Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements.

A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO2) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO2 concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R2 = 0.71 for prediction of NO2 data in 2002 and 2004) uses daily average NO2 concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO2 levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO2 patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.

Transboundary and traffic influences on air pollution across two Caribbean islands.

Exposure to ambient air pollution has been linked to adverse health outcomes ranging from asthma to premature mortality. However, little to no information exists on the exposure of residents and visitors in the Caribbean islands. While a few previous studies have quantified levels of PM10 (particulate matter <10 µm) from Sahara dust in Trinidad, our study focussed on a local source of air pollution, traffic emissions. Mass concentrations of black carbon (BC) and PM2.5 (PM <2.5 µm) were measured at ten locations across the islands of Trinidad and Tobago over a three-week period. PM2.5 concentrations were observed to be heavily influenced by air masses showing origins from the Sahara Desert (31%), North America (26%) and Atlantic Ocean (42%), which resulted in similar average concentrations between the two islands. Average concentrations of BC were five times higher in Trinidad than Tobago (2.0 vs 0.43 µg/m3). In addition, BC in Trinidad was three times higher near than away from major roads (2.21 vs. 0.72 µg/m3), with concentrations reaching levels comparable to those near highways in large Metropolitan cities. The elevated BC concentrations observed in this study suggests that significant exposure to diesel exhaust is occurring in Trinidad, with significant contributions from traffic.

Near-Road Air Pollutant Measurements: Accounting for Inter-Site Variability Using Emission Factors.

A daily integrated emission factor (EF) method was applied to data from three near-road monitoring sites to identify variables that impact traffic related pollutant concentrations in the near-road environment. The sites were operated for 20 months in 2015-2017, with each site differing in terms of design, local meteorology, and fleet compositions. Measurement distance from the roadway and local meteorology were found to affect pollutant concentrations irrespective of background subtraction. However, using emission factors mostly accounted for the effects of dilution and dispersion, allowing intersite differences in emissions to be resolved. A multiple linear regression model that included predictor variables such as fraction of larger vehicles (>7.6 m in length; i.e., heavy-duty vehicles), vehicle speed, and ambient temperature accounted for intersite variability of the fleet average NO, NO x, and particle number EFs (R2:0.50-0.75), with lower model performance for CO and black carbon (BC) EFs (R2:0.28-0.46). NO x and BC EFs were affected more than CO and particle number EFs by the fraction of larger vehicles, which also resulted in measurable weekday/weekend differences. Pollutant EFs also varied with ambient temperature and because there were little seasonal changes in fleet composition, this was attributed to changes in fuel composition and/or post-tailpipe transformation of pollutants.