Abstract
ABSTRACT A key component of air quality management in urban areas is the analysis of pollutant emission distribution and hotspots, which are valuable information for developing emission reduction strategies and as input in air quality models. For vehicular emissions, this task is difficult because the sources are in constant movement and the availability of accurate and realistic vehicular activity information is scarce; a common issue in cities of emerging countries. Hence, exercises for estimating atmospheric pollutant emissions are developed only in a macro-scale way, without allocating in space and time the emissions. In this study, we present a new computational algorithm named DROVE, for the disaggregation of on-road vehicle emissions in space and time. DROVE is a free code developed in R and provides emission fluxes distribution from the estimation of disaggregation factors. It was designed with three possible approximations for spatial emissions disaggregation, considering the input information that would be available in the city: length of road segments (LRS), LRS + type of roads; and LRS + traffic flows. Two temporal distribution options are available for obtaining gridded hourly emissions. We evaluated the capabilities of DROVE for performing the PM10 emission distribution fluxes in the medium-sized cities of Manizales, Colombia, Antofagasta, Chile, and the megacity of Bogota, Colombia. Results suggest that DROVE was able to allocate emission hotspots in zones of high traffic and main avenues (when information of the type of roads or traffic flow is available). Emissions distribution did not reflect this behavior when only LRS was used as input data, obtaining 50% of grid cells with percentage emission differences higher than 100% against the use of LRS + traffic flows. DROVE can be implemented in any region worldwide, could contribute with air quality management and provide disaggregated emission fluxes for air quality modeling.
Highlights
Urban areas in Latin American countries have been exposed during the last decades to a fast urbanization and economic growth, being the increases in traffic levels the most important contributor to air quality problems (Sun et al, 2016; Mangones et al, 2019)
The spatial disaggregation of PM10 vehicular emissions performed with DROVE in Manizales, suggests that PM10 emission hotspots are associated with the principal avenues of the city, where main hotspots were concentrated at downtown area and south region - zone characterized by the presence of the municipalbus terminal
We introduce a new open-source code named as DROVE for the space and time disaggregation of on-road vehicle emissions, with the flexibility to be implemented in any region of the world
Summary
Urban areas in Latin American countries have been exposed during the last decades to a fast urbanization and economic growth, being the increases in traffic levels the most important contributor to air quality problems (Sun et al, 2016; Mangones et al, 2019). This is a key issue that affects human health and could be sever in cities of emerging countries (González et al, 2018). The analysis of emission fluxes from a high spatial resolution could provide information about air pollution hotspots in urban areas, which is a valuable information for developing emission reduction strategies. For vehicular emissions this task is difficult because the sources are in constant movement and it is necessary accurate information related with the emission activity such as, driving patterns, traffic flows and road network structure (Gómez et al, 2018)
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