Jules Kerckhoffs1, Gerard Hoek2, Ulrike Gehring2, Roel Vermeulen3. 1. Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, the Netherlands. Electronic address: j.kerckhoffs@uu.nl. 2. Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, the Netherlands. 3. Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, the Netherlands.
Abstract
BACKGROUND: Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), however is lacking. Reason for this is the shortage of empirical UFP land use regression models spanning large geographical areas including cities with varying topographies, peri-urban and rural areas. The aim of this paper is to combine targeted mobile monitoring and long-term regional background monitoring to develop national UFP models. METHOD: We used an electric car to monitor UFP concentrations in selected cities and towns across the Netherlands over a 14-month period in 2016-2017. Routes were monitored 3 times and concentrations were averaged per road segment. In addition, we used kriging maps based on regional background monitoring (20 sites; 3 × 2 weeks) over the same period to assess annual average regional background concentrations. All road segments were used to model spatial variation of UFP with three different land-use (regression) approaches: supervised stepwise regression, LASSO and random forest. For each approach, we also tested a deconvolution method, which segregates the average concentration at each road segment into a local and background signal. Model performance was evaluated with short-term (400 sites across the Netherlands; 3 × 30 minutes) and external longer-term measurements (42 sites in two major cities; 3 × 24 hours). We also compared predictions of all six models at 1000 random addresses spread over the country. RESULTS: We found similar predictive performance for the six models, with validation R2 values from 0.25 to 0.35 for short-term measurements and 0.52 to 0.60 for longer-term external measurements. Models with and without deconvolution had similar predictive performance. All models based on the deconvolution method included a regional background kriging map as important predictor. Correlations between predictions at random addresses were high with Pearson correlations from 0.84 to 0.99. Models overestimated exposure at the short-term and long-term sites by about 20-30% in all cases, with small differences between regions and road types. CONCLUSION: We developed robust nation-wide models for long-term UFP exposure combining mobile monitoring with long-term regional background monitoring. Minor differences in predictive performance between different algorithms were found, but the deconvolution approach is considered more physically realistic. The models will be applied in Dutch nation-wide health studies.
BACKGROUND: Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), however is lacking. Reason for this is the shortage of empirical UFP land use regression models spanning large geographical areas including cities with varying topographies, peri-urban and rural areas. The aim of this paper is to combine targeted mobile monitoring and long-term regional background monitoring to develop national UFP models. METHOD: We used an electric car to monitor UFP concentrations in selected cities and towns across the Netherlands over a 14-month period in 2016-2017. Routes were monitored 3 times and concentrations were averaged per road segment. In addition, we used kriging maps based on regional background monitoring (20 sites; 3 × 2 weeks) over the same period to assess annual average regional background concentrations. All road segments were used to model spatial variation of UFP with three different land-use (regression) approaches: supervised stepwise regression, LASSO and random forest. For each approach, we also tested a deconvolution method, which segregates the average concentration at each road segment into a local and background signal. Model performance was evaluated with short-term (400 sites across the Netherlands; 3 × 30 minutes) and external longer-term measurements (42 sites in two major cities; 3 × 24 hours). We also compared predictions of all six models at 1000 random addresses spread over the country. RESULTS: We found similar predictive performance for the six models, with validation R2 values from 0.25 to 0.35 for short-term measurements and 0.52 to 0.60 for longer-term external measurements. Models with and without deconvolution had similar predictive performance. All models based on the deconvolution method included a regional background kriging map as important predictor. Correlations between predictions at random addresses were high with Pearson correlations from 0.84 to 0.99. Models overestimated exposure at the short-term and long-term sites by about 20-30% in all cases, with small differences between regions and road types. CONCLUSION: We developed robust nation-wide models for long-term UFP exposure combining mobile monitoring with long-term regional background monitoring. Minor differences in predictive performance between different algorithms were found, but the deconvolution approach is considered more physically realistic. The models will be applied in Dutch nation-wide health studies.
Authors: Magali N Blanco; Amanda Gassett; Timothy Gould; Annie Doubleday; David L Slager; Elena Austin; Edmund Seto; Timothy V Larson; Julian D Marshall; Lianne Sheppard Journal: Environ Sci Technol Date: 2022-08-02 Impact factor: 11.357
Authors: Travis Hee Wai; Joshua S Apte; Maria H Harris; Thomas W Kirchstetter; Christopher J Portier; Chelsea V Preble; Ananya Roy; Adam A Szpiro Journal: Atmos Environ (1994) Date: 2022-03-23 Impact factor: 5.755
Authors: Jelle Vlaanderen; Kees de Hoogh; Gerard Hoek; Annette Peters; Nicole Probst-Hensch; Augustin Scalbert; Erik Melén; Cathryn Tonne; G Ardine de Wit; Marc Chadeau-Hyam; Klea Katsouyanni; Tõnu Esko; Karin R Jongsma; Roel Vermeulen Journal: Environ Epidemiol Date: 2021-07-01
Authors: Mar Viana; Kostas Karatzas; Athanasios Arvanitis; Cristina Reche; Miguel Escribano; Edurne Ibarrola-Ulzurrun; Paolo Emilio Adami; Fréderic Garrandes; Stéphane Bermon Journal: Int J Environ Res Public Health Date: 2022-03-17 Impact factor: 3.390