Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors

Long- and short-term exposure to airborne pollutants results in adverse health effects. Regulatory monitors can be used to determine if regional concentrations meet regulatory standards of air pollution. As assessments of air pollutant exposure become more spatially resolved, evaluation is needed to assess the spatial representativeness of monitors in different environments. We measured NO2, ultrafine particle concentration (UFP), and PM1 with both stationary and mobile platforms in Pittsburgh, PA in 2016 and 2017. We sampled in eight -1 km(2) neighborhoods representing different land use and exposure regimes (e.g., urban and suburban, high and low traffic). Mobile sampling was conducted on up to 25 days in each neighborhood to study fine-scale spatial variation in pollutant concentrations. NO2 exhibited within-neighborhood spatial variation, with hotspots elevated by up to a factor of 5 above the regional background. Spatial differences in UFP within the same 1 km(2) neighborhoods could be a factor of 2.4 times regional background. PM1 was more regional and less spatially variable. Most neighborhoods exhibited less than 1 mu gm(-3) spatial variability in PM1. Spatial variability of NO2 and UFP showed moderate correlation (R-2 > 0.5) with traditional land use covariates such as traffic volume and restaurant density. We used the Wilcoxon rank-sum test to calculate the fraction of each neighborhood represented by the same underlying concentration distribution. PM1 was the most spatially homogeneous, with 80-100% of each 1 km(2) area being statistically similar to a reference location. Quantifying pollutant spatial patterns with high fidelity (e.g.,< 2 ppb NO2 or < 1 mu gm(-3) PM1) seems unattainable in many urban areas unless the sampling network is significantly dense, with more than one or two nodes per km(2).