Disentangling vehicular emission impact on air pollution using ethanol

 Abstract

The Sao Paulo Metropolitan Area is a unique case worldwide due to the extensive use of biofuel, particularly ethanol, by its large fleet of nearly 8 million cars. Based on source apportionment analysis of Organic Aerosols in downtown Sao Paulo, and using ethanol as tracer of passenger vehicles, we have identified primary emissions from light-duty-vehicles (LDV) and heavy-duty-vehicles (HDV), as well as secondary process component. Each of those factors mirror a relevant primary source or secondary process in this densely occupied area. Using those factors as predictors in a multiple linear regression analysis of a wide range of pollutants, we have quantified the role of primary LDV or HDV emissions, as well as atmospheric secondary processes, on air quality degradation. Results show a significant contribution of HDV emissions, despite contributing only about 5% of vehicles number in the region. The latter is responsible, for example, of 40% and 47% of benzene and black carbon atmospheric concentration, respectively. This work describes an innovative use of biofuel as a tracer of passenger vehicle emissions, allowing to better understand the role of vehicular sources on air quality degradation in one of most populated megacities worldwide.

Introduction

Large urban conglomerates are well-known air pollution hotspots, with impacts ranging from local air quality degradation¹ up to global climate^2,3, where emission from the transportation sector plays a pivotal role⁴. Within the Sao Paulo Metropolitan Area (SPMA), passenger vehicles outnumber buses by over 100 to 1, and trucks by over 30 to 1. Nonetheless, on a vehicle basis, buses and trucks emission of pollutants such as nitric oxide (NO) and particulate matter tends to exceed those of passenger vehicles by roughly the same order of magnitude^5,6,7. Furthermore, distinct vehicle circulation patterns lead to heterogeneous spatial and temporal air pollutant emissions. Combined with a complex atmospheric chemistry and dynamics, the identification and quantification of the role of vehicle types on air quality remains largely an open issue^8,9,10.
In 1975, the Brazilian government created a national program to stimulate the use of ethanol as vehicle fuel, which mandated its mixture in gasoline. Since then, ethanol penetration in Brazil has largely varied throughout the years, depending not only on available vehicle technology, but also on the price of oil, the price of sugarcane derivatives (e.g. sugar), government incentives and so forth. In early 1990s there was a peak in hydrous ethanol (E100) fuelled passenger cars within the SPMA, which accounted for half of the fleet, whereas the other half was gasohol fuelled (with 25% ethanol mix in gasoline, E25)¹¹. In 2003, with the introduction of flex-fuelled passenger vehicles, consumers were able to choose at the petrol station any mixture of ethanol between E25 and E100. Around the world, there is a tendency of increase of biofuel consumption by the transportation sector¹², nonetheless its global penetration remain fairly small. For a basis of comparison, in 2013 (the time of our measurements) average fuel consumption by passenger vehicles within SPMA amounted to 55% ethanol and 45% gasoline (on fleet-wide average, the equivalent of an E55 fuel). Conversely, in the UK the fraction of ethanol is about 5% (E5) and expected to rise in the near future to E10, a comparable fraction to other countries in the world.
Given that ethanol is used as fuel uniquely by Light Duty Vehicles (LDV, cars and motorcycles), in contrast to diesel fuelled Heavy Duty Vehicles (e.g. buses and trucks), we propose its use to disentangle traffic emissions on a number of atmospheric pollutants. To our knowledge, this study represents the first use of ethanol as a real-time tracer of passenger car emissions on a source apportionment analysis. As biofuel use continue to rise worldwide¹², atmospheric ethanol concentration should prove a valuable tracer in understanding the primary emission of different fractions of transportation sector in urban environments. In times when understanding air pollution health effects are becoming deeper and wider¹³, policy makers should be presented clear paths to improve urban air quality.

Results and Discussion

The range of atmospheric measurements conducted in downtown Sao Paulo is described in Table 1. The basis of our study is the organic aerosol source apportionment analysis based on Positive Matrix Factorization (PMF) on nearly real-time Aerosol Mass Spectrometry measurements (Table 2)¹⁴. This type of analysis allows the identification of some primary sources (e.g. traffic¹⁵, cooking¹⁶, biomass burning¹⁷) and secondary processes (such as particulate matter formation via oxidation of isoprene¹⁸). The PMF factors identification is possible via spectral signatures analysis and correlation with known tracers (e.g. Carbon Monoxide, CO, for traffic or Ozone for secondary processes through photochemistry). The use of ethanol as tracer for factor identification is discussed in the following.

Table 1 Description of the different data sets used in this study and summary statistics. Measurement data has been averaged into 1-h bins, resulting in 741 valid data points from 08 February to 08 April 2013.

From: Disentangling vehicular emission impact on urban air pollution using ethanol as a tracer

Variable and unit of measurement	Method	Data source	Median (and IQ)
PM10 mass concentration (µg m−3)	Beta continuous	CETESB	28 (19–40)
PM1 mass concentration (µg m−3)	ACSM + MAAP	Own	10.80 (7.49–16.31)
BC mass concentration (µg m−3)	MAAP	Own	2.71 (1.55–4.27)
Aerosol particle larger than 7 nm (N) number concentration (10,000 cm−3)	DMPS	Own	1.36 (1.03–1.71)
CO mixing ratio (ppm)	IR Photometry	CETESB	0.6 (0.4–0.9)
NO mixing ratio (ppb)	Chemiluminescence	CETESB	23 (7–47)
Ozone mixing ratio (ppb)	UV Photometry	CETESB	21.9 (13.2–30.9)
Ethanol mixing ratio (ppb)	PTRMS	Own	24.20 (20.35–29.52)
Acetaldehyde mixing ratio (ppb)	PTRMS	Own	3.25 (2.28–4.33)
Benzene mixing ratio (ppb)	PTRMS	Own	0.58 (0.41–0.87)
Toluene mixing ratio (ppb)	PTRMS	Own	1.59 (0.99–2.59)

Table 2 Concentration of PMF factor loadings and ambient concentration of chemical species in the submicrometric size range. Measurement data has been averaged into 1-h bins, from similar statistics as Table 1.

From: Disentangling vehicular emission impact on urban air pollution using ethanol as a tracer

Variable and unit of measurement	Median (and IQ)
Factor LDV-OA (µg m−3)	1.22 (0.73–1.80)
Factor HDV-OA (µg m−3)	0.85 (0.44–1.60)
Factor OOA-I (µg m−3)	0.68 (0.36–1.15)
Factor OOA-II (µg m−3)	1.57 (0.78–2.78)
Organic Aerosol (µg m−3)	4.91 (3.26–7.43)
Sulphate (µg m−3)	1.64 (1.14–2.38)
Nitrate (µg m−3)	0.44 (0.24–0.96)
Ammonium (µg m−3)	0.66 (0.33–1.13)

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Authors:

• Joel Brito,
• Samara Carbone,
• Djacinto A. Monteiro dos Santos,
• Pamela Dominutti,
• Nilmara de Oliveira Alves,
• Luciana V. Rizzo &
• Paulo Artaxo  

Source: Nature Scientific Reports.