Temporal and spatial trends in aerosols near the English Channel – An air quality success story?

We present a detailed analysis of long-term aerosol measurements from four sun photometer sites (from west to east: Plymouth, Chilbolton, Dunkirk, Oostende) and four Department for Environment, Food & Rural Affairs surface sites (from west to east: Plymouth, Southampton, Portsmouth, Eastbourne) near the English Channel. From the early 2000s to about 2016, annual mean Aerosol Optical Depth (AOD) from all sun photometer sites decreased by an overall average of 23% decade-1 (range of 15–28% decade-1). From 2010 to 2017, annual mean concentration of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) from all the surface sites decreased by an overall average of 44% decade-1 (range of 7–64% decade-1). Seasonally, the highest aerosol loading is generally found around the springtime, and this maximum has been decreasing much faster over recent years than during the other seasons. This is driven by the interaction between the seasonal weather patterns (e.g. reduced westerly flow and drier weather in the spring) and the main emission sources being predominantly from the European Continent. We find clear spatial gradients in the aerosol loading as well as aerosol composition. From west to east along the English Channel, PM2.5 concentration increases with a mean gradient of about 0.007 μg m-3 km-1. At the westernmost site Plymouth, sea spray is estimated on average to account for 16% of the AOD and 13% of the particulate matter with aerodynamic diameter less than 10 μm (PM10). The importance of sea spray is reduced by at least a factor of two at the more eastern sites. The long-term decrease in aerosol loading along the English Channel appears to be more strongly driven by the reduced anthropogenic emissions, rather than by changes in the large-scale circulation such as the North Atlantic Oscillation. Clean ups in road vehicles and ship emissions, however, do not appear to be strong drivers for the long-term trends in aerosol loading at these coastal sites

Characterization of Smoke/Dust Episode over West Africa: Comparison of MERRA-2 Modeling with Multiwavelength Mie-Raman Lidar Observations

Observations of multiwavelength Mie-Raman lidar taken during the SHADOW field campaign are used to analyze a smoke/dust episode over West Africa on 24-27 December 2015. For the case considered, the dust layer extended from the ground up to approximately 2000 m while the elevated smoke layer occurred in the 2500 m - 4000 m range. The profiles of lidar measured backscattering, extinction coefficients and depolarization ratios are compared with the vertical distribution of aerosol parameters provided by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). The MERRA-2 model simulated the correct location of the near-surface dust and elevated smoke layers. The value of modeled and observed aerosol extinction coefficients at both 355 nm and 532 nm are also rather close. In particular, for the episode reported, the mean value of difference between the measured and modeled extinction coefficients at 355 nm is 0.01 km(exp -1) with standard deviation of 0.042 km(exp -1). The model predicts significant concentration of dust particles inside the elevated smoke layer, which is supported by an increased depolarization ratio of 15% observed in the center of this layer. The modeled at 355 nm the lidar ratio of 65 sr in the near-surface dust layer is close to the observed value (70+/-10) sr. At 532 nm however, the simulated lidar ratio (about 40 sr) is lower than measurements (55+/-8 sr). The results presented demonstrate that the lidar and model data are complimentary and the synergy of observations and models is a key to improve the aerosols characterization

Principales applications des complexes d’inclusion cyclodextrine/substrat

Cyclodextrins are cyclic oligosaccharides obtained by the enzymatic degradation of starch. They possess a hydrophobic cavity that allows them to encapsulate other compounds. The formation of such inclusion complexes, also called "host/guest complexes" is likely to modify the physical, chemical and/or biological characteristics of the guest molecule. This article describes the main applications of cyclodextrin/substrate inclusion complexes through several examples published in the literature. All industrial fields are concerned, from pharmaceuticals to cosmetics, through chromatography, food, medical, biotechnology or catalysis.Les cyclodextrines sont des oligosaccharides cycliques obtenus par dégradation enzymatique de l’amidon. Elles possèdent une cavité hydrophobe qui leur permet d’encapsuler divers composés. La formation de tels complexes d’inclusion dits « hôte/invité » peut modifier les caractéristiques physiques, chimiques et/ou biologiques de la molécule invitée. Cet article décrit les principales applications des complexes d’inclusion cyclodextrine/substrat illustrées par des exemples décrits dans la littérature. Tous les domaines industriels sont concernés, de la pharmacie aux cosmétiques, en passant par la chromatographie, l’agroalimentaire, le médical, les biotechnologies ou encore la catalyse

Synthesis of silica materials containing cyclodextrin and their applications in wastewater treatment

International audienc

Fundamentals and Applications of Cyclodextrins

Cyclodextrins are natural oligosaccharides obtained from starch. They were discovered in 1891 by Villiers, and attracted major scientific and industrial interests from the late 1970s. Actually, cyclodextrins are among the most remarkable macrocyclic molecules with major theoretical and practical interest for chemistry and biology. Cyclodextrins belong to the family of cage molecules due to their structure, which is composed of a hydrophobic cavity that can encapsulate other molecules. Indeed, the most characteristic feature of cyclodextrins is their ability to form inclusion complexes with various molecules through host-guest interactions. Cyclodextrins and their derivatives have a wide variety of practical applications including pharmacy, medicine, foods, cosmetics, toiletries, catalysis, chromatography, biotechnology, nanotechnology, and textile production. Cyclodextrins are also the object of numerous fundamental studies. Between 2011 and 2015, 18,430 cyclodextrin-related publications have been published. In this chapter, after a brief description of cyclodextrin basics, we highlight selected works on cyclodextrins published over the last 5 years by various research groups

Advanced methods of identification of sea-breeze and low-level jet events from near ground measurements with specific implication for energy production by offshore wind farms

International audienceSince an uniform high-speed wind is required for maximum power production, it is important to survey the meteorological phenomena which can boost up or down the power production by offshore wind turbines. The study is focused on developing and validation of advanced methods of detection of such mete-orological phenomena. In situ measurements were performed at an experimental site located in Dunkirk, northern France. The wind variability was measured by Sonic anemometer during a period starting from 11th January 2018 to 18th December 2019. Automatic detection algorithms have been developed to detect sea-breeze (SB) and nocturnal low-level jet (NLLJ) events from Sonic anemometer measurements near ground. The SB detection is based on a recurrent neural network algorithm (RNN). The accuracy of event identifica-tion by this network is 95%. We found 67 and 78 SB days in 2018 and 2019 respectively. NLLJ detection al-gorithms developed, using wavelet transformation methods, show a better performance than other existing methods. A total of 192 and 168 NLLJ days were found in 2018 and 2019 respectively. The wind speed was found higher during the nighttime for NLLJ than for non-NLLJ days, which can increase the peak power pro-duction up to 40 times, compared to normal days. To evaluate the skill of detection algorithms based on ane-mometer measurements, simultaneous Sonic and lidar wind measurements have been done at site for 86-day long period. The wind speed and turbulence kinetic energy were computed from Sonic anemometer and com-pared to the lidar measurements. The comparison suggests that the point measurements by Sonic anemometer can be very useful for the algorithms of automatic detection of meteorological events

130 years of cyclodextrin discovery for health, food, agriculture, and the industry: a review

International audienceCyclodextrins are a group of cyclic oligosaccharides obtained by enzymatic degradation of starch. They are remarkable macrocyclic molecules that have led major theoretical and practical advances in chemistry, biology, biochemistry, health science, and agriculture. Their molecular structure is composed of a hydrophobic cavity that can encapsulate other compounds to form inclusion complexes through host-guest interactions. This unique feature is at the origin of many applications. Cyclodextrins and their derivatives have a wide variety of practical applications in almost all sectors of the industry, including pharmacy, medicine, foods, cosmetics, chromatography, catalysis, biotechnology, and the textile industry. Villiers published the first reference to cyclodextrins in 1891, and since then, these molecules have continued to fascinate academia and industry. Currently, more than 2000 publications on cyclodextrins are published each year. On the occasion of the 130th anniversary of their discovery, in this review, we present an historical overview of the development and applications of cyclodextrins. First, we present the discovery and first chemical studies on cyclodextrins. Then, the main results obtained during the 1911-1970 exploration period are discussed. A third part presents the historical landmarks in the development of cyclodextrins from 1970 to the present day

Turbulence of Landward and Seaward Wind during Sea-Breeze Days within the Lower Atmospheric Boundary Layer

Reynolds stress anisotropy is estimated from the stress spheroids, based on 20 Hz ultrasonic anemometer measurements, performed in the coastal area of northern France, over a 1.5-year long period. Size and shape variation (i.e., prolate, oblate, disk, rod, etc.) of stress spheroids are used for the characterization of energy redistribution by turbulent eddies. The sea-breeze (SB) events were identified using a change in wind direction from seaward (SWD) to landward (LWD) during the day time. We found that the LWD wind creates more turbulent anisotropic states than SWD wind. The prolate-shaped stress spheroids correspond to small-scale turbulence observed during LWD wind, while oblate spheroids are found during SWD winds. Moreover, it was found that during LWD winds, large turbulence kinetic energy (TKE) in the flow field produces large stress spheroids. On the contrary, during SWD winds, a smaller level of TKE is responsible for small-size stress spheroid formation. The average volume of the corresponding Reynolds stress spheroids during the LWD is 13% larger than that of during SWD wind

Machine Learning and Deterministic Methods for Detection Meteorological Phenomena from Ground Measurements: Application for Low-Level Jet and Sea-Breeze Identification in Northern France

This study focused on the detection of mesoscale meteorological phenomena, such as the nocturnal low-level jet (NLLJ) and sea breeze (SB), using automatic deterministic detection wavelet technique algorithms (HWTT and SWT) and the machine learning recurrent neural network (RNN) algorithm. The developed algorithms were applied for detection of NLLJ and SB events from ultrasonic anemometer measurements, performed between January 2018 and December 2019 at a nearshore experimental site in the north of France. Both algorithms identified the SB and NLLJ days successfully. The accuracy of SB event detection by the RNN algorithm attained 95%, and we identified 67 and 78 SB days in 2018 and 2019, respectively. Additionally, a total of 192 and 168 NLLJ days were found in 2018 and 2019, respectively. To demonstrate the capability of the algorithms to detect SB and NLLJ events from near-ground ultrasonic anemometer measurements, analysis of the simultaneous wind lidar measurements available for 86 days were performed. The results show a good agreement between the RNN-based detection method and the lidar observations, detecting 88% of SB. Deterministic algorithms (HWTT and SWT) detected a similar number of NLLJ events and provided high correlation (0.98) with the wind lidar measurements. The meteorological phenomena studied can significantly affect the energy production of offshore wind farms. It was found that the maximum hourly average peak power production could be to 5 times higher than that of the reference day due to higher wind speed observed during NLLJ events. During SB events, hourly average peak power production could be up to 2.5 times higher. In this respect, the developed algorithms applied for analysis, from near-ground anemometer measurements, may be helpful for monitoring and forecasting the meteorological phenomena capable of disturbing the energy production of offshore wind turbines

Machine Learning and Deterministic Methods for Detection Meteorological Phenomena from Ground Measurements: Application for Low-Level Jet and Sea-Breeze Identification in Northern France

This study focused on the detection of mesoscale meteorological phenomena, such as the nocturnal low-level jet (NLLJ) and sea breeze (SB), using automatic deterministic detection wavelet technique algorithms (HWTT and SWT) and the machine learning recurrent neural network (RNN) algorithm. The developed algorithms were applied for detection of NLLJ and SB events from ultrasonic anemometer measurements, performed between January 2018 and December 2019 at a nearshore experimental site in the north of France. Both algorithms identified the SB and NLLJ days successfully. The accuracy of SB event detection by the RNN algorithm attained 95%, and we identified 67 and 78 SB days in 2018 and 2019, respectively. Additionally, a total of 192 and 168 NLLJ days were found in 2018 and 2019, respectively. To demonstrate the capability of the algorithms to detect SB and NLLJ events from near-ground ultrasonic anemometer measurements, analysis of the simultaneous wind lidar measurements available for 86 days were performed. The results show a good agreement between the RNN-based detection method and the lidar observations, detecting 88% of SB. Deterministic algorithms (HWTT and SWT) detected a similar number of NLLJ events and provided high correlation (0.98) with the wind lidar measurements. The meteorological phenomena studied can significantly affect the energy production of offshore wind farms. It was found that the maximum hourly average peak power production could be to 5 times higher than that of the reference day due to higher wind speed observed during NLLJ events. During SB events, hourly average peak power production could be up to 2.5 times higher. In this respect, the developed algorithms applied for analysis, from near-ground anemometer measurements, may be helpful for monitoring and forecasting the meteorological phenomena capable of disturbing the energy production of offshore wind turbines