Article
Nuclear Science & Technology
Zhihong Tang, Suxiang Jiang, Jiejin Cai, Qiong Li, Xuezhong Li, Ziqi Gong
Summary: In response to the Fukushima nuclear accident, the CTBTO and other countries conducted extensive monitoring. The study used a global dispersion model to simulate the transport of radioactive materials, showing that the radioactive plume spread from west to east and reached China through three routes. This research helps understand the potential threat of future nuclear accidents and supports monitoring and emergency measures.
PROGRESS IN NUCLEAR ENERGY
(2021)
Article
Geosciences, Multidisciplinary
JoffreyDumont Le Brazidec, Marc Bocquet, Olivier Saunier, Yelva Roustan
Summary: The accident at the Fukushima Daiichi NPP resulted in significant and rapidly changing releases of atmospheric radionuclides. Inverse modelling methods, particularly Bayesian inversion, can effectively assess these releases and their uncertainties by combining an atmospheric transport model with a range of observations. This study developed Bayesian algorithms to estimate the magnitude and temporal evolution of the Fukushima Daiichi NPP's cesium 137 (137Cs) release, taking into account the spatio-temporal information and uncertainties associated with observations, models, and source estimates.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
Article
Environmental Sciences
Magnus Ulimoen, Erik Berge, Heiko Klein, Brit Salbu, Ole Christian Lind
Summary: Atmospheric dispersion models are crucial for nuclear risk assessment and emergency response, but uncertainties are mainly attributed to meteorology, source terms, and model parameters. Ensemble predictions, including both meteorological and source term ensembles, show higher prediction skill compared to individual runs and improve decision-making in the early phase after a nuclear accident.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Sheng Fang, Xinwen Dong, Shuhan Zhuang, Zhijie Tian, Yungang Zhao, Yun Liu, Yuanyuan Liu, Li Sheng
Summary: This paper presents an objective method that can adaptively recover missing releases caused by temporal absences in observation records. By assuming that accident releases of radionuclides are piecewise-constant and comprise both peaks and constant releases, the proposed method minimizes the total variation of the estimated source term to recover the missing releases. Applied to the Fukushima accident, the method effectively recovers the missing releases and improves the simulation of air concentrations and deposition patterns.
ENVIRONMENTAL POLLUTION
(2023)
Article
Construction & Building Technology
Zhenzhe Liu, Xiaofeng Li
Summary: The layout of sensors in urban neighborhood has a significant impact on the estimation of dangerous or polluting gas leak sources. In this study, Computational Fluid Dynamics (CFD) approaches are used to analyze the effect of sensor layout on the estimation performance. The best sensor layout in the urban neighborhood is determined. The influence of source location and the border effect of measuring points on the performance of estimation are analyzed. Results show that placing average two sensors around each building is the best configuration for identifying source location parameters. There is no obvious advantage in sensor configuration regarding source intensity. The difficulty of estimation increases as the distance between pollutants and the city increases. Considering the border effect of measuring points improves the accuracy of estimation slightly.
BUILDING AND ENVIRONMENT
(2022)
Article
Environmental Sciences
Youness El-Ouartassy, Irene Korsakissok, Matthieu Plu, Olivier Connan, Laurent Descamps, Laure Raynaud
Summary: This study uses a probabilistic approach to investigate meteorological uncertainties in local and medium-distance atmospheric dispersion simulations. The quality of weather ensemble forecasts is confirmed and the performance of dispersion simulations is evaluated using probabilistic scores. The results demonstrate the added value of ensemble forecasts compared to deterministic predictions in the decision-making process during crises.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2022)
Article
Geosciences, Multidisciplinary
Olivier Evrard, Caroline Chartin, J. Patrick Laceby, Yuichi Onda, Yoshifumi Wakiyama, Atsushi Nakao, Olivier Cerdan, Hugo Lepage, Hugo Jaegler, Rosalie Vandromme, Irene Lefevre, Philippe Bonte
Summary: A study compiled data on gamma-emitting artificial radionuclide activities measured in 782 sediment samples collected during 16 fieldwork campaigns in Japan from November 2011 to November 2020. This dataset may help in evaluating and anticipating the post-accidental redistribution of radionuclides in the environment and validating models simulating the transfer of radiocesium across continental landscapes.
EARTH SYSTEM SCIENCE DATA
(2021)
Article
Environmental Sciences
Roseane A. S. Albani, Vinicius V. L. Albani, Luiz E. S. Gomes, Helio S. Migon, Antonio J. Silva Neto
Summary: We propose a methodology that combines a data-driven dispersion model with Bayesian inference and uncertainty quantification to identify multiple pollutant sources in the atmosphere. The dispersion model incorporates a realistic wind field based on measured wind components time series. Bayesian inference accounts for uncertainty in concentration data and balances the prior and likelihood. Source parameters are estimated using a Metropolis in Gibbs MCMC algorithm with adaptive steps, initialized with a maximum a posteriori estimator obtained with particle swarm optimization. The proposed methodology seems to outperform inversion techniques from previous works.
ENVIRONMENTAL POLLUTION
(2023)
Article
Environmental Sciences
Roseane A. S. Albani, Vinicius V. L. Albani, Helio S. Migon, Antonio J. Silva Neto
Summary: This study addresses source characterization of atmospheric releases using adaptive strategies in Bayesian inference, numerical solution of the dispersion problem by a stabilized finite element method, and uncertainty quantification in measurements. The adaptive techniques accelerate the convergence of algorithms, leading to accurate reconstructions of source parameters. Results show errors in reconstructions ranging from 0.11% to 8.67% of the search region, with similar computational time compared to deterministic techniques.
ENVIRONMENTAL POLLUTION
(2021)
Article
Multidisciplinary Sciences
Marius Marinescu, Alberto Olivares, Ernesto Staffetti, Junzi Sun
Summary: This paper focuses on the estimation of the spatio-temporal wind velocity field and proposes a Gaussian process regression method for both reconstruction and prediction. The method is statistically consistent and provides probability distributions and confidence intervals for the estimates.
Article
Environmental Sciences
Tsuyoshi Thomas Sekiyama, Mizuo Kajino, Masaru Kunii
Summary: The study utilized single-model initial-perturbed ensemble simulations to quantify uncertainty in aerosol dispersion modeling, focusing on a point-source radioactive aerosol emitted from the Fukushima Daiichi Nuclear Power Plant in March 2011. The simulations showed that variation in wind speed resulted in increased uncertainties in aerosol concentrations.
Article
Nuclear Science & Technology
Ke Li, Weihua Chen, Manchun Liang, Jianqiu Zhou, Yunfu Wang, Shuijun He, Jie Yang, Dandan Yang, Hongmin Shen, Xiangwei Wang
Summary: The paper introduces a parameter bias transformation method combined with Lagrangian puff model to improve the reliability and accuracy of atmospheric dispersion model for nuclear emergency. It considers the uncertainty of release rate, wind speed, wind direction, and plume height, using particle swarm optimization for optimal parameter search. Twin and Kincaid experiments show the method effectively enhances model prediction and parameter estimation.
NUCLEAR ENGINEERING AND TECHNOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Lasse Johansson, Ari Karppinen, Mona Kurppa, Anu Kousa, Jarkko Niemi, Jaakko Kukkonen
Summary: This article presents an operational urban air quality modelling system called ENFUSER, which is evaluated against measured data. ENFUSER combines various dispersion modelling approaches, utilizes data assimilation, and extracts information continuously from online, global open-access sources. The model covers a global range as the geographic datasets used are globally available. Urban dispersion is addressed using a combination of Gaussian puff and Gaussian plume modelling, while long-range transport of pollutants is accounted for with a separate regional model. The data assimilation method adjusts emission factors and regional background values on an hourly basis and supports the use of AQ sensors.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Engineering, Environmental
Ondrej Tichy, Vaclav Smidl, Nikolaos Evangeliou
Summary: Estimation of source term of atmospheric radionuclide emissions is crucial for nuclear emergency response and accident analysis, but often inaccurate due to biases in atmospheric transport and meteorological analysis. A method for atmospheric plume bias correction using information from atmospheric transport model is proposed, showing improved accuracy in source term estimation and validation in real cases.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Engineering, Civil
Chao Lin, Ryozo Ooka, Hideki Kikumoto
Summary: In this study, a limiter for turbulent concentration diffusivity, which takes into account travel time, was introduced into the Reynolds-averaged Navier-Stokes equations (RANS) and the Eulerian dispersion model. By adjusting the combination of model parameters in the limiter, the proposed method effectively controlled the turbulent diffusivity near the source and in the downwind region, leading to accurate predictions of dispersion characteristics.
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS
(2022)
Article
Meteorology & Atmospheric Sciences
Cecile L. Defforge, Bertrand Carissimo, Marc Bocquet, Raphael Bresson, Patrick Armand
Summary: This study explores the application of data assimilation methods in urban conditions and enhances scalar-dispersion modelling accuracy by assimilating perturbed measurements inside the urban canopy.
BOUNDARY-LAYER METEOROLOGY
(2021)
Article
Multidisciplinary Sciences
Julien Brajard, Alberto Carrassi, Marc Bocquet, Laurent Bertino
Summary: This study introduces a new method for parametrizing atmospheric models using machine learning and data assimilation techniques, training models to better simulate system states and improving forecast skill by integrating machine learning parameters in a hybrid model.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Alban Farchi, Marc Bocquet, Patrick Laloyaux, Massimo Bonavita, Quentin Malartic
Summary: Recent studies have shown the potential of combining machine learning with data assimilation to reconstruct dynamic systems and correct model errors; utilizing tendency correction can significantly improve data assimilation performance.
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Article
Meteorology & Atmospheric Sciences
Q. Malartic, A. Farchi, M. Bocquet
Summary: This article presents a method for learning chaotic dynamics and state trajectory using local ensemble Kalman filters with sequentially acquired observations. The authors propose algorithms for covariance and local domain localisation to estimate both global and local model parameters. The approach is tested on the Lorenz model and demonstrated using a two-dimensional illustration based on a multilayer Lorenz model.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Mathematics, Interdisciplinary Applications
Marc Bocquet
Summary: The breakthroughs of deep learning in computer vision and natural language processing have greatly benefited the climate sciences. These advances have been applied to various aspects such as parameterization, model error correction, model discovery, and surrogate modeling. This article reviews recent progress in the intersection of dynamical systems, data assimilation, and machine learning, focusing on physical model error correction. Technical challenges in implementing these techniques in high-dimensional operational systems are discussed, along with questions about the combined use of data assimilation and machine learning and uncertainty quantification.
FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS
(2023)
Article
Geography, Physical
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, Veronique Dansereau
Summary: We introduce a proof of concept for parametrising the unresolved subgrid scale of sea-ice dynamics using deep learning techniques. A single neural network is trained to correct all model variables at the same time, rather than parametrising single processes. The approach is tested in a regional sea-ice model, and the results show that neural networks can significantly reduce forecast errors for sea-ice dynamics.
Article
Geosciences, Multidisciplinary
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Gregoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, Thomas Lauvaux
Summary: Under the Copernicus programme, an operational CO2 Monitoring Verification and Support system (CO2MVS) is being developed to exploit data from future satellites monitoring the distribution of CO2 within the atmosphere. This study investigates the potential of deep learning methods, particularly convolutional neural networks, to identify plume-specific spatial features in satellite images for accurate estimation of local CO2 emissions from cities or power plants.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Elise Potier, Gregoire Broquet
Summary: In the near future, many satellites equipped with high-resolution instruments will be launched to capture images of atmospheric gaseous compounds and monitor greenhouse gas emissions and pollutants. However, comparing and analyzing these images to accurately estimate emissions can be challenging due to modeling errors and uncertainties. This article discusses different metrics and solutions for comparing the images to simulated concentrations, taking into account position errors and wind direction.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2023)
Article
Geosciences, Multidisciplinary
JoffreyDumont Le Brazidec, Marc Bocquet, Olivier Saunier, Yelva Roustan
Summary: The accident at the Fukushima Daiichi NPP resulted in significant and rapidly changing releases of atmospheric radionuclides. Inverse modelling methods, particularly Bayesian inversion, can effectively assess these releases and their uncertainties by combining an atmospheric transport model with a range of observations. This study developed Bayesian algorithms to estimate the magnitude and temporal evolution of the Fukushima Daiichi NPP's cesium 137 (137Cs) release, taking into account the spatio-temporal information and uncertainties associated with observations, models, and source estimates.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
Proceedings Paper
Computer Science, Hardware & Architecture
Tifenn Hirtzlin, Thomas Dalgaty, Marc Bocquet, Jean-Michel Portal, Jacques-Olivier Klein, Clement Turck, Kamel-Eddine Harabi, Damien Querlioz, Elisa Vianello
Summary: This article introduces two methods for energy-efficient hardware implementation using resistive memory technology. One method is to use binarized neural networks that are resilient to errors and operate at low energy consumption. The other method is to utilize memristor variability to implement Markov chain Monte Carlo sampling configured as a Bayesian machine learning model. The research demonstrates the robustness of these methods to device variability and degradation, and based on simulations, the total energy consumption for classification and model training is estimated to be two orders of magnitude lower than CMOS-based approaches.
2022 INTERNATIONAL CONFERENCE ON IC DESIGN AND TECHNOLOGY (ICICDT)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
F. Jebali, E. Muhr, M. Alayan, M. C. Faye, D. Querlioz, F. Andrieu, E. Vianello, G. Molas, M. Bocquet, J. M. Portal
Summary: This paper presents an embedded measurement circuit dedicated to extracting the SET switching time of RRAM memory cells. The design and operation of the measurement circuit, as well as the test setup and conditions, are described in detail. The results show that the resistance and SET switching time values obtained using this circuit are consistent with those obtained through heavy waveguide measurement setups in the literature.
2022 IEEE 34TH INTERNATIONAL CONFERENCE ON MICROELECTRONIC TEST STRUCTURES (ICMTS)
(2022)
Article
Geosciences, Multidisciplinary
Colin Grudzien, Marc Bocquet
Summary: This research proposes a novel ensemble variational method for weakly nonlinear forecast error dynamics. By combining the classic ensemble Kalman smoother and 4D smoother, the method shows significant performance advantages in short-term forecasting, providing a theoretical and computational framework.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Proceedings Paper
Computer Science, Hardware & Architecture
J. Minguet Lopez, F. Rummens, L. Reganaz, A. Heraud, T. Hirtzlin, L. Grenouillet, G. Navarro, M. Bernard, C. Carabasse, N. Castellani, V Meli, S. Martin, T. Magis, E. Vianello, C. Sabbione, D. Deleruyelle, M. Bocquet, J. M. Portal, G. Molas, F. Andrieu
Summary: This study experimentally validates the sub-threshold reading strategy in OxRAM+OTS crossbar arrays for low precision inference in Binarized Neural Networks. Through an experimental and theoretical study, the sub-threshold current margin in 1S1R stacked HfO2-based devices with various OTS technologies is optimized. The accuracy and power consumption of a Binarized Neural Network designed in 28nm CMOS are estimated with Monte Carlo simulations.
2022 14TH IEEE INTERNATIONAL MEMORY WORKSHOP (IMW 2022)
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
T. Francois, J. Coignus, A. Makosiej, B. Giraud, C. Carabasse, J. Barbot, S. Martin, N. Castellani, T. Magis, H. Grampeix, S. Van Duijn, C. Mounet, P. Chiquet, U. Schroeder, S. Slesazeck, T. Mikolajick, E. Nowak, M. Bocquet, N. Barrett, F. Andrieu, L. Grenouillet
Summary: This study demonstrates 16kbit FeRAM arrays at the 130nm node with TiN/HfO2:Si/TiN ferroelectric capacitors integrated in the Back-End-of-Line (BEOL), achieving zero bit failure, fully open memory window at 2.5V programming voltage, reduced capacitor area, and fast switching speed. Promising endurance up to 10^7 cycles is reported at the array level. Solder reflow compatibility is also demonstrated for the first time for HfO2-based FeRAM, paving the way for competitive ultra-low power embedded non-volatile memories at more advanced nodes.
2021 IEEE INTERNATIONAL ELECTRON DEVICES MEETING (IEDM)
(2021)
Article
Environmental Sciences
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, Yelva Roustan
Summary: Using Bayesian framework and MCMC algorithms to estimate atmospheric pollutant sources in the inverse problem has proven fruitful. Several methods, such as ensemble forecasting, error distribution selection, and covariance matrix design, are applied to better quantify the uncertainties of the source, leading to improved source term reconstruction and understanding of the release's origin.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2021)
Article
Environmental Sciences
Muhammad Waqas, Majid Nazeer, Man Sing Wong, Wu Shaolin, Li Hon, Joon Heo
Summary: The socio-economic restriction measures implemented in the United States have significantly reduced nitrogen dioxide (NO2) emissions. The study highlights the impact of factors such as human mobility, population density, income, climate, and stationary sources on the reduction of NO2 at different stations. The research emphasizes the scientific impacts of the NO2 reduction and income inequality revealed by the pandemic on air quality and health disparities.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Guorui Zhi, Jinhong Du, Aizhong Chen, Wenjing Jin, Na Ying, Zhihui Huang, Peng Xu, Di Wang, Jinghua Ma, Yuzhe Zhang, Jiabao Qu, Hao Zhang, Li Yang, Zhanyun Ma, Yanjun Ren, Hongyan Dang, Jianglong Cui, Pengchuan Lin, Zhuoshi He, Jinmin Zhao, Shuo Qi, Weiqi Zhang, Wenjuan Zhao, Yingxin Li, Qian Liu, Chen Zhao, Yi Tang, Peng Wei, Jingxu Wang, Zhen Song, Yao Kong, Xiangzhe Zhu, Yi Shen, Tianning Zhang, Yangxi Chu, Xinmin Zhang, Jiafeng Fu, Qingxian Gao, Jingnan Hu, Zhigang Xue
Summary: An comprehensive emission inventory for China in 2019, which includes both air pollutants and greenhouse gases, was developed in this study. The inventory utilizes existing frameworks and data to provide comparable emissions data and demonstrates the relationship between emissions and economic development.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
I-Ting Ku, Yong Zhou, Arsineh Hecobian, Katherine Benedict, Brent Buck, Emily Lachenmayer, Bryan Terry, Morgan Frazier, Jie Zhang, Da Pan, Lena Low, Amy Sullivan, Jeffrey L. Collett Jr
Summary: Unconventional oil and natural gas development (UOGD) in the United States has expanded rapidly in recent decades, raising concerns about its impact on air quality. This study conducted extensive air monitoring during the development of several large well pads in Broomfield, Colorado, providing a unique opportunity to examine changes in local air toxics and VOC concentrations during well drilling and completions and production. The study identified significant increases in VOC concentrations during drilling operations, highlighting the importance of emissions from synthetic drilling mud. The findings suggest opportunities to mitigate emissions during UOGD operations.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Puji Lestari, Akbar R. Tasrifani, Wiranda I. Suri, Martin J. Wooster, Mark J. Grosvenor, Yusuke Fujii, Vissia Ardiyani, Elisa Carboni, Gareth Thomas
Summary: This study developed field emission factors for various pollutants in peatland fires and estimated the total emissions. Gas samples were collected using an analyzer, while particulate samples were collected using air samplers. The study found significant emissions of CO2, CO, PM2.5, carbon aerosols, water-soluble ions, and elements from the fires in Central Kalimantan, Indonesia in 2019.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Ligang Li, Yuyu Chen, Lu Fan, Dong Sun, Hu He, Yongshou Dai, Yong Wan, Fangfang Chen
Summary: A high-precision retrieval method based on a deep convolutional neural network and satellite remote sensing data is proposed to obtain accurate methane vertical profiles.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Hyung Joo Lee, Toshihiro Kuwayama, Michael Fitzgibbon
Summary: This study investigated the changes in nitrogen dioxide (NO2) air pollution levels and their disparities in California, U.S. during the pandemic of coronavirus disease 2019 (COVID-19). The results showed a decrease in NO2 concentrations, especially in urban and high-traffic areas. However, socially vulnerable populations still experienced higher levels of NO2 exposure. The study suggests that reducing NO2 disparities, particularly racial inequity, can be achieved through continued regulatory actions targeting traffic-related NOx emissions.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Maria Chiara Pietrogrande, Beatrice Biffi, Cristina Colombi, Eleonora Cuccia, Umberto Dal Santo, Luisa Romanato
Summary: This study investigates the chemical composition and oxidative potential of PM10 particles in the Po Valley, Italy, and demonstrates the impact of high levels of atmosphere ammonia. The rural area had significantly higher ammonia concentrations compared to the urban site, resulting in higher levels of secondary inorganic aerosol. Although the SIA components did not contribute significantly to the PM10 oxidative reactivity, they were correlated with the oxidative potential measurements. This suggests that the contribution of SIA to PM oxidative toxicity cannot be ignored.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Natalie Allen, Jan Gacnik, Sarrah M. Dunham-Cheatham, Mae Sexauer Gustin
Summary: Accurate measurement of atmospheric reactive mercury is challenging due to its reactivity and low concentrations. The University of Nevada, Reno Reactive Mercury Active System (RMAS) has been shown to be more accurate than the industry standard, but has limitations including long time resolution and sampling biases. Increasing the sampling flow rate negatively affected RM concentrations, but did not impact the chemical composition of RM captured on membranes.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Chin-Yu Hsu, Wei-Ting Hsu, Ching-Yi Mou, Pei-Yi Wong, Chih-Da Wu, Yu-Cheng Chen
Summary: This study estimated the daily exposure concentrations of PM2.5 for elderly individuals residing in different regions of Taiwan using land use regression with machine learning (LUR_ML) and microenvironmental exposure (ME) models. The accuracy of the models varied across regions, with the ME models exhibiting higher predictions and lower biases. The use of region-specific microenvironmental measurements in the ME model showed potential for accurate prediction of personal PM2.5 exposure.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Xiaohan Si, Kerrie Mengersen, Chuchu Ye, Wenbiao Hu
Summary: This study found that there is an interactive effect between air pollutants and weather factors, which significantly affects influenza transmission. Future research should consider the interactive effects between pollutants and temperature or humidity to evaluate the environment-influenza association.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Luxi Xu, Ruijun Xu, Yunshao Ye, Rui Wang, Jing Wei, Chunxiang Shi, Qiaoxuan Lin, Ziquan Lv, Suli Huang, Qi Tian, Yuewei Liu
Summary: This study aimed to evaluate the impact of ambient air pollution on hospital admissions for angina. The results showed that exposure to ambient particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone are associated with an increased risk of hospital admissions for angina. The association with nitrogen dioxide exposure was found to be the strongest.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Xinyu Yu, Man Sing Wong, Majid Nazeer, Zhengqiang Li, Coco Yin Tung Kwok
Summary: This study proposes a novel method to address the challenge of missing values in satellite-derived AOD products and creates a comprehensive daily AOD dataset for the Guangdong-Hong Kong-Macao Greater Bay Area. By reconstructing missing values and developing a new model, the derived dataset outperforms existing products and agrees well with ground-based observations. Additionally, the dataset exhibits consistent temporal patterns and more spatial details.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Yidan Zhang, Yifan Xu, Bo Peng, Wu Chen, Xiaoyu Cui, Tianle Zhang, Xi Chen, Yuan Yao, Mingjin Wang, Junyi Liu, Mei Zheng, Tong Zhu
Summary: This study developed a sensitive method to measure the metallic components of atmospheric fine particulate matter (PM2.5) and compared the results with different analysis methods. The concentrations of metallic components in personal PM2.5 samples were found to be significantly different from corresponding fixed-site samples. Personal sampling can reduce exposure misclassifications, and measuring metallic components is useful for exploring health risks and identifying sources of PM2.5.
ATMOSPHERIC ENVIRONMENT
(2024)
Review
Environmental Sciences
Jamie Leonard, Lea Ann El Rassi, Mona Abdul Samad, Samantha Prehn, Sanjay K. Mohanty
Summary: Increasing concentrations of microplastics in the Earth's atmosphere could have adverse effects on ecosystems and human health. The deposition rate of airborne microplastics is influenced by both land use and climate, and a global analysis suggests that climate may have a greater impact on the concentration and deposition rate of microplastics than land use.
ATMOSPHERIC ENVIRONMENT
(2024)
Article
Environmental Sciences
Tian Zhou, Xiaowen Zhou, Zining Yang, Carmen Cordoba-Jabonero, Yufei Wang, Zhongwei Huang, Pengbo Da, Qiju Luo, Zhijuan Zhang, Jinsen Shi, Jianrong Bi, Hocine Alikhodja
Summary: This study investigated the long-range transport and effects of North African and Middle Eastern dust in East Asia using lidar observations and model simulations. The results showed that the dust originated from multiple sources and had a long transport time. The vertical distribution of the dust was found to be crucial for assessing its impacts.
ATMOSPHERIC ENVIRONMENT
(2024)
