Air pollution from industries and asthma onset in childhood: A population-based birth cohort study using dispersion modeling.

BACKGROUND: Despite evidence that ambient air pollution may play a role in the development of asthma, little is known about the potential contribution of industrial emissions. OBJECTIVE: We used a population-based birth cohort to investigate the association between asthma onset in childhood and residential exposure to industrial emissions, estimated from atmospheric dispersion modeling. METHODS: The study population comprised all children born in the province of Quebec, Canada, 2002-2011. Asthma onset were ascertained from health administrative databases with validated algorithms. We used atmospheric dispersion modeling to develop time-varying annual mean concentration of ambient PM2.5, NO2 and SO2 at participants' residence from industries. For each pollutant, we assessed the association between industrial emissions exposure and childhood asthma onset using Cox proportional hazard model, adjusted for sex, material and social deprivation and calendar year. Sensitivity analysis included adjusting for long-term regional and traffic-related ambient PM2.5 and NO2, and assessing potential confounding by unmeasured secondhand smoke. RESULTS: The cohort included 722,667 children and 66,559 incident cases of asthma. For all pollutants, we found a non-linear association between childhood asthma onset and residential ambient air pollutant concentration from industries, with stronger effects at lower concentrations. A change from 25th to the 75th percentile in the mean annual ambient concentration of PM2.5 (0.13 µg/m3), NO2 (1.0 µg/m3) and SO2 (1.6 µg/m3) from industrial emissions was associated with a 19% (95% CI: 17-20%), 21% (95% CI: 19-23%) and 23% (95% CI: 21-24%) increase in the risk of asthma onset in children, respectively. For PM2.5 and NO2, associations were persisting after adjustments for long-term regional PM2.5 and traffic-related NO2 ambient concentration. CONCLUSION: Residential exposure to industrial emissions estimated from dispersion modeling was associated with asthma onset in childhood. Importantly, associations were stronger at lower concentrations and independent from those of other sources, thus adding up to the burden of regional and traffic-related air pollution.

Influence of travel behaviour and daily mobility on exposure to traffic-related air pollution.

This study evaluates the daily exposure of urban residents across various commuting modes and destinations by intersecting data from a travel survey with exposure surfaces for ultrafine particles and black carbon, in Toronto, Canada. We demonstrate that exposure misclassification is bound to arise when we approximate daily exposure with the concentration at the home location. We also identify potential inequities in the distribution of exposure to traffic-related air pollution whereby those who are mostly responsible for the generation of traffic-related air pollution (drivers and passengers) are exposed the least while active commuters and transit riders, are exposed the most.

Environmental and health impacts of transportation and land use scenarios in 2061.

We compared numbers of trips and distances by transport mode, air pollution and health impacts of a Business As Usual (BAU) and an Ideal scenario with urban densification and reductions in car share (76%-62% in suburbs; 55%-34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a demographic model and population projections. Year 2031 (Y2031) trips (from mode choice modeling) and distances were used to estimate those of Y2061. Emissions of nitrogen dioxide (NO2) and carbon dioxide (CO2) were estimated and NO2 used with dispersion modeling to estimate concentrations. Walking and Public Transit (PT) use and corresponding distances walked in Y2061 were >70% higher for the Ideal scenario vs the BAU, while car share and distances were <40% lower. NO2 levels were slightly lower in the Ideal scenario vs the BAU, but always higher in the urban core. Health impacts, summarized with disability adjusted life years (DALY), differed between urban and suburb areas but globally, the Ideal scenario reduced the impacts of the Y2061 BAU by 33% DALY. Percentages of car and PT trips were similar for the Y2031 and Y2061 BAU but kms travelled by car, CO2 and NO2 increased, due to increased populations. Drastic measures to decrease car share appear necessary to substantially reduce impacts of transportation.

Within-City Spatial Variations in Multiple Measures of PM2.5 Oxidative Potential in Toronto, Canada.

Few studies have characterized within-city spatial variations in the oxidative potential of fine particulate air pollution (PM2.5). In this study, we evaluated multiple measures of PM2.5 oxidative potential across Toronto, Canada (2016-2017), including glutathione/ascorbate-related oxidative potential (OPGSH and OPAA) and dithiothreitol depletion (OPDTT). Integrated 2-week samples were collected from 67 sites in summer and 42 sites in winter. Multivariable linear models were developed to predict OP based on various land use/traffic factors, and PM2.5 metals and black carbon were also examined. All three measures of PM2.5 oxidative potential varied substantially across Toronto. OPAA and OPDTT were primarily associated with traffic-related components of PM2.5 (i.e., Fe, Cu, and black carbon) whereas OPGSH was not a strong marker for traffic during either season. During summer, multivariable models performed best for OPAA ( RCV2 = 0.48) followed by OPDTT ( RCV2 = 0.32) and OPGSH ( RCV2 = 0.22). During winter, model performance was best for OPDTT ( RCV2 = 0.55) followed by OPGSH ( RCV2 = 0.50) and OPAA ( RCV2 = 0.23). Model parameters varied between seasons, and between-season differences in PM2.5 mass concentrations were weakly/moderately correlated with seasonal differences in OP. Our findings highlight substantial within-city variations in PM2.5 oxidative potential. More detailed information is needed on local sources of air pollution to improve model performance.

Estimating the health benefits of planned public transit investments in Montreal.

BACKGROUND: Since public transit infrastructure affects road traffic volumes and influences transportation mode choice, which in turn impacts health, it is important to estimate the alteration of the health burden linked with transit policies. OBJECTIVE: We quantified the variation in health benefits and burden between a business as usual (BAU) and a public transit (PT) scenarios in 2031 (with 8 and 19 new subway and train stations) for the greater Montreal region. METHOD: Using mode choice and traffic assignment models, we predicted the transportation mode choice and traffic assignment on the road network. Subsequently, we estimated the distance travelled in each municipality by mode, the minutes spent in active transportation, as well as traffic emissions. Thereafter we estimated the health burden attributed to air pollution and road traumas and the gains associated with active transportation for both the BAU and PT scenarios. RESULTS: We predicted a slight decrease of overall trips and kilometers travelled by car as well as an increase of active transportation for the PT in 2031 vs the BAU. Our analysis shows that new infrastructure will reduce the overall burden of transportation by 2.5 DALYs per 100,000 persons. This decrease is caused by the reduction of road traumas occurring in the inner suburbs and central Montreal region as well as gains in active transportation in the inner suburbs. CONCLUSION: Based on the results of our study, transportation planned public transit projects for Montreal are unlikely to reduce drastically the burden of disease attributable to road vehicles and infrastructures in the Montreal region. The impact of the planned transportation infrastructures seems to be very low and localized mainly in the areas where new public transit stations are planned.

Spatial variations in ambient ultrafine particle concentrations and the risk of incident prostate cancer: A case-control study.

BACKGROUND: Diesel exhaust contains large numbers of ultrafine particles (UFPs, <0.1µm) and is a recognized human carcinogen. However, epidemiological studies have yet to evaluate the relationship between UFPs and cancer incidence. METHODS: We conducted a case-control study of UFPs and incident prostate cancer in Montreal, Canada. Cases were identified from all main Francophone hospitals in the Montreal area between 2005 and 2009. Population controls were identified from provincial electoral lists of French Montreal residents and frequency-matched to cases using 5-year age groups. UFP exposures were estimated using a land use regression model. Exposures were assigned to residential locations at the time of diagnosis/recruitment as well as approximately 10-years earlier to consider potential latency between exposure and disease onset. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated per interquartile range (IQR) increase in UFPs (approximately 4000 particles/cm3) using logistic regression models adjusting for individual-level and ecological covariates. RESULTS: Ambient UFP concentrations were associated with an increased risk of prostate cancer (OR=1.10, 95% CI: 1.01, 1.19) in fully adjusted models when exposures were assigned to residences 10-years prior to diagnosis. This risk estimate increased slightly (OR=1.17, 95% CI; 1.01, 1.35) when modeled as a non-linear natural spline function. A smaller increased risk (OR=1.04, 95% CI: 0.97, 1.11) was observed when exposures were assigned to residences at the time of diagnosis. CONCLUSIONS: Exposure to ambient UFPs may increase the risk of prostate cancer. Future studies are needed to replicate this finding as this is the first study to evaluate this relationship.

The association between the incidence of postmenopausal breast cancer and concentrations at street-level of nitrogen dioxide and ultrafine particles.

BACKGROUND: There is scant information as to whether traffic-related air pollution is associated with the incidence of breast cancer. Nitrogen dioxide (NO2) and ultrafine particles (UFPs, <0.1µm), are two pollutants that capture intra-urban variations in traffic-related air pollution and may also be associated with incidence. METHODS: We conducted a population-based, case-control study of street-level concentrations of NO2 and UFPs and incident postmenopausal breast cancer in Montreal, Canada. Incident cases were identified between 2008 and 2011 from all but one hospital that treated breast cancer in the Montreal area. Population controls were identified from provincial electoral lists of Montreal residents and frequency-matched to cases using 5-year age groups. Concentrations of NO2 and UFPs were estimated using two separate land-use regression models. Exposures were assigned to residential locations at the time of recruitment, and we identified residential histories of women who had lived in these residences for 10 years or more. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using logistic regression models adjusting for individual-level and ecological covariates. We assessed the functional form of NO2 and UFP exposures using natural cubic splines. RESULTS: We found that the functional form of the response functions between incident postmenopausal breast cancer and concentrations of NO2 and UFPs were consistent with linearity. For NO2, we found increasing risks of breast cancer for all subjects combined and stronger associations when analyses were restricted to those women who had lived at their current address for 10 years or more. Specifically, the OR, adjusted for personal covariates, per increase in the interquartile range (IQR=3.75 ppb) of NO2 was 1.08 (95%CI: 0.92-1.27). For women living in their homes for 10 years or more, the adjusted OR was 1.17 (95%CI: 0.93-1.46; IQR=3.84 ppb); for those not living at that home 10 years before the study, it was 0.93 (95%CI: 0.64, 1.36; IQR=3.65 ppb). For UFPs, the ORs were lower than for NO2, with little evidence of association in any of the models or sub-analyses and little variability in the ORs (about 1.02 for an IQR of ~3500cm-3). On the other hand, we found higher ORs amongst cases with positive oestrogen and progesterone receptor status; namely for NO2, the OR was 1.13 (95%CI: 0.94-1.35) and for UFPs it was 1.05 (95%CI: 0.96-1.14). CONCLUSIONS: Our findings suggest that exposure to ambient NO2 and UFPs may increase the risk of incident postmenopausal breast cancer especially amongst cases with positive oestrogen and progesterone receptor status.

An examination of population exposure to traffic related air pollution: Comparing spatially and temporally resolved estimates against long-term average exposures at the home location.

Air pollution in metropolitan areas is mainly caused by traffic emissions. This study presents the development of a model chain consisting of a transportation model, an emissions model, and atmospheric dispersion model, applied to dynamically evaluate individuals' exposure to air pollution by intersecting daily trajectories of individuals and hourly spatial variations of air pollution across the study domain. This dynamic approach is implemented in Montreal, Canada to highlight the advantages of the method for exposure analysis. The results for nitrogen dioxide (NO2), a marker of traffic related air pollution, reveal significant differences when relying on spatially and temporally resolved concentrations combined with individuals' daily trajectories compared to a long-term average NO2 concentration at the home location. We observe that NO2 exposures based on trips and activity locations visited throughout the day were often more elevated than daily NO2 concentrations at the home location. The percentage of all individuals with a lower 24-hour daily average at home compared to their 24-hour mobility exposure is 89.6%, of which 31% of individuals increase their exposure by more than 10% by leaving the home. On average, individuals increased their exposure by 23-44% while commuting and conducting activities out of home (compared to the daily concentration at home), regardless of air quality at their home location. We conclude that our proposed dynamic modelling approach significantly improves the results of traditional methods that rely on a long-term average concentration at the home location and we shed light on the importance of using individual daily trajectories to understand exposure.

Investigating the role of transportation models in epidemiologic studies of traffic related air pollution and health effects.

In two earlier case-control studies conducted in Montreal, nitrogen dioxide (NO2), a marker for traffic-related air pollution was found to be associated with the incidence of postmenopausal breast cancer and prostate cancer. These studies relied on a land use regression model (LUR) for NO2 that is commonly used in epidemiologic studies for deriving estimates of traffic-related air pollution. Here, we investigate the use of a transportation model developed during the summer season to generate a measure of traffic emissions as an alternative to the LUR model. Our traffic model provides estimates of emissions of nitrogen oxides (NOx) at the level of individual roads, as does the LUR model. Our main objective was to compare the distribution of the spatial estimates of NOx computed from our transportation model to the distribution obtained from the LUR model. A secondary objective was to compare estimates of risk using these two exposure estimates. We observed that the correlation (spearman) between our two measures of exposure (NO2 and NOx) ranged from less than 0.3 to more than 0.9 across Montreal neighborhoods. The most important factor affecting the "agreement" between the two measures in a specific area was found to be the length of roads. Areas affected by a high level of traffic-related air pollution had a far better agreement between the two exposure measures. A comparison of odds ratios (ORs) obtained from NO2 and NOx used in two case-control studies of breast and prostate cancer, showed that the differences between the ORs associated with NO2 exposure vs NOx exposure differed by 5.2-8.8%.

Acute cardiovascular health effects in a panel study of personal exposure to traffic-related air pollutants and noise in Toronto, Canada.

Urban populations are often simultaneously exposed to air pollution and environmental noise, which are independently associated with cardiovascular disease. Few studies have examined acute physiologic responses to both air and noise pollution using personal exposure measures. We conducted a repeated measures panel study of air pollution and noise in 46 non-smoking adults in Toronto, Canada. Data were analyzed using linear mixed-effects models and weighted cumulative exposure modeling of recent exposure. We examined acute changes in cardiovascular health effects of personal (ultrafine particles, black carbon) and regional (PM2.5, NO2, O3, Ox) measurements of air pollution and the role of personal noise exposure as a confounder of these associations. We observed adverse changes in subclinical cardiovascular outcomes in response to both air pollution and noise, including changes in endothelial function and heart rate variability (HRV). Our findings show that personal noise exposures can confound associations for air pollutants, particularly with HRV, and that impacts of air pollution and noise on HRV occur soon after exposure. Thus, both noise and air pollution have a measurable impact on cardiovascular physiology. Noise should be considered alongside air pollution in future studies to elucidate the combined impacts of these exposures in urban environments.

Assessing the transferability of landuse regression models for ultrafine particles across two Canadian cities.

Land use regression (LUR) models have been increasingly used to predict intra-city variations in the concentrations of different air pollutants. However, limited research assessing the transferability of these models between cities has been published to date. In this study, LUR models were generated for Ultra-Fine Particles (UFP) (<0.1 um) using data collected from mobile monitoring campaigns in two Canadian cities, Montreal and Toronto. City-specific models were first generated for each city before the models were transferred to the second city with and without recalibration. The calibrated transferred models showed only a slight decrease in performance, with the coefficient of determination (R2), dropping from 0.49 to 0.36 for Toronto and from 0.41 to 0.38 for Montreal. Transferring models between cities with no calibration resulted in low R2; 0.11 in Toronto and 0.18 in Montreal. Moreover, two additional models were generated by combining data from the two cities. The first combined model (CM1) assumed a spatially invariant effect of the predictors, while the second (CM2) relaxed the assumption of spatial invariance for some of the model coefficients. The performance of both combined models (R2 ranged between 0.41 for CM1 and 0.43 for CM2; root mean squared error (RMSE) ranged between 0.34 for CM1 and 0.33 for CM2) was found to be on par with the Toronto city-specific model and outperformed the Montreal model. The results of this study highlight that the UFP LUR models appear to support transferability of model structures between cities with similar geographical characteristics, with a minor drop in model fit and predictive skill.

Spatial variations in the estimated production of reactive oxygen species in the epithelial lung lining fluid by iron and copper in fine particulate air pollution.

BACKGROUND: Certain metals may play an important role in the adverse health effects of fine particulate air pollution (PM2.5), but few models are available to predict spatial variations in these pollutants. METHODS: We conducted large-scale air monitoring campaigns during summer 2016 and winter 2017 in Toronto, Canada, to characterize spatial variations in iron (Fe) and copper (Cu) concentrations in PM2.5. Information on Fe and Cu concentrations at each site was paired with a kinetic multilayer model of surface and bulk chemistry in the lung epithelial lining fluid to estimate the possible impact of these metals on the production of reactive oxygen species (ROS) in exposed populations. Land use data around each monitoring site were used to develop predictive models for Fe, Cu, and their estimated combined impact on ROS generation. RESULTS: Spatial variations in Fe, Cu, and ROS greatly exceeded that of PM2.5 mass concentrations. In addition, Fe, Cu, and estimated ROS concentrations were 15, 18, and 9 times higher during summer compared with winter with little difference observed for PM2.5. In leave-one-out cross-validation procedures, final multivariable models explained the majority of spatial variations in annual mean Fe (R 2 = 0.68), Cu (R 2 =0.79), and ROS (R 2 = 0.65). CONCLUSIONS: The combined use of PM2.5 metals data with a kinetic multilayer model of surface and bulk chemistry in the human lung epithelial lining fluid may offer a novel means of estimating PM2.5 health impacts beyond simple mass concentrations.

Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model.

Exposure models are needed to evaluate the chronic health effects of ambient ultrafine particles (<0.1 µm) (UFPs). We developed a land use regression model for ambient UFPs in Toronto, Canada using mobile monitoring data collected during summer/winter 2010-2011. In total, 405 road segments were included in the analysis. The final model explained 67% of the spatial variation in mean UFPs and included terms for the logarithm of distances to highways, major roads, the central business district, Pearson airport, and bus routes as well as variables for the number of on-street trees, parks, open space, and the length of bus routes within a 100 m buffer. There was no systematic difference between measured and predicted values when the model was evaluated in an external dataset, although the R(2) value decreased (R(2) = 50%). This model will be used to evaluate the chronic health effects of UFPs using population-based cohorts in the Toronto area.

Near roadway air pollution across a spatially extensive road and cycling network.

This study investigates the variability in near-road concentrations of ultra-fine particles (UFP). Our results are based on a mobile data collection campaign conducted in 2012 in Montreal, Canada using instrumented bicycles and covering approximately 475 km of unique roadways. The spatial extent of the data collected included a diverse array of roads and land use patterns. Average concentrations of UFP per roadway segment varied greatly across the study area (1411-192,340 particles/cm(3)) as well as across the different visits to the same segment. Mixed effects linear regression models were estimated for UFP (R(2) = 43.80%), incorporating a wide range of predictors including land-use, built environment, road characteristics, and meteorology. Temperature and wind speed had a large negative effect on near-road concentrations of UFP. Both the day of the week and time of day had a significant effect with Tuesdays and afternoon periods positively associated with UFP. Since UFP are largely associated with traffic emissions and considering the wide spatial extent of our data collection campaign, it was impossible to collect traffic volume data. For this purpose, we used simulated data for traffic volumes and speeds across the region and observed a positive effect for volumes and negative effect for speed. Finally, proximity to truck routes was also associated with higher UFP concentrations.

Wavelet transform-based artificial neural networks (WT-ANN) in PM10 pollution level estimation, based on circular variables.

INTRODUCTION: In this paper, a novel method in the estimation and prediction of PM(10) is introduced using wavelet transform-based artificial neural networks (WT-ANN). DISCUSSION: First, the application of wavelet transform, selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in input training set data, is presented. Afterward, the circular statistical indices which are used in this method are formally introduced in order to investigate the relation between PM(10) levels and circular meteorological variables. Then, the results of the simulation of PM(10) based on WT-ANN by use of MATLAB software are discussed. The results of the above-mentioned simulation show an enhanced accuracy and speed in PM(10) estimation/prediction and a high degree of robustness compared with traditional ANN models.