Temat: TROPOMI - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: CO level over the Republic of Croatia using SENTINEL-5P
Autorzy:: Mamić, Luka
Powiązania:: https://bibliotekanauki.pl/articles/2191383.pdf
Data publikacji:: 2021
Wydawca:: Stowarzyszenie SILGIS
Tematy:: air pollution
carbon monoxide
CO
GIS
remote sensing
TROPOMI
zanieczyszczenie powietrza
tlenek węgla
teledetekcja
Opis:: This paper deals with the issue of air pollution in the territory of the Republic of Croatia by monitoring the level of one of the largest air pollutants – carbon monoxide. For the study area, total carbon monoxide levels observed by the TROPOMI SENTINEL- 5P mission device were taken and used to show carbon monoxide levels for the period from January to September 2020 for every fifteenth day of the month. The entire process of downloading, georeferencing and processing TROPOMI data is described. The analysis examines the relationship between carbon monoxide levels and urban areas, major roads, and altitude. Also, the time frame of observation covers the period of the most severe measures and lockdown due to the coronavirus pandemic and studies the impact of these measures on the level of carbon monoxide in the territory of the Republic of Croatia.
Źródło:: GIS Odyssey Journal; 2021, 1, 1; 61--82
2720-2682
Pojawia się w:: GIS Odyssey Journal
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Fusing multiple open-source remote sensing data to estimate $\text{PM}_\text{2.5}$ and $\text{PM}_\text{10}$ monthly concentrations in Croatia
Autorzy:: Mamić, Luka
Kaplan, Gordana
Gašparović, Mateo
Powiązania:: https://bibliotekanauki.pl/articles/2191359.pdf
Data publikacji:: 2022
Wydawca:: Stowarzyszenie SILGIS
Tematy:: air quality
TROPOMI
machine learning
PM2.5
PM10
remote sensing
jakość powietrza
uczenie maszynowe
teledetekcja
Opis:: The objective of this study is to create a methodology for accurately estimating atmospheric concentrations of PM2.5 and PM10 using Sentinel-5P and other open-source remote sensing data from the Google Earth Engine (GEE) platform on a monthly basis for June, July and August which are considered as months of non-heating season in Croatia, and December, January and February, which, on the other hand, are considered as months of the heating season. Furthermore, machine learning algorithms were employed in this study to build models that can accurately identify air quality. The proposed method uses open-source remote sensing data accessible on the GEE platform, with in-situ data from Croatian National Network for Continuous Air Quality Monitoring as ground truth data. A common thing for all developed monthly models is that the predicted values slightly underestimate the actual ones and appear slightly lower. However, all models have shown the general ability to estimate PM2.5 and PM10 levels, even in areas without high pollution. All developed models show moderate to high correlation between in-situ and estimated PM2.5 and PM10 values, with overall better results for PM2.5 than for PM10 concentrations. Regarding PM2.5 models, the model with the highest correlation (r = 0.78) is for January. The PM10 model with the highest correlation (r = 0.79) is for December. All things considered, developed models can effectively detect all PM2.5 and PM10 hotspots.
Źródło:: GIS Odyssey Journal; 2022, 2, 2; 59--77
2720-2682
Pojawia się w:: GIS Odyssey Journal
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "TROPOMI" wg kryterium: Temat

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język