Low-cost sensor networks and land-use regression: interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California
| Autor: | Lena Weissert, Kyle A. Alberti, Elaine Miles, Geoff S. Henshaw, Georgia Miskell, Jennifer Salmond, Vasileios Papapostolou, David E. Williams, Brandon Feenstra, Andrea Polidori, Hamesh Patel |
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| Rok vydání: | 2019 |
| Předmět: |
Pollution
FOS: Computer and information sciences Atmospheric Science Measure (data warehouse) 010504 meteorology & atmospheric sciences Land use Calibration (statistics) media_common.quotation_subject 010501 environmental sciences 01 natural sciences Statistics - Applications Random forest Environmental science Applications (stat.AP) Wireless sensor network Image resolution Air quality index 0105 earth and related environmental sciences General Environmental Science media_common Remote sensing |
| DOI: | 10.48550/arxiv.1911.03130 |
| Popis: | The development of low-cost sensors and novel calibration algorithms offer new opportunities to supplement existing regulatory networks to measure air pollutants at a high spatial resolution and at hourly and sub-hourly timescales. We use a random forest model on data from a network of low-cost sensors to describe the effect of land use features on local-scale air quality, extend this model to describe the hourly-scale variation of air quality at high spatial resolution, and show that deviations from the model can be used to identify particular conditions and locations where air quality differs from the expected land-use effect. The conditions and locations under which deviations were detected conform to expectations based on general experience. Comment: 26 pages, 11 figures |
| Databáze: | OpenAIRE |
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