Assessing the spatial representativeness of PM₂.₅ and O₃ measurements from the National Air Pollutant Surveillance System

Our literature review demonstrated that within-city spatial variation of fine particulate matter (PM₂.₅) mass is case specific, and that in some cities significant spatial variation exists while in others PM₂.₅ mass is spatially homogenous (15 studies reviewed identified within-city PM₂.₅ mass concentration spatial variation greater than 20%, while 21 studies found PM₂.₅ mass concentration spatial variation less than 20%). Primary PM₂.₅, including Black Carbon and ultrafine particle mass and number concentration, demonstrated significant within-city spatial variation (20 of 22 studies reviewed identified within-city spatial variation in primary PM₂.₅ greater than 20%). The average within-city spatial variation of primary PM₂.₅ was greater than 200%. In most circumstances, within-city ozone (O₃) variation is larger than within-city PM₂.₅ mass variation; however, much smaller than that seen for measures of primary PM₂.₅. Significant spatial variation in O₃ concentrations was seen at all spatial scales (local, neighborhood, city, urban/suburban and urban/rural) and due to scavenging by NO, large O₃ gradients are present at fine-scale spatial resolution (e.g. around major roads and other high traffic areas). Of the 26 studies reviews, 21 found within-city O₃ spatial variation greater than 20%. Recommendations: • Statistical methods could be applied to data from the NAPS monitoring network to examine within-city variability when multiple monitors exist. Currently, 18 metropolitan areas have greater than two O₃ monitors and 23 metropolitan areas have greater than two PM₂.₅ monitors. Correlation and coefficient of divergence measures could be calculated for monitor pairs using hourly, daily, monthly and yearly averages to identify areas where additional monitoring to characterize spatial variability could be prioritized. • The use of PM₂.₅ mass is not adequate for capturing primary PM₂.₅ and masks significant within-city spatial variation that exists in measures of primary PM₂.₅. Specific PM₂.₅ components of concern should be considered when examining the influence of within-city spatial variation. We demonstrated that existing Canadian city-specific land use regression (LUR) models could be used to assess the spatial representativeness of NAPS O₃ monitors. Annual, summer,and summer daylight models were able to explain 84%, 84%, and 79% of the spatial variation for 39 NAPS O₃ monitors. Small within-city O₃ spatial variation was present, however, likely due to monitor siting. From these models we illustrated how a spatial representative area could be defined for each monitor and show that these areas do not correspond to straight proximity (the most common extrapolation method used for NAPS data). Recommendations: • Existing city-specific NO₂ LUR models can be used to examine O₃ spatial variability and define representative areas around each NAPS monitor; however, further work is needed to incorporate more predictor variables and to examine the spatiotemporal stability of the O₃ and NO₂ relationship. With regard to population coverage of the current PM₂.₅ and O₃ NAPS monitoring network, 60% of the Canadian population is within 10km of a monitor. However, our preliminary analysis of NAPS monitor representative areas indicates that monitor proximity alone does not fully characterize population exposure. We derived objective monitor siting characteristics (e.g. proximity to roads, road density, landuse, industrial emissions, population density) for various buffer distances around each monitor using geographical information systems (GIS) and nation data. There was generally poor agreement between the existing NAPS monitor classifications (e.g. agricultural, residential, industrial) and landuse characteristics derived in a GIS. Recommendations: • NAPS monitor classifications could be refined to better provide information on source influences and representativeness. At minimum, a traffic influenced monitor code should be added to the current NAPS monitor classification. In addition, the full description of monitor siting characteristics should be made available with NAPS data as the siting characteristics of most importance will change based on different users. For this reason, simple monitor classifications can be misleading. • Additional siting variables could be derived for NAPS monitors using other readily available geographic data. • Land use may change around monitors over time and it is recommended that NAPS periodically evaluate and update monitor siting characteristics. Ideally, the spatial representativeness of NAPS monitors would be assessed using saturation measurements at various locations in space and time around each monitor. We reviewed measurement methods that could be used to assess representative areas and since complete monitoring coverage is not feasible, we also reviewed modeling methods. We briefly introduced each method and summarized their strengths and weaknesses for determining the spatial representativeness of NAPS monitors. Recommendations: • A representative area should be defined for each NAPS monitor, either using measurements or modeling approaches, such as those demonstrated using existing NO₂ LUR models. Representative areas would inform use of NAPS measurements and their spatial extrapolation, and future monitor siting. • Along with the representative area for each NAPS monitor, the population representativeness should also be determined (e.g. the percentage of the population in a CMA represented by the monitor). This would be useful for prioritizing NAPS stations and for future monitor siting.

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