 Interpolation biases in assessing spatial heterogeneity of outdoor air quality in Moscow, RussiaEvgeny Noi and Alan T. Murray Land Use Policy, 2022, vol. 112, issue C Abstract: Air quality monitoring is challenging in countries where public access to data is not enabled by government agencies along with open access policies. This is especially true for Russia, where the Federal environmental monitoring network is outdated, and those systems operated by municipalities are generally under-funded. In fact, only three municipal agencies in Russia make real-time information on pollutant concentrations available online. Utilizing data from MosEcoMonitoring, we structure analytical models to infer spatial heterogeneity of nitrogen dioxide in Moscow. Model results are validated using non-governmental air quality data. The developed approach represents an open-source analytical framework for air quality evaluation and population exposure assessment on a city and district level. Both visualizations and predictive performance suggest governmental data is biased in a number of ways, highlighting the importance of ancillary data in uncovering socio-environmental implications. Keywords: Land-use regression; Nitrogen dioxide; Spatial interpolation; GIS (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:lauspo:v:112:y:2022:i:c:s0264837721005068 DOI: 10.1016/j.landusepol.2021.105783 Access Statistics for this article Land Use Policy is currently edited by Jaap Zevenbergen More articles in Land Use Policy from Elsevier |
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