An Hourly Regression Model for Ultrafine Particles in a Near-Highway Urban Area

Abstract

Estimating ultrafine particle number concentrations (PNC) near highways for exposure assessment in chronic health studies requires models capable of capturing PNC spatial and temporal variations over the course of a full year. The objectives of this work were to describe the relationship between near-highway PNC and potential predictors, and to build and validate hourly log-linear regression models. PNC was measured near Interstate 93 (I-93) in Somerville, MA (USA) using a mobile monitoring platform driven for 234 hours on 43 days between August 2009 and September 2010. Compared to urban background, PNC levels were consistently elevated within 100–200 m of I-93, with gradients impacted by meteorological and traffic conditions. Temporal and spatial variables including wind speed and direction, temperature, highway traffic, and distance to I-93 and major roads contributed significantly to the full regression model. Cross-validated model R2 values ranged from 0.38–0.47, with higher values achieved (0.43–0.53) when short-duration PNC spikes were removed. The model predicts highest PNC near major roads and on cold days with low wind speeds. The model allows estimation of hourly ambient PNC at 20-m resolution in a near-highway neighborhood.

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

“…The absence of variables characterizing detailed pollution sources often leads to a smaller proportion of variability explained by LUR models, which was concluded by Allen et al (2013) and Saraswat et al (2013). Moreover, recently published spatiotemporal LUR models (Patton et al 2014;Smargiassi et al 2012) usually incorporated meteorological conditions, especially wind speed and direction, as these factors play a crucial role in the spatiotemporal pattern of air pollutants (Gupta and Christopher 2009;McKendry 2000). As for dynamic variables that change over time, they can generally improve the performance of spatiotemporal LUR models (Dons et al 2013).…”

“…Several recent LUR studies have attempted to capture temporal variation, mainly in three ways: (1) by modifying the intercept of LUR models based on a temporal variation observed at background monitoring sites (Gan et al 2011;Nethery et al 2008;Saraswat et al 2013;Slama et al 2007); (2) by adding dummy variables to reflect different periods (Dons et al 2013;Noth et al 2011);and (3) by building LUR models for different periods with dynamic or static variables (Dons et al 2013;Gulliver et al 2011). Other studies even integrated the above methods (Adam-Poupart et al 2014;Patton et al 2014). The first two approaches are limited to producing the same spatial pattern of pollutants over time, because they are based on a typical LUR model.…”

“…Our initial purpose was to explore factors that influence the spatial variation of increased PM 2.5 concentrations during peak hours by means of model comparison between peak and non-peak hours and building the model for incremental concentration. Only a few LUR studies (Dons et al 2013;Hong and Bae 2012;Patton et al 2014) have investigated the issue regarding traffic rush hours.…”

Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment

“…For example, Hasenfratz et al (2015) and Mueller et al (2016) present a study on the modelling of particle number concentrations in Zurich using data from a tram-based mobile sensor network. Hankey and Marshall (2015) use bicycle-based, mobile measurements to build LUR models, and in studies of Kanaroglou et al (2013), Patton et al (2014) and Weichenthal et al (2016b), van-based measurements are used. Mobile measurements can also be collected in participatory and community-based campaigns.…”

Zone of influence for particle number concentrations at signalised traffic intersections

“…The route was chosen with an intention to pass through a maximum number of TIs of different geometries and built-up area around them so that varying impact of PNC dispersion at these TIs can be assessed Kumar, 2014, 2015;Patton et al, 2014). The pavement of each of the studied 10 TIs was made of bituminous flexible pavement, which contained ~95% of asphalt and the remaining contents being filler and bituminous binder (Kumar et al, 2013).…”