ORCID Profile
0000-0002-2868-9343
Current Organisation
Nanjing University of Science and Technology
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Copernicus GmbH
Date: 26-09-2022
Publisher: Copernicus GmbH
Date: 13-02-2023
Publisher: Copernicus GmbH
Date: 23-01-2023
Abstract: Abstract. Ozone (O3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O3 pollution, near-surface O3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multi-source data. The ML model is trained with combined O3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O3 concentrations are projected to increase by 5 %–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and the Gangetic Plains under the high-forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O3 increases are primarily owing to the favorable meteorological conditions for O3 photochemical formation in most Asian regions. We also find that the summertime O3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of a climate change penalty on Asian O3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.
Publisher: Copernicus GmbH
Date: 26-09-2022
DOI: 10.5194/ACP-2022-550
Abstract: Abstract. Ozone (O3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O3 pollution, near-surface O3 concentrations over Asia in 2020–2100 are projected using a machine learning (ML) method along with multisource data. The ML model is trained with combined O3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O3 concentrations are projected to increase by 5–20 % over South China, Southeast Asia, and South India and less than 10 % over North China and Gangetic Plains under the high forcing scenarios in the last decade of 21st century, compared to the first decade of 2020–2100. The O3 increases are primarily owing to the favorable meteorological conditions for O3 photochemical formation in most Asian regions. We also find that the summertime O3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of climate change penalty on Asian O3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.
Publisher: Copernicus GmbH
Date: 13-02-2023
DOI: 10.5194/GMD-2022-301
Abstract: Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are trained using the historical measurement datasets independently collected at the environmental monitoring stations, and their operational forecasts onward by the inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions. In contrast, deterministic chemical transport models (CTM), which simulate the full life cycles of air pollutants, provide forecasts that are continuous in 3D field. However, owing to the complex error sources due to the emission, transport, and removal of pollutants, CTM forecasts are typically biased particularly in fine scale. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our recent regional-feature-selection machine learning prediction (RFSML v1.0) and a CTM forecast. The prediction fusion was conducted using the Bayesian theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to lify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented significant improvements than the pure CTM. Moreover, covariance inflation further enhanced the fused prediction particularly in the cases of severe underestimation.
Publisher: Copernicus GmbH
Date: 03-01-2023
Abstract: Abstract. The co-polluted days by ozone (O3) and PM2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less) (O3–PM2.5PDs) were frequently observed in the Beijing–Tianjin–Hebei (BTH) region in warm seasons (April–October) of 2013–2020. We applied the 3-D global chemical transport model (GEOS-Chem) to investigate the chemical and physical characteristics of O3–PM2.5PDs by composited analyses of such days that were captured by both the observations and the model. Model results showed that, when O3–PM2.5PDs occurred, the concentrations of hydroxyl radical and total oxidant, sulfur oxidation ratio, and nitrogen oxidation ratio were all high, and the concentrations of sulfate at the surface were the highest among all pollution types. We also found unique features in vertical distributions of aerosols during O3–PM2.5PDs concentrations of PM2.5 decreased with altitude near the surface but remained stable at 975–819 hPa. Process analyses showed that secondary aerosols (nitrate, ammonium, and sulfate) had strong chemical productions at 913–819 hPa, which were then transported downward, resulting in the quite uniform vertical profiles at 975–819 hPa on O3–PM2.5PDs. The weather patterns for O3–PM2.5PDs were characterized by anomalous high-pressure system at 500 hPa as well as strong southerlies and high RH at 850 hPa. The latter resulted in the strong chemical productions around 850 hPa on O3–PM2.5PDs. The physical and chemical characteristics of O3–PM2.5PDs are quite different from those of polluted days by either O3 alone or PM2.5 alone and have important implications for air quality management.
Publisher: Copernicus GmbH
Date: 20-09-2022
DOI: 10.5194/ACP-2022-557
Abstract: Abstract. The co-polluted days by ozone (O3) and PM2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 μm or less) (O3& PM2.5PD) were frequently observed in the Beijing–Tianjin–Hebei (BTH) region in warm seasons (April–October) of 2013–2020. We applied the 3-D global chemical transport model (GEOS-Chem) to investigate the chemical and physical characteristics of O3& PM2.5PD by composited analyses of such days that were captured by both the observations and the model. Model results showed that, when O3& PM2.5PD occurred, the concentrations of hydroxyl radical and total oxidant, sulfur oxidation ratio, and nitrogen oxidation ratio were all high, and the concentrations of sulfate at the surface were the highest among all aerosol species. We also found unique features in vertical distributions of aerosols during O3& PM2.5PD concentrations of PM2.5 decreased with altitude near the surface but remained stable at 975–819 hPa. Process analyses showed that secondary aerosols (nitrate, ammonium and sulfate) had strong chemical productions at 913–819 hPa, which were then transported downward, resulting in the quite uniform vertical profiles at 975–819 hPa in O3& PM2.5PD. The weather patterns for O3& PM2.5PD were characterized by a high pressure ridge of the Western Pacific Subtropical High at 850 hPa. The strong southerlies at 850 hPa brought moist air from the south, resulting in a high RH and hence the strong chemical productions around this layer in O3& PM2.5PD. The physical and chemical characteristics of O3& PM2.5PD are quite different from those of polluted days by either O3 alone or PM2.5 alone, which have important implications for air quality management.
Publisher: IOP Publishing
Date: 14-02-2023
Abstract: Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for ex le, Infrared Atmospheric Sounder Interferometer (IASI) and Cross-Track Infrared Sounder, which provide global coverage of ammonia distribution, have gained popularity in ammonia emission estimation through data assimilation methods. However, satellite-based emission inversion studies on China are limited. In this study, we propose a four-dimensional ensemble variational-based ammonia emission inversion system to optimize ammonia emissions in China. It was developed by assimilating the IASI ammonia retrievals onboard Meteorological Operational satellite A and B into a chemical transport model Goddard Earth Observing System Chemical model (GEOS-Chem). Monthly inversion experiments were conducted in April, July, and October 2016 to test the performance. The inversion result indicated that the prior inventory from the MEIC model captured ammonia spreads in general however, it heterogeneously underrated the emission intensity. The increments obtained in the assimilation were as high as 50% in North, East, and Northwest China. The posterior emission inventory presented a regional emission flux consistent with relevant studies. Driven by the optimized source estimate, GEOS-Chem provides superior results than using the prior in the evaluation of the assimilated IASI retrievals and the surface ammonia concentration measured by the ground-based Ammonia Monitoring Network in China.
Publisher: Copernicus GmbH
Date: 20-09-2022
Publisher: Copernicus GmbH
Date: 29-08-2023
Abstract: Abstract. Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to lify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particularly in cases of severe underestimation.
No related grants have been discovered for Jianbing Jin.