Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM₁₀) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM₁₀ concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM₁₀ at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM₁₀ concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM₁₀, gaseous pollutants and meteorological variables respectively.     

Development of an emissions inventory model for mobile sources

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO₂), oxides of nitrogen (NO_x), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM₁₀), 1,3-butadiene (C₄H₆) and benzene (C₆H₆) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.     

Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach

This paper applies artificial neural network to predict hourly air pollutant concentrations near an arterial in Guangzhou, China. Factors that influence pollutant concentrations are classified into four categories: traffic-related, background concentration, meteorological and geographical. The hourly averages of these influential factors and concentrations of carbon monoxide, nitrogen dioxide, particular matter and ozone were measured at three selected sites near the arterial using vehicular automatic monitoring equipments. Models based on back-propagation neural network were trained, validated and tested using the collected data. It is demonstrated that the models are able to produce accurate prediction of hourly concentrations of the pollutants respectively more than 10 h in advance. A comparison study shows that the neural network models outperform multiple linear regression models and the California line source dispersion model.     

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21%, 33%, 24% and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than non-commercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity.     

Influence of coordinated traffic lights parameters on roadside pollutant concentrations

The paper examines the effects of coordinated traffic lights on CO and C₆H₆ roadside concentrations in an urban area of Palermo in Southern Italy. Traffic loop detectors and one pollution-monitoring are used to collect data for use in DRACULA traffic microsimulator software. CO and C₆H₆ roadside concentrations associated with varying cycle and offset times of the coordinated traffic lights are estimated using a neural network. Two functions were set up describing the relations of pollutant concentrations in term of cycle and offset time.     

Using grey system theory to evaluate transportation effects on air quality trends in Japan

Japan’s Air Pollution Control Law signed in 1968 prescribes the maximum permissible limits of motor vehicle exhausts as well as establishing mechanisms for monitoring air pollution In this paper, the grey relational grade of air pollutants from ambient air pollution and roadside air pollution monitoring stations is used to look at the relationship between air pollution and transportation. The results indicated that the ambient and roadside air quality increased by rose from 1975 to 2004 but less fast than the growth in traffic. Some of this may be attributable to the legislation but there have also been other measures since 1968 that have also contributed.     

Urban roadside monitoring and prediction of CO,NO2 and SO2 dispersion from on-road vehicles in megacity Delhi

The study inspects the traffic-induced gaseous emission dispersion characteristics from the urban roadside sites in Delhi, India. The concentration of pollutants viz. CO, NO₂ and SO₂ along with traffic and ambient atmospheric conditions at five selected local urban road sites were simultaneously measured. A developed General Finite Line Source Model (GFLSM) was used to predict the local roadside CO, NO₂ and SO₂ concentrations. A comparison of the observed and predicted values emission parameters using GFLS model has shown that the predicted values for SO₂, CO and NO₂ at all the selected local urban roadside locations are found to lie within the error bands of 5%, 6%, and 7% respectively. A high level of agreement was found between the monitored and estimated CO, NO₂ and SO₂ concentration data. From the study, it has also been established that the developed model exhibits the capability of reasonably predicting the characteristics of gaseous pollutants dispersion from on-road vehicles for the urban city air quality.     

A tool for calculating aircraft emissions and its application to Greek airspace

This paper quantifies the impact of aircraft emissions on local air quality and climate change. Aircraft emissions during the cruise cycle and the landing/take-off cycle are considered. A tool is developed that computes emission values using real-time air traffic data derived from various databases. Emissions include carbon dioxide, hydrocarbons, carbon monoxide and nitrogen oxides. The overall output is a detailed ‘emissions map’ of a given territory that enables the identification of critical emission spots including routes, airports, season, aircraft type and flight category. The method can be used for real-time monitoring of airline emissions and for policy analysis. The proposed tool and resulting outputs are illustrated in the case of the Greek airport system using domestic, international and overflights. Demand volatility driven mainly by tourism and its impact on emissions is assessed.     

Queuing simulation of roadside survey station: Blocked traffic lane

External station travel surveys provide critical inputs to travel demand models. The results from these models are frequently used for statewide planning purposes. Although a roadside survey is very effective in obtaining useful information from road users, its major drawback is the excessive delay that is imposed onto road users particularly on high-volume facilities. In this paper, we used a discrete event simulation to model a blocked traffic lane survey, which is usually conducted for two-lane undivided highways. This type of survey station requires a complete stop of all oncoming traffic. Non-surveyed traffic has no ability to go around and thus has to wait in a queue in order to proceed through the survey station. Road users’ impacts are quantified in terms of delay and queue length while the performance of surveyors is measured by the number of surveys completed per unit time. Sensitivity analyses of simulation inputs reveal that simulation results are fairly insensitive to selected parameters. The results in this study provide a quick and useful guideline that roadside surveyors can use to estimate the road user impacts prior to the survey and to plan the survey procedure accordingly.     

Day of week effects on diurnal ozone/NOx cycles and transportation emissions in Southern California

Diurnal cycles of ground-level ozone and its precursor NOx concentrations stem from and reflect complex temporal patterning of many underlying factors, including transportation emissions. Investigating the complexity of diurnal ozone/NOx cycles at a finer temporal resolution allows a better understanding of ozone dynamics and helps in designing ozone control strategies. This study applied functional data analysis techniques to hourly resolved ozone and NOx measurement data from the 1997 Southern California Ozone Study. Functional analysis of variance on diurnal ozone/NOx cycles for urban and rural monitoring sites confirmed, in a new continuous functional form, the ozone weekend effect. Functional data analysis also allows for a direct examination of day-of-week effects on ozone formation/destruction rates. Comparisons of Sunday ozone rates to those on weekdays demonstrate earlier, faster, and longer duration of ozone accumulation on Sunday. The results are further interpreted from the transportation emissions perspective using hourly resolved weigh-in-motion traffic data.     

Long-term behavior of particulate matters at urban roadside and background locations in Seoul,Korea

The concentrations of particulate matter, PM_2.5, PM₁₀, and TSP at an urban roadside and an urban background station are analyzed. Data collected over a 10 year period are analyzed. The concentrations of the particulates measured at the urban site are systematically larger than at the background station. The mean PM values at the former also exhibit a slight fall over the decade unlike those at the background station. Overall, the particulate matters at both locations are in an intermediate range of global level, e.g., approximately two times lower than those in other Asian regions but higher than in Europe.     

Influence of vehicular traffic on urban air quality – A case study of Hyderabad,India

In the present study, impact of vehicular traffic emissions on black carbon aerosol mass concentration, trace gases and ground reaching solar radiation were analyzed during nationwide truck strike of 5–12 January, 2009 over urban environment of Hyderabad, India. A significant reduction of about 57%, 60%, 40% and 50% was observed in black carbon, particulate matter, carbon monoxide and ozone respectively during nationwide truck strike period. Results of the study are important for source apportionment of pollutants as the strike created natural laboratory for studying the impact of diesel operated trucks on urban air quality.     

Traffic emission pollution sampling and analysis on urban streets with high-rising buildings

Air pollution at many types of intersections and other roadside “hot spots” is not accurately characterized by state-of-the-practice models. In this study, data were collected on traffic flows, second-by-second CO and NO₂ ambient concentrations in Shanghai, China. The sampled data were compared with CAL3QHC modeling results. We found that: (1) intersection hot spot emission concentrations were explained primarily by queuing activities of motor vehicles; (2) air quality concentrations are difficult to predict because of complex dispersion processes near high-rise buildings; and (3) screening models such as CAL3QHC are prone to large errors in dense cities with mixed traffic and high-rising buildings. Suggestions are made for improved models relevant to dense developing cities.     

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.     

Linking an activity-based travel demand model with traffic emission and dispersion models: Transport’s contribution to air pollution in Toronto

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.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

The influence of workplace environment on lung function of shopkeepers working near National Highway in Jalgaon: A note

The inferior ambient air quality was observed near highway passing through Jalgaon urban center. Among the pollutants critical level of particulates are observed at the roadside during May 2003 to April 2004. The shopkeepers working at the highway sides are at high risk of exposure to the air pollution caused by heavy highway traffic. The lung function test of the shopkeepers shows significant decrease in forced vital capacity, forced expiratory volume in one second and peak expiratory flow rate. The regular periodic health checkup and use of nose mask will protect the health of shopkeepers working near National Highway passing through Jalgaon urban center.     

Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data

Ambient concentrations of pollutants are correlated with emissions, but the contribution to ambient air quality of on-road mobile sources is not necessarily equal to their contribution to regional emissions. This is true for several reasons such as the distribution of other pollution sources and regional topology, as well as meteorology. In this paper, using a dataset from a travel demand model for the Sacramento metropolitan area for 2005, regional vehicle emissions are disaggregated into hourly, gridded emission inventories, and transportation-related concentrations are estimated using an atmospheric dispersion model. Contributions of on-road motor vehicles to urban air pollution are then identified at a regional scale. The contributions to ambient concentrations are slightly higher than emission fractions that transportation accounts for in the region, reflecting that relative to other major pollution sources, mobile sources tend to have a close proximity to air quality monitors in urban areas. The contribution results indicate that the impact of mobile sources on PM₁₀ is not negligible, and mobile sources have a significant influence on both NO_x and VOC pollution that subsequently results in secondary particulate matter and ozone formation.     

How wide should be the adjacent area to an urban motorway to prevent potential health impacts from traffic emissions?

In recent years, several studies show that people who live, work or attend school near the main roadways have an increased incidence and severity of health problems that may be related with traffic emissions of air pollutants. The concentrations of near-road atmospheric pollutants vary depending on traffic patterns, environmental conditions, topography and the presence of roadside structures. In this study, the vertical and horizontal variation of nitrogen dioxide (NO₂) and benzene (C₆H₆) concentration along a major city ring motorway were analysed. The main goal of this study is to try to establish a distance from this urban motorway considered “safe” concerning the air pollutants human heath limit values and to study the influence of the different forcing factors of the near road air pollutants transport and dispersion. Statistic significant differences (p = 0.001, Kruskal–Wallis test) were observed between sub-domains for NO₂ representing different conditions of traffic emission and pollutants dispersion, but not for C₆H₆ (p = 0.335). Results also suggest significant lower concentrations recorded at 100 m away from roadway than at the roadside for all campaigns (p < 0.016 (NO₂) and p < 0.036 (C₆H₆), Mann–Whitney test). In order to have a “safe” life in homes located near motorways, the outdoor concentrations of NO₂ must not exceed 44–60.0 μg m⁻³ and C₆H₆ must not exceed 1.4–3.3 μg m⁻³. However, at 100 m away from roadway, 81.8% of NO₂ receptors exceed the annual limit value of human health protection (40 μg m⁻³) and at the roadside this value goes up to 95.5%. These findings suggest that the safe distance to an urban motorway roadside should be more at least 100 m. This distance should be further studied before being used as a reference to develop articulated urban mobility and planning policies.     

Assessing greenhouse gas and related air pollutant emissions from road traffic counts: A case study for Mauritius

The road transport sector is one of the major contributors of greenhouse gases and other air pollutants emissions. Regional emissions levels from road vehicles were investigated, in Mauritius, by applying a fuel-based approach. We estimated fuel consumption and air emissions based on traffic counts on the various types of classified roads at three different regional set ups, namely urban, semi urban and rural. The Relative Development Index (RDI), a composite index calculated from socio-economic and environmental indicators was used to classify regions. Our results show that the urban motorways were the most polluting due to heavy traffic. Some rural areas had important pollution levels as well. Our analysis of variance (ANOVA), however, showed little difference in emissions among road types and regions. The study can provide a simple tool for researchers in countries where data are very scarce, as is the case for many developing countries.