Changes in black carbon emissions over Europe due to COVID-19 lockdowns

This study has been supported by the Research Council of Norway (project ID: 275407, COMBAT - Quantification of Global Ammonia Sources constrained by a Bayesian Inversion Technique). Nikolaos Evangeliou and Sabine Eckhardt received funding from the Arctic Monitoring & Assessment Programme (AMAP). John Backman was supported by the Academy of Finland project Novel Assessment of Black Carbon in the Eurasian Arctic: From Historical Concentrations and Sources to Future Climate Impacts (NABCEA; project no. 296302), the Academy of Finland Centre of Excellence programme (project no. 307331) and COST Action CA16109 Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoL, COLOSSAL. The research leading to the ACTRIS measurements has received funding from the European Union's Horizon 2020 Research And Innovation programme (grant agreement no. 654109) and the Cloudnet project (European Union contract EVK2-2000-00611).All measurement data and model outputs used for the present publication are publicly available and can be downloaded from https://doi.org/10.21336/gen.b5vj-sn33 (Evangeliou et al., 2020) or upon request to the corresponding author. All prior emission datasets are also available for download. ECLIPSE emissions can be obtained from http://www.iiasa.ac.at/web/home/research/researchPrograms/air/Global_emissions.html (Klimont et al., 2017), EDGAR version HTAP_V2.2 from http://edgar.jrc.ec.europa.eu/methodology.php# (Janssens-Maenhout et al., 2015), ACCMIP version 5 from http://accent.aero.jussieu.fr/ACCMIP_metadata.php (Lamarque et al., 2010) and PKU from http://inventory.pku.edu.cn (Peking University, 2021). FLEXPART is publicly available and can be downloaded from https://www.flexpart.eu (Pisso et al., 2019) and FLEXINVERT+ from https://flexinvert.nilu.no (Thompson and Stohl, 2014). MERRA-2 reanalysis data can be obtained from https://disc.gsfc.nasa.gov (NASA Earth Data, 2021) and AERONET measurements from https://aeronet.gsfc.nasa.gov (Holben et al., 1998).The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-2675-2021-supplement.Following the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for COVID-19 in December 2019 in Wuhan (China) and its spread to the rest of the world, the World Health Organization declared a global pandemic in March 2020. Without effective treatment in the initial pandemic phase, social distancing and mandatory quarantines were introduced as the only available preventative measure. In contrast to the detrimental societal impacts, air quality improved in all countries in which strict lockdowns were applied, due to lower pollutant emissions. Here we investigate the effects of the COVID-19 lockdowns in Europe on ambient black carbon (BC), which affects climate and damages health, using in situ observations from 17 European stations in a Bayesian inversion framework. BC emissions declined by 23 kt in Europe (20 % in Italy, 40 % in Germany, 34 % in Spain, 22 % in France) during lockdowns compared to the same period in the previous 5 years, which is partially attributed to COVID-19 measures. BC temporal variation in the countries enduring the most drastic restrictions showed the most distinct lockdown impacts. Increased particle light absorption in the beginning of the lockdown, confirmed by assimilated satellite and remote sensing data, suggests residential combustion was the dominant BC source. Accordingly, in central and Eastern Europe, which experienced lower than average temperatures, BC was elevated compared to the previous 5 years. Nevertheless, an average decrease of 11 % was seen for the whole of Europe compared to the start of the lockdown period, with the highest peaks in France (42 %), Germany (21 %), UK (13 %), Spain (11 %) and Italy (8 %). Such a decrease was not seen in the previous years, which also confirms the impact of COVID-19 on the European emissions of BC.Research Council of Norwayrctic Monitoring & Assessment Programme (AMAP).Academy of Finland project Novel Assessment of Black Carbon in the Eurasian Arctic: From Historical Concentrations and Sources to Future Climate Impacts (NABCEA) 296302Academy of Finland 307331European Cooperation in Science and Technology (COST) CA16109European Union's Horizon 2020 Research And Innovation programme 654109Cloudnet project European Union EVK2-2000-0061

Dose calculations in aircrafts after Fukushima nuclear power plant accident – Preliminary study for aviation operations

There is little information to decision support in air traffic management in case of nuclear releases into the atmosphere. In this paper, the dose estimation due to both, external exposure (i.e. cloud immersion, deposition inside and outside the aircraft), and due to internal exposure (i.e, inhalation of radionuclides inside the aircraft) to passengers and crew is calculated for a worst-case emergency scenario. The doses are calculated for different radionuclides and activities. Calculations are mainly considered according to International Commission on Radiological Protection (ICRP) recommendations and Monte Carlo simulations. In addition, a discussion on potential detectors installed inside the aircraft for monitoring the aerosol concentration and the ambient dose equivalent rate, H*(10), for during-flight monitoring and early warning is provided together with the evaluation of a response of a generic detector. The results show that the probability that a catastrophic nuclear accident would produce significant radiological doses to the passengers and crew of an aircraft is very low. In the worst-case scenarios studied, the maximum estimated effective dose was about 1¿mSv during take-off or landing operations, which is the recommended yearly threshold for the public. However, in order to follow the ALARA (As Low As Reasonably Achievable) criteria and to avoid aircraft contamination, the installation of radiological detectors is considered. This would, on one hand help the pilot or corresponding decision maker to decide about the potential change of the route and, on the other, allow for gathering of 4D data for future studiesPostprint (published version

Development of a Versatile Modular Platform for Aerial Manipulators

The scope of this chapter is the development of an aerial manipulator platform using an octarotor drone with an attached manipulator. An on-board spherical camera provides visual information for the drone’s surroundings, while a Pan-Tilt-Zoom camera system is used to track targets. A powerful computer with a GPU offers significant on-board computational power for the visual servoing of the aerial manipulator system. This vision system, along with the Inertial Management Unit based controller provides exemplary guidance in confined and outdoor spaces. Coupled with the manipulator’s force sensing capabilities the system can interact with the environment. This aerial manipulation system is modular as far as attaching various payloads depending on the application (i.e., environmental sensing, facade cleaning and others, aerial netting for evader-drone geofencing, and others). Experimental studies using a motion capture system are offered to validate the system’s efficiency

Changes in black carbon emissions over Europe due to COVID-19 lockdowns

Following the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for COVID-19 in December 2019 in Wuhan (China) and its spread to the rest of the world, the World Health Organization declared a global pandemic in March 2020. Without effective treatment in the initial pandemic phase, social distancing and mandatory quarantines were introduced as the only available preventative measure. In contrast to the detrimental societal impacts, air quality improved in all countries in which strict lockdowns were applied, due to lower pollutant emissions. Here we investigate the effects of the COVID-19 lockdowns in Europe on ambient black carbon (BC), which affects climate and damages health, using in situ observations from 17 European stations in a Bayesian inversion framework. BC emissions declined by 23 kt in Europe (20% in Italy, 40% in Germany, 34% in Spain, 22% in France) during lockdowns compared to the same period in the previous 5 years, which is partially attributed to COVID-19 measures. BC temporal variation in the countries enduring the most drastic restrictions showed the most distinct lockdown impacts. Increased particle light absorption in the beginning of the lockdown, confirmed by assimilated satellite and remote sensing data, suggests residential combustion was the dominant BC source. Accordingly, in central and Eastern Europe, which experienced lower than average temperatures, BC was elevated compared to the previous 5 years. Nevertheless, an average decrease of 11% was seen for the whole of Europe compared to the start of the lockdown period, with the highest peaks in France (42 %), Germany (21 %), UK (13 %), Spain (11 %) and Italy (8 %). Such a decrease was not seen in the previous years, which also confirms the impact of COVID-19 on the European emissions of BC.Peer reviewe

Microplastics and nanoplastics in the marine-atmosphere environment

The discovery of atmospheric micro(nano)plastic transport and ocean-atmosphere exchange points to a highly complex marine plastic cycle, with negative implications for human and ecosystem health. Yet, observations are currently limited. In this Perspective, we quantify the processes and fluxes of the marine-atmospheric micro(nano)plastic cycle, with the aim of highlighting the remaining unknowns in atmospheric micro(nano)plastic transport. Between 0.013 and 25 million metric tons per year of micro(nano)plastics are potentially being transported within the marine atmosphere and deposited in the oceans. However, the high uncertainty in these marine-atmospheric fluxes is related to data limitations and a lack of study intercomparability. To address the uncertainties and remaining knowledge gaps in the marine-atmospheric micro(nano)plastic cycle, we propose a future global marine-atmospheric micro(nano)plastic observation strategy, incorporating novel sampling methods and the creation of a comparable, harmonized and global data set. Together with long-term observations and intensive investigations, this strategy will help to define the trends in marine-atmospheric pollution and any responses to future policy and management actions. Atmospheric transport of microplastics could be a major source of plastic pollution to the ocean, yet observations currently remain limited. This Perspective quantifies the known budgets of the marine-atmospheric micro(nano)plastic cycle and proposes a future global observation strategy.Peer reviewe

Simulating CH_4 and CO_2 over South and East Asia using the zoomed chemistry transport model LMDz-INCA

The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO_2) and methane (CH_4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH_4 and CO_2 using zoomed versions (abbreviated as ZAs) of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of  ∼ 0.66° in longitude and  ∼ 0.51° in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH_4 and CO_2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50° in longitude and 1.27° in latitude (abbreviated as STs), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH_4 annual gradients between stations in SEA, with reduction of RMSE by 16–20% compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH_4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH_4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO_2 suggests that the CO_2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH_4 and CO_2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions

Simulating CH_4 and CO_2 over South and East Asia using the zoomed chemistry transport model LMDz-INCA

The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO_2) and methane (CH_4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH_4 and CO_2 using zoomed versions (abbreviated as ZAs) of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of  ∼ 0.66° in longitude and  ∼ 0.51° in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH_4 and CO_2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50° in longitude and 1.27° in latitude (abbreviated as STs), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH_4 annual gradients between stations in SEA, with reduction of RMSE by 16–20% compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH_4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH_4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO_2 suggests that the CO_2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH_4 and CO_2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions

Cybersecurity for industrial Internet of Things: architecture, models and lessons learned

Modern industrial systems now, more than ever, require secure and efficient ways of communication. The trend of making connected, smart architectures is beginning to show in various fields of the industry such as manufacturing and logistics. The number of IoT (Internet of Things) devices used in such systems is naturally increasing and industry leaders want to define business processes which are reliable, reproducible, and can be effortlessly monitored. With the rise in number of connected industrial systems, the number of used IoT devices also grows and with that some challenges arise. Cybersecurity in these types of systems is crucial for their wide adoption. Without safety in communication and threat detection and prevention techniques, it can be very difficult to use smart, connected systems in the industry setting. In this paper we describe two real-world examples of such systems while focusing on our architectural choices and lessons learned. We demonstrate our vision for implementing a connected industrial system with secure data flow and threat detection and mitigation strategies on real-world data and IoT devices. While our system is not an off-the-shelf product, our architecture design and results show advantages of using technologies such as Deep Learning for threat detection and Blockchain enhanced communication in industrial IoT systems and how these technologies can be implemented. We demonstrate empirical results of various components of our system and also the performance of our system as-a-whole

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCV