Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions
| Autor: | Timothy Gould, Mei W. Tessum, Joel D. Kaufman, Sverre Vedal, Lianne Sheppard, Timothy V. Larson |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Pollutant
Air Pollutants Air pollutant concentrations Meteorology Mobile broadband Air pollution Sampling (statistics) General Chemistry 010501 environmental sciences medicine.disease_cause 01 natural sciences Metropolitan area Los Angeles Article Term (time) Air Pollution Baltimore medicine Environmental Chemistry Environmental science Particulate Matter Spatial analysis 0105 earth and related environmental sciences Environmental Monitoring |
| Zdroj: | Environ Sci Technol |
| ISSN: | 1520-5851 |
| Popis: | Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005–2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single two-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO(2) and NO(x) concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs, and only use mobile data collected within 300m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R(2) changing from 0.40 to 0.82), but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases additional spatial information from mobile monitoring and personal sampling are potentially cost-efficient inexpensive ways of improving exposure predictions at both two-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|