Matches in Nanopublications for { <https://w3id.org/ro-id/374d0d3a-4807-4925-be83-b9eea52356e3/> ?p ?o ?g. }
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "impact of covid 19" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "pandemic lockdown" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "reg AP emission inventory" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Phrase "threshold temperature value" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The datasetprovides a statistical summary for each of the air pollutant species, all air pollutants are converted toan Air Quality Index (AQI) with the U.S. Environmental Protection Agency (EPA) standard calculation." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The ECHAM HAM and CESM NoT ensembles allow more freedom for temperature adjustment." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Figure summarizes the main statistics (normalized meanbias, NMB; normalized root mean square error, NRMSE;and correlation, r) obtained from the comparison betweenmeasured and ML based electricity demand during the first months of for selected countries." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "In this study, ML models are used for predictingthe fluctuations of electricity demand based on the temper ature (and additional time features) assuming that temper ature is a strong driver of electricity demand (for heatingand air conditioning) However, temperature is obviously notthe only driver of electricity demand variability that can beinfluenced by various other factors (e.g. change of technol ogy, behaviour, regulation) In addition, the GBM modelsused in this study are non parametric, meaning that they can not extrapolate, i.e. predict electricity demand values out side the range of values used during the training phase." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Thepoorest performance was obtained in Finland (r.) dueto a strong negative anomaly (on average) of elec tricity demand in January February compared to pre vious years used for training." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The daily median AQI was used in this study for each of major cities." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The average temperature was not availablefor Los Angeles (LA) New York City (NYC) and Sydney, so the maximum temperature was used,as the maximum temperature is important for ozone formation." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The ECHAM HAM simulation set up is most similar to the CESM NoT ensemble described below." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Bibliography on air quality before, during and after lockdown." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "The same emissions scenario (from Forster et al.) is run as for CESM using monthly emissions." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 Sentence "Collection of scientific articles and other communications related to the impact of COVID-19 pandemic lockdown on air quality pollution." assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 TimeReference "winter" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 TimeReference "in spring" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 TimeReference "the past winter" assertion.
- 374d0d3a-4807-4925-be83-b9eea52356e3 author mailto:jean.iaquinta@geo.uio.no assertion.