Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community
- 15 March 2019
- journal article
- research article
- Published by American Chemical Society (ACS) in Environmental Science & Technology
- Vol. 53 (8), 4305-4315
- https://doi.org/10.1021/acs.est.8b05249
Abstract
Most empirical air quality models (e.g., land use regression) focus on urban areas. Mobile monitoring for model development offers the opportunity to explore smaller, rural communities – an understudied population. We use mobile monitoring to systematically sample all daylight hours (7am-7pm) to develop empirical models capable of estimating hourly concentrations in Blacksburg, VA – a small town in rural Appalachia (population: 182,635). We collected ~120 hours of mobile monitoring data for Particle Number (PN) and Black Carbon (BC). We developed (1) daytime (12-hour average) models that approximate long-term concentrations and (2) spatiotemporal models for estimating hourly concentrations. Model performance for the daytime models is consistent with previous fixed-site and short-term sampling studies; adjusted R2 (10-fold CV R2) was 0.80 (0.69) for the PN model and 0.67 (0.58) for the BC model. The spatiotemporal models had comparable performance (10-fold CV R2 for the PN [BC] models: 0.42 [0.25]) to previous mobile monitoring studies that isolate specific time periods. Temporal and spatial model coefficients had similar magnitudes in the spatiotemporal models suggesting both factors are important for exposure. We observed similar spatial patterns in Blacksburg (e.g., roadway gradients) as in other studies in urban areas suggesting similar exposure disparities exist in small, rural communities.Keywords
Funding Information
- Institute for Critical Technologies and Applied Science, Virginia Tech
This publication has 48 references indexed in Scilit:
- Application and evaluation of two model fusion approaches to obtain ambient air pollutant concentrations at a fine spatial resolution (250m) in AtlantaEnvironmental Modelling & Software, 2018
- Cross-comparison and evaluation of air pollution field estimation methodsAtmospheric Environment, 2018
- Mortality risk and PM2.5 air pollution in the USA: an analysis of a national prospective cohortAir Quality, Atmosphere & Health, 2017
- National Spatiotemporal Exposure Surface for NO2: Monthly Scaling of a Satellite-Derived Land-Use Regression, 2000–2010Environmental Science & Technology, 2015
- A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth dataAtmospheric Environment, 2014
- Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE projectThe Lancet, 2013
- Development of Land Use Regression Models for Particle Composition in Twenty Study Areas in EuropeEnvironmental Science & Technology, 2013
- Validation of a Spatiotemporal Land Use Regression Model Incorporating Fixed Site MonitorsEnvironmental Science & Technology, 2010
- A review of land-use regression models to assess spatial variation of outdoor air pollutionAtmospheric Environment, 2008
- A Review of Land-use Regression Models for Characterizing Intraurban Air Pollution ExposureInhalation Toxicology, 2007