On-road traffic emissions in a megacity
Introduction
Megacities are very large urban agglomerations with populations that exceed 10 million inhabitants, differing from urban areas not only in population size but also in the scale of their economy, infrastructure and associated environmental impacts (Gurjar and Lelieveld, 2005). Rapid urbanization has resulted in increasing air pollution emissions, typically arising from transportation, energy production and industrial activities, concentrated in densely populated areas (Gurjar et al., 2008) and surpassing the limits of the megacities' physically occupied area, thus contributing significantly to air quality on a global scale through the long range transport of air pollutants (Gurjar and Lelieveld, 2005, Butler et al., 2008, Butler and Lawrence, 2009).
This work was developed within the framework of the inter-American project “South American Emissions, Megacities and Climate” aimed at developing a number of tools for the chemical weather forecast in the South American cities of Bogotá, Buenos Aires, Lima, Santiago and Sao Paulo. To the best of our knowledge this is the first inventory for the on-road transport categories of the metropolitan area of Buenos Aires that has estimated emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), carbon monoxide (CO), nitrogen oxides (NOx), non-methane volatile organic compounds (NMVOC), particulate matter (PM) and sulfur dioxide (SO2) in two spatial disaggregation levels.
Road transport became a significant source of air pollution in the last century and is currently one of the largest emission sources in megacities with subsequent adverse effects on human health (Faiz, 1993, Colvile et al., 2001). In Argentina, road transport emissions accounted for 58% of total CO emissions in 2000 (Fundación Bariloche, 2005), share that is expected to rise because of the rising global demand for private mobility (Zachariadis et al., 2001). Real-world vehicle emissions are difficult to estimate because many factors are involved, and its accurate estimation is crucial for the formulation of efficient air quality management.
Emission inventories provide the necessary information required for air quality control policies, the development of future scenarios and modeling purposes (Parra et al., 2006, Zachariadis and Samaras, 2001, van Aardenne, 2002). There are two approaches for inventorying emissions: top-down and bottom up. Both methodologies share the same basic structure but present considerable differences regarding input data, assumptions and parameters (Reynolds and Broderick, 2000). The selection of the approach and the quality and consistency of the inventory depends almost entirely on the availability of the data and its degree of detail (Reynolds and Broderick, 2000, Tsilingiridis et al., 2002). It is futile to expect emission calculations to achieve results that are superior in accuracy to that of the original survey data (Sturm et al., 1996).
Differences are expected when comparing national emission inventories and specific megacity inventories. In the former, data on fuel consumption, technologies and installed abatement measures are applied uniformly over the country, while in the latter, the data employed is city-specific, the geographical units covered are smaller and the resulting emissions are distributed using more detailed maps of the emissions sources (Butler et al., 2008).
The metropolitan area of Buenos Aires (MABA) is composed of the city itself and 24 radially surrounding districts that are part of the province of Buenos Aires (Fig. 1), comprising an area of 3647 km2. Being the 10th megalopolis in the world and the 3rd in Latin America it holds 32% of the total population in only 0.14% of the national territory, with a population density of 4600 inhabitants km−2 and 2.4 million circulating vehicles. It is the greatest center of activities and the political, economical, and administrative center of the country, having the highest population density of the country, which diminishes from the center to its surrounding districts as poverty levels increase. In all its extent, the MABA is characterized by social and territorial inequities.
Three governmental levels coexist in the MABA (municipal, provincial and national) with different responsibilities and territorial limits within which each one may exercise its authority. Road transport regulation and management are key examples of this situation. For instance, emissions control for passenger vehicles is enforced at the municipal level in the city of Buenos Aires and at the provincial level in the 24 districts of the MABA; heavy-duty trucks are subject to national regulation, whereas light-duty trucks, being registered as passenger vehicles for private use, are also subject to municipal regulation; buses in the city of Buenos Aires are regulated at the national level while buses running in the provincial territory are subject to local regulation. This situation results in a juxtaposition of actors leading to diversity in sources of information and data. More importantly, this superimposing governmental levels combined with the fact that the MABA lies on a flat terrain with relatively good ventilation has been an excuse for a relatively poor air quality management in this highly populated urban zone, which lacks an official inventory of emissions of mobile sources. In addition, the availability of local and process-specific information on emissions represents a serious data gap, a common feature of many cities in developing countries (Guttikunda et al., 2005).
Section snippets
Methodology
A distance traveled approach was implemented to develop spatially disaggregated emission inventories for 2006. Annual emissions were estimated following the COPERT methodology (Ntziachristos and Samaras, 2000, Ntziachristos and Samaras, 2000b), for which total emissions by category and species are estimated as the contribution of hot, cold and evaporative emissions. Hot emissions were calculated on the basis of Eq. (1).where, Ei,j,k: emissions of species i, vehicle
Emission inventories
Annual emissions for the metropolitan area of Buenos Aires for 2006 from on-road mobile sources are presented as follows, indicating the level of uncertainty between brackets (Gg): CO2 11,524 (10,071–12,195), CH4 10.6, N2O 1.04, CO 569 (406–829), NOx 81.9 (63.4–97.6), NMVOC 69.8 (43.8–96.8), PM 6.37 (3.27–9.11) and SO2 6.60 (5.38–6.90). As the level of uncertainty associated with CH4 and N2O emissions was 140% the corresponding range was not reported above because of its negative lower bound.
Conclusions
The on-road transport emission inventory for the metropolitan area of Buenos Aires for greenhouse gases and criteria pollutants was developed as part of an inter-American project aimed at chemical weather forecasting in South American megacities. Our inventory, which is presented for two spatial disaggregation levels, is to the best of our knowledge the first study with these characteristics for this megacity.
Within the scope of this research the relationship between the MABA emissions and
Acknowledgements
This work was carried out with the aid of a grant from the Inter-American Institute for Global Change Research (IAI) CRN II 2017 which is supported by the US National Science Foundation (Grant GEO-0452325) within the framework of the project South American Emissions, Megacities and Climate and support from Projects PICT 32494 (Agencia Nacional de Promoción Científica y Tecnológica, Argentina) and UBACyT I031 (Universidad Buenos Aires). The authors are thankful for information and support
References (38)
- et al.
The representation of emissions from megacities in global emission inventories
Atmospheric Environment
(2008) The sensitivity of the one-sample and two-sample student t statistics
Computational Statistics & Data Analysis
(1988)- et al.
The transport sector as a source of air pollution
Atmospheric Environment
(2001) Automotive emissions in developing countries-relative implications for global warming, acidification and urban air quality
Transportation Research Part A: Policy and Practice
(1993)- et al.
Evaluation of emissions and air quality in megacities
Atmospheric Environment
(2008) - et al.
New directions: megacities and global change
Atmospheric Environment
(2005) - et al.
Speed-dependent representative emission factors for catalyst passenger cars and influencing parameters
Atmospheric Environment
(2000) - et al.
Development of the high spatial resolution EMICAT2000 emission model for air pollutants from the north-eastern Iberian Peninsula (Catalonia, Spain)
Journal of Environmental Pollution
(2006) - et al.
Development of an emissions inventory model for mobile sources
Journal of Transportation Research Part D
(2000) - et al.
Determination of traffic emissions – intercomparison of different calculation methods
The Science of the Total Environment
(1996)
Spatial and temporal characteristics of air pollutant emissions in Thessaloniki, Greece: investigation of emission abatement measures
The Science of the Total Environment
The effect of age and technological change on motor vehicle emissions
Transportation Research Part D
Validation of road transport statistics through energy efficiency calculations
Energy
The influence of megacities on global atmospheric chemistry: a modeling study
Environmental Chemistry
Generación de factores de emisión para vehículos livianos, medianos y pesados de la Región Metropolitana, Informe final
Cited by (90)
One-pot synthesis of regenerative calcium counterions from waste chicken eggshells for SO<inf>2</inf>-to-sulfur reduction
2024, Materials Today SustainabilityRoad transport exhaust emissions in Colombia. 1990–2020 trends and spatial disaggregation
2023, Transportation Research Part D: Transport and EnvironmentOn-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador
2022, Science of the Total EnvironmentReal-world and bottom-up methodology for emission inventory development and scenario design in medium-sized cities
2022, Journal of Environmental Sciences (China)Citation Excerpt :Besides, emission inventories are used as a tool to predict future emissions, apply air pollution control and mitigation strategies, and determine their effectiveness, as well as cost-benefit analysis and prioritization of various solutions. The fact that emission inventories are an integral part of air pollution management justifies the extensive efforts to provide them (D'Angiola et al., 2010; Baltar de Souza Leão et al., 2020). Due to the importance of compiling the emission inventories in air pollution studies, many studies have been done in this regard, but most of these studies require a vast amount of data and enormous infrastructures that make their use difficult and in some cases impossible for underdeveloped and developing countries.
Emission factors and emission inventory of diesel vehicles in Nepal
2022, Science of the Total Environment