extremely high PM_2.5 peaks during the night. These uncertainties associated with nPBLH and nPBLH–PM_2.5 simulations suggest that PM_2.5 peaks predicted from nighttime or next-morning nPBLH simulations should be interpreted with caution. Additionally, we discuss uncertainties among PBL parameterization schemes in relation to PM_2.5 simulations. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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17 pages, 4230 KiB

Open AccessArticle

AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images

by Maqsood Ahmed, Yonglin Shen, Mansoor Ahmed, Zemin Xiao, Ping Cheng, Nafees Ali, Abdul Ghaffar and Sabir Ali

Remote Sens. 2022, 14(22), 5732; https://doi.org/10.3390/rs14225732 - 13 Nov 2022

Cited by 9 | Viewed by 2881

Abstract

Air quality has a significant influence on the environment and health. Instruments that efficiently and inexpensively detect air quality could be extremely valuable in detecting air quality indices. This study presents a robust deep learning model named AQE-Net, for estimating air quality from mobile images. The algorithm extracts features and patterns from scene photographs collected by the camera device and then classifies the images according to air quality index (AQI) levels. Additionally, an air quality dataset (KARACHI-AQI) of high-quality outdoor images was constructed to enable the model’s training and assessment of performance. The sample data were collected from an air quality monitoring station in Karachi City, Pakistan, comprising 1001 hourly datasets, including photographs, PM2.5 levels, and the AQI. This study compares and examines traditional machine learning algorithms, e.g., a support vector machine (SVM), and deep learning models, such as VGG16, InceptionV3, and AQE-Net on the KHI-AQI dataset. The experimental findings demonstrate that, compared to other models, AQE-Net achieved more accurate categorization findings for air quality. AQE-Net achieved 70.1% accuracy, while SVM, VGG16, and InceptionV3 achieved 56.2% and 59.2% accuracy, respectively. In addition, MSE, MAE, and MAPE values were calculated for our model (1.278, 0.542, 0.310), which indicates the remarkable efficacy of our approach. The suggested method shows promise as a fast and accurate way to estimate and classify pollutants from only captured photographs. This flexible and scalable method of assessment has the potential to fill in significant gaps in the air quality data gathered from costly devices around the world. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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14 pages, 3766 KiB

Open AccessArticle

Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea

by Jaein I. Jeong, Jisu Seo and Rokjin J. Park

Remote Sens. 2022, 14(21), 5310; https://doi.org/10.3390/rs14215310 - 24 Oct 2022

Cited by 5 | Viewed by 1771

Abstract

Fine particulate matter (PM) significantly affects visibility, a sensitive indicator of air pollution. Despite a continuous decrease in the PM concentrations in South Korea, the public generally believes that PM air pollution has worsened over the past years. To explain this disparity, we analyzed the characteristics of recent visibility changes using observations of visibility and PM component data observed in Seoul, South Korea, from 2012 to 2018. A significant negative correlation (R = −0.96) existed between visibility and concentrations of PM, with an aerodynamic diameter ≤ 2.5 μm (PM_2.5); a high PM_2.5 concentration was the most important contributor to poor visibility. Annual mean PM_2.5 concentrations in Seoul decreased by −5.1% yr⁻¹ during 2012–2018, whereas annual mean visibility improved by 2.1% yr⁻¹. We found that a lower improvement in visibility was associated with changes in the PM component. Among the PM components affecting poor visibility, contributions of ammonium nitrate (NH₄NO₃) significantly increased during 2012–2018 (from 48% in 2012 to 59% in 2018). Increases in NO₃⁻ aerosol concentrations were owing to SO_x emission reduction and the resulting decreases in SO₄²⁻ aerosol concentrations, which led to an increase in NH₃ available for additional NH₄NO₃ production in the atmosphere. Despite decreased PM concentrations in Seoul, the change of PM components has compromised visibility improvement; thus, NO₃⁻ concentrations need to be reduced. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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17 pages, 4723 KiB

Open AccessArticle

A Numerical Analysis of the Changes in O₃ Concentration in a Wildfire Plume

by Dongjin Kim, Wonbae Jeon, Jaehyeong Park, Jeonghyeok Mun, Hyunsik Choi, Cheol-Hee Kim, Hyo-Jung Lee and Hyun-Young Jo

Remote Sens. 2022, 14(18), 4549; https://doi.org/10.3390/rs14184549 - 12 Sep 2022

Cited by 5 | Viewed by 2205

Abstract

This study analyzed the characteristics of changes in O₃ concentration in a plume induced by a wildfire in Andong, South Korea, from 24 to 26 April 2020, using the Community Multi-scale Air Quality (CMAQ) model. Fire INventory from National Center for Atmospheric Research (FINN) emissions data were used for the wildfire emissions. The increases in the concentrations of primary pollutants (CO, NO_x, and volatile organic compounds (VOCs)) due to the wildfire peaked near the source at 09 LST and, as the plume was transported, the reduction in the supply of pollutants from wildfire, as well as chemical reactions, advection and diffusion, and deposition, caused the concentrations to continuously decrease. In contrast, O₃ concentration showed a sustained increase during transport due to photochemical reactions caused by precursors (e.g., NO_x, VOCs) emitted during the wildfire, peaking (1.40 ppb) at approximately 1 km at 13 LST over 60 km from the source. To analyze these results, a process analysis was conducted. Integrated process rate (IPR) analysis results showed that the production rate of O₃ and loss rates of NO_x and VOCs peaked at 09 LST due to the photochemical reactions of NO_x and VOCs emitted due to wildfire. Then, as the plume was transported, the loss rates of NO_x and VOCs that contributed to O₃ production continued to decrease at 11 LST. The O₃ production rate also decreased at 11 LST but increased at 13 LST due to increasing solar radiation. This indicates that the O₃ concentration is complexly determined by O₃ precursors and solar radiation. Additionally, IRR analysis revealed that NO and NO₂ emitted during wildfire and solar radiation contributed to the production and loss processes of O₃; the production reactions of O₃ were predominant, and O₃ was accumulated and transported in the plume, leading to the peak O₃ concentration at 13 LST. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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22 pages, 5110 KiB

Open AccessArticle

Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations

by Seunghee Lee, Ganghan Kim, Myong-In Lee, Yonghan Choi, Chang-Keun Song and Hyeon-Kook Kim

Remote Sens. 2022, 14(9), 2123; https://doi.org/10.3390/rs14092123 - 28 Apr 2022

Cited by 2 | Viewed by 2362

Abstract

This study examines the performance of a data assimilation and forecasting system that simultaneously assimilates satellite aerosol optical depth (AOD) and ground-based PM₁₀ and PM_2.5 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The data assimilation case for the surface PM₁₀ and PM_2.5 concentrations exhibits a higher consistency with the observed data by showing more correlation coefficients than the no-assimilation case. The data assimilation also shows beneficial impacts on the PM₁₀ and PM_2.5 forecasts for South Korea for up to 24 h from the updated initial condition. This study also finds deficiencies in data assimilation and forecasts, as the model shows a pronounced seasonal dependence of forecasting accuracy, on which the seasonal changes in regional atmospheric circulation patterns have a significant impact. In spring, the forecast accuracy decreases due to large uncertainties in natural dust transport from the continent by north-westerlies, while the model performs reasonably well in terms of anthropogenic emission and transport in winter. When the south-westerlies prevail in summer, the forecast accuracy increases with the overall reduction in ambient concentration. The forecasts also show significant accuracy degradation as the lead time increases because of systematic model biases. A simple statistical correction that adjusts the mean and variance of the forecast outputs to resemble those in the observed distribution can maintain the forecast skill at a practically useful level for lead times of more than a day. For a categorical forecast, the skill score of the data assimilation run increased by up to 37% compared to that of the case with no assimilation, and the skill score was further improved by 10% through bias correction. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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21 pages, 8952 KiB

Open AccessArticle

An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data

by Hyeon-Kook Kim, Seunghee Lee, Kang-Ho Bae, Kwonho Jeon, Myong-In Lee and Chang-Keun Song

Remote Sens. 2022, 14(2), 389; https://doi.org/10.3390/rs14020389 - 14 Jan 2022

Cited by 4 | Viewed by 2233

Abstract

Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM_2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM_2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM_2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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22 pages, 8242 KiB

Open AccessArticle

Optimization and Evaluation of SO₂ Emissions Based on WRF-Chem and 3DVAR Data Assimilation

by Yiwen Hu, Zengliang Zang, Dan Chen, Xiaoyan Ma, Yanfei Liang, Wei You, Xiaobin Pan, Liqiong Wang, Daichun Wang and Zhendong Zhang

Remote Sens. 2022, 14(1), 220; https://doi.org/10.3390/rs14010220 - 4 Jan 2022

Cited by 25 | Viewed by 3457

Abstract

Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO₂) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO₂ concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO₂ emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO₂ forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO₂ emission optimization methodology is computationally cost-effective. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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19 pages, 5786 KiB

Open AccessArticle

Comparative Analysis of PM_2.5 and O₃ Source in Beijing Using a Chemical Transport Model

by Wei Wen, Song Shen, Lei Liu, Xin Ma, Ying Wei, Jikang Wang, Yi Xing and Wei Su

Remote Sens. 2021, 13(17), 3457; https://doi.org/10.3390/rs13173457 - 31 Aug 2021

Cited by 10 | Viewed by 2832

Abstract

For many years, Beijing has suffered from severe air pollution. At present, fine particulate matter (PM_2.5) pollution in the winter and ozone (O₃) pollution in the summer constitute serious environmental problems. In this study, the combination of a comprehensive air quality model with particulate matter source apportionment technology (CAMx-PAST) and monitoring data was used for the high-spatial resolution source apportionment of secondary inorganic components (SNA: SO₄²⁻, NO₃⁻, and NH₄⁺) in PM_2.5; their corresponding precursor gases (SO₂, NO₂, and NH₃); and O₃ in the winter and summer over Beijing. Emissions from residents, industry, traffic, agriculture, and power accounted for 54%, 25%, 14%, 5%, and 2% of PM_2.5 in the winter, respectively. In the summer, the emissions from industry, traffic, residents, agriculture, and power accounted for 42%, 24%, 20%, 10%, and 4% of PM_2.5, respectively. The monthly transport ratio of PM_2.5 was 27% and 46% in the winter and summer, respectively. The regional transport of residential and industrial emissions accounted for the highest proportion of PM_2.5. The regional transport of emissions had a significant effect on the SO₄²⁻ and NO₃⁻ concentrations, whereas SO₂ and NO₂ pollution were mainly affected by local emissions, and NH₄⁺ and NH₃ were mainly attributed to agricultural emissions. Industrial and traffic sources were two major emission sectors that contributed to O₃ pollution in Beijing. The monthly transport ratios of O₃ were 31% and 65% in the winter and summer, respectively. The high-spatial resolution regional source apportionment results showed that emissions from Langfang, Baoding, and Tangshan had the greatest impact on Beijing’s air pollution. This work’s methods and results will provide scientific guidance to support the government in its decision-making processes to manage the PM_2.5 and O₃ pollution issues. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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19 pages, 8309 KiB

Open AccessArticle

Enhanced Simulation of an Asian Dust Storm by Assimilating GCOM-C Observations

by Yueming Cheng, Tie Dai, Daisuke Goto, Hiroshi Murakami, Mayumi Yoshida, Guangyu Shi and Teruyuki Nakajima

Remote Sens. 2021, 13(15), 3020; https://doi.org/10.3390/rs13153020 - 1 Aug 2021

Cited by 7 | Viewed by 2781

Abstract

Dust aerosols have great effects on global and regional climate systems. The Global Change Observation Mission-Climate (GCOM-C), also known as SHIKISAI, which was launched on 23 December 2017 by the Japan Aerospace Exploration Agency (JAXA), is a next-generation Earth observation satellite that is used for climate studies. The Second-Generation Global Imager (SGLI) aboard GCOM-C enables the retrieval of more precious global aerosols. Here, the first assimilation study of the aerosol optical thicknesses (AOTs) at 500 nm observed by this new satellite is performed to investigate a severe dust storm in spring over East Asia during 28–31 March 2018. The aerosol observation assimilation system is an integration of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-Hydrostatic Icosahedral Atmospheric Model (NICAM). Through verification with the independent observations from the Aerosol Robotic Network (AERONET) and the Asian Dust and Aerosol Lidar Observation Network (AD-Net), the results demonstrate that the assimilation of the GCOM-C aerosol observations can significantly enhance Asian dust storm simulations. The dust characteristics over the regions without GCOM-C observations are better revealed from assimilating the adjacent observations within the localization length, suggesting the importance of the technical advances in observation and assimilation, which are helpful in clarifying the temporal–spatial structure of Asian dust and which could also improve the forecasting of dust storms, climate prediction models, and aerosol reanalysis. Full article

(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)

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