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Development of historic monthly land use regression models of SO2, NOx and suspended particulate matter for birth cohort ELSPAC

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00130568" target="_blank" >RIV/00216224:14310/23:00130568 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://www.sciencedirect.com/science/article/pii/S1352231023001140?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1352231023001140?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.atmosenv.2023.119688" target="_blank" >10.1016/j.atmosenv.2023.119688</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Development of historic monthly land use regression models of SO2, NOx and suspended particulate matter for birth cohort ELSPAC

  • Popis výsledku v původním jazyce

    Vulnerable windows in child development in utero and after birth are critical time points for uncovering the links between environment and health. Particular attention is paid to the first 1000 days of life from conception to the second year of life.The ELSPAC (European Longitudinal Study of Pregnancy and Childhood) birth cohort, launched in the early 1990s, is a rich source of longitudinal data about health and life events, based mainly in Brno, Czechia. There are currently no air quality concentration maps that can be used to assess exposure to air pollutants for this period of the 1990s in Central Europe. Simply transferring current models to the 1990's is burdened with the error introduced by the temporal change in emission sources and land use of the area. Therefore, Czech air quality monitoring data were used to develop monthly land use regression (LUR) models, which combine collected spatial variables with monitoring data to predict the variation in exposures to pollutants. Monthly pollutant concentrations were regressed against the GIS-based potential predictor variables to develop LUR models, following a supervised forward linear regression, with several predefined constraints.We constructed 180 LUR monthly models for sulphur dioxide (SO2), nitrogen oxides (NOx) and suspended particulate matter (SPM) for 1990-1994, that completely cover the first 1000 days for all ELSPAC study par-ticipants. The final models showed, on average reasonably good performance (adjusted R2 = 0.59 with hold-out validation (HOV) R2 = 0.40 for SO2; adjusted R2 = 0.75 with HOV R2 = 0.35 for NOx; and adjusted R2 = 0.61 with HOV R2 = 0.31 for SPM; with a mean number of stations of 74, 38 and 41, respectively). For these models, roads and greenness were predominantly selected as the best predictors.The modelled exposures will serve in many subsequent ELSPAC epidemiological studies, but our models may be also used in other Czech and possibly other Central European cities in that period.

  • Název v anglickém jazyce

    Development of historic monthly land use regression models of SO2, NOx and suspended particulate matter for birth cohort ELSPAC

  • Popis výsledku anglicky

    Vulnerable windows in child development in utero and after birth are critical time points for uncovering the links between environment and health. Particular attention is paid to the first 1000 days of life from conception to the second year of life.The ELSPAC (European Longitudinal Study of Pregnancy and Childhood) birth cohort, launched in the early 1990s, is a rich source of longitudinal data about health and life events, based mainly in Brno, Czechia. There are currently no air quality concentration maps that can be used to assess exposure to air pollutants for this period of the 1990s in Central Europe. Simply transferring current models to the 1990's is burdened with the error introduced by the temporal change in emission sources and land use of the area. Therefore, Czech air quality monitoring data were used to develop monthly land use regression (LUR) models, which combine collected spatial variables with monitoring data to predict the variation in exposures to pollutants. Monthly pollutant concentrations were regressed against the GIS-based potential predictor variables to develop LUR models, following a supervised forward linear regression, with several predefined constraints.We constructed 180 LUR monthly models for sulphur dioxide (SO2), nitrogen oxides (NOx) and suspended particulate matter (SPM) for 1990-1994, that completely cover the first 1000 days for all ELSPAC study par-ticipants. The final models showed, on average reasonably good performance (adjusted R2 = 0.59 with hold-out validation (HOV) R2 = 0.40 for SO2; adjusted R2 = 0.75 with HOV R2 = 0.35 for NOx; and adjusted R2 = 0.61 with HOV R2 = 0.31 for SPM; with a mean number of stations of 74, 38 and 41, respectively). For these models, roads and greenness were predominantly selected as the best predictors.The modelled exposures will serve in many subsequent ELSPAC epidemiological studies, but our models may be also used in other Czech and possibly other Central European cities in that period.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    10511 - Environmental sciences (social aspects to be 5.7)

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/EF17_043%2F0009632" target="_blank" >EF17_043/0009632: CETOCOEN Excellence</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Ostatní

  • Rok uplatnění

    2023

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Atmospheric Environment

  • ISSN

    1352-2310

  • e-ISSN

  • Svazek periodika

    301

  • Číslo periodika v rámci svazku

    May 2023

  • Stát vydavatele periodika

    GB - Spojené království Velké Británie a Severního Irska

  • Počet stran výsledku

    11

  • Strana od-do

    1-11

  • Kód UT WoS článku

    000953490800001

  • EID výsledku v databázi Scopus

    2-s2.0-85149475804