New methods to derive street-scale spatial patterns of air pollution from mobile monitoring

Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area

“…This background subtraction and enhancement identification process is illustrated in section S3. While this approach of identifying enhancements in pollutant time series has been used in previous studies, ,, it should be noted that these observed enhancements indicate the combined effect of various factors (e.g., number of sources, distance between the source and sampling point, and amounts of dispersion, dilution, mixing, etc., occurring in the interim) and thus should not be used to directly infer source properties, e.g., emission factors.…”

“…This temporally varying background contribution could be from a spatially invariant, regional background, or in the case of mobile sampling, it could also be an intermediate, neighborhood-scale background, e.g., when the GSV car drives along a suburban-to-street-canyon-to-suburban transect. To identify local enhancements, we employed an enhancement identification method following Padilla et al 54 Briefly, for each 1 Hz observation, a “background” concentration is identified (first percentile of the observations in the 150 s before and 150 s after; at a typical speed of 30 km h –1 , this translates to a distance of just <2.5 km). Next, the difference between the total concentration and the background is defined as a local enhancement (Δ).…”

“…In this study, we address this gap by presenting mobile, high-spatial resolution, repeated, on-road measurements of LDSA in central London, along with simultaneous measurements of optically measured PM 2.5 mass, BC mass, and NO 2 mixing ratios. These measurements were conducted as part of the first year-long mobile air monitoring campaign in central London. , The large data set generated from repeated, year-long mobile measurements of these pollutants enables us to prepare high-resolution maps of long-term central tendencies of individual pollutant concentrations, as well as statistical analyses of uncertainty, local enhancements, interspecies ratios of enhancements, and, finally, spatial correlations between these metrics and two ubiquitiously occurring urban particulate sources: major roadways and restaurants (land-use covariate proxies for vehicular and cooking emissions, respectively). To the best of our knowledge, this is the first study reporting high-spatial resolution, year-long, mobile measurements of LDSA.…”

“…Probability of Exceedance. This methodology is described in detail by Padilla et al 54 and described here briefly. Because only two GSVs performed sampling within the 22 km 2 central London region, sampling coverage can be sparse on some roads (section S5).…”

Optimizing Urban Air Pollution Detection Systems

“…In order to solve air pollution monitoring problems, various methods are used, including probability theory and statistical methods [30][31][32][33][34]. They are commonly used to investigate the location and identification of emission sources (for example, conditional probability functions are used for source identification).…”