Developing PM2.5 and PM10 prediction models on a national and regional scale using open-source remote sensing data

Clean air is the precursor to a healthy life. Air quality is an issue that has been getting under its well-deserved spotlight in the last few years. From a remote sensing point of view, the first Copernicus mission with the main purpose of monitoring the atmosphere and tracking air pollutants, the Sentinel-5P TROPOMI mission, has been widely used worldwide. Particulate matter of a diameter smaller than 2.5 and 10 mu m (PM2.5 and PM10) significantly determines air quality. Still, there are no available satellite sensors that allow us to track them remotely with high accuracy, but only using ground stations. This research aims to estimate PM2.5 and PM10 using Sentinel-5P and other open-source remote sensing data available on the Google Earth Engine (GEE) platform for heating (December 2021, January, and February 2022) and non-heating seasons (June, July, and August 2021) on the territory of the Republic of Croatia. Ground stations of the National Network for Continuous Air Quality Monitoring were used as a starting point and as ground truth data. Raw hourly data were matched to remote sensing data, and seasonal models were trained at the national and regional scale using machine learning. The proposed approach uses a random forest algorithm with a percentage split of 70% and gives moderate to high accuracy regarding the temporal frame of the data. The mapping gives us visual insight between the ground and remote sensing data and shows the seasonal variations of PM2.5 and PM10. The results showed that the proposed approach and models could efficiently estimate air quality

Upotreba alternativne i komplementarne medicine u bolesnika s malignim bolestima u velikom onkološkom centru i gledišta na budućnost

Usage of complementary and alternative medicine (CAM) is steadily increasing over the last decades, gaining medical, economic and sociological importance. The aim of the present study was to assess the use of complementary and alternative therapies in cancer patients. A crosssectional, descriptive survey design was used to collect data through an anonymous questionnaire. A total of 267 patients were included in the study. The prevalence of CAM use among cancer patients in this study was 60.3%. It was found that 61 heterogeneous CAM therapies were used, the most popular among patients being naturopathy/folk medicine. In multivariate logistic regression analysis, independent predictors of CAM use were high income, divorced status, female sex and younger age. In conclusion, considering the fact that a large proportion of patients used at least one CAM approach, we need to continue our efforts to improve the patient-oncologist communication in order to deliver most reliable information to patients and to better understand the possible standard medicine-CAM interactions. According to results of the latest studies, CAM therapies that help manage pain, nausea, fatigue, anxiety, and other symptoms should be integrated into the patient overall care.Proširenost upotrebe komplementarne i alternativne medicine (KAM) u posljednjih nekoliko desetljeća u stalnom je porastu, dobivajući sve veće medicinsko, ekonomsko i sociološko značenje. Cilj ovoga istraživanja bio je procijeniti proširenost upotrebe KAM u onkoloških bolesnika u Hrvatskoj. Studija je dizajnirana kao presječno, deskriptivno, pregledno istraživanje te je provedena u obliku anonimnog upitnika. Istraživanje je provedeno na onkološkoj i hematološkoj klinici kliničkog bolničkog centra s velikim obrtajem onkoloških bolesnika. Ukupno 267 bolesnika koji su dali svoj informirani pristanak bilo je uključeno u studiju. Nakon statističke analize utvrđena je učestalost upotrebe KAM kod onkoloških bolesnika 60,3%. Pokazalo se da je korišten ukupno 61 različit oblik KAM, a najpopularnija alternativna terapija među bolesnicima bila je naturopatija/narodna medicina. U multivarijatnoj logističkoj regresijskoj analizi nezavisni prediktori povezani uz korištenje KAM bili su visoka primanja, razveden/a stanje, ženski spol i mlađa dob. S obzirom na činjenicu da značajan udio bolesnika koristi barem jedan oblik KAM nužno je nastaviti djelovanje u smjeru poboljšanja komunikacije između bolesnika i onkologa te razmisliti o integraciji onih KAM terapija koje imaju pozitivan učinak u uklanjanju boli, mučnine, umora, anksioznosti i drugih simptoma u cjelokupnu onkološku skrb

Fusing multiple open-source remote sensing data to estimate PM2.5\text{PM}_\text{2.5} and PM10\text{PM}_\text{10} monthly concentrations in Croatia

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