(9,13) Land use regression (LUR) has been a popular approach (14) because of its simplicity, interpretability, and ability to predict fine-scale variations in pollution. Numerous approaches exist for quantifying intraurban variation in air pollutant concentrations. Data-only mapping performed poorly with few (1–2) repeated drives but obtained better cross-validation R 2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.
#GOOGLE MAPS CAR FULL#
Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R 2 for log-transformed NO and BC of 0.65 and 0.43. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations here, measurements from only a subset of roads or repeat visits are considered. First, we explore a “data-only” approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore approaches to minimize data requirements for mapping a city’s air quality using mobile monitors with “data-only” versus predictive modeling approaches.
Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution.
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