{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T17:03:49Z","timestamp":1790528629530,"version":"4.1.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T00:00:00Z","timestamp":1552348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T00:00:00Z","timestamp":1552348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO<jats:sub>2<\/jats:sub>, NO<jats:sub>x<\/jats:sub>, PM<jats:sub>2.5<\/jats:sub>, PM<jats:sub>2.5absorbance<\/jats:sub> and PM<jats:sub>10.<\/jats:sub> The raster datasets on 5\u00d75 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.<\/jats:p>","DOI":"10.1038\/sdata.2019.35","type":"journal-article","created":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T14:03:35Z","timestamp":1552399415000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["High resolution annual average air pollution concentration maps for the Netherlands"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0493-851X","authenticated-orcid":false,"given":"Oliver","family":"Schmitz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rob","family":"Beelen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maciej","family":"Strak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerard","family":"Hoek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Soenario","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bert","family":"Brunekreef","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilonca","family":"Vaartjes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin J.","family":"Dijst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diederick E.","family":"Grobbee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Karssenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,3,12]]},"reference":[{"key":"BFsdata201935_CR1","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1016\/S0140-6736(02)11274-8","volume":"360","author":"B Brunekreef","year":"2002","unstructured":"Brunekreef, B. & Holgate, S. T. Air pollution and health. Lancet 360, 1233\u20131242 (2002).","journal-title":"Lancet"},{"key":"BFsdata201935_CR2","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.jaci.2004.08.030","volume":"114","author":"JA Bernstein","year":"2004","unstructured":"Bernstein, J. A. et al. Health effects of air pollution. J. Allergy Clin. Immunol. 114, 1116\u20131123 (2004).","journal-title":"J. Allergy Clin. Immunol."},{"key":"BFsdata201935_CR3","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1080\/10473289.2006.10464485","volume":"56","author":"CA Pope III","year":"2006","unstructured":"Pope, C. A. III & Dockery, D. W. Health Effects of Fine Particulate Air Pollution: Lines that Connect. J. Air Waste Manage. Assoc. 56, 709\u2013742 (2006).","journal-title":"J. Air Waste Manage. Assoc."},{"key":"BFsdata201935_CR4","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1136\/jech.2005.044263","volume":"61","author":"TS Nawrot","year":"2007","unstructured":"Nawrot, T. S. et al. Stronger associations between daily mortality and fine particulate air pollution in summer than in winter: evidence from a heavily polluted region in western Europe. J. Epidemiol. Community Health 61, 146\u2013149 (2007).","journal-title":"J. Epidemiol. Community Health"},{"key":"BFsdata201935_CR5","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1183\/16000617.0034-2016","volume":"25","author":"V Siroux","year":"2016","unstructured":"Siroux, V., Agier, L. & Slama, R. The exposome concept: a challenge and a potential driver for environmental health research. Eur. Respir. Rev. 25, 124\u2013129 (2016).","journal-title":"Eur. Respir. Rev."},{"key":"BFsdata201935_CR6","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.envint.2017.08.017","volume":"108","author":"M Strak","year":"2017","unstructured":"Strak, M. et al. Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey. Environ. Int. 108, 228\u2013236 (2017).","journal-title":"Environ. Int."},{"key":"BFsdata201935_CR7","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1080\/136588197242158","volume":"11","author":"DJ Briggs","year":"1997","unstructured":"Briggs, D. J. et al. Mapping urban air pollution using GIS: a regression-based approach. Int. J. Geogr. Inf. Sci. 11, 699\u2013718 (1997).","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"BFsdata201935_CR8","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1038\/sj.jea.7500388","volume":"15","author":"M Jerrett","year":"2005","unstructured":"Jerrett, M. et al. A review and evaluation of intraurban air pollution exposure models. J. Expo. Anal. Environ. Epidemiol 15, 185\u2013204 (2005).","journal-title":"J. Expo. Anal. Environ. Epidemiol"},{"key":"BFsdata201935_CR9","doi-asserted-by":"publisher","first-page":"7561","DOI":"10.1016\/j.atmosenv.2008.05.057","volume":"42","author":"G Hoek","year":"2008","unstructured":"Hoek, G. et al. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos. Environ. 42, 7561\u20137578 (2008).","journal-title":"Atmos. Environ."},{"key":"BFsdata201935_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.atmosenv.2013.02.037","volume":"72","author":"R Beelen","year":"2013","unstructured":"Beelen, R. et al. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project. Atmos. Environ. 72, 10\u201323 (2013).","journal-title":"Atmos. Environ."},{"key":"BFsdata201935_CR11","doi-asserted-by":"publisher","first-page":"4407","DOI":"10.1021\/es103578x","volume":"45","author":"EV Novotny","year":"2011","unstructured":"Novotny, E. V., Bechle, M. J., Millet, D. B. & Marshall, J. D. National Satellite-Based Land-Use Regression: NO2 in the United States. Environ. Sci. Technol. 45, 4407\u20134414 (2011).","journal-title":"Environ. Sci. Technol."},{"key":"BFsdata201935_CR12","doi-asserted-by":"publisher","first-page":"7045","DOI":"10.1007\/s11356-014-3893-5","volume":"22","author":"J Wu","year":"2015","unstructured":"Wu, J. et al. Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China. Environ. Sci. Pollut. Res. 22, 7045\u20137061 (2015).","journal-title":"Environ. Sci. Pollut. Res."},{"key":"BFsdata201935_CR13","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.envsoft.2015.07.008","volume":"74","author":"M Dirgawati","year":"2015","unstructured":"Dirgawati, M. et al. Development of Land Use Regression models for predicting exposure to NO2 and NOx in Metropolitan Perth, Western Australia. Environ. Modell. Softw. 74, 258\u2013267 (2015).","journal-title":"Environ. Modell. Softw."},{"key":"BFsdata201935_CR14","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.envsoft.2017.06.029","volume":"95","author":"MM Rahman","year":"2017","unstructured":"Rahman, M. M., Yeganeh, B., Clifford, S., Knibbs, L. D. & Morawska, L. Development of a land use regression model for daily NO2 and NOx concentrations in the Brisbane metropolitan area, Australia. Environ. Modell. Softw. 95, 168\u2013179 (2017).","journal-title":"Environ. Modell. Softw."},{"key":"BFsdata201935_CR15","doi-asserted-by":"publisher","first-page":"11195","DOI":"10.1021\/es301948k","volume":"46","author":"M Eeftens","year":"2012","unstructured":"Eeftens, M. et al. Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project. Environ. Sci. Technol. 46, 11195\u201311205 (2012).","journal-title":"Environ. Sci. Technol."},{"key":"BFsdata201935_CR16","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.envres.2016.02.039","volume":"147","author":"M Shekarrizfard","year":"2016","unstructured":"Shekarrizfard, M., Faghih-Imani, A. & Hatzopoulou, M. An examination of population exposure to traffic related air pollution: Comparing spatially and temporally resolved estimates against long-term average exposures at the home location. Environ. Res. 147, 435\u2013444 (2016).","journal-title":"Environ. Res."},{"key":"BFsdata201935_CR17","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.healthplace.2016.10.002","volume":"43","author":"YM Park","year":"2017","unstructured":"Park, Y. M. & Kwan, M.-P. Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored. Health Place 43, 85\u201394 (2017).","journal-title":"Health Place"},{"key":"BFsdata201935_CR18","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.envint.2018.01.009","volume":"113","author":"R Tang","year":"2018","unstructured":"Tang, R. et al. Integrating travel behavior with land use regression to estimate dynamic air pollution exposure in Hong Kong. Environ. Int. 113, 100\u2013108 (2018).","journal-title":"Environ. Int."},{"key":"BFsdata201935_CR19","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1021\/es1023042","volume":"45","author":"G Hoek","year":"2011","unstructured":"Hoek, G. et al. Land Use Regression Model for Ultrafine Particles in Amsterdam. Environ. Sci. Technol. 45, 622\u2013628 (2011).","journal-title":"Environ. Sci. Technol."},{"key":"BFsdata201935_CR20","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1136\/oem.2010.061135","volume":"68","author":"M Eeftens","year":"2011","unstructured":"Eeftens, M. et al. Stability of measured and modelled spatial contrasts in NO2 over time. Occup. Environ. Med. 68, 765\u2013770 (2011).","journal-title":"Occup. Environ. Med."},{"key":"BFsdata201935_CR21","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.envres.2017.03.050","volume":"156","author":"M Strak","year":"2017","unstructured":"Strak, M. et al. Associations between lifestyle and air pollution exposure: potential for confounding in large administrative data cohorts. Environ. Res. 156, 364\u2013373 (2017).","journal-title":"Environ. Res."},{"key":"BFsdata201935_CR22","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.envsoft.2009.10.004","volume":"25","author":"D Karssenberg","year":"2010","unstructured":"Karssenberg, D., Schmitz, O., Salamon, P., de Jong, K. & Bierkens, M. F. P. A software framework for construction of process-based stochastic spatio-temporal models and data assimilation. Environ. Modell. Softw. 25, 489\u2013502 (2010).","journal-title":"Environ. Modell. Softw."},{"key":"BFsdata201935_CR23","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1001\/jama.287.9.1132","volume":"287","author":"CA Pope III","year":"2002","unstructured":"Pope, C. A. III et al. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. J. Am. Med. Assoc 287, 1132\u20131141 (2002).","journal-title":"J. Am. Med. Assoc"},{"key":"BFsdata201935_CR24","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","volume":"3","author":"M Kottek","year":"2006","unstructured":"Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the K\u00f6ppen-Geiger climate classification updated. Meteorol. Z. 3, 259\u2013263 (2006).","journal-title":"Meteorol. Z."},{"key":"BFsdata201935_CR25","doi-asserted-by":"publisher","first-page":"180214","DOI":"10.1038\/sdata.2018.214","volume":"5","author":"HE Beck","year":"2018","unstructured":"Beck, H. E. et al. Present and future K\u00f6ppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).","journal-title":"Sci. Data"},{"key":"BFsdata201935_CR26","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.atmosenv.2012.07.080","volume":"62","author":"J Cyrys","year":"2012","unstructured":"Cyrys, J. et al. Variation of NO2 and NOx concentrations between and within 36 European study areas: Results from the ESCAPE study. Atmos. Environ. 62, 374\u2013390 (2012).","journal-title":"Atmos. Environ."},{"key":"BFsdata201935_CR27","first-page":"228","volume":"14","author":"M Brauer","year":"2003","unstructured":"Brauer, M. et al. Estimating Long-Term Average Particulate Air Pollution Concentrations: Application of Traffic Indicators and Geographic Information Systems. Epidemiology 14, 228\u2013239 (2003).","journal-title":"Epidemiology"},{"key":"BFsdata201935_CR28","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MCSE.2011.37","volume":"13","author":"S Van Der Walt","year":"2011","unstructured":"Van Der Walt, S., Colbert, S. C. & Varoquaux, G. The NumPy array: A structure for efficient numerical computation. Comput. Sci. Eng. 13, 22\u201330 (2011).","journal-title":"Comput. Sci. Eng."},{"key":"BFsdata201935_CR29","unstructured":"Tomlin, C. D . Geographic Information Systems and Cartographic Modeling. (Prentice Hall: Englewood Cliffs, NJ, 1990)."},{"key":"BFsdata201935_CR30","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1080\/13658810601064009","volume":"21","author":"EJ Pebesma","year":"2007","unstructured":"Pebesma, E. J., de Jong, K. & Briggs, D. Interactive visualization of uncertain spatial and spatio-temporal data under different scenarios: an air quality example. Int. J. Geogr. Inf. Sci. 21, 515\u2013527 (2007).","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"BFsdata201935_CR31","doi-asserted-by":"publisher","first-page":"8712","DOI":"10.1021\/es505791g","volume":"49","author":"DR Montagne","year":"2015","unstructured":"Montagne, D. R. et al. Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation. Environ. Sci. Technol. 49, 8712\u20138720 (2015).","journal-title":"Environ. Sci. Technol."},{"key":"BFsdata201935_CR32","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.1408591","author":"O. Schmitz","year":"2018","unstructured":"Schmitz, O. et al. Zenodo https:\/\/doi.org\/10.5281\/zenodo.1408591 (2018)"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/sdata201935.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/sdata201935","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.nature.com\/articles\/sdata201935","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/www.nature.com\/articles\/sdata201935.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T04:57:42Z","timestamp":1671253062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/sdata201935"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,12]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3,12]]}},"alternative-id":["BFsdata201935"],"URL":"https:\/\/doi.org\/10.1038\/sdata.2019.35","relation":{"references":[{"id-type":"doi","id":"10.5281\/zenodo.1408591","asserted-by":"subject"}]},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,12]]},"assertion":[{"value":"5 September 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"190035"}}