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Modelling the Spatio-Temporal Distribution of Ambient Nitrogen Dioxide and Investigating the Effects of Public Transit Policies on Population Exposure
Abstract
Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NOx) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO2) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO2 concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO2 hot spots. This is associated with a reduction in total NOx emissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals’ daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO2 concentration across the study domain and the mean NO2 exposure across the sample population results from the fact that NO2 concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains.
... In the common industrial practice, air quality studies are usually carried out by employing large scale models (Bezyk et al., 2021;Isakov et al., 2017;Shekarrizfard et al., 2017), which can predict pollutants dispersion in large areas where environmental impact assessment should be performed. However, these models can have accuracy issues when dealing with the assessment of dispersion in the local scale of urban districts. ...
The use of Computational Fluid Dynamics (CFD) for environmental studies is continuously growing. In this context, simulations are usually carried out solving Reynolds-averaged Navier–Stokes (RANS) equations that require an appropriate implementation of the Atmospheric Boundary Layer (ABL).
This paper proposes a framework based on the Shear Stress Transport (SST) k−ω turbulence model, which is highly recommended for microclimate analysis, since it is known to well reproduce homogeneous ABL flows in the context of RANS simulations. Besides, a new blending approach is developed to account for the presence of obstacles in the domain. The model is implemented in the open-source OpenFOAM code and validated against well-known benchmark cases spanning different configurations, i.e., an empty fetch, a single building and an array of buildings.
The performance of the proposed model is very satisfactory. For instance, the Factor-of-2 validation metric is FAC2>0.8 for both velocity and turbulent kinetic energy in nearly all cases.
... In the common industrial practice, air quality studies are usually carried out by employing large scale models (Bezyk et al., 2021;Isakov et al., 2017;Shekarrizfard et al., 2017), which can predict pollutants dispersion in large areas where environmental impact assessment should be performed. 10 However, these models can have accuracy issues when dealing with the assessment of dispersion in the local scale of urban districts, since they are characterized by a low level of turbulence treatment. ...
... Concerns over population exposure to air pollution have grown with an increasing number of health studies reporting links between combustion-related air pollutants and adverse health effects (Sevik et al., 2020;Tobías et al., 2018;Vette et al., 2013;Weichenthal, 2012). Near-road air quality, especially pollutants such as ultrafine particles (UFP) and black carbon (BC), varies significantly over small distances and short periods in urban environments (Parvez & Wagstrom, 2019;Shekarrizfard et al., 2017;Wang et al., 2016;Weichenthal et al., 2014;. Mobile measurements of air pollution using high-time-resolution portable instruments on mobile platforms have gained popularity in recent years due to their ability to characterize the spatiotemporal variability of nearroad air quality in urban areas (W. ...
This study harnesses the power of mobile data in developing a spatial model for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300 km were travelled across a widespread 525 km of routes. Reported average BC values were around 20 µg/m3, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared—in terms of mean square error (MSE) and mean absolute error (MAE)—with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively).
... Ewing and Cevero (2010) performed a meta analysis of studies on travel and the built environment with the primary objective of providing planners with data in the form of elasticities that could be used to estimate the effects of built environment factors on mode choice and travel demand. Most recently, Shekarrizfard et al. (2017) designed an integrated transportation and air quality modeling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. This complex modeling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO 2 concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO 2 hot spots. ...
Regional travel-demand forecasting models are complex and cumbersome to use. Furthermore, they are also not sensitive to neighborhood level sustainable land use policies such as transit-oriented developments (TOD). There is a need for developing simple sketch planning tools and methodologies for taking measurements on the impacts of land use policies on mobility and environment. The primary objective of this research is to develop a methodology for deriving household-level emission footprints of auto travel from household travel survey data. The methodology was demonstrated by comparing and contrasting emission footprints for TOD and Non-TOD land uses in the Washington DC metropolitan area. A TOD is defined as the area encircling stations along line-haul Metrorail service. Statistical analyses indicated that Non-TOD emission footprints are significantly higher than the TOD footprints. Differences amongst pairs of TODs showed no statistical significance. Some exceptions to this generalized observation were also noted. The utility of the methodology was also demonstrated by comparing aggregate emission footprints at county level. The methodology can also be used for deriving emission footprints for any logical aggregate group of traffic analysis zones (TAZ). Thus, this research advances the utility of travel surveys to establishing baselines on emission footprints for select geographies.
... The most common approach is to consider home location as a proxy of population location [19][20][21][22][23][24] and constitutes the basis for most air quality health impact assessment studies [25,26]. However, several studies have reported important discrepancies between personal exposure and exposure at residence [27][28][29]. It has been demonstrated that travel behavior significantly influences on exposure to air pollution [30][31][32][33]. ...
Air pollution is one of the greatest challenges cities are facing today and improving air quality is a pressing need to reduce negative health impacts. In order to efficiently evaluate which are the most appropriate policies to reduce the impact of urban pollution sources (such as road traffic), it is essential to conduct rigorous population exposure assessments. One of the main limitations associated with those studies is the lack of information about population distribution in the city along the day (population dynamics). The pervasive use of mobile devices in our daily lives opens new opportunities to gather large amounts of anonymized and passively collected geolocation data allowing the analysis of population activity and mobility patterns. This study presents a novel methodology to estimate population dynamics from mobile phone data based on a user-centric mobility model approach. The methodology was tested in the city of Madrid (Spain) to evaluate population exposure to NO2. A comparison with traditional census-based methods shows relevant discrepancies at disaggregated levels and highlights the need to incorporate mobility patterns into population exposure assessments.
... Although transport has been identified as one of the key sectors for a long time (Colvile et al., 2001) most of air quality exceedances in Europe are still related to road traffic (EEA, 2017). Traffic-related policies have been shown particularly relevant for NO 2 exposure (Shekarrizfard et al., 2017) since this is the main contributor to ambient air levels in many urban areas worldwide including Madrid (Spain). According to Borge et al. (2014), road traffic is responsible for up to 90% of NO 2 concentration levels in the city centre. ...
... Although transport has been identified as one of the key sectors for a long time (Colvile et al., 2001) most of air quality exceedances in Europe are still related to road traffic (EEA, 2017). Traffic-related policies have been shown particularly relevant for NO 2 exposure (Shekarrizfard et al., 2017) since this is the main contributor to ambient air levels in many urban areas worldwide including Madrid (Spain). According to Borge et al. (2014), road traffic is responsible for up to 90% of NO 2 concentration levels in the city centre. ...
Air pollution continues to be one of the main issues in urban areas. In addition to air quality plans and emission abatement policies, additional measures for high pollution episodes are needed to avoid exceedances of hourly limit values under unfavourable meteorological conditions such as the Madrid's short-term action NO2 protocol. In December 2016 there was a strong atmospheric stability episode that turned out in generalized high NO2 levels, causing the stage 3 of the NO2 protocol to be triggered for the first time in Madrid (29th December). In addition to other traffic-related measures, this involves access restrictions to the city centre (50% to private cars). We simulated the episode with and without measures under a multi-scale modelling approach. A 1 km2 resolution modelling system based on WRF-SMOKE-CMAQ was applied to assess city-wide effects while the Star-CCM+ (RANS CFD model) was used to investigate the effect at street level in a microscale domain in the city centre, focusing on Gran Vía Avenue. Changes in road traffic were simulated with the mesoscale VISUM model, incorporating real flux measurements during those days. The corresponding simulations suggest that the application of the protocol during this particular episode may have prevented concentrations to increase by 24 μg·m-3 (14% respect to the hypothetical no action scenario) downtown although it may have cause NO2 to slightly increase in the city outskirts due to traffic redistribution. Speed limitation and parking restrictions alone (stages 1 and 2 respectively) have a very limited effect. The microscale simulation provides consistent results but shows an important variability at street level, with reduction above 100 μg·m-3 in some spots inside Gran Vía. Although further research is needed, these results point out the need to implement short-term action plans and to apply a consistent multi-scale modelling assessment to optimize urban air quality abatement strategies.
Exposure to excessive ambient light at night (ALAN) has been proved to have a statistical association with human diseases. But current studies on ALAN exposure inequity have likely underestimated exposure levels due to the neglect of personal mobility. Based on mobile phone positioning big data and night-light satellite imagery, we conducted an empirical study on the inequity of ALAN exposure in Tokyo, Japan. We quantified the intensity of ALAN on the grid of mobile phone positioning data. Then we used the Gini coefficient and population-weighted mean exposure to evaluate the inequity of ALAN exposure among individuals and between different population groups. As a result, we found evidence of the inequity of ALAN exposure in Tokyo. For age inequity, younger people suffer higher exposure to light pollution at night, but children are an exception. For gender inequity, there is almost no inequity between men and women. For residence inequity, the average ALAN exposure of non-residents can reach up to about twice that of residents. At time and space nodes where there are more travel behaviors, such as central Tokyo during 18:00–24:00, we have detected higher exposure and stronger inequity, indicating that ignoring personal mobility will cause underestimation.
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.
On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because they often only incorporate data on household travel, not including commercial vehicle movements, taxis, ride hailing services, and other trips typically underreported within travel surveys. In contrast, traffic counts embed all types of on‐road vehicles; however, they are only conducted at selected locations in an urban area. Traffic counts are typically spatially correlated, which enables the development of methods that can interpolate traffic data at selected monitoring stations across an urban road network and in turn develop emission estimates. This paper presents a new and universal methodology designed to use traffic count data for the prediction of periodic and annual volumes as well as greenhouse gas emissions at the level of each individual roadway and for multiple years across a large road network. The methodology relies on the data collected and the spatio‐temporal relationships between traffic counts at various stations; it recognizes patterns in the data and identifies locations with similar trends. Traffic volumes and emissions prediction can be made even on roads where no count data exist. Data from the City of Toronto traffic count program were used to validate the output of various algorithms, indicating robust model performance, even in areas with limited data.
This paper describes the design and application of an integrated model for the prediction of exposure to traffic related air pollution in an urban area as a result of transport policy scenarios. For this purpose, a travel demand model linked with models for traffic assignment, emissions, and air quality was used to simulate population exposure to ambient Nitrogen Dioxide (NO2) in a base year (2008) and in a horizon year (2031) while incorporating population and demographic projections. The integrated model was used to evaluate the impacts of the planned regional transit and vehicle technology improvements on exposure to NO2. In the 2031 business as usual scenario, an average decrease of 19% in exposure to NO2 is observed across the sample population, compared to the 2008 base case. This decrease is primarily attributed to projected improvements in vehicle technology. In the 2031 transit scenario, we observed an average 10% decrease in exposure compared to the 2031 business as usual. In terms of the spatial variability in air pollution, the transit scenario was observed to achieve large reductions in NO2 concentrations within the downtown area and moderate reductions throughout the suburbs.
Background:
The recent diesel scandal has again highlighted the impact that the transport sector can have on public health.
Aim:
To describe the current impact of transport planning on public health.
Result:
Transport is fundamental to our cities' economic and social development, but causes large health effects and impact through accidents, air pollution, noise, green space and lack of physical activity.
Conclusion:
There is an urgent need to rebalance and provide better and safer infrastructures and policy support for transport, and particularly, active transport modes, building a new culture for it. A parallel transition in transport and urban planning is needed to improve, in a global and structural way, the relations between urban mobility and health.
An air quality assessment was completed in support of a permit renewal application for a backup diesel generating station in Dawson City, Yukon. Due to the fact that the plant is situated in a valley and experiences stagnant conditions each year that can inhibit the movement of air, the CALPUFF model was considered appropriate to determine the effect of plant emissions on community air quality. However, because it was also anticipated that building downwash would be a primary factor in determining maximum predicted contaminant concentrations in close proximity to the powerhouse, a computational fluid dynamics (CFD) modelling analysis was also undertaken to verify the accuracy of near-field CALPUFF modelling results. The diesel engines at the station are housed in a low building (<5 m), vented through short, curved exhaust stacks such that the exhaust gas is released horizontally rather than vertically. Emissions from four diesel engines, ranging in size from 800 to 1500 kW, were modelled in CALPUFF as individual point sources for both actual operations in 2010 and 2011, as well as for a hypothetical scenario of maximum sustained operation at 100% capacity factor. Based on the highest predicted concentrations from CALPUFF modelling, meteorological conditions conducive to building downwash were identified and selected for CFD modelling. The CFD modelling results indicated that the predicted concentrations due to building downwash were closely correlated with wind speed, with the highest concentrations occurring with the highest wind speeds. The CFD modelling results were consistent with those derived from the CALPUFF model, with CFD maximum predicted concentrations being less than 10% higher than those estimated using the CALPUFF model. However, whereas the CALPUFF model predicted the maximum point of impingement to occur at the facility property line beside the powerhouse, the CFD model predicted the maximum concentrations to occur 10-20 metres from the property line. The comparison of modelling results showed that the differences between the maximum predicted contaminant concentrations in close proximity to the source for building downwash effects derived using the CALPUFF model were not very different from those that would be derived from a more complex simulation of wind flow around a small building using CFD modelling techniques. The analysis provides justification for using the CALPUFF model to represent near-field contaminant concentrations in similar regulatory applications. In addition, the CFD and the CALPUFF analyses were performed for higher building heights or higher stacks. Additional analysis would be required to verify whether the two models would continue to provide similar results for more complex building configuration stacks.
The first report on the development of the Operational Street Pollution Model (OSPM)
Well-being impact assessments of urban interventions are a difficult challenge, as there is no agreed methodology and scarce evidence on the relationship between environmental conditions and well-being. The European Union (EU) project "Urban Reduction of Greenhouse Gas Emissions in China and Europe" (URGENCHE) explored a methodological approach to assess traffic noise-related well-being impacts of transport interventions in three European cities (Basel, Rotterdam and Thessaloniki) linking modeled traffic noise reduction effects with survey data indicating noise-well-being associations. Local noise models showed a reduction of high traffic noise levels in all cities as a result of different urban interventions. Survey data indicated that perception of high noise levels was associated with lower probability of well-being. Connecting the local noise exposure profiles with the noise-well-being associations suggests that the urban transport interventions may have a marginal but positive effect on population well-being. This paper also provides insight into the methodological challenges of well-being assessments and highlights the range of limitations arising from the current lack of reliable evidence on environmental conditions and well-being. Due to these limitations, the results should be interpreted with caution.
Shifting to active modes of transport in the trip to work can achieve substantial co-benefits for health, social equity and climate change mitigation. Previous integrated modeling of transport scenarios has assumed active transport mode share and been unable to incorporate acknowledged system feedbacks.
To compare the effects of policies to increase bicycle commuting in a car-dominated city and to explore the role of participatory modeling to support transport planning in the face of complexity.
We used system dynamics modeling (SDM) to compare realistic policies, incorporating feedback effects, non-linear relationships, and time delays between variables. We developed an SD model of commuter bicycling through interviews and workshops with policy, community, and academic stakeholders. We incorporated best available evidence to simulate five policy scenarios over the next 40 years in Auckland, New Zealand. Injury, physical activity, fuel costs, air pollution, and carbon emissions outcomes were simulated.
Using the simulation model we demonstrated the kinds of policies that would likely be needed to change a historical pattern of decline in cycling into a pattern of growth that would meet policy goals. Our model projections suggest that transforming urban roads over the next 40 years, using best practice physical separation on main roads and bicycle-friendly speed reduction on local streets, would yield benefits 10-25 times greater than costs.
To our knowledge, this is the first integrated simulation model of future specific bicycling policies. Our projections provide practical evidence that may be used by health and transport policy-makers to optimize the benefits of transport bicycling while minimizing negative consequences in a cost effective manner. The modeling process enhanced understanding by a range of stakeholders of cycling as a complex system. Participatory SDM can be a helpful method for integrating health and environmental outcomes in transport and urban planning.
Objectives:
There is a paucity of information on environmental risk factors for prostate cancer. We conducted a case-control study in Montreal to estimate associations with exposure to ground-level nitrogen dioxide (NO2), a marker for traffic-related air pollution.
Methods:
Cases were 803 men with incident prostate cancer, ≤75 years of age, and diagnosed across all French hospitals in Montreal. Concurrently, 969 controls were drawn from electoral lists of French-speaking individuals residing in the same electoral districts as the cases and frequency-matched by age. Concentrations of NO2 were measured across Montreal in 2005-2006. We developed a land use regression model to predict concentrations of NO2 across Montreal for 2006. These estimates were back-extrapolated to 1996. Estimates were linked to residential addresses at the time of diagnosis or interview. Unconditional logistic regression was used, adjusting for potential confounding variables.
Results:
For each increase of 5 parts per billion of NO2, as estimated from the original land use regression model in 2006, the OR5ppb adjusted for personal factors was 1.44 (95% CI 1.21 to 1.73). Adding in contextual factors attenuated the OR5ppb to 1.27 (95% CI 1.03 to 1.58). One method for back-extrapolating concentrations of NO2 to 1996 (about 10 years before the index date) gave the following OR5ppb: 1.41 (95% CI 1.24 to 1.62) when personal factors were included, and 1.30 (95% CI 1.11 to 1.52) when contextual factors were added.
Conclusions:
Exposure to ambient concentrations of NO2 at the current address was associated with an increased risk of prostate cancer. This novel finding requires replication.
Few studies have modeled the effects of policy measures on population exposure. This work assessed for the first time the impact of a policy measure on population exposure to nitrogen dioxide (NO2) by using the activity-based model ALBATROSS. Activity-based models can be of great value in evaluating the effect of integrated policies and measures that have no obvious relation with transport or air quality. The scenario
considered in this study involved changing the hours during which shops could be open to allow shopping earlier in the morning and later in the evening. Both emissions and population distribution of this policy measure could be derived from the activity travel behavior predicted by the activity-based model. It was found that extending the opening hours changed the activity pattern of the adult population in the Netherlands.
Approximately 6% more nondaily and 0.5% more daily shopping hours were predicted. The change in activity pattern resulted in more transport (+0.5% more vehicle kilometers driven). As a consequence of this, emissions and air pollutant concentrations were also altered. When the concentration maps were matched with the dynamic population, an increase in population exposure to NO2 was observed. Absolute differences were small (up to 0.40 μg/m3). On an average weekday, NO2 exposure increased by 0.15 μg/m3. The relative change in exposure on an average weekday was 0.4%. In certain neighborhoods and at certain hours a more substantial increase could be observed.
The authors examined whether air pollution at school (nitrogen dioxide) is associated with poorer child cognition and health and whether adjustment for air pollution explains or moderates previously observed associations between aircraft and road traffic noise at school and children's cognition in the 2001-2003 Road Traffic and Aircraft Noise Exposure and Children's Cognition and Health (RANCH) project. This secondary analysis of a subsample of the United Kingdom RANCH sample examined 719 children who were 9-10 years of age from 22 schools around London's Heathrow airport for whom air pollution data were available. Data were analyzed using multilevel modeling. Air pollution exposure levels at school were moderate, were not associated with a range of cognitive and health outcomes, and did not account for or moderate associations between noise exposure and cognition. Aircraft noise exposure at school was significantly associated with poorer recognition memory and conceptual recall memory after adjustment for nitrogen dioxide levels. Aircraft noise exposure was also associated with poorer reading comprehension and information recall memory after adjustment for nitrogen dioxide levels. Road traffic noise was not associated with cognition or health before or after adjustment for air pollution. Moderate levels of air pollution do not appear to confound associations of noise on cognition and health, but further studies of higher air pollution levels are needed.
The PM 10 and PM 2.5 levels at a highway toll station were monitored from October to December 2008. Experimental results show that hourly average PM 10 and PM 2.5 levels at the highway toll station were 10.6–208.4 μg/m 3 and 6.6–187.9μg/m 3 , respectively. Additionally, the PM 2.5 -to-PM 10 ratio at the highway toll station was 0.73, indicating that emissions from traffic sources are dominant in PM 2.5 fraction. At the highway toll station, the time variations of the PM 10 and PM 2.5 levels were not strongly correlated with traffic volumes; however, traffic on the highway markedly elevated ambient PM 10 and PM 2.5 levels. The PM 10 and PM 2.5 levels at the highway toll station are higher than those at monitoring stations in the vicinity to the toll station by factors of 1.3–1.4 and 1.4–1.8 times, respectively. The low wind speeds and low mixing-layer heights lead to relatively high PM 10 and PM 2.5 levels. Moreover, high wind speed also could have resulted in high PM 10 and PM 2.5 levels due to the re-suspension of particulate matter under well dispersed conditions. Measurements indicate that both traffic emissions and meteorological conditions drive PM 10 and PM 2.5 levels at the highway toll station.
This paper reviews methods to evaluate the performance of air quality models, which are tools that predict the fate of gases and aerosols upon their release into the atmosphere. Because of the large economic, public health, and environmental impacts often associated with the use of air quality model results, it is important that these models be properly evaluated.
A comprehensive model evaluation methodology makes use of scientific assessments of the model technical algorithms, statistical evaluations using field or laboratory data, and operational assessments by users in real-world applications. The focus of the current paper is on the statistical evaluation component. It is important that a statistical model evaluation exercise should start with clear definitions of the evaluation objectives and specification of hypotheses to be tested. A review is given of a set of model evaluation methodologies, including the BOOT and the ASTM evaluation software, Taylor’s nomogram, the figure of merit in space, and the CDF approach. Because there is not a single best performance measure or best evaluation methodology, it is recommended that a suite of different performance measures be applied. Suggestions are given concerning the magnitudes of the performance measures expected of “good” models. For example, a good model should have a relative mean bias less than about 30% and a relative scatter less than about a factor of two.
In order to demonstrate some of the air quality model evaluation methodologies, two simple baseline urban dispersion models are evaluated using the Salt Lake City Urban 2000 field data. The importance of assumptions concerning details such as minimum concentration and pairing of data are shown. Typical plots and tables are presented, including determinations of whether the difference in the relative mean bias between the two models is statistically significant at the 95% confidence level.
Previous methodologies for modeling hazardous air pollutant emissions for onroad mobile sources are based on using spatial surrogates to allocate county level emissions to grid cells. A disadvantage of this process is that it spreads onroad emissions throughout a grid cell instead of along actual road locations. Recent air quality modeling in Portland, Oregon, using the CALPUFF dispersion model assigned emissions to specific roadway links. The resulting data were used to develop a regression model to approximate the CALPUFF predicted concentrations, determine the impacts of roadway proximity on ambient concentrations of three hazardous air pollutants, benzene, 1,3-butadiene, and diesel PM, and to estimate the zone of influence around roadways.Independent variables in the model included emission rates and traffic volumes for individual roadway links, distance and direction between roadway links and receptors, and distributions of wind speeds and directions. Dependent variables were derived from simulated annual average pollutant concentrations from motor vehicles at modeled receptor locations, predicted using CALPUFF. The regression model had limited capability to predict CALPUFF concentrations with an R-squared value of about 0.6. The model indicated the zone of influence around a roadway as between 200 and 400 m. The results support the thesis that in order to capture localized impacts of hazardous air pollutants in a dispersion model, there is a need to include individual roadway links.
Automobile exhaust contains precursors to ozone and fine particulate matter (PM ≤ 2.5 µm in aerodynamic diameter; PM2.5), posing health risks. Dependency on car commuting also reduces physical fitness opportunities.
In this study we sought to quantify benefits from reducing automobile usage for short urban and suburban trips.
We simulated census-tract level changes in hourly pollutant concentrations from the elimination of automobile round trips ≤ 8 km in 11 metropolitan areas in the upper midwestern United States using the Community Multiscale Air Quality (CMAQ) model. Next, we estimated annual changes in health outcomes and monetary costs expected from pollution changes using the U.S. Environmental Protection Agency Benefits Mapping Analysis Program (BenMAP). In addition, we used the World Health Organization Health Economic Assessment Tool (HEAT) to calculate benefits of increased physical activity if 50% of short trips were made by bicycle.
We estimate that, by eliminating these short automobile trips, annual average urban PM2.5 would decline by 0.1 µg/m3 and that summer ozone (O3) would increase slightly in cities but decline regionally, resulting in net health benefits of 0.2 billion, 3.8 billion/year from avoided mortality and reduced health care costs (95% CI: 5.0 billion]. We estimate that the combined benefits of improved air quality and physical fitness would exceed $8 billion/year.
Our findings suggest that significant health and economic benefits are possible if bicycling replaces short car trips. Less dependence on automobiles in urban areas would also improve health in downwind rural settings.
Introduction
In this paper, a novel method in the estimation and prediction of PM10 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 PM10 levels and circular meteorological variables. Then, the results of the simulation of PM10 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 PM10 estimation/prediction and a high degree of robustness compared with traditional ANN models.
Substantial policy changes to control obesity, limit chronic disease, and reduce air pollution emissions, including greenhouse gasses, have been recommended. Transportation and planning policies that promote active travel by walking and cycling can contribute to these goals, potentially yielding further co-benefits. Little is known, however, about the interconnections among effects of policies considered, including potential unintended consequences.
We review available literature regarding health impacts from policies that encourage active travel in the context of developing health impact assessment (HIA) models to help decision-makers propose better solutions for healthy environments. We identify important components of HIA models of modal shifts in active travel in response to transport policies and interventions.
Policies that increase active travel are likely to generate large individual health benefits through increases in physical activity for active travelers. Smaller, but population-wide benefits could accrue through reductions in air and noise pollution. Depending on conditions of policy implementations, risk tradeoffs are possible for some individuals who shift to active travel and consequently increase inhalation of air pollutants and exposure to traffic injuries. Well-designed policies may enhance health benefits through indirect outcomes such as improved social capital and diet, but these synergies are not sufficiently well understood to allow quantification at this time.
Evaluating impacts of active travel policies is highly complex; however, many associations can be quantified. Identifying health-maximizing policies and conditions requires integrated HIAs.
Only about 30% of cases of breast cancer can be explained by accepted risk factors. Occupational studies have shown associations between the incidence of breast cancer and exposure to contaminants that are found in ambient air.
We sought to determine whether the incidence of postmenopausal breast cancer is associated with exposure to urban air pollution.
We used data from a case-control study conducted in Montreal, Quebec, in 1996-1997. Cases were 383 women with incident invasive breast cancer, and controls were 416 women with other incident, malignant cancers, excluding those potentially associated with selected occupational exposures. Concentrations of nitrogen dioxide (NO2) were measured across Montreal in 2005-2006. We developed a land-use regression model to predict concentrations of NO2 across Montreal for 2006, and developed two methods to extrapolate the estimates to 1985 and 1996. We linked these estimates to addresses of residences of subjects at time of interview. We used unconditional logistic regression to adjust for accepted and suspected risk factors and occupational exposures.
For each increase of 5 ppb NO2 estimated in 1996, the adjusted odds ratio was 1.31 (95% confidence interval, 1.00-1.71). Although the size of effect varied somewhat across periods, we found an increased risk of approximately 25% for every increase of 5 ppb in exposure.
We found evidence of an association between the incidence of postmenopausal breast cancer and exposure to ambient concentrations of NO2. Further studies are needed to confirm whether NO2 or other components of traffic-related pollution are indeed associated with increased risks.
Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures.
Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions.
Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM(2.5) were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.
The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.
The development and use of dispersion models that simulate traffic-related air pollution in urban areas has risen significantly in support of air pollution exposure research. In order to accurately estimate population exposure, it is important to generate concentration surfaces that take into account near-road concentrations as well as the transport of pollutants throughout an urban region. In this paper, an integrated modelling chain was developed to simulate ambient Nitrogen Dioxide (NO2) in a dense urban neighbourhood while taking into account traffic emissions, the regional background, and the transport of pollutants within the urban canopy. For this purpose, we developed a hybrid configuration including 1) a street canyon model, which simulates pollutant transfer along streets and intersections, taking into account the geometry of buildings and other obstacles, and 2) a Gaussian puff model, which resolves the transport of contaminants at the top of the urban canopy and accounts for regional meteorology. Each dispersion model was validated against measured concentrations and compared against the hybrid configuration. Our results demonstrate that the hybrid approach significantly improves the output of each model on its own. An underestimation appears clearly for the Gaussian model and street-canyon model compared to observed data. This is due to ignoring the building effect by the Gaussian model and undermining the contribution of other roads by the canyon model. The hybrid approach reduced the RMSE (of observed vs. predicted concentrations) by 16%–25% compared to each model on its own, and increased FAC2 (fraction of predictions within a factor of two of the observations) by 10%–34%.
This research is motivated by the need to improve transportation policy analysis through the development of a holistic framework to evaluate transportation externalities. Traditionally, transportation planning has been focused primarily on the improvement of transportation infrastructure and network performance and little attention has been paid to the resulting externalities that negatively impact public health. The paper presents a holistic analysis framework that enables policy makers analyze the chain effect of transportation demand on air quality and population health exposure. Holism is achieved by incorporating the interactions between transportation demand, network performance measurement, vehicular emissions, air quality modeling and population exposure assessment. The eventual impact of vehicular emissions on population is measured through the use of an intake fraction metric, which measures the fraction of pollutant inhaled by an exposed population over a defined period of time. The proposed framework takes advantage of the existing state-of-the-art domain specific models so there is no need to re-invent the wheels. Instead, the focus of the this research is to provide a prescriptive process of addressing data gaps and resolution matching between these models as well as other models alike. The proposed population exposure assessment incorporates key parameters including different microenvironments and inhalation rates not accounted for in the existing literature of exposure assessment. The entire framework is evaluated with the three city sub-region of Maricopa County in Arizona. Further investigations demonstrate the importance of differentiating microenvironments and inhalation rates to properly capturing population exposure.
A dynamic city-wide air pollution exposure assessment study has been carried out for the urban population of Rome, Italy, by using time resolved population distribution maps, derived by mobile phone traffic data, and modelled air pollutants (NO2, O3 and PM2.5) concentrations obtained by an integrated air dispersion modeling system. More than a million of persons were tracked during two months (March and April 2015) for their position within the city and its surroundings areas, with a time resolution of 15 minutes and mapped over an irregular grid system with a minimum resolution of 0.26x0.34 Km2. In addition, demographics information (as gender and age ranges) were available in a separated dataset not connected with the total population one. Such BigData were matched in time and space with air pollution model results and then used to produce hourly and daily resolved cumulative population exposures during the studied period. A significant mobility of population was identified with higher population densities in downtown areas during daytime increasing of up to 1000 people/Km2 with respect to nigh-time one, likely produced by commuters, tourists and working age population. Strong variability (up to ±50% for NO2) of population exposures were detected as an effect of both mobility and time/spatial changing in pollutants concentrations. A comparison with the correspondent stationary approach based on National Census data, allows detecting the inability of latter in estimating the actual variability of population exposure. Significant underestimations of the amount of population exposed to daily PM2.5 WHO guideline was identified for the Census approach. Very small differences (up to a few μg/m3) on exposure were detected for gender and age ranges population classes.
Advances in micro-sensor 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 6 to 12 times, for 20 minutes, 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 the locations were not monitored at the same time. Two exposure surfaces were then developed for NO2 and O3 - as averages over spring, summer and fall. Validation against 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, with great potential for capturing temporal variability.
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 fine particulate matter from vehicle exhaust is associated with increased health risk. This study develops a new approach for creating spatially detailed regional maps of fine particulate matter concentration from vehicle exhaust using a dispersion model to better evaluate these risks. The spatial extent, diurnal, and seasonal patterns of concentration fields across Los Angeles County, California are evaluated and population exposure and exposure equity by race and income are investigated. The results demonstrate how this modeling approach can create new knowledge about vehicle emissions exposure. This approach also provides a method for proactively screening out regional plans, or specific projects within these plans, that are likely to cause air quality concerns. A proactive and regional air quality assessment can identify potential problems earlier in the planning process and a wider range of solutions, saving time, money and protecting public health. The detailed concentration maps can also be used to improve the siting of regulatory air quality monitors and provide more accurate exposure data for epidemiology studies.
Modelling approaches for simulating air and stormwater pollution due to on-road vehicles are reviewed and discussed. Models for traffic, emissions, atmospheric dispersion, and stormwater contamination are studied with particular emphasis on their couplings to create a modelling chain. The models must be carefully selected according to the requirements and level of detail of the integrated modelling chain. Although a fair amount of research has been conducted to link air pollution and road traffic, many questions related to spatio-temporal scales, domains of validity, consistency among models, uncertainties of model simulation results, and interfaces between models remain open. The aim of this work is to review the current status of the relationships between traffic, emissions, air quality, and water quality models, to recommend modelling approaches and to propose some directions for improving the state of the science. The difficulties and challenges associated with model coupling are illustrated with specific examples.
In developed countries such as Canada and United States, a significant number of individuals depend on automobile as their main mode of transport. There has been a stronger push towards analyzing travel behavior at the individual level so that transportation agencies can formulate appropriate strategies to reduce the auto dependency. Towards this pursuit of enhancing our understanding on travel behavior, we examine individual home to work/school commute patterns in Montreal, Canada with an emphasis on the transit mode of travel. The overarching theme of this paper is to examine the effect of the performance of the public transportation system on commuter travel mode and transit route choice (for transit riders) in Montreal. We investigate two specific aspects of commute mode choice: (1) the factors that dissuade individuals from commuting by public transit and (2) the attributes that influence transit route choice decisions (for those individuals who commute by public transit). This study employs a unique survey conducted by researchers as part of the McGill University Sustainability project. The survey collected information on commute patterns of students, faculty and staff from McGill University. In addition, detailed socio-demographic and residential location information was also collected. The analysis was undertaken using multinomial logit model for the travel mode choice component and a mixed multinomial logit model for the transit route choice component. The model estimation results were employed to conduct policy sensitivity analysis that allows us to provide recommendations to public transportation and metropolitan agencies.
There is often a large discrepancy between the questions raised by policy makers and the responses offered by scientists. Current modeling approaches do not answer some of the typical questions that decision-makers face, as they do not provide solutions to policy-makers dealing with concrete political negotiation and decisions. In this paper, we try to bridge the gap by creating an integrated model chain that can respond to such concrete policy questions. The paper describes a model chain consisting of an activity-based transport model, a road traffic emission model, a bi-gaussian atmospheric dispersion model and a concentration measurement interpolation model. Subsequently results are compared to observations, in order to test its usability for simulating air quality and assessing dynamic exposure. The model is shown to represent the main cycles governing air quality, such as the intra-daily, the intra-weekly and the intra-annual cycle. Finally, this paper provides an example of the use of such a model chain by assessing the impact of different trip motives on the intra-daily NO2 cycle.
Objectives:
We quantified health benefits of transportation strategies to reduce greenhouse gas emissions (GHGE).
Methods:
Statistics on travel patterns and injuries, physical activity, fine particulate matter, and GHGE in the San Francisco Bay Area, California, were input to a model that calculated the health impacts of walking and bicycling short distances usually traveled by car or driving low-emission automobiles. We measured the change in disease burden in disability-adjusted life years (DALYs) based on dose-response relationships and the distributions of physical activity, particulate matter, and traffic injuries.
Results:
Increasing median daily walking and bicycling from 4 to 22 minutes reduced the burden of cardiovascular disease and diabetes by 14% (32,466 DALYs), increased the traffic injury burden by 39% (5907 DALYS), and decreased GHGE by 14%. Low-carbon driving reduced GHGE by 33.5% and cardiorespiratory disease burden by less than 1%.
Conclusions:
Increased physical activity associated with active transport could generate a large net improvement in population health. Measures would be needed to minimize pedestrian and bicyclist injuries. Together, active transport and low-carbon driving could achieve GHGE reductions sufficient for California to meet legislative mandates.
Transportation policy measures often aim to change travel behaviour towards more efficient transport. While these policy measures do not necessarily target health, these could have an indirect health effect. We evaluate the health impact of a policy resulting in an increase of car fuel prices by 20% on active travel, outdoor air pollution and risk of road traffic injury. An integrated modelling chain is proposed to evaluate the health impact of this policy measure. An activity-based transport model estimated movements of people, providing whereabouts and travelling kilometres. An emission- and dispersion model provided air quality levels (elemental carbon) and a road safety model provided the number of fatal and non-fatal traffic victims. We used kilometres travelled while walking or cycling to estimate the time in active travel. Differences in health effects between the current and fuel price scenario were expressed in Disability Adjusted Life Years (DALY).
A 20% fuel price increase leads to an overall gain of 1650 (1010–2330) DALY. Prevented deaths lead to a total of 1450 (890–2040) Years Life Gained (YLG), with better air quality accounting for 530 (180–880) YLG, fewer road traffic injuries for 750 (590–910) YLG and active travel for 170 (120–250) YLG. Concerning morbidity, mostly road safety led to 200 (120–290) fewer Years Lived with Disability (YLD), while air quality improvement only had a minor effect on cardiovascular hospital admissions. Air quality improvement and increased active travel mainly had an impact at older age, while traffic safety mainly affected younger and middle-aged people.
This modelling approach illustrates the feasibility of a comprehensive health impact assessment of changes in travel behaviour. Our results suggest that more is needed than a policy rising car fuel prices by 20% to achieve substantial health gains. While the activity-based model gives an answer on what the effect of a proposed policy is, the focus on health may make policy integration more tangible. The model can therefore add to identifying win–win situations for both transport and health.
By focussing on air pollutant concentration levels only, the variation in population mobility is not taken
into account when assessing the exposure. Transportation policies have an impact on both concentration
levels and mobility patterns. The impact of a fuel price increase policy on population exposure to
elemental carbon (EC) was evaluated and compared to the base scenario (current situation), taking into
account time-activity patterns e including time in commute. We assessed the effect on exposure of both
the change in concentrations and whereabouts. The decrease in exposure due to the fuel price increase
using residential information only was limited to areas near highways and urban centres. Integrating
population movement, exposures to EC were higher and the decrease in exposure was no longer limited
to areas near traffic hotspots. For inhabitants of urban areas, the exposure integrating time-activity
patterns was more similar to the residential exposure, as they spent more time in their own neighbourhood.
For people living further away from traffic hotspots, the estimated impact of the policy was
higher than expected for residential exposure. These people profited both from the higher decrease in
concentrations at their work/shop/leisure destinations in more urban areas and, as they have to travel
longer, also had a larger gain from the high decrease in concentrations during transport. Therefore, the
impact of changing concentrations is underestimated when using residential exposure only. These
results show the importance of taking into activity-travel patterns when planning future actions
Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM2.5, NOx, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.
This paper describes the development of an integrated approach for assessing ambient air quality and population exposure as a result of road passenger transportation in large urban areas. A microsimulation activity-based travel demand model for the Greater Toronto Area – the Travel Activity Scheduler for Household Agents – is extended with capabilities for modelling and mapping of traffic emissions and atmospheric dispersion. Hourly link-based emissions and zone-based soak emissions were estimated. In addition, hourly roadway emissions were dispersed at a high spatial resolution and the resulting ambient air concentrations were linked with individual time-activity patterns derived from the model to assess person-level daily exposure. The method results in an explicit representation of the temporal and spatial variation in emissions, ambient air quality, and population exposure.
Concentrations of traffic-related air pollution can be highly variable at the local scale and can have substantial seasonal variability. This study was designed to provide estimates of intra-urban concentrations of ambient nitrogen dioxide (NO2) in Montreal, Canada, that would be used subsequently in health studies of chronic diseases and long-term exposures to traffic-related air pollution. We measured concentrations of NO2 at 133 locations in Montreal with passive diffusion samplers in three seasons during 2005 and 2006. We then used land use regression, a proven statistical prediction method for describing spatial patterns of air pollution, to develop separate estimates of spatial variability across the city by regressing NO2 against available land-use variables in each of these three periods. We also developed a “pooled” model across these sampling periods to provide an estimate of an annual average. Our modelling strategy was to develop a predictive model that maximized the model R2. This strategy is different from other strategies whose goal is to identify causal relationships between predictors and concentrations of NO2.Observed concentrations of NO2 ranged from 2.6 ppb to 31.5 ppb, with mean values of 12.6 ppb in December 2005, 14.0 ppb in May 2006, and 8.9 ppb in August 2006. The greatest variability was observed during May. Concentrations of NO2 were highest downtown and near major highways, and they were lowest in the western part of the city. Our pooled model explained approximately 80% of the variability in concentrations of NO2. Although there were differences in concentrations of NO2 between the three sampling periods, we found that the spatial variability did not vary significantly across the three sampling periods and that the pooled model was representative of mean annual spatial patterns.
Recent air quality studies have highlighted that important differences in pollutant concentrations can occur over the day and between different locations. Traditional exposure analyses, however, assume that people are only exposed to pollution at their place of residence. Activity-based models, which recently have emerged from the field of transportation research, offer a technique to micro-simulate activity patterns of a population with a high resolution in space and time. Due to their characteristics, this model can be applied to establish a dynamic exposure assessment to air pollution.This paper presents a new exposure methodology, using a micro-simulator of activity–travel behaviour, to develop a dynamic exposure assessment. The methodology is applied to a Dutch urban area to demonstrate the advantages of the approach for exposure analysis. The results for the exposure to PM10 and PM2.5, air pollutants considered as hazardous for human health, reveal large differences between the static and the dynamic approach, mainly due to an underestimation of the number of hours spent in the urban region by the static method.We can conclude that this dynamic population modelling approach is an important improvement over traditional methods and offers a new and more sensitive way for estimating population exposure to air pollution. In the light of the new European directive, aimed at reducing the exposure of the population to PM2.5, this new approach contributes to a much more accurate exposure assessment that helps evaluate policies to reduce public exposure to air pollution.
Speed reduction measures rank among the most common schemes to improve traffic safety. Recently many urban streets or entire districts were converted into 30 kph zones and in many European countries the maximum permissible speed of trucks on motorways is under discussion. However, besides contributing to traffic safety, reducing the maximum speed is also seen as beneficial to the environment due to the associated reduced fuel consumption and lower emissions. These claims however are often unsubstantiated.
To gain greater insight into the impact of speed management policies on emissions, this paper examines the impact on different traffic types (urban versus highway traffic) with different modelling approaches (microscopic versus macroscopic). Emissions were calculated for specific types of vehicles with the microscopic VeTESS-tool using real-world driving cycles and compared with the results obtained using generalized Copert-like macroscopic methodologies. We analyzed the relative change in pollutants emitted before and after the implementation of a speed reduction measure for passenger cars on local roads (50–30 kph) and trucks on motorways (90–80 kph). Results indicate that emissions of most classic pollutants for the research undertaken do not rise or fall dramatically. For the passenger cars both methods indicate only minor changes to the emissions of NOx and CO2. For PM, the macroscopic approach predicts a moderate increase in emissions whereas microscopic results indicate a significant decrease. The effects of specific speed reduction schemes on PM emissions from trucks are ambiguous but lower maximums speed for trucks consistently result in lower emissions of CO2 and lower fuel consumption. These results illustrate the scientific uncertainties that policy makers face when considering the implementation of speed management policies.
Compact city forms are associated with minimal consumption of land and energy, hence, they are often promoted as being the more sustainable thus preferred mode of urban development. In this context, numerical simulations were performed to evaluate the effect of urban sprawl on air quality and associated human exposure. Working on a highly urbanised area in the German Ruhrgebiet, models dealing with satellite data processing, traffic flows, pollutant emission and atmospheric dispersion were applied in an integrated fashion, under conditions representative of the urbanised area as it is today. A fair agreement was obtained between simulated and observed meteorological variables, as well as between simulated and observed concentrations of ozone and particulate matter. Simulated atmospheric pollution fields were found to closely reflect urbanisation patterns. In a companion paper [De Ridder, K., Lefebre, F., Adriaensen, S., Arnold, U., Beckroege, W., Bronner, C., Damsgaard, O., Dostal, I., Dufek, J., Hirsch, J., IntPanis, L., Kotek, Z., Ramadier, T., Thierry, A., Vermoote, S., Wania, A., Weber, C., 2008. Simulating the impact of urban sprawl on air quality and population exposure in the German Ruhr area. Part II: Development and evaluation of an urban growth scenario], the results of this base case simulation will be compared with those of a scenario simulation, designed to mimic urban sprawl, so as to allow the evaluation of the latter on air quality and associated human exposure.
Based on wind tunnel experiments, theoretical considerations and measurements, the dispersion model CAR (calculation of air pollution from road traffic) has been developed for determining air quality in city streets. CAR International, which is a simple parameterized model using readily available input data, calculates annual percentile values and average concentrations close to streets (at the kerbside) for non-reactive air pollutants and NO2. The accuracy of the model has proved to be good and is within the limits set by Dutch air quality decrees. CAR International can be used for a quick survey of city street air quality. It is also a valuable tool for judging the effects of traffic management plans and for scenario studies.
Nowadays urban pollution exposure from road transport has become a great concern in major cities throughout the world. A modelling framework has been developed to simulate Personal Exposure Frequency Distributions (PEFDs) as a function of urban background and roadside pollutant concentrations, under different traffic conditions. In this paper, we present a technique for classifying roads, according to their traffic conditions, using the traffic characteristics and fleet compositions. The pollutant concentrations data for 2001 from 10 Roadside Pollution Monitoring (RPM) units in the city of Leicester were analysed to understand the spatial and temporal variability of the pollutant concentrations patterns. It was found that variability of pollutants during the day can be associated with specific road traffic conditions. Statistical analysis of two urban and two rural Automated Urban and Rural Network (AURN) background sites for particulates suggests that PM2.5 and PM10 are closely related at urban sites but not at rural sites. The ratio of the two pollutants observed at Marylebone was found to be 0.748, which was applied to Leicester PM10 data to obtain PM2.5 profiles. These results are being used as an element in the PEFDs model to estimate the impact of urban traffic on exposure.
Local air quality management requires the use of screening and advanced modelling tools that are able to predict roadside pollution levels under a variety of meteorological and traffic conditions. So far, more than 200 air pollution hotspots have been identified by local authorities in the UK, many of them associated with NO2 and/or PM10 exceedences in heavily trafficked urban streets that may be classified as street canyons or canyon intersections. This is due to the increased traffic-related emissions and reduced natural ventilation in such streets. Specialised dispersion models and empirical adjustment factors have been commonly used to account for the entrapment of pollutants in street canyons. However, most of the available operational tools have been validated using experimental datasets from relatively deep canyons (H/W⩾1) from continental Europe. The particular characteristics of low-rise street canyons (H/W<1), which are a typical feature of urban/sub-urban areas in the UK, have been rarely taken into account.The main objective of this study is to review current practice and evaluate three widely used regulatory dispersion models, WinOSPM, ADMS-Urban 2.0 and AEOLIUS Full. The model evaluation relied on two comprehensive datasets, which included CO, PM10 and NOx measurements, traffic information and relevant meteorological data from two busy street canyons in Birmingham and London for a 1-year period. The performance of the selected models was tested for different times of the day/days of the week and varying wind conditions. Furthermore, the ability of the models to reproduce roadside NO2/NOx concentration ratios using simplified chemistry schemes was evaluated for one of the sites. Finally, advantages and limitations of the current regulatory street canyon modelling practice in the UK, as well as needs for future research, have been identified and discussed.
Studies on the health effects of long-term average exposure to outdoor air pollution have played an important role in recent health impact assessments. Exposure assessment for epidemiological studies of long-term exposure to ambient air pollution remains a difficult challenge because of substantial small-scale spatial variation. Current approaches for assessing intra-urban air pollution contrasts include the use of exposure indicator variables, interpolation methods, dispersion models and land-use regression (LUR) models. LUR models have been increasingly used in the past few years. This paper provides a critical review of the different components of LUR models.
Children are particularly vulnerable to the health effects of climate change, the biggest global health threat of the 21st century. However, the worst effects on child health can be avoided, and well-designed climate policies can have important benefits for child health and equity. We call on child health professionals to seize opportunities to prevent climate change, improve child health and reduce inequalities, and suggest useful actions that can be taken. © 2011 Paediatrics and Child Health Division (Royal Australasian College of Physicians).