Spatio-temporal Land Use Regression Modelling of Ozone Levels in Athens, Greece.View Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000696</doi_batch_id><timestamp>1714154757</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 1</title></titles><publication_date><month>03</month><year>2024</year></publication_date><journal_volume><volume>22</volume></journal_volume><issue>1</issue></journal_issue><journal_article><titles><title>Spatio-temporal Land Use Regression Modelling of Ozone Levels in Athens, Greece.</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>Spatio-temporal methods have been developed for the estimation of concentrations of pollutants such as particulate matter and nitrogen dioxide for application in epidemiological studies. A limited number of city-specific spatio-temporal ozone (O3) models have been proposed until today. Our aim was to develop a spatio-temporal land use regression (LUR) model that estimates daily concentrations of O3, for the whole year, as well as the warm (April-September) and cold season (October-March), within the greater Athens area. We developed models using a semiparametric approach including linear and smooth functions of spatial and temporal covariates and a bivariate smooth thin plate function. The final set of explanatory variables was selected based on the adjusted-R2. We tested the final model in temporal and spatial terms following a leave-one out monitor approach. The adjusted-R2 in the leave-one-out cross validation was 0.73 for the annual model (warm: 0.65 and cold: 0.70). The spatial terms in our annual model explained 32.9% and the temporal 63.2% of the variability in O3. The developed models showed good validity when comparing predicted and observed measurements for the 2015 data. Spatio-temporal LUR modeling provides a useful tool for estimating O3 spatio-temporal variability with adequate accuracy for subsequent use in epidemiological studies.</p> </p></abstract><publication_date><month>01</month><day>22</day><year>2020</year></publication_date><pages><first_page>85</first_page><last_page>94</last_page></pages><publisher_item><identifier id_type="pii">gnest_03233</identifier></publisher_item><doi_data><doi>10.30955/gnj.003233</doi><timestamp>1714154757</timestamp><resource>https://journal.gnest.org/publication/gnest_03233</resource></doi_data></journal_article></journal></body></doi_batch> </div>
