Alternative techniques to assess road traffic emissions

Today, one of the most important environmental problems in urban areas is air pollution. According to the World Health Organization, air pollution represents a serious risk to human health in many cities of the world. Road traffic is one of the main sources of pollution in cities. Besides, vehicular emissions are released in close proximity to population, increasing the adverse health effects. Road traffic emissions are highly uncertain in developing countries. Existing techniques to assess this source of pollution are expensive and not always accurate. It results difficult for a city from the developing world to afford these techniques and thus, adequate abatement strategies can not be adopted. It is essential to develop methodologies to reduce these uncertainties to manage air quality more effectively. This PhD thesis aims to develop and to implement alternative techniques to assess road traffic emissions. This work focuses on the study of Volatile Organic Compounds (VOCs), this family of pollutants have not been sufficiently studied in cities. The developed techniques were tested during an intensive measuring campaign conducted in Ho Chi Minh City (HCMC), Vietnam. The motorcycles are the main mode of transport in HCMC, this means of transport has contributed to the progress of this rapidly developing city. The high levels of air pollution, the elevated number of motorcycles and especially the limited funding available, made HCMC an interesting place to test the developed methodologies. In a first stage the results of the measuring campaign were used to assess the roadside levels of pollution in HCMC. During the HCMC campaign, 19 C2-C6 VOCs were monitored on-line at roadside level together with other important pollutants. Results show that there is a severe air pollution problem in HCMC. The pollutants that are produced by road traffic are identified by means of a Principal Components Analysis (PCA) receptor Model. The PCA shows that all the VOCs monitored (except isoprene) are produced by motorcycles. It is necessary to develop and to encourage the use of alternative modes of transport in HCMC. The use of the on-line roadside pollutants monitoring together with the PCA showed to be a good first approach to assess the road traffic emissions. This is an affordable technique and it is strongly recommended to use it in cities with limited financial means to study the air pollution problem. This work also presents a new method to estimate road traffic emission factors (EFs). This method is based on a long term tracer experiment conducted during the HCMC campaign. The results of the HCMC tracer experiment were used together with traffic counts and pollutant measurements to calculate the dispersion factors and afterwards the EFs. Estimated EFs for HCMC are within the range of EFs estimated in other studies. Additionally, a Computational Fluid Dynamics Model (CFD) is used to critically evaluate the proposed methodology. The evaluations show that it is possible to accurately estimate the EFs from tracer studies. The methodology proposed here results in an interesting and promising alternative to estimate the EFs. All the techniques presented in this work offer several advantages. Data obtained serve for different purposes at the same time and their use can provide valuable information for urban air quality assessment. For example, concentrations of pollutants are determined at roadside level, as well as their evolution in time, this information is useful for exposure studies. Roadside monitoring can be used to identify the pollutants that are directly emitted by road traffic. Results from the tracer study can be used to estimate the EFs under real urban conditions and to validate dispersion models which in turn can be used in the future to evaluate abatement strategies for such streets

Evidence of traffic-generated air pollution in Havana

In Havana, transport is blamed as a likely source of pollution issues, which is usually supported on arguments referring to a vehicle fleet mainly made of old cars (i.e., most models are American from the 1950s or Russian from the 1980s) with poor technical conditions. Most of the existing studies are based on measurements from passive samplers collected for 24 h, which may not be representative of conditions where pollutant concentrations (particles or gases) fluctuate or are not homogeneous, such as transport-related pollution. The goal of this paper is to explore the transport-generated pollution by examining short-time correlations between traffic flows, pollutant concentrations and meteorological parameters. To do that, statistical relationships among all variables were analyzed, which revealed that PM10, NO2 and SO2 concentration levels are influenced by vehicular traffic, mainly with low-speed winds blowing perpendicular to the street axis. Furthermore, southeast and northeast winds force drag pollution from sources other than traffic. These conclusions depend on the specific conditions of the summer season at the measurement area. A more complete analysis could be conducted when more data becomes available for each season

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O-3 and the total gaseous oxidant (O-X = NO2 + O-3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O-3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of similar to 70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between similar to 25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to similar to 40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of similar to 60%). Analysis of the total oxidant (OX = NO2 + O-3) showed that primary NO2 emissions at urban locations were greater than the O-3 production, whereas at background sites, O-X was mostly driven by the regional contributions rather than local NO2 and O-3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.Peer reviewe

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.World Meteorological Organization Global Atmospheric Watch programme is gratefully acknowledged for initiating and coordinating this study and for supporting this publication. We acknowledge the following projects for supporting the analysis contained in this article: Air Pollution and Human Health for an Indian Megacity project PROMOTE funded by UK NERC and the Indian MOES, Grant reference number NE/P016391/1; Regarding project funding from the European Commission, the sole responsibility of this publication lies with the authors. The European Commission is not responsible for any use that may be made of the information contained therein. This project has received funding from the European Commission’s Horizon 2020 research and innovation program under grant agreement No 874990 (EMERGE project). European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Estonian Research Council (project PRG714); Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (KKOBS, project 2014-2020.4.01.20-0281). European network for observing our changing planet project (ERAPLANET, grant agreement no. 689443) under the European Union’s Horizon 2020 research and innovation program, Estonian Ministry of Sciences projects (grant nos. P180021, P180274), and the Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (3.2.0304.11-0395). Eastern Mediterranean and Middle East—Climate and Atmosphere Research (EMME-CARE) project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 856612) and the Government of Cyprus. INAR acknowledges support by the Russian government (grant number 14.W03.31.0002), the Ministry of Science and Higher Education of the Russian Federation (agreement 14.W0331.0006), and the Russian Ministry of Education and Science (14.W03.31.0008). We are grateful to to the following agencies for providing access to data used in our analysis: A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Sciences; Agenzia Regionale per la Protezione dell’Ambiente della Campania (ARPAC); Air Quality and Climate Change, Parks and Environment (MetroVancouver, Government of British Columbia); Air Quality Monitoring & Reporting, Nova Scotia Environment (Government of Nova Scotia); Air Quality Monitoring Network (SIMAT) and Emission Inventory, Mexico City Environment Secretariat (SEDEMA); Airparif (owner & provider of the Paris air pollution data); ARPA Lazio, Italy; ARPA Lombardia, Italy; Association Agr´e´ee de Surveillance de la Qualit´e de l’Air en ˆIle-de- France AIRPARIF / Atmo-France; Bavarian Environment Agency, Germany; Berlin Senatsverwaltung für Umwelt, Verkehr und Klimaschutz, Germany; California Air Resources Board; Central Pollution Control Board (CPCB), India; CETESB: Companhia Ambiental do Estado de S˜ao Paulo, Brazil. China National Environmental Monitoring Centre; Chandigarh Pollution Control Committee (CPCC), India. DCMR Rijnmond Environmental Service, the Netherlands. Department of Labour Inspection, Cyprus; Department of Natural Resources Management and Environmental Protection of Moscow. Environment and Climate Change Canada; Environmental Monitoring and Science Division Alberta Environment and Parks (Government of Alberta); Environmental Protection Authority Victoria (Melbourne, Victoria, Australia); Estonian Environmental Research Centre (EERC); Estonian University of Life Sciences, SMEAR Estonia; European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Finnish Meteorological Institute; Helsinki Region Environmental Services Authority; Haryana Pollution Control Board (HSPCB), IndiaLondon Air Quality Network (LAQN) and the Automatic Urban and Rural Network (AURN) supported by the Department of Environment, Food and Rural Affairs, UK Government; Madrid Municipality; Met Office Integrated Data Archive System (MIDAS); Meteorological Service of Canada; Minist`ere de l’Environnement et de la Lutte contre les changements climatiques (Gouvernement du Qu´ebec); Ministry of Environment and Energy, Greece; Ministry of the Environment (Chile) and National Weather Service (DMC); Moscow State Budgetary Environmental Institution MOSECOMONITORING. Municipal Department of the Environment SMAC, Brazil; Municipality of Madrid public open data service; National institute of environmental research, Korea; National Meteorology and Hydrology Service (SENAMHI), Peru; New York State Department of Environmental Conservation; NSW Department of Planning, Industry and Environment; Ontario Ministry of the Environment, Conservation and Parks, Canada; Public Health Service of Amsterdam (GGD), the Netherlands. Punjab Pollution Control Board (PPCB), India. R´eseau de surveillance de la qualit´e de l’air (RSQA) (Montr´eal); Rosgydromet. Mosecomonitoring, Institute of Atmospheric Physics, Russia; Russian Foundation for Basic Research (project 20–05–00254) SAFAR-IITM-MoES, India; S˜ao Paulo State Environmental Protection Agency, CETESB; Secretaria de Ambiente, DMQ, Ecuador; Secretaría Distrital de Ambiente, Bogot´a, Colombia. Secretaria Municipal de Meio Ambiente Rio de Janeiro; Mexico City Atmospheric Monitoring System (SIMAT); Mexico City Secretariat of Environment, Secretaría del Medio Ambiente (SEDEMA); SLB-analys, Sweden; SMEAR Estonia station and Estonian University of Life Sciences (EULS); SMEAR stations data and Finnish Center of Excellence; South African Weather Service and Department of Environment, Forestry and Fisheries through SAAQIS; Spanish Ministry for the Ecological Transition and the Demographic Challenge (MITECO); University of Helsinki, Finland; University of Tartu, Tahkuse air monitoring station; Weather Station of the Institute of Astronomy, Geophysics and Atmospheric Science of the University of S˜ao Paulo; West Bengal Pollution Control Board (WBPCB).http://www.elsevier.com/locate/envintam2023Geography, Geoinformatics and Meteorolog

Evaluación espacial y temporal de PM10 y PM2.5 con datos de CAMS, MODIS-AOD y mediciones de superficie en de calidad del aire de Colombia

La estimación de los niveles de material particulado PM10 y PM2.5 es parte importante de la evaluación de la calidad del aire.  En zonas en las que no se tiene monitoreo de calidad del aire en superficie, el uso de datos satelitales y reanálisis es una alternativa viable y de bajo costo aplicable en cualquier lugar del mundo. El paquete de datos “Copernicus Atmosphere Monitoring Service” (CAMS) cubre todo el globo con una resolución espacial de 0.1° (11 km), una resolución temporal de 1 hora y reporta información de la composición atmosférica y los procesos que tienen lugar en la misma, en tiempo real y predicciones de 4 días en el futuro para muchos contaminantes. Estas características hacen de CAMS un paquete de datos con posibilidad de ser aplicado para analizar la calidad del aire en países en vía de desarrollo. Por otra parte, la profundidad óptica de aerosol (AOD) de 550 nm tomada de los satélites “Moderate Resolution Imaging Spectroraiometer” (MODIS) de la NASA, tiene una resolución espacial de 1° (111 km), resolución temporal de 1 hora, sujeta a disponibilidad de datos por trayectorias polares de los satélites Terra-Aqua, y nubosidad. AOD de MODIS es una variable que ha sido utilizada en estudios previos para predecir niveles de PM10 en Colombia. El objetivo de este estudio es evaluar la calidad del aire en Colombia para PM2.5 y PM10 utilizando los datos de CAMS y MODIS durante el periodo de tiempo de 2003 a 2015.  En este estudio se comparan los datos de MODIS y CAMS con datos obtenidos de las redes de monitoreo de calidad del aire de 3 ciudades de Colombia: Bogotá, Bucaramanga y Medellín. Se evaluaron los coeficientes de correlación (R) y el índice de correspondencia (IA) con el fin de evaluar el desempeño de los dos paquetes de datos (MODIS y CAMS). El análisis indica que hay una correlación estadisticamente significativa entre MODIS, CAMS y mediciones en superficie. Estos paquetes de datos pueden ser usados para analizar la calidad del aire en Colombia y en otros paises en via de desarrollo

Multi-year Observations of Black Carbon and Brown Carbon in Bogota, Colombia: Identification of potential biomass burning emission areas through the relation of Tracers and Number of Fires.

Biomass burning pollution sources can produce regional and global impacts on air quality. South America is one of largest contributors to biomass burning emissions (BB) globally. After Amazonia, BB emissions from the grassland plains of Northern South America (NSA), where both wildfires and agricultural burns occur regularly, are the most significant. The BB season in NSA is characterized by a different seasonality compared to that of Amazonia, with numerous fires occurring between January and March. In this work, we report 3 years of continuous equivalent Black Carbon (eBC) and Brown Carbon (BrC) measurements from an Aethalometer AE33-7. This data is used to identify and quantify the contribution of biomass burning from NSA to Bogota, Colombia's. The measurement site is located upwind of Bogota, at a hill-top 500 meters above the plateau where the city is located. Additionally, PM2.5 off-line data using a low-vol sampler and 37 mm quartz filters, has been collected during two three-month long field campaigns. The first campaign was carried out from January to March 2018 (high BB emissions in NSA) and the second one between July and August 2018 (low BB emissions in NSA). The filter samples were analyzed in Colorado State University quantifying biomass burning tracers such as Levoglucosan and potassium ion. OC/EC data was also retrieved from the filter samples. MODIS Active Fire Data and HYSPLIT back‐trajectories were used to support the identification of potential biomass burning plumes transported to the city during the fires season. We analyzed the relationship between BrC, OC, Potassium ion, and levoglucosan to identify signals of regional transport of BB aerosols. We found a maximum BB contribution of 10% to light-absorbing aerosols during the high number of fires season and a 1% BB contribution during the low number of fires season. Our results indicate potential biomass burning transport events from wildfires were observed during the months of January and April. Besides, we found a higher correlation between BB tracers and fires located in 400 km buffer. In addition, we identified potential source regions that could impact Bogotas air quality

An evaluation of the estimation of road traffic emission factors from tracer studies

Road traffic emission factors (EFs) are one of the main sources of uncertainties in emission inventories; it is necessary to develop methods to reduce these uncertainties to manage air quality more efficientl

Analysis of Pollutant Dynamics in a High Andean City using satellite data, a Lagrangian approach and Statistical Learning Techniques: Classification Tree and Random Forest

Understanding the dynamic of atmospheric pollution is an important tool for managing air quality. The use of statistical learning tools has not been widely used in the study of air pollutants. The purpose of this work is to identify the key variables controlling daily and seasonal variations of PMcoarse, PM2.5 and CO concentrations. This analysis is performed to untangle the contribution from local and regional or meteorological factors impacting air quality. Two statistical learning techniques were used: Classification and Regression Trees (CART) and Random Forest- The latter combines up to 1000 classification trees with a majority-vote weighting criterion. Wind speed, wind direction, temperature, precipitation, humidity, solar radiation and barometric pressure were used as local meteorological input variables. Daily Aerosol Optical Depth (AOD) segregated into BC, OM, Dust, sulfate from CAMS-Copernicus were retrieved and used as local and regional chemical input variables. A long-record of radiosonde profiles associated to boundary layer structure also were used as local meteorological input influencing variations of pollutant levels. Back-trajectories of air masses arriving Bogotá were performed to analyze the long-range transport of pollutants. The number of MODIS active fires and their fire radiative (FRP) power from Northern South America selected in the vicinity of the air masses arriving Bogotá were included as regional variables. All analyses were performed daily for the period ranging from January 2008 to December 2018. Classification Tree and Random Forest techniques were implemented through R script to classify PMcoarse, PM2.5, and CO concentrations into 5 categories (5 quantiles), finding the best variables deciphering changes between concentration categories. The main variable that better classified seasonal concentration levels of pollutants was the Fire Radiative Power, and the local pressure in Bogotá as indicator of the intertropical Convergence Zone. Anomalous levels of concentration were described mainly by mixing height, wind direction, and wind speed. Moreover,  AOD segregated was important to understand the seasonal and daily variations

Air quality modelling over Bogota, Colombia: Combined techniques to estimate and evaluate emission inventories

Two versions of the Emission Inventory (EI) are generated for the city of Bogota, Colombia. In the first version (EI-1), CORINAIR traffic emission factors (EFs) are used. In the second (EI-2), bulk traffic EFs calculated for the city, using in situ measurements and inverse modelling techniques at street level, are used. EI-2 traffic emissions are 5, 4 and 3 times bigger than the corresponding values in EI-1, for CO, PM10 and NMVOCs, respectively. The main goal of this study consists in evaluating the two versions of the EI when introduced into a mesoscale air quality model. The AOT (accumulated exposure over a threshold) index is calculated for comparison between observed and simulated concentrations of primary pollutants. Simulated concentrations using EI-2 are closer to the observed values. This comparison allows us to extract some conclusions of the methodology used to calculate the EFs. Local factors like the driving behavior, the altitude, vehicle technology and an aged fleet cannot be totally included and corrected in the standard methodologies, and seem to be more important than obtaining very detailed and precise information on the classification of the fleet or driving speeds. Under financially limited and fast changing situations, as in the case of many developing countries, a simple methodology to estimate bulk traffic EFs and to evaluate the EI, is of utmost importance. The use of combined techniques such as in situ measurements to estimate bulk traffic EFs, and further evaluation of the inventories with numerical models, proved to be a useful tool for this purpose

Air quality modelling over Bogota, Colombia: Combined techniques to estimate and evaluate emission inventories

Two versions of the Emission Inventory (EI) are generated for the city of Bogota, Colombia. In the first version (EI-1), CORINAIR traffic emission factors (EFs) are used. In the second (EI-2), bulk traffic EF's calculated for the city, using in situ measurements and inverse modelling techniques at street level, are used. EI-2 traffic emissions are 5, 4 and 3 times bigger than the corresponding values in EI-1, for CO, PM10 and NMVOCs, respectively. The main goal of this study consists in evaluating the two versions of the El when introduced into a mesoscale air quality model. The AOT (accumulated exposure over a threshold) index is calculated for comparison between observed and simulated concentrations of primary pollutants. Simulated concentrations using EI-2 are closer to the observed values. This comparison allows us to extract some conclusions of the methodology used to calculate the EFs. Local factors like the driving behavior, the altitude, vehicle technology and an aged fleet cannot be totally included and corrected in the standard methodologies, and seem to be more important than obtaining very detailed and precise information on the classification of the fleet or driving speeds. Under financially limited and fast changing situations, as in the case of many developing countries, a simple methodology to estimate bulk traffic Ef's and to evaluate the Ell, is of utmost importance. The use of combined techniques such as in situ measurements to estimate bulk traffic EFs, and further evaluation of the inventories with numerical models, proved to be a useful too] for this purpose. (c) 2007 Elsevier Ltd. All rights reserved