Marianne Hatzopoulou

Integrating an Activity-Based Travel Demand Model with Dynamic Traffic Assignment and Emission Models: Implementation in the Greater Toronto, Canada, Area

Microsimulation is becoming more popular in transportation research. This research explores the potential of microsimulation by integrating an existing activity-based travel demand model, TASHA, with a dynamic agent-based traffic simulation model, MATSim. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modeling frameworks using TASHA and static assignment using Emme/2. The resulting model is then used for light-duty vehicle emission modeling where the traditional average-speed modeling approach is improved by exploiting agent-based traffic simulation results. This improved method of emission modeling is more sensitive to the effect of congestion, and the linkage between individual vehicles and link emissions is preserved. The results have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation. The framework can be improved by further enhancing the sensitivity of TASHA to travel time.

Exposure to traffic-related air pollution during physical activity and acute changes in blood pressure, autonomic and micro-vascular function in women: a cross-over study

BackgroundTraffic-related air pollution may contribute to cardiovascular morbidity. In urban areas, exposures during physical activity are of interest owing to increased breathing rates and close proximity to vehicle emissions.MethodsWe conducted a cross-over study among 53 healthy non-smoking women in Montreal, Canada during the summer of 2013. Women were exposed to traffic pollutants for 2-hours on three separate occasions during cycling on high and low-traffic routes as well as indoors. Personal air pollution exposures (PM2.5, ultrafine particles (UFP), black carbon, NO2, and O3) were evaluated along each route and linear mixed-effects models with random subject intercepts were used to estimate the impact of air pollutants on acute changes in blood pressure, heart rate variability, and micro-vascular function in the hours immediately following exposure. Single and multi-pollutant models were examined and potential effect modification by mean regional air pollution concentrations (PM2.5, NO2, and O3) was explored for the 24-hour and 5-day periods preceding exposure.ResultsIn total, 143 exposure routes were completed. Each interquartile increase (10,850/cm3) in UFP exposure was associated with a 4.91% (95% CI: -9.31, -0.512) decrease in reactive hyperemia index (a measure of micro-vascular function) and each 24 ppb increase in O3 exposure corresponded to a 2.49% (95% CI: 0.141, 4.84) increase in systolic blood pressure and a 3.26% (95% CI: 0.0117, 6.51) increase in diastolic blood pressure 3-hours after exposure. Personal exposure to PM2.5 was associated with decreases in HRV measures reflecting parasympathetic modulation of the heart and regional PM2.5 concentrations modified these relationships (p < 0.05). In particular, stronger inverse associations were observed when regional PM2.5 was higher on the days prior to the study period. Regional PM2.5 also modified the impact of personal O3 on the standard deviation of normal to normal intervals (SDNN) (p < 0.05): a significant inverse relationship was observed when regional PM2.5 was low prior to study periods and a significant positive relationship was observed when regional PM2.5 was high.ConclusionExposure to traffic pollution may contribute to acute changes in blood pressure, autonomic and micro-vascular function in women. Regional air pollution concentrations may modify the impact of these exposures on autonomic function.

The impact of traffic volume, composition, and road geometry on personal air pollution exposures among cyclists in Montreal, Canada

Cyclists may experience increased exposure to traffic-related air pollution owing to increased minute ventilation and close proximity to vehicle emissions. The aims of this study were to characterize personal exposures to air pollution among urban cyclists and to identify potential determinants of exposure including the type of cycling lane (separated vs on-road), traffic counts, and meteorological factors. In total, personal air pollution exposure data were collected over 64 cycling routes during morning and evening commutes in Montreal, Canada, over 32 days during the summer of 2011. Measured pollutants included ultrafine particles (UFPs), fine particles (PM2.5), black carbon (BC), and carbon monoxide (CO). Counts of diesel vehicles were important predictors of personal exposures to BC, with each 10 vehicle/h increase associated with a 15.0% (95% confidence interval (CI): 5.7%, 24.0%) increase in exposure. Use of separated cycling lanes had less impact on personal exposures with a 12% (95% CI: −43%, 14%) decrease observed for BC and smaller decreases observed for UFPs (mean: −1.3%, 95% CI: −20%, 17%) and CO (mean: −5.6%, 95% CI: −17%, 4%) after adjusting for meteorological factors and traffic counts. On average, PM2.5 exposure increased 7.8% (95% CI: −17%, 35%) with separate cycling lane use, but this estimate was imprecise and not statistically significant. In general, our findings suggest that diesel vehicle traffic is an important contributor to personal BC exposures and that separate cycling lanes may have a modest impact on personal exposure to some air pollutants. Further evaluation is required, however, as the impact of separate cycling lanes and/or traffic counts on personal exposures may vary between regions.

Investigating the Use Of Portable Air Pollution Sensors to Capture the Spatial Variability Of Traffic-Related Air Pollution.

Advances in microsensor technologies for air pollution monitoring encourage a growing use of portable sensors. This study aims at testing their performance in the development of exposure surfaces for nitrogen dioxide (NO2) and ozone (O3). In Montreal, Canada, a data-collection campaign was conducted across three seasons in 2014 for 76 sites spanning the range of land uses and built environments of the city; each site was visited from 6 to 12 times, for 20 min, using NO2 and O3 sensors manufactured by Aeroqual. Land-use regression models were developed, achieving R(2) values of 0.86 for NO2 and 0.92 for O3 when adjusted for regional meteorology to control for the fact that all of the locations were not monitored at the same time. A total of two exposure surfaces were then developed for NO2 and O3 as averages over spring, summer, and fall. Validation against the fixed-station data and previous campaigns suggests that Aeroqual sensors tend to overestimate the highest NO2 and O3 concentrations, thus increasing the range of values across the city. However, the sensors suggest a good performance with respect to capturing the spatial variability in NO2 and O3 and are very convenient to use, having great potential for capturing temporal variability.

Characterizing the impact of traffic and the built environment on near-road ultrafine particle and black carbon concentrations.

BACKGROUND Increasing evidence suggests that ultrafine particles (UFPs) may contribute to cardiorespiratory morbidity. We examined the relationship between near road UFPs and several traffic and built environment factors to identify predictors that may be used to estimate exposures in population-based studies. Black carbon (BC) was also examined. METHODS Data were collected on up to 6 occasions at 73 sites in Montreal, Canada over 6-week period during summer, 2012. After excluding highly correlated variables, road width, truck ratio (trucks/total traffic), building height, land zoning parameters, and meteorological factors were evaluated. Random-effect models were used to estimate percent changes in UFP and BC concentrations with interquartile changes in each candidate predictor adjusted for meteorological factors. RESULTS Mean pollutant concentrations varied substantially across sites (UFP range: 1977-94, 798 particles/cm(3); BC range: 29-9460 ng/m(3)). After adjusting for meteorology, interquartile increases in road width (14%, 95% CI: 0, 30), building height (13%, 95% CI: 5, 22), and truck ratio (13%, 95% CI: 3, 23) were the most important predictors of mean UFP concentrations. Road width (28%, 95% CI: 9, 51) and industrial zoning (18%, 95% CI: 2, 37) were the strongest predictors of maximum UFP concentrations. Industrial zoning (35%, 95% CI: 9, 67) was the strongest predictor of BC. CONCLUSIONS A number of traffic and built environmental factors were identified as important predictors of near road UFP and BC concentrations. Exposure models incorporating these factors may be useful in evaluating the health effects of traffic related air pollution.

Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics.

The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LAeq24h, Lnight, and Lden to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LAeq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R2) was 0.68, 0.59, and 0.69 for LAeq24h, Lnight, and Lden, respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LAeq24h levels computed over 5 days at road-traffic sites (bias: −0.7 dB(A)), but not at rail (−2.1 dB(A)) nor at air (−2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure.

Integrating Vehicle Emission Modeling with Activity-Based Travel Demand Modeling: Case Study of the Greater Toronto, Canada, Area

An initial attempt is made to quantify vehicle emissions in the Greater Toronto Area (GTA) in Canada by exploiting travel information provided by activity-based 24-h models rather than conventional trip-based models. For this purpose, travel activity inputs to the Canadian version of the MOBILE6.2 model (MOBILE6.2C) are generated by relying on the travel demand modeling capabilities of the Travel Activity Scheduler for Household Agents (TASHA), a next-generation activity-based model of travel demand for the GTA. Additional input data supplied to MOBILE6.2C are obtained from Canadian sources and by running traffic assignment (using EMME/2) on the trip distribution matrix generated by TASHA. The integration of MOBILE6.2 with TASHA has provided estimates of the time of day that vehicle emissions occur. TASHA provides an explicit representation of trip starts and ends, which results in improved engine start emissions. Overall, because TASHA provides a better behavioral framework for modeling travel than conventional trip-based models, it is expected to lead to better emissions estimates. Such an effort also provides insight and experience that will be used later in the integration of TASHA with more advanced emission models, thus refining the reliability of practical tools that can be used to assess the environmental sustainability of policies.

A web-based route planning tool to reduce cyclists' exposures to traffic pollution: A case study in Montreal, Canada

We developed a web-based route planning tool for cyclists in Montreal, Canada, using spatial monitoring data for ambient nitrogen dioxide (NO2). With this tool, we estimated exposures to NO2 along shortest routes and lower exposure alternatives using origin-destination survey data. On average, exposures were estimated to be lower by 0.76 ppb (95% CI: 0.72, 0.80) relative to the shortest route, with decreases of up to 6.1 ppb for a single trip. Cumulative exposure levels (ppb km) decreased by approximately 4%. In general, the benefits of decreased exposure could be achieved with little increase (less than 1 km) in the overall route length.

Socioeconomic status and environmental noise exposure in Montreal, Canada

BackgroundThis study’s objective was to determine whether socioeconomically deprived populations are exposed to greater levels of environmental noise.MethodsIndicators of socioeconomic status were correlated with LAeq24h noise levels estimated with a land-use regression model at a small geographic scale.ResultsWe found that noise exposure was associated with all socioeconomic indicators, with the strongest correlations found for median household income, proportion of people who spend over 30% of their income on housing, proportion of people below the low income boundary and with a social deprivation index combining several socio-economic variables.ConclusionOur results were inconsistent with a number of studies performed elsewhere, indicating that locally conducted studies are imperative to assessing whether this double burden of noise exposure and low socioeconomic status exists in other contexts. The primary implication of our study is that noise exposure represents an environmental injustice in Montreal, which is an issue that merits both investigation and concern.