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| DOI | 10.1016/j.atmosenv.2021.118851 |
| New methods to derive street-scale spatial patterns of air pollution from mobile monitoring | |
| Padilla L.E.; Ma G.Q.; Peters D.; Dupuy-Todd M.; Forsyth E.; Stidworthy A.; Mills J.; Bell S.; Hayward I.; Coppin G.; Moore K.; Fonseca E.; Popoola O.A.M.; Douglas F.; Slater G.; Tuxen-Bettman K.; Carruthers D.; Martin N.A.; Jones R.L.; Alvarez R.A. | |
| 发表日期 | 2021 |
| ISSN | 1352-2310 |
| 英文摘要 | The benefits of monitoring ambient air pollution with instruments mounted to ground-based, moving platforms include increased spatial resolution of measurements and synchronous, fast-response measurements close to road sources for emissions analyses. However, these come at the cost of obtaining a suitable number of repeat visits at each location in order to achieve reliable and representative pollution estimates at the desired spatial and temporal resolution. Thus, methods that maximize the information content derived from limited repeat coverage of mobile platforms are needed in order to realize the spatial and emissions source benefits possible from mobile air pollution data collection. This work builds upon previous methods by providing generalizable approaches to quantifying sampling uncertainty that enable greater data inclusion, make sampling uncertainty an integral component of air quality findings and provide decision-makers with options to fit uncertainty analysis to their purpose. To demonstrate the uncertainty estimation methods, we analyzed mobile monitoring data collected in the Breathe London pilot project in three distinct use cases. We derived insights from two key measures of pollution: total ambient NO2 concentrations and the ratio of NOx to CO2 enhancements – a marker of the intensity of NOx pollution from emission sources. The results were useful information for London public health policymakers on street-by-street level differences in pollution, and the effects of the Ultra Low Emission Zone. The future use of these flexible uncertainty methods will allow decision-makers to best leverage the information embedded in available air pollution data. © 2021 The Authors |
| 关键词 | Air qualityEmission ratiosExceedance probabilityMobile monitoringPollution hotspotsSampling uncertainty |
| 语种 | 英语 |
| scopus关键词 | Decision making; Information use; Monitoring; Nitrogen oxides; Uncertainty analysis; Decision makers; Emission ratio; Emission sources; Exceedance probability; Hotspots; Mobile monitoring; NO x; Pollution hotspot; Sampling uncertainties; Spatial patterns; Air quality |
| 来源期刊 | ATMOSPHERIC ENVIRONMENT
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/248781 |
| 作者单位 | Environmental Defense Fund, 18 Tremont Street, Boston, MA 02108, United States; National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, United Kingdom; Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom; Cambridge Environmental Research Consultants, 3 King's Parade, Cambridge, CB2 1SJ, United Kingdom; ACOEM UK Ltd, Ground Floor, C1 the Courtyard, Tewkesbury Business Park TewkesburyGL20 8GD, United Kingdom; Environmental Defense Fund Europe, 3rd Floor, 41 Eastcheap, London, EC3M 1DT, United Kingdom; Google Earth Outreach, 1600 Amphitheatre Parkway, Mountain View, CA 94062, United States |
| 推荐引用方式 GB/T 7714 | Padilla L.E.,Ma G.Q.,Peters D.,et al. New methods to derive street-scale spatial patterns of air pollution from mobile monitoring[J],2021. |
| APA | Padilla L.E..,Ma G.Q..,Peters D..,Dupuy-Todd M..,Forsyth E..,...&Alvarez R.A..(2021).New methods to derive street-scale spatial patterns of air pollution from mobile monitoring.ATMOSPHERIC ENVIRONMENT. |
| MLA | Padilla L.E.,et al."New methods to derive street-scale spatial patterns of air pollution from mobile monitoring".ATMOSPHERIC ENVIRONMENT (2021). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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