Found 2647 publications. Showing page 52 of 265:
The Arctic middle atmosphere was affected by major sudden stratospheric warmings (SSW) in February 2018 and January 2019, respectively. In this article, we report for the first time the impact of these two events on the middle atmospheric nitric oxide (NO) abundance. The study is based on measurements obtained during two dedicated observation campaigns, using the Sub-Millimetre Radiometer (SMR) aboard the Odin satellite, measuring NO globally since 2003. The SSW of February 2018 was similar to other, more dynamically quiet, Arctic winters in term of NO downward transport from the upper mesosphere–lower thermosphere to lower altitudes (referred to as energetic particle precipitation indirect effect EPP-IE). On the contrary, the event of January 2019 led to one of the strongest EPP-IE cases observed within the Odin operational period. Important positive NO anomalies were indeed observed in the lower mesosphere–upper stratosphere during the three months following the SSW onset, corresponding to NO volume mixing ratios more than 50 times higher than the climatological values. These different consequences on the middle atmospheric composition are explained by very different dynamical characteristics of these two SSW events.
Elsevier
2021
10-year satellite-constrained fluxes of ammonia improve performance of chemistry transport models
In recent years, ammonia emissions have been continuously increasing, being almost 4 times higher than in the 20th century. Although an important species, as its use as a fertilizer sustains human living, ammonia has major consequences for both humans and the environment because of its reactive gas-phase chemistry that makes it easily convertible to particles. Despite its pronounced importance, ammonia emissions are highly uncertain in most emission inventories. However, the great development of satellite remote sensing nowadays provides the opportunity for more targeted research on constraining ammonia emissions. Here, we used satellite measurements to calculate global ammonia emissions over the period 2008–2017. Then, the calculated ammonia emissions were fed to a chemistry transport model, and ammonia concentrations were simulated for the period 2008–2017.
The simulated concentrations of ammonia were compared with ground measurements from Europe, North America and Southeastern Asia, as well as with satellite measurements. The satellite-constrained ammonia emissions represent global concentrations more accurately than state-of-the-art emissions. Calculated fluxes in the North China Plain were seen to be more increased after 2015, which is not due to emission changes but due to changes in sulfate emissions that resulted in less ammonia neutralization and hence in larger atmospheric loads. Emissions over Europe were also twice as much as those in traditional datasets with dominant sources being industrial and agricultural applications. Four hot-spot regions of high ammonia emissions were seen in North America, which are characterized by high agricultural activity, such as animal breeding, animal farms and agricultural practices. South America is dominated by ammonia emissions from biomass burning, which causes a strong seasonality. In Southeastern Asia, ammonia emissions from fertilizer plants in China, Pakistan, India and Indonesia are the most important, while a strong seasonality was observed with a spring and late summer peak due to rice and wheat cultivation. Measurements of ammonia surface concentrations were better reproduced with satellite-constrained emissions, such as measurements from CrIS (Cross-track Infrared Sounder).
2021
This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23 % to −37 % in clean to slightly polluted conditions but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2 Pmolec cm−2, i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec cm−2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm−2) but exceed those for the tropospheric column data (0.7 Pmolec cm−2). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm−2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.
2021
Similarities and differences in the submicron atmospheric aerosol chemical composition are analyzed from a unique set of measurements performed at 21 sites across Europe for at least one year. These sites are located between 35 and 62°N and 10° W – 26°E, and represent various types of settings (remote, coastal, rural, industrial, urban). Measurements were all carried out on-line with a 30-min time resolution using mass spectroscopy based instruments known as Aerosol Chemical Speciation Monitors (ACSM) and Aerosol Mass Spectrometers (AMS) and following common measurement guidelines. Data regarding organics, sulfate, nitrate and ammonium concentrations, as well as the sum of them called non-refractory submicron aerosol mass concentration ([NR-PM1]) are discussed. NR-PM1 concentrations generally increase from remote to urban sites. They are mostly larger in the mid-latitude band than in southern and northern Europe. On average, organics account for the major part (36–64%) of NR-PM1 followed by sulfate (12–44%) and nitrate (6–35%). The annual mean chemical composition of NR-PM1 at rural (or regional background) sites and urban background sites are very similar. Considering rural and regional background sites only, nitrate contribution is higher and sulfate contribution is lower in mid-latitude Europe compared to northern and southern Europe. Large seasonal variations in concentrations (μg/m³) of one or more components of NR-PM1 can be observed at all sites, as well as in the chemical composition of NR-PM1 (%) at most sites. Significant diel cycles in the contribution to [NR-PM1] of organics, sulfate, and nitrate can be observed at a majority of sites both in winter and summer. Early morning minima in organics in concomitance with maxima in nitrate are common features at regional and urban background sites. Daily variations are much smaller at a number of coastal and rural sites. Looking at NR-PM1 chemical composition as a function of NR-PM1 mass concentration reveals that although organics account for the major fraction of NR-PM1 at all concentration levels at most sites, nitrate contribution generally increases with NR-PM1 mass concentration and predominates when NR-PM1 mass concentrations exceed 40 μg/m³ at half of the sites.
Elsevier
2021
2021
DNA damage and repair activity are often assessed in blood samples from humans in different types of molecular epidemiology studies. However, it is not always feasible to analyse the s#38les on the day of collection without any type of storage. For instance, certain studies use repeated sampling of cells from the same subject or samples from different subjects collected at different time-points, and it is desirable to analyse all these samples in the same comet assay experiment. In addition, flawless comet assay analyses on frozen samples opens up for the possibility of using this technique on biobank material. In this article we discuss the use of cryopreserved peripheral blood mononuclear cells (PBMCs), buffy coat (BC) and whole blood (WB) for analysis of DNA damage and repair using the comet assay. The published literature and the authors’ experiences indicate that various types of blood samples can be cryopreserved with only minor effect on the basal level of DNA damage. There is evidence to suggest that WB and PBMCs can be cryopreserved for several years without much effect on the level of DNA damage. However, care should be taken when cryopreserving WB and BCs. It is possible to use either fresh or frozen samples of blood cells, but results from fresh and frozen cells should not be used in the same dataset. The article outlines detailed protocols for the cryopreservation of PBMCs, BCs and WB samples.
Oxford University Press
2021
The long-term time trends of atmospheric pollutants at eight Arctic monitoring stations are reported. The work was conducted under the Arctic Monitoring and Assessment Programme (AMAP) of the Arctic Council. The monitoring stations were: Alert, Canada; Zeppelin, Svalbard; Stórhöfði, Iceland; Pallas, Finland; Andøya, Norway; Villum Research Station, Greenland; Tiksi and Amderma, Russia. Persistent organic pollutants (POPs) such as α- and γ-hexachlorocyclohexane (HCH), polychlorinated biphenyls (PCBs), α-endosulfan, chlordane, dichlorodiphenyltrichloroethane (DDT) and polybrominated diphenyl ethers (PBDEs) showed declining trends in air at all stations. However, hexachlorobenzene (HCB), one of the initial twelve POPs listed in the Stockholm Convention in 2004, showed either increasing or non-changing trends at the stations. Many POPs demonstrated seasonality but the patterns were not consistent among the chemicals and stations. Some chemicals showed winter minimum and summer maximum concentrations at one station but not another, and vice versa. The ratios of chlordane isomers and DDT species showed that they were aged residues. Time trends of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) were showing decreasing concentrations at Alert, Zeppelin and Andøya. The Chemicals of Emerging Arctic Concern (CEAC) were either showing stable or increasing trends. These include methoxychlor, perfluorohexane sulfonic acid (PFHxS), 6:2 fluorotelomer alcohol, and C9-C11 perfluorocarboxylic acids (PFCAs). We have demonstrated the importance of monitoring CEAC before they are being regulated because model calculations to predict their transport mechanisms and fate cannot be made due to the lack of emission inventories. We should maintain long-term monitoring programmes with consistent data quality in order to evaluate the effectiveness of chemical control efforts taken by countries worldwide.
Elsevier
2021
American Meteorological Society (AMS)
2021
Implementing Citizen Science in Primary Schools: Engaging Young Children in Monitoring Air Pollution
Most European cities have air pollution levels that exceed the threshold for human health protection. Children are sensitive to air pollution and thus it is important to ensure they are not exposed to high concentrations of air pollutants. In order to make a positive change toward cleaner air, a joint effort is needed, involving all civil society actors. Schools and local communities have a decisive role, and can, for example, become engaged in citizen science initiatives and knowledge coproduction. In 2019, with the aim of raising awareness for air quality, NILU developed a citizen science toolbox to engage primary schools in monitoring air quality using a simple and affordable measuring method based on paper and petroleum jelly. This is a very visual method, where the students can clearly see differences from polluted and non-polluted places by looking at “how dirty” is the paper. In addition to the qualitative analysis, we have developed an air meter scale making possible for the students to obtain an indicative measurement of the air pollution level. The comparison between the paper and petroleum jelly method against reference PM10 data collected at two official air quality stations showed a good agreement. The method is a strong candidate for dust monitoring in citizen science projects, making participation possible and empowering people with simple tools at hand. The toolbox is targeted at primary schools and children aged 6–12 years, although it can easily be adapted to other age groups. The main objective of the toolbox is to involve young children who are usually not targeted in air quality citizen science activities, to develop research skills and critical thinking, as well as increase their awareness about the air they breathe. The toolbox is designed to engage students in hands-on activities, that challenge them to create hypotheses, design scientific experiments, draw conclusions and find creative solutions to the air pollution problem. The toolbox includes all the necessary material for the teachers, including guidance, background information and templates facilitating the incorporation in the school curricula. The toolbox was launched as part of the Oslo European Green Capital in March 2019 and was later included as part of the European Clean Air Day initiative coordinated by the European Citizen Science Association (ECSA) working group on air quality. A total of 30 schools and 60 4th grade classes (aged 8–9 years) participated in the Oslo campaign. The citizen science approach employed in the schools, combined the four key elements that promote knowledge integration: elicit ideas, add new ideas, distinguish among ideas and reflect and sort out ideas. Although the main goal of the study was to provide simple but robust tools for engaging young children in air quality monitoring, we also carried out ex-ante and ex-post evaluations in 12 of the participating classes using a 10-question multiple choice test to have an indication of the contribution of the activity to knowledge integration. The results show that there is an increase in the number of correct answers, as well as a reduction in the misconceptions after conducting the activity. These results indicate that applying a citizen science approach improved science instruction and helped knowledge integration by including students' views and taking advantage of the diverse ideas students generated. Citizen science gives learners an insight into the ways that scientists generate solutions for societal problems. But more important, citizen science provides a way to differ from the classic view of the learner as an absorber of information, by considering the social context of instruction and making the topic personally relevant.
Frontiers Media S.A.
2021
Instead of a flag valid/non-valid usually proposed in the quality control (QC) processes of air quality (AQ), we proposed a method that predicts the p-value of each observation as a value between 0 and 1. We based our error predictions on three approaches: the one proposed by the Working Group on Guidance for the Demonstration of Equivalence (European Commission (2010)), the one proposed by Wager (Journal of Machine Learning Research, 15, 1625–1651 (2014)) and the one proposed by Lu (Journal of Machine Learning Research, 22, 1–41 (2021)). Total Error framework enables to differentiate the different errors: input, output, structural modeling and remnant. We thus theoretically described a one-site AQ prediction based on a multi-site network using Random Forest for regression in a Total Error framework. We demonstrated the methodology with a dataset of hourly nitrogen dioxide measured by a network of monitoring stations located in Oslo, Norway and implemented the error predictions for the three approaches. The results indicate that a simple one-site AQ prediction based on a multi-site network using Random Forest for regression provides moderate metrics for fixed stations. According to the diagnostic based on predictive qq-plot and among the three approaches used in this study, the approach proposed by Lu provides better error predictions. Furthermore, ensuring a high precision of the error prediction requires efforts on getting accurate input, output and prediction model and limiting our lack of knowledge about the “true” AQ phenomena. We put effort in quantifying each type of error involved in the error prediction to assess the error prediction model and further improving it in terms of performance and precision.
MDPI
2021