Matches in Nanopublications for { ?s <http://schema.org/description> ?o ?g. }
- b7601048-d964-4c6f-92ac-f6956817dd44 description "The aim of the project was to to understand and map the use of pesticides and fertilizers in the context of home farming and gardening. Simultaneously, it aimed to disseminate information on the topic with the final aim of reducing the use of pesticides and fertilizers." assertion.
- 02f9d9ac-8775-4508-9b15-5235689f0f4e description "Description of project, process, take aways and impact." assertion.
- 2683e86b-2558-4f67-88bd-ea9197b7c81e description "Project Final Report" assertion.
- 48aed26d-c46e-437f-b62e-6c98df6aaadf description "In My Backyard: On-Site Survey Responses Raw Dataset" assertion.
- 81ca717a-1e5a-4075-a840-3743438fc130 description "In My Backyard is a citizen science project promoted by Rio Neiva – Environmental NGO and its partner CEA – Municipal Centre for Environmental Education, both based in Esposende, Portugal. It was funded through the ACTION project. In My Backyard aimed to understand the use of harmful pesticides and fertilizers in home farming and gardening and uncovering sustainable alternatives practiced within domestic backyards." assertion.
- ed577e68-3589-4206-93ac-63a4f40fd5ae description "Project key insights - Presentation at EU Week of Regions and Cities, 8th October, Session Citizens safeguarding the environment - https://europa.eu/regions-and-cities/programme/sessions/1451_en" assertion.
- 4776fc21-01a3-4806-b248-70a577cbc6b0 description "The Sonic Kayak system is a low cost open hardware for gathering and mapping fine-scale marine environmental data, which has not been previously possible to obtain. Data is sonified through an onboard speaker allowing paddlers to seek out areas of interest and gain real time feedback of the data. At the beginning of the project, the system included underwater temperature sensors and a hydrophone for measuring underwater sound, each recording data every second with GPS, time and date. Working with ACTION, two new environmental sensors have been designed and integrated into the existing system (turbidity and air quality). New data have been gathered and citizens have been engaged in two online citizen science style surveys. In the first one people could try out 4 different data sonification approaches and see which was the most straightforward for understanding the underlying environmental data, and also give their preferences on which sounds they liked the best. In the second one, feedback on the pilot activities were gathered." assertion.
- 30375d66-2993-4579-823b-26b90e0fffc6 description "This post is to let you know about the changes we've made and new things available within the project, and to call for your feedback and thoughts via the survey at the end." assertion.
- 372295f4-02a5-404b-becf-e261f988a6ef description "Originally based on the Sonic Bikes system, a Raspberry Pi based citizen science project where kayaks become musical & scientific instruments for investigating the marine world." assertion.
- 6bc655ee-7e2b-45c9-825b-304e6256c64b description "Sonic Kayaks are rigged with sensors, both underwater (temperature, sound, and turbidity) and above water (air pollution). As the kayaker paddles around, the sensors pick up changes in the environment, and these are played to the kayaker in real time through an on-board speaker." assertion.
- 75751713-39bd-4f15-ac1e-f7d65a5a955e description "These data sets are the result of five trips using Sonic Kayaks to collect data as part of the ACTION Project. The sampling was carried out in the Penryn river, around Falmouth docks and the Helford estuary. A variety of sensors were used:" assertion.
- a5056b12-fa95-4777-914e-ce04c53500a6 description "Post that describes the device" assertion.
- b34b8b18-c6dd-4368-b9ba-39931bd8d2b4 description "Collection of maps based on the measurements taken by devices" assertion.
- d6ad4d13-2fcd-4e48-b5a1-e97a1de90e5b description "Magizine of Rasberry Pi projects. It includes an article about Sonic Kayacs" assertion.
- 370d93ab-df01-46de-982e-0ef74b3acf8a description "The Noise Maps project focused on deploying a citizen science process in the neighborhoods of Sagrada Familia and the Raval (Barcelona) to address the challenge of noise pollution, a serious problem related to health problems (lack of sleep, psychological ailments, cardiovascular disease, risk of higher stroke) and negative social effects (weakness of social cohesion and coexistence, reduced quality of life, loss of cultural diversity). Noise pollution was an urgent problem in the pilot areas, with active community groups on the lookout for a solution to help improve their living conditions." assertion.
- 1c88a369-bddc-4c85-86ab-3ec43988470f description "Platform of the Geographical Institute of Catalunya" assertion.
- 325cc034-a394-4caa-8c08-c502de58ca87 description "Dashboards created in Grafana to visualze data" assertion.
- 45234eb7-d500-4a56-b2db-460ebedc2b8f description "Collection of ambient urban ourdoors audios from Raval" assertion.
- 4bcf424f-f5a0-4d27-8ff8-eda67bf38827 description "The Noise Maps project focused on deploying a citizen science process in the Barcelona neighborhoods of Sagrada Familia and the Raval to address the challenge of noise pollution. The sound data was generated between May and September 2020." assertion.
- 5ddb1f2b-a2a7-4eed-9393-9ad7be4f8a58 description "This repository contains an AudioMoth firmware adaptation to calculate the Sound Pressure Level (SPL). This is based on the 1.3.0 version of AudioMoth firmware (published on AudioMoth-Project and AudioMoth-Firmware-Basic). We include the SPL library (src/spl.c and inc/spl.h) that implement all the functions related to the SPL estimation." assertion.
- a114366b-3b77-45f6-be84-63ae42e9f0aa description "This presentation will help ACTION pilots to create their own dashboards" assertion.
- a48a8958-311d-42d8-98fe-28ec802cfbe9 description "Results were presented in the ArsElectronica 2020 congress" assertion.
- 959fa202-b251-4fcd-8d5f-8ed83740fe43 description "Norway is the land of fjords, trolls and – electric cars. By actively promoting the purchase of electric cars, the Norwegian government is aiming at protecting the environment and not least improving air quality, especially in urban areas. Air quality is still a reason for concern in many European countries, including the Nordic countries. Not many people are aware of this fact, and this is where the Norwegian pilot of the ACTION project comes in. The pilot gives high school students in Oslo and the larger Oslo area the opportunity to design and carry out their own air quality projects, using an off-the-shelf air quality sensor platform. The aim is to create awareness about the sources of air pollution, make the students think of ways to reduce both emission and exposure and teach them scientific working methods. We use the Nova SDS011 sensor for measuring PM2.5 and PM10 that is transmitting data to an Arduino board. The data can be obtained through an SD card." assertion.
- 0f121a00-24ef-44cc-a261-f039512ef7ac description "Forskningsprosjekt luftforurensning" assertion.
- 227345a8-a64b-4653-a1dd-75f8121050e8 description "This poster has been created by students of the Ullern Upper Secondary School, located in Oslo, Norway." assertion.
- 52dff073-d2fb-441d-afee-7e97f1ab1655 description "Measurements taken by a DIY sensor designed by the project air:bit (http://airbit.uit.no/#english). Measurements were taken by students of the school Lambertseter VGS, located in the district of Nordstrand in Oslo, Norway." assertion.
- 629de82e-6a07-4577-9653-88119aa38a27 description "This poster has been created by students of the Ullern Upper Secondary School, located in Oslo, Norway." assertion.
- 71e19d7c-93ac-4ebf-a6a8-f4280e98be9c description "Firmware of an Arduino board integrated with a Nova SDS011 sensor for measuring PM2.5 and PM10. The data can be obtained through an SD card." assertion.
- c953024b-6a89-4a8b-92b6-3532a608a7c6 description "This deliverable serves as a handbook for air quality projects in high schools. It contains information about the ACTION air quality pilot in high schools, tips and lessons learned as well as material that has been used and created within the high school projects." assertion.
- fb5ff35b-cfe1-4415-abd0-122ec94b3539 description "Measurements taken by a DIY sensor (Sensor 2) designed by the project air:bit (http://airbit.uit.no/#english). Measurements were taken by students of the school Lambertseter VGS, located in the district of Nordstrand in Oslo, Norway." assertion.
- 0c470650-84d9-40e1-bc80-4591a27f6c4d description "Aromatic compounds are those chemical compounds (most commonly organic) that contain one or more rings with pi electrons delocalized all the way around them. In contrast to compounds that exhibit aromaticity, aliphatic compounds lack this delocalization. The term "aromatic" was assigned before the physical mechanism determining aromaticity was discovered, and referred simply to the fact that many such compounds have a sweet or pleasant odour; however, not all aromatic compounds have a sweet odour, and not all compounds with a sweet odour are aromatic compounds. Aromatic hydrocarbons, or arenes, are aromatic organic compounds containing solely carbon and hydrogen atoms. The configuration of six carbon atoms in aromatic compounds is called a "benzene ring", after the simple aromatic compound benzene, or a phenyl group when part of a larger compound. Not all aromatic compounds are benzene-based; aromaticity can also manifest in heteroarenes, which follow Hückel's rule (for monocyclic rings: when the number of its π electrons equals 4n + 2, where n = 0, 1, 2, 3, ...). In these compounds, at least one carbon atom is replaced by one of the heteroatoms oxygen, nitrogen, or sulfur. Examples of non-benzene compounds with aromatic properties are furan, a heterocyclic compound with a five-membered ring that includes a single oxygen atom, and pyridine, a heterocyclic compound with a six-membered ring containing one nitrogen atom." assertion.
- 90e03a9e-5748-4640-a657-f71298b2ff57 description "In organic chemistry, a homologous series is a sequence of compounds with the same functional group and similar chemical properties in which the members of the series can be branched or unbranched, or differ by -CH2.[1] This can be the length of a carbon chain,[1] for example in the straight-chained alkanes (paraffins), or it could be the number of monomers in a homopolymer such as amylose.[2] Compounds within a homologous series typically have a fixed set of functional groups that gives them similar chemical and physical properties. (For example, the series of primary straight-chained alcohols has a hydroxyl at the end of the carbon chain.) These properties typically change gradually along the series, and the changes can often be explained by mere differences in molecular size and mass. The name "homologous series" is also often used for any collection of compounds that have similar structures or include the same functional group, such as the general alkanes (straight and branched), the alkenes (olefins), the carbohydrates, etc. However, if the members cannot be arranged in a linear order by a single parameter, the collection may be better called a "chemical family" or "class of homologous compounds" than a "series"." assertion.
- b5b86f8e-5c07-4b43-9b21-6df3bb6ebe72 description "Street Spectra is a citizen science project to map and characterize public lighting sources. Volunteers use a low cost diffraction grating on top of their smartphones’ camera to take pictures of the street lamps and their emission spectra." assertion.
- 1880a929-c2d8-4ca4-a587-248ba0fade49 description "Application in Epicollect to collect data" assertion.
- 1f63a9a8-e073-42cf-9fad-6c567b4f3382 description "This document describes the different templates that are going to be developed in ACTION for helping pilots to export/use external platforms. Also, a new tool to create Data Management Plan documents based on a questionnaire will be described. Finally, a mini guide has been included to help users to create a CS project using the external platforms Epicollect and Zooniverse." assertion.
- b4f1f691-6b50-42fc-adc1-88e7dbf61219 description "This lesson plan is to be used in the classroom of 12 and 13 years old students and aims to educate its users on the topic of light pollution. Aside from gaining awareness, the students will be introduced to the Street Spectra citizen science project through which they will learn how to analyze and classify sources of light pollution contributing to science as a citizen scientist. https://streetspectra.actionproject.eu/ These pages will discuss: artificial light at night in general, different types of light pollution, their negative effects as well as the most efficient way to install lighting sources in such a way that any negative impact is minimized. The Street Spectra project with its objectives as well as its relationship to citizen science are explained during the course. Theory is accompanied with suggested activities adapted to the level of the students. With this unit the authors intend to gather contents that can be implemented in the classroom, and which can serve as a guide so that both students and teachers can participate in this citizen science project. In order for a citizen science project to grow the input of researchers, disseminators and a wide range of volunteers are needed. The participation of the students and teachers will directly help the study of light pollution." assertion.
- b5ef6b92-6204-44a1-8417-c234cb37a908 description "This document explains all the steps to obtain the spectra of street lights, how to determine their nature, and also how to contribute with this information to the StreetSpectra citizen science project. The first sections are devoted to introduce the StreetSpectra project, and also the light pollution (LP) problem. We have also included some of the science basics (LP and simple physics of spectra)." assertion.
- 97985638-81ed-42b1-ae48-ab432f14db52 description "Supplementary materials of the paper "A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective - The Adriatic Sea experience" Oceanologia 2022 (in press)" assertion.
- 13ab86ae-dfbf-40f2-beff-5ed8683bcf04 description "UML structure of the theme Geophysics shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system" assertion.
- 160b1eb5-a7bc-4a0b-a7e7-2c1468a61f6e description "UML structure of the theme Seafloor Mapping shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system" assertion.
- 3996852d-7831-4dcd-8545-08fdc1ecdc58 description "UML structure of the theme Water Column shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system" assertion.
- 4889392e-3921-44d3-bc27-790b285fd695 description "UML structure built in Enterprise Architect (© Sparx System) representing the theme “Water Column”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes." assertion.
- 5bf32ec6-68c6-4e6f-b6fc-85a580acf32d description "UML structure built in Enterprise Architect (© Sparx System) representing the theme “Geology”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes." assertion.
- 5de6e1a4-a358-461c-a32a-242d2d61595f description "The table shows the complete evaluation of the FAIR principles related to the Marine Spatial Data Infrastructure (MSDI) focus of the scientific paper" assertion.
- 7590862a-8a88-475b-8f9e-7609e6b0edbd description "UML structure built in Enterprise Architect (© Sparx System) representing the theme “Geophysics”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes." assertion.
- 99cdc922-dd1c-44d8-935a-91ce27b7f841 description "UML structure of the theme Geology shared as .xml file and WGS 1984 Web Mercator (Auxiliary sphere) as reference system" assertion.
- bf860bfe-76ac-48b7-aca6-91ad8490b20f description "UML structure built in Enterprise Architect (© Sparx System) representing the theme “Seafloor Mapping”. Boxes represent Feature datasets (yellow) Feature classes (orange), Object classes (green), and Raster Catalogues (pink), while continuous lines portray the relationships between classes." assertion.
- d767e6c3-6cfd-4e68-a989-0b4cbe9236b5 description "Trajectory model (backwards) such as FLEXPART or HYSPLIT or LAGRANTO. Backward trajectories to find origin of events where high concentrations of pollutants have been found" assertion.
- 8fb3edaf-c8f3-4a5d-9ed0-b09bb88dae9a description "Contains information and links to input data that are needed for executing this executable RO" assertion.
- cfbabab0-137e-46cd-ae0e-22f87604b6f1 description "Contains the tools used for executing this RO" assertion.
- d20ff2aa-af5c-4924-a3f4-7f09e3d04185 description "Contains all the outputs generated by this executable RO" assertion.
- f663c0e0-9c12-41a7-af85-17d1b93fa3fd description "Contains bibliographic resources related to this RO" assertion.
- df54b782-824b-421a-8449-39ea2e31e8a2 description "The Lagrangian particle dispersion model FLEXPART in its original version in the mid-1990s was designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as those released after an accident in a nuclear power plant. Over the past decades, the model has evolved into a comprehensive tool for multi-scale atmospheric transport modeling and analysis and has attracted a global user community. This version of FLEXPART (10.4) works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS)." assertion.
- 5f2e7ab8-8122-41b3-8d53-060e09855fa7 description "Compare CAMS analysis with available observations on specific locations." assertion.
- 5a148d13-8ed9-4065-94c2-640d4d58919f description "Contains the tools used for executing this RO" assertion.
- 5b84c130-66d7-4c88-ae28-c3ec3eefa2ff description "contains information and links to input data that are needed for executing this executable RO" assertion.
- d5db85d6-b4e7-41bc-a419-56dc83b0c575 description "Contains bibliographic resources related to this RO" assertion.
- e82b7efa-bc39-46a5-aa13-380b8624fa60 description "Contains all the outputs generated by this executable RO" assertion.
- 03e3106c-2500-47d2-8945-27ccb9035599 description "Norway - Hurdal (NO0056R) - low_vol_sampler - pm25_mass - pm25 [2014.01.13-2021.01.04]" assertion.
- 0d3223a9-f663-4eed-a105-5f484b65596e description "Working progress. This Jupyter notebook is in Github and is regularly updated." assertion.
- 0f1890e0-3083-4bf7-bb4d-f2b1f999eac8 description "Norway - Hurdal (NO0056R) - low_vol_sampler - pm25_mass - pm25 [2014.01.13-2021.01.04]" assertion.
- 2459dc9b-fcad-4673-be8c-f0fec599f73f description "NO2 timeseries at a given location and for different years (2019, 2020, 2021). Here the timeseries is for NO2 in Madrid." assertion.
- 60c99a6a-0ac6-45d1-9dc7-9d62ebd03f8e description "This is the rendered version (with jupyter-book) of the Jupyter Notebook comparing CAMS and air quality measurements." assertion.
- 63c02ce2-3601-495d-ae40-3493af5b3609 description "Copernicus Atmosphere Time Series of O3 over several location before, during and after then lockdown (March-June 2019, 2020 and 2021)" assertion.
- 7fe38fd0-dc1e-4781-a2a1-c485b0308e0e description "CAMS timeseries of NO2 for 2019, 2020 and 2021 at selected locations" assertion.
- 9637df5a-e396-4d0d-b128-da562185c397 description "Copernicus Atmosphere Time Series of NO2 over several location before, during and after then lockdown (March-June 2019, 2020 and 2021)" assertion.
- 9d6d5b1c-c6f1-4ec1-9041-b78cdc600722 description "Information about EBAS data format" assertion.
- bd43e070-f469-4d65-acc4-799b9a527f7e description "Copernicus Atmosphere Time Series of PM2.5 over several location before, during and after then lockdown (March-June 2019, 2020 and 2021)" assertion.
- 66613047-4680-459d-8c60-509c349830d5 description "This report puts together some of the results obtained on the subject and summarizes the main findings. In particular, it reuses other Research Objects we or other have created, including automatically generated Bibliographic Research objects." assertion.
- e43d2b1c-0d14-4d3d-8500-393fb10610c6 description "Work in progress" assertion.
- 67edd16f-c3d8-4879-a3a5-2d223e7dce6d description "Galaxy workflow and executed Galaxy histories for getting air quality "metrics" from CADS on a specified location" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 description "Collection of scientific articles and other communications related to the impact of COVID-19 pandemic lockdown on air quality pollution." assertion.
- 116dfa4e-7918-403c-b4ef-1f995d91c775 description "The COVID-19 pandemic led to dramatic changes in economic activity in 2020. In this paper the authors use estimates of emission changes for 2020 in two Earth System Models (ESMs) to simulate the impacts of the COVID-19 economic changes." assertion.
- 1189b793-4040-49f4-bd1a-de0a03b83de8 description "The COVID-19 pandemic has affected severely the economic structure and health care system, among others, of India and the rest of the world. The magnitude of its aftermath is exceptionally devastating in India, with the first case reported in January 2020, and the number has risen to ~31.3 million as of July 23, 2021. India imposed a complete lockdown on March 25, which severely impacted migrant population, industrial sector, tourism industry, and overall economic growth. Herein, the impacts of lockdown and unlock phases on ambient atmospheric air quality variables have been assessed across 16 major cities of India covering the north-to-south stretch of the country. In general, all assessed air pollutants showed a substantial decrease in AQI values during the lockdown compared with the reference period (2017–2019) for almost all the reported cities across India. On an average, about 30–50% reduction in AQI has been observed for PM2.5, PM10, and CO, and maximum reduction of 40–60% of NO2 has been observed herein, while the data was average for northern, western, and southern India. SO2 and O3 showed an increase over a few cities as well as a decrease over the other cities. Maximum reduction (49%) in PM2.5 was observed over north India during the lockdown period. Furthermore, the changes in pollution levels showed a significant reduction in the first three phases of lockdown and a steady increase during subsequent phase of lockdown and unlock period. Our results show the substantial effect of lockdown on reduction in atmospheric loading of key anthropogenic pollutants due to less-to-no impact from industrial activities and vehicular emissions, and relatively clean transport of air masses from the upwind region. These results indicate that by adopting cleaner fuel technology and avoiding poor combustion activities across the urban agglomerations in India could bring down ambient levels of air pollution at least by 30%." assertion.
- 6021e080-1951-495c-97d1-3fd1b4e3b0bb description "There is a strong body of evidence to show how air pollution affects different aspects of health at even lower concentrations than previously understood. But here’s what hasn’t changed: every year, exposure to air pollution is still estimated to cause millions of deaths and the loss of healthy years of life." assertion.
- 729c0b49-df8a-42de-8059-1a220c51f353 description "In this paper the authors quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Their estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality." assertion.
- 9c87dcef-0fd1-4225-8134-a79e4eaa2da3 description "Waste natural gas from industrial oil and gas fields could be a source of nitrogen dioxide and black carbon pollution, according to new research." assertion.
- a64fed5b-e924-4d23-b778-9832e56115bc description "This study has used, in a first stage, Sentinel-5P and CAMS service to analyze the air quality in the territory of Iberian Peninsula, as well as assess in detail major cities within the region (Lisbon, Porto and Madrid), for a period from January 2018 to April 2020. On a later stage, the data from Sentinel-3A and 3B allowed the analysis of water quality during the months March, April and May 2020, in the Portuguese coast. Regarding the air quality, NO2 and PM10 levels in the Iberian Peninsula were consistently lower compared to the same periods in the two past years." assertion.
- acb77822-d471-413d-a825-ad569e28f4dd description "In this work the authors investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread. Part of the analysis employs a variety of machine learning tools." assertion.
- b37476c6-244e-4b80-8268-03c5fc1c7de6 description "Paris coronavirus. Man wearing a mask walking in front of the Eiffel Tower on the first day of Paris lock-down. Photo by The Paris Photographer on Unsplash" assertion.
- bc3d57ea-d82b-4261-9af7-24e949d1d5f5 description "Concawe has undertaken a city-level analysis to quantify the ways in which the Covid-19 lockdown measures have had an impact on air quality in Europe. This article presents the results of the analysis for particulate matter (PM 2.5 ), nitrogen dioxide (NO2 ) and ozone (O3 )." assertion.
- bc6922f1-debf-46ae-ba79-d574f6f5c064 description "To assess the impact of the COVID-19 pandemic lockdown on air quality worldwide, Air Quality Index (AQI) data was used to estimate the change in air quality in 20 major cities on six continents. Our results show significant declines of AQI in NO2 , SO2 , CO, PM 2.5 and PM 10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown." assertion.
- 53aa90bf-c593-4e6d-923f-d4711ac4b0e1 description "The COVID-19 pandemic has led to significant reductions in economic activity, especially during lockdowns. Several studies has shown that the concentration of nitrogen dioxyde and particulate matter levels have reduced during lockdown events. Reductions in transportation sector emissions are most likely largely responsible for the NO2 anomalies. In this study, we analyze the impact of lockdown events on the air quality using data from Copernicus Atmosphere Monitoring Service over Europe and at selected locations." assertion.
- 4b86d722-c40d-45a6-b200-c76ea7b08452 description "This folder contains the Jupyter Notebooks used for this analysis" assertion.
- 7e3b7179-2cb2-4f8c-8bb7-23cd6c7109b1 description "This folder contains data and information generated by the Jupyter notebooks." assertion.
- 7f6fe5ab-bbae-4acc-a941-7196619a9489 description "Bibliography and other references used for this work." assertion.
- e5aad685-6a07-4492-8ad3-7cdf00e2e306 description "Information about the input data used for executing the Jupyter notebook" assertion.
- 0fc39920-a4b1-40e6-8154-922c11648611 description "Rendered notebook showing the impact of the Covid-19 Lockdown on Air quality over Europe" assertion.
- 14abf6e3-4ecc-462e-9aa4-4c8a925c304a description "Study the impact of the lockdown during the covid-19 pandemic on different cities in Europe." assertion.
- 1afcaec9-3b79-42d4-bb87-4349579eaac2 description "PM2.5 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube" assertion.
- 1d453d7c-72a5-4b23-88a1-4b34fc73a0b0 description "Link to ADAM viewer to explore NO2 CAMS data cube collection." assertion.
- 1fbe484e-51ae-4e16-ac6f-60aa06e298ba description "Plot over Europe of surface NO2 on May 1st, 2020" assertion.
- 25c2aa47-dc11-41b4-ad96-b516e66c628a description "O3 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube" assertion.
- 499fb80e-b2a8-4263-811f-bdea94730e57 description "Maps of the 3-yearly averages (done over the period March to June) for NO2, PM2.5 and O3." assertion.
- 7781b8c4-96f3-4310-878c-ce669b13a95e description "Maximum NO2 values over Europe during March-June for 3 different years: 2019 (before pandemic), 2020 (during the lockdown) and 2021 (after the lockdown)." assertion.
- 79d1e316-23f5-4d93-ba9c-b4e48c291797 description "CAMS European air quality forecasts: Ozone (O3, µg m-3) startDate: 2018-07-12T00:00:00Z endDate: 2022-10-19T23:00:00Z" assertion.
- c7b8d37a-3cf5-4fb4-8464-99f6e9e4a8aa description "Comparisons of PM2.5 changes for 2019, 2020 and 2021." assertion.
- d813c1f0-667a-49f5-996a-9226b922f39b description "PM10 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube." assertion.
- e5bcb8af-fcbc-4173-8a10-7501a02db5ab description "NO2 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube." assertion.
- f39bd071-a179-4718-a36f-c8bc1cbe3072 description "CAMS PM2.5, Data Cube Collection" assertion.