Seasonal effects in land use regression models for nitrogen dioxide, coarse particulate matter, and gaseous ammonia in Cleveland, Ohio

AbstractPassive ambient air sampling for nitrogen dioxide (NO2), coarse particulate matter (PMc), and gaseous ammonia (NH3) was conducted at 22 monitoring sites, a compliance site, and a background site in the Cleveland, Ohio, USA area during summer 2009 and winter 2010. This air monitoring network was established to assess intra–urban gradients of air pollutants and evaluate the impact of traffic and urban emissions on air quality. Method evaluations of passive monitors, which were weeklong in duration for NO2 and PMc and two–weeklong for NH3, demonstrated the ability of the NO2 and NH3 monitors to adequately measure air pollution concentrations, while the precision of the PMc sampler showed the need for improvement. Seasonal differences were obvious from visual inspection for NO2 (higher in winter) and NH3 (higher in summer) but were less apparent for PMc levels. Land use regression models (LURs) revealed spatial gradients for NO2 and PMc from traffic and industrial sources. A strong summer/winter seasonal influence was detected in the LURs, with season being the only significant predictor of NH3. Explicit use of summer and winter seasons in the LURs revealed both a seasonal effect, per se, and also seasonal interaction with other predictor variables