Matches in Nanopublications for { ?s ?p ?o <https://w3id.org/np/RA14jLK0VGzQFeoD6z7ypPPUOa2QrnzW7a7yqi8cZngyo/assertion>. }
- 8fb0d0f3-cfc3-4b09-8920-7837cbe3964d type MediaObject assertion.
- 92623c2c-5dd7-4f49-9175-0a67bdb2e549 type MediaObject assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd type MediaObject assertion.
- 957715d6-ad3e-4ef8-bcd7-e993c0f5d6ef type MediaObject assertion.
- a7079779-ea87-43ca-a840-a2a7a0cf246d type MediaObject assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 type MediaObject assertion.
- 00vn06n10 type Organization assertion.
- 8546b63a-68d5-48fe-a174-a70a9b462d92 type Place assertion.
- c84ef3c6-8dcb-4c90-81cb-a5a8f0fefe44 type Place assertion.
- f631ebeb-31a9-44b6-8424-c15766c875fa type Place assertion.
- fc8cb233-3e99-4b7f-a845-59301e87cebf type Place assertion.
- 4a946757-943c-4bf8-986e-d8984276aed1 type Geometry assertion.
- b2b4129f-0457-4816-9717-c496bc1ef497 type Geometry assertion.
- f557d130-b63e-46cd-868b-6144d36da7c5 type Geometry assertion.
- 424a95ac-8243-40ef-86c6-0e0648c7765e type Geometry assertion.
- 3f5b49d5-6f99-4ddb-88ac-4b662e8edd90 type community assertion.
- 8dbdc420-0063-452d-ada6-2679c3137cd9 type community assertion.
- 4a946757-943c-4bf8-986e-d8984276aed1 type Polygon assertion.
- b2b4129f-0457-4816-9717-c496bc1ef497 type Polygon assertion.
- f557d130-b63e-46cd-868b-6144d36da7c5 type Polygon assertion.
- 424a95ac-8243-40ef-86c6-0e0648c7765e type Polygon assertion.
- mailto:rpalma@man.poznan.pl type Agent assertion.
- 0000-0003-2388-0744 type Agent assertion.
- 0000-0001-6084-1504 type Agent assertion.
- 0000-0003-0745-4155 type Agent assertion.
- 0000-0002-2736-0052 type Agent assertion.
- enrichment_service-account-enrichment type Agent assertion.
- mailto:mantovani@meeo.it type Agent assertion.
- 00vn06n10 type Agent assertion.
- 0000-0002-1784-2920 type Agent assertion.
- 0000-0002-8763-1643 type Agent assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 mainEntity "Jupyter Notebook" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 isFinalized "False" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 isSnapshotOf "https://w3id.org/ro-id/998dccd6-7192-4d88-af39-6018c71e6bdf" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 snapshotedAtTime "2023-02-19 13:23:07.862706+00:00" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 snapshotedBy "https://orcid.org/0000-0002-1784-2920" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 doi "https://doi.org/10.24424/656f-rf51" assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 dataType "Float32" assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd dataType "Float32" assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 dataType "Float32" assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 datasetManager "mailto:govoni@meeo.it" assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd datasetManager "mailto:govoni@meeo.it" assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 datasetManager "mailto:govoni@meeo.it" assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd maxValue "[1.354510459350422e-07]" assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 maxValue "[2.2007016298175586e-07]" assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 maxValue "[709.8012084960938]" assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 minValue "[0.0]" assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd minValue "[0.0]" assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 minValue "[0.0]" assertion.
- 3952 description "" assertion.
- 2919 description "" assertion.
- 3949 description "" assertion.
- 515ff865-2d30-4450-b209-3f79bf89c94f description "Jupyter Notebook to analyse the changes in NO2 during the Covid-19 Lockdown in the Venice Lagoon. Datasets are from Copernicus Atmosphere Monitoring Forecasts and in-situ measurement for water quality" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 description "In this study, we focusing on understanding changes in air and water quality during the Covid-19 lockdown in the Venice Lagoon. We are re-using existing Research Objects, and in particular Jupyter Notebooks that were created in previous studies." assertion.
- 09d7eee4-13ea-4df2-adef-4f07a796aa44 description "Integration of data on Air and Water quality in the Venice Lagoon to assess the impact of the Covid-19 Lockdown" assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd description "CAMS NITROGEN DIOXIDE" assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 description "CAMS OZONE" assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 description "CAMS SURFACE PARTICULATE METTER D<2.5" assertion.
- 8fb0d0f3-cfc3-4b09-8920-7837cbe3964d description "Dataset shows monthly values and error bars." assertion.
- 957715d6-ad3e-4ef8-bcd7-e993c0f5d6ef 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.
- 7719f92a-5ad6-4314-b8ac-6645255a835f description "The goal is to compare values of NO2 water quality before and during the covid-19 lockdown." assertion.
- 92623c2c-5dd7-4f49-9175-0a67bdb2e549 description "This is a case study of snapshot project http://snapshot.cnr.it/ to investigate the lockdown impact on the water quality at a selected site in the northern Adriatic Sea, precisely in Northern Adriatic Sea, the case of the Gulf of Venice using Machine Learning model." assertion.
- 875fafc1-3eb1-4273-8380-da3e51d5399e description "Bar plot showing NO2 averaged between March and June for 2019 and 2020. The goal is to compare values before and during the covid-19 lockdown." assertion.
- 37d7f44c-b1ac-40ec-8130-b01dc771fb13 description "Reduction in the impact of human-induced factors is capable of enhancing the environmental health. In view of COVID-19 pandemic, lockdowns were imposed in India. Travel, fishing, tourism and religious activities were halted, while domestic and industrial activities were restricted. Comparison of the pre- and post-lockdown data shows that water parameters such as turbidity, nutrient concentration and microbial levels have come down from pre- to post-lockdown period, and parameters such as dissolved oxygen levels, phytoplankton and fish densities have improved. The concentration of macroplastics has also dropped from the range of 138 ± 4.12 and 616 ± 12.48 items/100 m2 to 63 ± 3.92 and 347 ± 8.06 items/100 m2. Fish density in the reef areas has increased from 406 no. 250 m−2 to 510 no. 250 m−2. The study allows an insight into the benefits of effective enforcement of various eco-protection regulations and proper management of the marine ecosystems to revive their health for biodiversity conservation and sustainable utilization." assertion.
- 24c5035b-6451-4e84-944f-059b1d224566 description "Data at the Acqua Alta oceanographic tower is a collection of physical and biogeochemical observation in the northern Adriatic Sea https://www.comune.venezia.it/it/content/3-piattaforma-ismar-cnr http://www.ismar.cnr.it/infrastrutture/piattaforma-acqua-alta" assertion.
- 489f296f-7053-452b-a254-521e4e9ce898 description "NO2 CAMS over Europe March-June 2019, 2020 and 2021 extracted from ADAM data cube." assertion.
- 5048537a-2da7-4e24-89b4-14b4def8d9e2 description "In order to fight against the spread of COVID-19, the most hard-hit countries in the spring of 2020 implemented different lockdown strategies. 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, PM2.5 and PM10 in most cities, mainly due to the reduction of transportation, industry and commercial activities during lockdown. This work shows the reduction of primary pollutants, especially NO2, is mainly due to lockdown policies. However, preexisting local environmental policy regulations also contributed to declining NO2, SO2 and PM2.5 emissions, especially in Asian countries. In addition, higher rainfall during the lockdown period could cause decline of PM2.5, especially in Johannesburg. By contrast, the changes of AQI in ground-level O3 were not significant in most of cities, as meteorological variability and ratio of VOC/NOx are key factors in ground-level O3 formation." assertion.
- a7079779-ea87-43ca-a840-a2a7a0cf246d description "The natural-color Landsat images above show some of the MOSE engineering efforts that are visible above the water line near the Lido Inlet. The top image was acquired on June 20, 2000, by the Enhanced Thematic Mapper+ on Landsat 7. The second image, from the Operational Land Imager on Landsat 8, was collected on September 4, 2013. Turn on the image comparison tool to make the changes easier to see. (Note that Landsat 8 has a greater dynamic range than Landsat 7, so the Landsat 8 image is crisper the Landsat 7 image.)" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 contentLocation 8546b63a-68d5-48fe-a174-a70a9b462d92 assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd contentLocation c84ef3c6-8dcb-4c90-81cb-a5a8f0fefe44 assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 contentLocation f631ebeb-31a9-44b6-8424-c15766c875fa assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 contentLocation fc8cb233-3e99-4b7f-a845-59301e87cebf assertion.
- 7719f92a-5ad6-4314-b8ac-6645255a835f contentSize "39986" assertion.
- 8fb0d0f3-cfc3-4b09-8920-7837cbe3964d contentSize "46709" assertion.
- 875fafc1-3eb1-4273-8380-da3e51d5399e contentSize "63516" assertion.
- 4ac2a84f-ecf7-4e52-acbc-b0502cc29f6a contentSize "147673" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 contentSize "293999" assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 contributor 0000-0003-0745-4155 assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 contributor mailto:mantovani@meeo.it assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 contributor 0000-0002-8763-1643 assertion.
- eec6faaa-e133-47d4-b377-44f7d06a9654 dateCreated "2023-01-08 18:47:51.996769+00:00" assertion.
- 957715d6-ad3e-4ef8-bcd7-e993c0f5d6ef dateCreated "2023-01-08 19:14:03.311972+00:00" assertion.
- 92623c2c-5dd7-4f49-9175-0a67bdb2e549 dateCreated "2023-01-08 19:15:20.212877+00:00" assertion.
- 09d7eee4-13ea-4df2-adef-4f07a796aa44 dateCreated "2023-01-08 19:19:35.675216+00:00" assertion.
- 5048537a-2da7-4e24-89b4-14b4def8d9e2 dateCreated "2023-01-08 19:21:48.221333+00:00" assertion.
- 37d7f44c-b1ac-40ec-8130-b01dc771fb13 dateCreated "2023-01-08 19:24:00.526730+00:00" assertion.
- 8fb0d0f3-cfc3-4b09-8920-7837cbe3964d dateCreated "2023-01-08 19:35:05.569853+00:00" assertion.
- 24c5035b-6451-4e84-944f-059b1d224566 dateCreated "2023-01-08 19:37:15.986538+00:00" assertion.
- 489f296f-7053-452b-a254-521e4e9ce898 dateCreated "2023-01-08 19:38:36.937507+00:00" assertion.
- 92c1ed12-2aef-4045-9116-01e1bff3eddd dateCreated "2023-01-08 19:40:14.176174+00:00" assertion.
- 80885ab4-6622-4b21-9e52-5e696aa2d377 dateCreated "2023-01-08 19:41:17.789149+00:00" assertion.
- eb1a3994-a796-41c8-8673-80b74c6e3a81 dateCreated "2023-01-08 19:42:25.690080+00:00" assertion.
- a7079779-ea87-43ca-a840-a2a7a0cf246d dateCreated "2023-01-08 19:58:47.516622+00:00" assertion.
- 875fafc1-3eb1-4273-8380-da3e51d5399e dateCreated "2023-01-08 20:22:56.960407+00:00" assertion.
- 7719f92a-5ad6-4314-b8ac-6645255a835f dateCreated "2023-01-08 20:25:23.565475+00:00" assertion.
- 4ac2a84f-ecf7-4e52-acbc-b0502cc29f6a dateCreated "2023-05-24 19:00:14.547814+00:00" assertion.
- f631ebeb-31a9-44b6-8424-c15766c875fa geo d8df8044-90b4-4c5e-9c18-177fb380cab7 assertion.
- c84ef3c6-8dcb-4c90-81cb-a5a8f0fefe44 geo 0a58960c-e659-466a-b373-1825605e4e02 assertion.
- 8546b63a-68d5-48fe-a174-a70a9b462d92 geo 31b0b68d-7999-4e04-8be8-001d4496c246 assertion.
- fc8cb233-3e99-4b7f-a845-59301e87cebf geo 6a752df4-6c69-432b-b96d-1b201c42fc0f assertion.