Street-scale dispersion modelling framework of road-traffic derived air pollution in Hanoi, Vietnam

Traffic is an important source of air pollution in Vietnamese cities. The spatio-temporal variation of air pollution derived from traffic is poorly understood. Application of dispersion modelling can help but is hindered by the local scarcity of suitable input data. This study fills the data gap, by establishing a framework employing open-access global data to model emission from traffic activities in Hanoi. The outlined methodology explicitly defines road sources, calculates their emission, and employs background pollution profiles from Copernicus Atmospheric Monitoring Service (CAMS) to produce street-scale distribution maps for CO, PM10 and PM2.5. Pollution hotspots are found near major traffic flows with the highest hourly average CO, PM10 and PM2.5 concentrations at 1206, 87.5 and 61.5 μgm−3, respectively. The relationship between concentrations and properties of the road network is assessed. Motorcycles are the main emitters of the traffic sector. Emission from Heavy Good Vehicles dominate during the night, with contribution percentages increase as it gets further away from the city core. Modelled concentrations are underestimated mainly due to low vehicular emission factor. Adjusting emission factors according to vehicle quality in Vietnam greatly improves agreement. The presence of non-traffic emission sources contributes to the model underestimation. Results for comparisons of daily averaged PM values are broadly in agreement between models and observations; however, diurnal patters are skewed. This results partly from the uncertainties linked with background pollution levels from CAMS, and partly from non-traffic sources which are not accounted for here. Further work is needed to assess the use of CAMS's concentrations in Vietnam. Meteorological input contributes to the temporal disagreement between the model and observations. The impact is most noticeable with CO concentrations during morning traffic rush hours. This study recommends approaches to improve input for future model iterations and encourage applications of dispersion modelling studies in similar economic settings

Road traffic pollution monitoring and modelling tools and the UK national air quality strategy.

This paper provides an assessment of the tools required to fulfil the air quality management role now expected of local authorities within the UK. The use of a range of pollution monitoring tools in assessing air quality is discussed and illustrated with evidence from a number of previous studies of urban background and roadside pollution monitoring in Leicester. A number of approaches to pollution modelling currently available for deployment are examined. Subsequently, the modelling and monitoring tools are assessed against the requirements of Local Authorities establishing Air Quality Management Areas. Whilst the paper examines UK based policy, the study is of wider international interest

Towards a real-time microscopic emissions model

This article presents a new approach to microscopic road traffic exhaust emission modelling. The model described uses data from the SCOOT demand-responsive traffic control system implemented in over 170 cities across the world. Estimates of vehicle speed and classification are made using data from inductive detector loops located on every SCOOT link. This data feeds into a microscopic traffic model to enable enhanced modelling of the driving modes of vehicles (acceleration, deceleration, idling and cruising). Estimates of carbon monoxide emissions are made by applying emission factors from an extensive literature review. A critical appraisal of the development and validation of the model is given before the model is applied to a study of the impact of high emitting vehicles. The article concludes with a discussion of the requirements for the future development and benefits of the application of such a model

Modelling photochemistry in alpine valleys

Peer reviewe

The air quality impact of cordon and distance based road user charging: an empirical study of Leeds, U.K

Traffic assignment, pollutant emission and dispersion models were applied to a major UK city so as to assess the air quality impacts of five road pricing schemes. Schemes were evaluated with reference to: exceedence of air quality standards for six pollutants; greenhouse gas emission; redistribution of pollution, an environmental justice concern; and road network performance as traffic speed and trip distance. Results were compared to alternatives of do nothing, network development and clean fuel promotion. The air quality benefits of a modest distance based charge are highlighted. However, whilst road pricing shows potential as an air quality management tool, its value and suitability are strongly sensitive to prior air quality and emission source apportionment in the application city

The effect of short-term changes in air pollution on respiratory and cardiovascular morbidity in Nicosia, Cyprus.

Presented at the 6th International Conference on Urban Air Quality, Limassol, March, 2007. Short-paper was submitted for peer-review and appears in proceedings of the conference.This study investigates the effect of daily changes in levels of PM10 on the daily volume of respiratory and cardiovascular admissions in Nicosia, Cyprus during 1995-2004. After controlling for long- (year and month) and short-term (day of the week) patterns as well as the effect of weather in Generalized Additive Poisson models, some positive associations were observed with all-cause and cause-specific admissions. Risk of hospitalization increased stepwise across quartiles of days with increasing levels of PM10 by 1.3% (-0.3, 2.8), 4.9% (3.3, 6.6), 5.6% (3.9, 7.3) as compared to days with the lowest concentrations. For every 10μg/m3 increase in daily average PM10 concentration, there was a 1.2% (-0.1%, 2.4%) increase in cardiovascular admissions. With respects to respiratory admissions, an effect was observed only in the warm season with a 1.8% (-0.22, 3.85) increase in admissions per 10μg/m3 increase in PM10. The effect on respiratory admissions seemed to be much stronger in women and, surprisingly, restricted to people of adult age

CARBOTRAF: A decision Support system for reducing pollutant emissions by adaptive traffic management

Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions. Black carbon (BC) is a good indicator of combustion-related air pollution and results in negative health effects. Both BC and CO2 emissions are also known to contribute significantly to global warming. Current traffic control systems are designed to improve traffic flow and reduce congestion. The CARBOTRAF system combines real-time monitoring of traffic and air pollution with simulation models for emission and local air quality prediction in order to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimizing the traffic flows. The system is implemented and evaluated in two pilot cities, Graz and Glasgow. Model simulations link traffic states to emission and air quality levels. A chain of models combines micro-scale traffic simulations, traffic volumes, emission models and air quality simulations. This process is completed for several ITS scenarios and a range of traffic boundary conditions. The real-time DSS system uses all these model simulations to select optimal traffic and air quality scenarios. Traffic and BC concentrations are simultaneously monitored. In this paper the effects of ITS measures on air quality are analysed with a focus on BC

Air quality impact of a decision support system for reducing pollutant emissions: CARBOTRAF

Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions. Black carbon (BC) is a good indicator of combustion-related air pollution and results in negative health effects. Both BC and CO2 emissions are also known to contribute significantly to global warming. Current traffic control systems are designed to improve traffic flow and reduce congestion. The CARBOTRAF system combines real-time monitoring of traffic and air pollution with simulation models for emission and local air quality prediction in order to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimizing the traffic flows. The system is implemented and evaluated in two pilot cities, Graz and Glasgow. Model simulations link traffic states to emission and air quality levels. A chain of models combines micro-scale traffic simulations, traffic volumes, emission models and air quality simulations. This process is completed for several ITS scenarios and a range of traffic boundary conditions. The real-time DSS system uses these off-line model simulations to select optimal traffic and air quality scenarios. Traffic and BC concentrations are simultaneously monitored. In this paper the effects of ITS measures on air quality are analysed with a focus on BC

A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation

Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as microscopic land-use regression (mu LUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010-2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The mu LUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers