Average Carbon Number Analysis and Relationship with Octane Number and PIONA Analysis of Premium and Regular Gasoline Expended in Ecuador

Abstract

The quality of fuel depends on its chemical composition, which influences engine performance. Gas chromatography, a cornerstone of global oil and fuel R&D, remains crucial for ensuring the quality of petroleum products and regulatory compliance. Scientists use the most accurate analysis (PIONA) as a tool derived from gas chromatography coupled with mass spectrometry to identify and quantify hydrocarbons that influence resistance to detonation, which is determined by the research octane number (RON). This study introduces the “average carbon number (ACN)”, calculated from the molar chemical composition of commercial gasoline samples sold in Ecuador (Extra gasoline and Súper gasoline). A quantitative comparison of the ACN with techniques applied using standardized international procedures reveals that the ACN characterizes gasoline samples by providing insight into the distribution shape of carbon graphs. A comprehensive statistical analysis demonstrates the potential usefulness of ACN in characterizing fuel composition, highlighting its relevance in broader fuel quality assessments without the need for carbon distribution plots.

1. Introduction

Gasoline is a petroleum-derived liquid fuel commonly used in internal combustion engines and obtained by refining fossil fuels through fractional distillation and then subsequent blending [1,2]. Chemically, it is a complex mixture of volatile hydrocarbons, such as paraffins, naphthenes (cyclic alkanes), aromatics, and olefins, consisting of carbon atoms between C4 and C12 and boiling points ranging from 30 to 260 °C [3,4]. Commercially, it is manufactured to meet the needs of the automotive industry, following the specifications and regulations of the place of production and distribution [5,6]. Thus, gasoline production involves: (i) distilling crude oil and separating it into fractions based on boiling points; (ii) performing cracking, branching/isomerization to refine these fractions, and (iii) removing undesirable contents, such as sulfur [7,8]. Catalytic cracking, a widely employed and crucial process in refineries, converts heavy oils into commercially valuable gasoline by controlling temperature and pressure to break down the long-chain hydrocarbons found in heavy gas oil [9,10]. In the 1950s, the catalytic cracking method began to be promoted for breaking down long-chain molecules into shorter ones, improving gasoline yields [10,11]. Octane rating refers to the pressure and temperature to which a fuel must be subjected before it mixes with air before reaching the auto-ignition point. Therefore, gasoline with a higher octane rating will have better anti-knock characteristics, thereby resulting in smoother and more efficient vehicle performance [12,13]. In summary, refining practices, the origin of oil, and the added additives alter the properties of gasoline [14,15]. The characteristics that gasoline should have include antiknock resistance (related to octane rating), desirable volatility, good fuel economy, minimal deposition on engine surfaces, efficient combustion, and low pollutant emissions. To determine whether gasoline meets these parameters, properties such as density, octane number, vapor pressure, distillation range, oxygen content, and chemical composition are often evaluated [16,17]. Octane number (ON) is defined as the volume percentage of i-octane in a mixture of n-heptane and i-octane that produces the same knock intensity as a fuel in an internal combustion engine test under ASTM standard conditions [18,19].

The assessment of crude oils is crucial for predicting the physical properties of gasoline, monitoring and evaluating refining processes, and determining the quality of final products [20,21]. For example, the composition of automotive gasoline varies depending on the crude oil source, the refining process, and even the presence of residues in the storage tank [22,23]. According to some references, typical gasoline compounds have concentrations (% v/v) as follows (

Table 1. Typical bulk composition of gasoline [7,24,25].

Group	Compound Class	Gasoline Composition (%)
		[7]	[24]	[25]
Saturates			50–60
	Alkanes	4–7	45–55	4–8
	Isoalkanes	25–40		25–40
	Cyclo-alkanes	6–16	~5	3–7
Olefins		6–16	5–10	2–5 (30 *)
Cicloalkenes				1–4
Aromatics		20–50	25–40	20–50
	BTEX		15–35
	PAHs
Sulfur			<0.05
Metals (ppm)

* Some cases have documented contents up to this percentage. BTEX: acronym for benzene, toluene, ethylbenzene, and xylenes. PAHs: abbreviation of polycyclic aromatic hydrocarbons.

):

Gasoline may also contain some chemical compounds comprised of oxygen, nitrogen, or sulfur [26], additives such as aliphatic alcohols and methyl ethers [27], and, in some cases, impurities. The chemical composition of gasoline, in particular the proportion of the various compounds and the additives added by each refinery, determines its physical properties and engine performance characteristics [5]. For example, isoparaffins are more desirable in gasoline because of their higher resistance to engine knock compared to paraffins, which break down easily [28]. On the other hand, aromatic hydrocarbons can increase research octane number (RON) and motor octane number (MON) [26], which are directly related to octane number measurements [18]. Olefins can improve fuel octane number and anti-knock performance, as well as increase the reactivity of gasoline in combustion processes [29]. Consequently, gasoline containing long-chain paraffins with fewer branches and olefins with more than four carbon atoms is more prone to autoignition, while one with high levels of branched paraffins, olefins, and aromatic compounds is more resistant to the same phenomenon [30,31].

Globally regulated compounds such as lead, sulfur, benzene, and other aromatic compounds, as well as olefins and oxygenates, are part of the main chemical composition of gasoline [32]. These compounds have a significant impact on vehicle exhaust emissions [33]. Additives, such as tetraethyl and tetramethyl lead, are often added to gasoline to increase octane because they are less expensive yet can cause significant health effects [34]. Higher aromatic content reduces combustion efficiency, as aromatics break down more slowly than other hydrocarbons [35]. This is also associated with significantly increased emissions of non-methane hydrocarbons, carbon monoxide, polycyclic aromatic hydrocarbons, particulate matter, particulate number, and black carbon [36,37,38]. Fuels with high olefin content result in exhaust emissions with higher olefin content, which increases the potential for ozone formation as well as the propensity for the growth of gum deposits on intake valves and engine injectors [39]. Therefore, determining the chemical composition of commercially available gasoline is essential to verify its quality and even predicting the composition of exhaust emissions [40].

Several tests can be performed to verify whether the quality of gasoline meets the technical specifications set in each region or country. For instance, to measure the anti-knock capability of gasoline, three methods can be used: the motor octane number (MON) which is determined at high speeds; the research octane number (RON), which is determined at low speeds; and the running octane number, which shows the real behavior in a vehicle [1]. Research octane number (RON) is a measure of the resistance of the fuel to ignite early [41]. Premature igniting causes knocking and diminishes efficiency in reciprocating engines [26]. RON is obtained using a CFR (Cooperative Fuel Research) engine according to ASTM D2699-21 [42]. Worldwide, gasoline on the market is differentiated by its RON, with at least two types of gasoline available in most countries [43].

The European Standard EN 228 specifies that the minimum RON of gasoline should be 95 [44]. However, outside Europe, gasoline sold may still be below this minimum octane number. Octane number is mainly affected by the presence of aromatic hydrocarbons, isoparaffins, olefins, and additives [45], as mentioned above. MON is often preferred over RON since it is evaluated under conditions similar to those of a real engine [46]. It is evaluated using a CFR test engine with a preheated fuel mixture, higher engine speed, and variable ignition timing to further probe the fuel knock resistance [47]. Complementarily, over the years, many scientists have attempted to estimate and/or predict the octane number (ON and RON) of fuels with alternative methods to those already established by international standards (ASTM 2699-23) [48], where fuel composition plays a more important role. To mention some examples, articles have been published with results relating to different kinds of mathematical correlations [49,50] like linear relationship [51], partial quadratic regression [52,53], non-linear regression [54,55], multi-linear combination [56], hydrocarbon blending ratios [45,57,58,59], thermodynamics modeling and simulation [60,61], QSPR models [62,63], computational formulations [64,65], artificial neural networks [66,67], quantum mechanics methods [68], extreme machine learning [69], among others [44,70,71,72] As technology advances, it can be deduced that increasingly sophisticated approaches are being used that can bridge the gap between experimental and empirical methods.

Several types of gasoline are marketed in Ecuador, although not all are offered throughout the country. The two types of gasoline expended in Quito, the capital, are assembled in the Esmeraldas refinery. They are called Súper gasoline and Extra gasoline. These two types of gasoline, distinguished mainly by their RON, must comply with the specifications established in the Ecuadorian Technical Standard INEN 935. It requires that the RON reach a minimum of 85 and 92 for Extra gasoline and Súper gasoline, respectively. Regarding the chemical composition of gasoline, INEN 935 regulates the compounds that contribute to atmospheric pollution, establishing maximum levels for aromatics, benzene, and olefins at 30%, 1%, and 18% for 85 RON gasoline and 35%, 2%, and 25% for 92 RON gasoline [73]. Súper gasoline has many advantages, such as high engine performance, which decreases premature self-ignition of the fuel-air mixture, avoiding knocking. It also improves service life by reducing maintenance costs of vehicles and environmentally harmful combustion gases [74]. Its vapor pressure is 60 KPa, the maximum sulfur percentage is 0.065, and its gum content is 4 mg/100 mL [14]. Currently, some users claim to blend Súper gasoline and Extra gasoline for different reasons. A 70/30 blend of Súper gasoline and Extra gasoline, respectively, is optimal for combustion, while a 50/50 mix is more affordable since it maintains the same combustion characteristics while saving more fuel [75]. According to several studies, Súper gasoline outperforms other types of gasoline available in Ecuador in terms of mileage per gallon because lower-octane gasoline can cause problems of rattling, reduction in power, and therefore the efficiency of the engine, leading to higher maintenance costs [76]. In addition, its emissions analysis shows that it is the most environmentally friendly gasoline [11]. By June 2024, the price of Súper gasoline was $4.11 per gallon, while the price of Extra gasoline was $2.465 per gallon. This paper also aims to provide Ecuadorian customers with insights into the composition of these two gasoline grades, enabling them to make informed decisions about which grade is more suitable for their vehicles. Another fuel known as ECOPAIS is composed of 95% base gasoline and 5% ethanol, which has been proven to increase the power and torque of vehicles [77]. This gasoline is more cost-effective and reduces the clogging of vehicle filters because ethanol is a good solvent and scrubber [76]. The problem with this type of gasoline is the high concentration of sulfur and its subsequent deposit inside car engines, which leads to higher sulfur dioxide (SO₂) emissions into the environment, causing toxic effects in the atmosphere [78].

Despite the valuable information that the chemical profile of gasoline can provide, published studies on the subject are scarce. The chemical profile of gasoline has been analyzed in some countries, such as the United Kingdom [79], Australia [80], Saudi Arabia [81], China [82], the United States [83], and Brazil [84], of which the last three are summarized in

Table 2. Gasoline composition classified by hydrocarbon groups, distributed in Saudi Arabia [81], China [82], and Brazil [84].

Country	Saudi Arabia [81] 1		China [82] 2				Brazil [84] 1
RON	91	95	92	93	95	97	N/A
Hydrocarbon Group	Volume Percent of Total Hydrocarbons per Group Type (% v/v)
Paraffins	25.32	29.22	50.20 4	42.40 4	37.40 4	35.80 4	8.21
Isoparaffins	26.74	27.96					16.08
Olefins	1.48	1.44	9.00	12.50	12.40	15.00	18.48
Naphthenes	2.96	4.10	N/A	N/A	N/A	N/A	9.64
Aromatics	29.09	29.06	31.90	37.00	41.40	40.90	15.87
Oxygenates	N/A 3	N/A	N/A	N/A	N/A	N/A	26.75
MTBE	N/A	N/A	4.30	4.60	1.80	2.90	N/A
Aniline	N/A	N/A	0.28	0.26	0.24	0.60	N/A
Total C14	3.95	2.04	N/A	N/A	N/A	N/A	N/A
Others	N/A	N/A	8.90	8.10	8.90	8.30	N/A
Unknowns	10.45	6.17	N/A	N/A	N/A	N/A	N/A

¹ The compounds were analyzed by GC-FID. ² The compounds were analyzed by GC-MS. ³ N/A: not analyzed or not reported. ⁴ The data are reported as alkanes (paraffins and isoparaffins).

since their results were reported by The Hydrocarbon Group. In 2014, a chemical characterization of gasoline distributed in Quito, Ecuador, was performed, specifically to quantify BTEX aromatic compounds [9]. However, in September 2015, a new Fluid Catalytic Cracking reactor came into operation at the Esmeraldas Refinery, which promised to obtain high-octane gasoline in addition to increasing operational capacity [85]. Since then, and to our knowledge, no new chemical characterization has yet been performed to verify the hydrocarbon profile. The present study aims to provide a detailed chemical profile of the two types of gasoline (Extra gasoline and Súper gasoline) marketed in Quito, Ecuador, using gas chromatography with mass spectrometry (GC-MS), classifying the compounds separately according to the hydrocarbon group to which they belong (PIONA analysis). As a novelty, the distribution of the number of carbons in gasoline was used to calculate an average carbon number, a feature recently proposed by Taco-Vásquez and Holtzapple [86]. It has not been employed before to describe gasoline, but it has to do with other types of hydrocarbons like alkane sulfonic acid [87]. Additionally, the RON of gasoline samples was verified using ASTM D2699-21 [42].

2. Materials and Methods

2.1. Sample Collection

Seventy-seven commercial gasoline samples (54 Extra gasoline and 23 Súper gasoline) were collected from several gas stations located primarily in Quito, Ecuador and other cities in neighboring provinces on specific days in June and July 2022. Thermodynamics II students collected samples of different volumes. Each type of gasoline was randomly picked up from the nearest gas station to the students’ location.

Table A1. Supplemental information on the samples collected for this work.

N°	Gasoline Type	Volume (mL)	Sampler	Gas Station	Location	Coordinates	Collection Date dd/mm/yy	Collection Time	Reception Date dd/mm/yy	Reception Time
1	Extra	-	Melanie Muela	Primax	Iñaquito	0°10′29″ S 78°29′16″ W	07/02/22	15:00:00	07/04/22	7:30:00
2	Extra	40	Liney Mejía	Primax	Carcelén	0°05′30″ S 78°28′36″ W	07/03/22	19:52:00	07/04/22	7:21:00
3	Extra	50	Guido Acevedo	Mobil	La Magdalena	0°14′27″ S 78°31′38″ W	07/03/22	17:00:00	07/04/22	7:20:00
4	Extra	260	Lizbeth Alarcón	Petroecuador	Quitumbe	0°17′49″ S 78°33′32″ W	07/03/22	16:44:00	07/04/22	7:22:00
5	Extra	40	Zaid Falcón	Primax	Mariscal Sucre	0°11′48″ S 78°29′58″ W	07/03/22	12:30:00	07/04/22	7:10:00
6	Extra	60	Jessica Totoy	Primax	Sangolquí	0°21′56″ S 78°28′30″ W	07/03/22	10:37:00	07/04/22	7:20:00
7	Extra	-	Gabriela Calvache	Primax	La Mena	0°15′24″ S 78°32′36″ W	07/02/22	14:00:00	07/04/22	7:20:00
8	Extra	125	Angie Avilés	Petroecuador	Posmasqui	0°03′27″ S 78°27′18″ W	07/03/22	18:05:00	07/04/22	7:20:00
9	Extra	250	Karen Ponce	Petroecuador	Calderón	0°06′30″ S 78°27′01″ W	07/10/22	17:15:00	07/11/22	10:30:00
10	Extra	-	Janina Chusquillo	Primax	Jipijapa	0°08′38″ S 78°28′11″ W	06/09/22	10:00:00	06/10/22	11:00:00
11	Extra	340	Melanie Chamorro	Primax	La Magdalena	0°14′49″ S 78°31′12″ W	07/04/22	17:00:00	07/05/22	13:54:00
12	Extra	-	Cassidy Garzón	Primax	Mariscal Sucre	0°11′48″ S 78°29′58″ W	07/04/22	11:00:00	07/05/22	14:00:00
13	Extra	100	Josselyn López	Primax	Mariscal Sucre	0°11′48″ S 78°29′58″ W	07/03/22	11:00:00	07/05/22	13:50:00
14	Extra	110	María Pino	Primax	Kennedy	0°08′03″ S 78°28′26″ W	07/04/22	19:00:00	07/05/22	13:50:00
15	Extra	50	Rodrigo Andrade	Petroecuador	Conocoto	0°15′12″ S 78°29′02″ W	07/03/22	21:00:00	07/07/22	15:00:00
16	Extra	1000	Andrea Pico	PDV	Comité del Pueblo	0°06′39″ S 78°28′45″ W	07/07/22	18:50:00	07/08/22	7:51:00
17	Extra	600	Job Segovia	Masgas	Chimbacalle	0°14′41″ S 78°31′06″ W	07/07/22	7:59:00	07/08/22	7:26:00
18	Extra	50	Javier Villegas	Mobil	La Magdalena	0°14′27″ S 78°31′38″ W	07/07/22	21:45:00	07/08/22	7:20:00
19	Extra	350	María Gavilanes	E. S. Figueroa	Salcedo	1°02′29″ S 78°35′08″ W	07/05/22	10:35:00	07/07/22	7:23:00
20	Extra	200	Alisson Clavijo	Primax	Quitumbe	0°18′13″ S 78°32′32″ W	07/08/22	16:00:00	07/09/22	7:00:00
21	Extra	25	Salma Cadena	Primax	Sangolquí	0°18′30″ S 78°26′44″ W	06/09/22	15:00:00	06/10/22	10:50:00
22	Extra	25	Salomé Zapata	Terpel	Cochapamba	0°09′17″ S 78°29′49″ W	06/09/22	16:20:00	--	--
23	Extra	500	Melany Carrera	Masgas	Tumbaco	0°12′42″ S 78°23′15″ W	07/08/22	14:00:00	07/11/22	13:15:00
24	Extra	200	Carlos Cadena	Primax	Sangolquí	0°18′30″ S 78°26′44″ W	07/08/22	17:30:00	07/11/22	10:01:00
25	Extra	220	Laura Palacios	Petroecuador	Comité del Pueblo	0°06′56″ S 78°28′45″ W	07/10/22	17:38:00	07/11/22	9:23:00
26	Extra	200	Cynthia Carvajal	Petroecuador	Quitumbe	0°17′49″ S 78°33′32″ W	07/09/22	10:00:00	07/11/22	9:00:00
27	Extra	100	Samanta Ortiz	Primax	El Condado	0°06′19″ S 78°30′00″ W	07/10/22	15:30:00	07/11/22	11:00:00
28	Extra	100	Dayerlin Guerrero	Primax	El Inca	0°09′33″ S 78°28′59″ W	07/10/22	12:36:00	07/11/22	7:10:00
29	Extra	250	Daniel Cervantes	P&S	San Antonio	0°00′24″ N 78°26′25″ W	07/10/22	13:00:00	07/11/22	11:00:00
30	Extra	-	Patricia Moreira	Terpel	Conocoto	0°17′27″ S 78°27′51″ W	07/10/22	16:36:00	07/11/22	10:36:00
31	Extra	350	Pamela Bonilla	Petroecuador	Guamaní	0°18′41″ S 78°32′50″ W	07/10/22	12:35:00	07/12/22	11:06:00
32	Extra	200	Fernanda Moreno	Primax	Iñaquito	0°11′48″ S 78°29′35″ W	07/10/22	18:20:00	07/11/22	11:05:00
33	Extra	100	Cristian Sopa	Petroecuador	Guamaní	0°20′56″ S 78°32′57″ W	07/09/22	19:30:00	07/11/22	11:00:00
34	Extra	750	Daniel Guerrero	Petroecuador	La Mena	0°15′50″ S 78°33′00″ W	07/10/22	14:40:00	07/11/22	8:45:00
35	Extra	250	Karen Ponce	Petroecuador	Calderón	0°06′30″ S 78°27′01″ W	07/10/22	17:15:00	07/11/22	10:30:00
36	Extra	1000	Katherine Chulca	Petroecuador	Calderón	0°06′30″ S 78°27′01″ W	07/10/22	21:15:00	07/11/22	11:00:00
37	Extra	500	Mishell Vega	Masgas	Tumbaco	0°12′42″ S 78°23′15″ W	07/08/22	14:00:00	07/11/22	7:15:00
38	Extra	-	Patricia Moreira	Terpel	Conocoto	0°17′27″ S 78°27′51″ W	07/10/22	16:36:00	07/11/22	10:36:00
39	Extra	200	Stalin Aurea	Petroecuador	Ambato	1°13′42″ S 78°36′16″ W	07/08/22	20:00:00	07/11/22	7:00:00
40	Extra	300	Gabriel Espinoza	Primax	Sangolquí	0°20′43″ S 78°27′19″ W	07/09/22	8:30:00	11/07/22	9:00:00
41	Extra	200	Katherine Martínez	Terpel	La Magdalena	0°14′58″ S 78°31′16″ W	07/10/22	17:42:00	07/11/22	9:00:00
42	Extra	100	Bryan Tupe	Terpel	Tambillo	0°24′29″ S 78°32′50″ W	07/10/22	20:00:00	07/11/22	11:00:00
43	Extra	50	Lizeth Loachamin	Primax	Sangolquí	0°20′43″ S 78°27′19″ W	07/10/22	10:20:00	07/11/22	8:40:00
44	Extra	50	Jhon Suquitana	Primax	Amaguaña	0°20′43″ S 78°27′19″ W	07/10/22	10:00:00	07/11/22	7:20:00
45	Extra	150	Sandra Chango	Primax	Ambato	1°15′00″ S 78°37′58″ W	07/08/22	17:27:00	07/11/22	11:00:00
46	Extra	-	Jhuliana Vinueza	Primax	Alangasí	0°19′36″ S 78°23′43″ W	07/10/22	10:30:00	07/11/22	8:40:00
47	Extra	200	Katherine Vega	Masgas	Atuntaqui	0°18′50″ N 78°13′26″ W	07/10/22	15:00:00	07/10/22	7:20:00
48	Extra	250	Robinson Yandún	P&S	Cotocollao	0°07′16″ S 78°30′18″ W	07/11/22	21:45:00	07/12/22	8:30:00
50	Extra	100	Dayana Merizalde	Mobil	La Magdalena	0°14′27″ S 78°31′38″ W	07/10/22	16:07:00	07/11/22	7:00:00
51	Extra	50	Lizbeth Abril	Petroecuador	Sangolquí	0°18′11″ S 78°27′57″ W	06/06/22	17:00:00	06/07/22	14:00:00
52	Extra	500	Juliett Fernández	Primax	Jipijapa	0°08′38″ S 78°28′11″ W	07/09/22	12:45:00	07/11/22	10:30:00
53	Extra		Santiago Pati	Primax	Comité del Pueblo	0°06′02″ S 78°28′17″ W	07/09/22
54	Extra	-	Juan Noroña	Terpel	Conocoto	0°17′27″ S 78°27′51″ W	07/11/22	5:45:00	07/11/22	13:15:00
1	Súper	-	Melanie Muela	Primax	Iñaquito	0°10′29″ S 78°29′16″ W	07/02/22	15:00:00	07/04/22	7:30:00
2	Súper	40	Liney Mejía	Primax	Carcelén	0°05′30″ S 78°28′36″ W	07/03/22	19:52:00	07/04/22	7:21:00
3	Súper	50	Guido Acevedo	Mobil	La Magdalena	0°14′27″ S 78°31′38″ W	07/03/22	17:00:00	07/04/22	7:20:00
4	Súper	260	Lizbeth Alarcón	Petroecuador	Quitumbe	0°17′49″ S 78°33′32″ W	07/03/22	16:44:00	07/04/22	7:22:00
5	Súper	40	Zaid Falcón	Primax	Mariscal Sucre	0°11′48″ S 78°29′58″ W	07/03/22	12:30:00	07/04/22	7:10:00
6	Súper	25	Mario Guilcapi	Petroecuador	Chimbacalle	0°14′13″ S 78°30′29″ W	06/10/22	10:30:00	06/10/22	10:50:00
7	Súper	25	Mario Guilcapi	Primax	Chimbacalle	0°14′49″ S 78°30′20″ W	06/10/22	10:25:00	06/10/22	10:50:00
8	Súper	-	Janina Chusquillo	Primax	Mariscal Sucre	0°08′38″ S 78°28′11″ W	06/09/22	10:00:00	06/10/22	11:00:00
9	Súper	-	Cassidy Garzón	Primax	Mariscal Sucre	0°11′48″ S 78°29′58″ W	07/04/22	11:00:00	07/05/22	14:00:00
10	Súper	200	Esteban Morales	Shell	Iñaquito	0°11′31″ S 78°29′19″ W	07/02/22	17:30:00	07/05/22	13:30:00
11	Súper	1000	Andrea Pico	PDV	Comité del Pueblo	0°06′39″ S 78°28′45″ W	07/07/22	18:55:00	07/08/22	7:51:00
12	Súper	600	Job Segovia	Masgas	Chimbacalle	0°14′41″ S 78°31′06″ W	07/07/22	7:59:00	07/08/22	7:26:00
13	Súper	50	Javier Villegas	Mobil	La Magdalena	0°14′27″ S 78°31′38″ W	07/07/22	21:45:00	07/08/22	7:20:00
14	Súper	25	Salma Cadena	Primax	Sangolquí	0°18′30″ S 78°26′44″ W	06/09/22	15:00:00	06/10/22	10:50:00
15	Súper	25	Salomé Zapata	Terpel	Cochapamba	0°09′17″ S 78°29′49″ W	06/09/22	16:20:00
16	Súper	220	Laura Palacios	Petroecuador	Comité del Pueblo	0°06′56″ S 78°28′45″ W	07/10/22	17:40:00	07/11/22	9:23:00
17	Súper	200	Stalin Aurea	Petroecuador	Ambato	1°13′42″ S 78°36′16″ W	07/08/22	20:00:00	07/11/22	7:00:00
18	Súper	250	Karen Ponce	Petroecuador	Calderón	0°06′30″ S 78°27′01″ W	07/10/22	17:15:00	07/11/22	10:30:00
19	Súper	1000	Landy Flores	Primax	Mariscal Sucre	0°12′00″ S 78°29′30″ W	07/11/22	8:45:00	07/11/22	9:15:00
20	Súper	5	Mateo Terán	Masgas	Iñaquito	0°11′22″ S 78°28′52″ W	07/10/22	12:03:00	07/11/22	9:00:00
21	Súper	50	Lizbeth Abril	Petroecuador	Sangolquí	0°18′11″ S 78°27′57″ W	06/06/22	17:00:00	06/07/22	14:00:00
22	Súper	-	Juan Noroña	Terpel	Conocoto	0°17′27″ S 78°27′51″ W	07/11/22	5:45:00	07/11/22	13:15:00
23	Súper	150	Luis Cueva	Petroecuador	Sangolquí	0°19′29″ S 78°26′26″ W	07/11/22	6:30:00	07/11/22	11:10:00

in Appendix A shows detailed information on the samples. To visualize more precisely the geographic position and distribution of the sampling sites, the following link is provided: https://earth.google.com/earth/d/1xTHaw_buZZzLJq1ytklQVfsNNk2nPrEL?usp=sharing (accessed on 10 August 2024); however, a general map is also shown in Figure 1. After sampling, samples were stored at 20 °C in suitable containers with screw caps until further analysis.

2.2. Determination of Research Octane Number

The Research Octane Number (RON) test was conducted at LACBAL-EPN (Laboratory of Fuels, Biofuels, and Lubricating Oils) using a Cooperative Fuels Research (CFR) engine under ASTM D2699-21 [42]. Certificates for each type of gasoline are presented in Supplementary Materials File S1-Octane Analysis. This standard describes how to measure the resistance to detonation of the fuel when the engine is fully loaded and operating at low revolutions per minute (rpm). The CFR consisted of a continuously variable compression ratio single-cylinder engine with appropriate load and accessory equipment and instrumentation, mounted on a stationary base. The engine operated at a constant speed of 600 ± 6 rpm. The equipment included a knock meter to determine the knock characteristics of the fuel. The octane number was quantified using a scale with heptane (n-C₇H₁₆) as zero and isooctane (C₈H₁₈: 2,2,4-trimethylpentane) as 100. The detailed procedure follows the ASTM standard mentioned above [42].

2.3. GC-MS Analysis

The separation and identification of all compounds present in the gasoline samples were performed by GC-MS, using a Clarus 590 gas chromatograph (Perkin Elmer, Hopkinton, MA, USA) coupled to a Clarus SQ 8 S mass spectrometer (Perkin Elmer, Hopkinton, MA, USA) and an autosampler. A Zebron ZB-5MS GC capillary column (30 m × 0.25 mm × 0.25 µm) from Phenomenex Inc. (Torrance, CA, USA) was used. Helium gas (99.99% purity) served as the carrier gas at a constant flow rate of 1 mL/min with an injection volume of 0.5 μL and a 300:1 split ratio. The injector temperature was maintained at 250 °C, and the ion-source temperature was 200 °C. The oven temperature program was: (i) initial temperature of 30 °C (isothermal for 35 min); (ii) ramped at 5 °C/min up to 125 °C; (iii) and then at 30 °C/min up to 290 °C (isothermal for 1 min). For GC-MS detection, an electron ionization system in electron impact mode with an ionization energy of 70 eV was employed. The relative percentage of each component was calculated by comparing its average peak area to the total area. The software adopted to manage the mass spectra and chromatograms was Turbo-Mass ver. 6.1.2, and the library database was NIST 2017.

2.4. PIONA Analysis

For the seventy-seven samples, each was analyzed twice using GC-MS, resulting in 154 spectrograms categorized by carbon number and hydrocarbon type. For Extra gasoline, the total number of observations was one hundred-eight, whereas for Súper gasoline, the number of observations was forty-six. The PIONA analysis determines the molar concentration (percentage) of paraffins (P), isoparaffins (I), olefins (O), naphthenes (N), and aromatics (A), which are the main hydrocarbon components, along with any non-hydrocarbon components such as phenols, acids, alcohols, and monoaromatic steroids, among others [88,89]. Each sample was analyzed multiple times with a gas chromatograph. From the chromatograms generated by the system, the areas of the peaks corresponding to each type of chemical compound were obtained with a mass spectrometer primarily based on their retention indices (RI) [90]. To determine the specific concentration of each of these hydrocarbons, three runs of three diverse sets of standard hydrocarbons were performed to establish correction factors to normalize the data through calibration curves. For example, the first set was composed of the following carbon number standards: C5-pentane, C6-hexane, C7-heptane, C8-octane, C12-dodecane, C16-hexadecane, cyclohexene, methyl-cyclohexane, benzene, toluene, o-xylene, p-xylene, naphthalene, cumene, and hexene; the second set was composed by cyclohexane and cyclohexene, and finally, the third set was composed by 1-hexene, 1-decene, and 1-dodecene. Hence, the real molar concentrations of each sample were obtained. The concentrations obtained from each sample were grouped according to the type of hydrocarbon with a certain number of carbons, and the oxygenates were classified through the use of a programming code generated in RStudio version 2023.06.0+421 “Mountain Hydrangea” (Supplementary Materials File S2-Script Data Carbon Number). With this information, we proceeded to calculate the average carbon number (ACN).

2.5. Calculation of the Average Carbon Number

According to Taco-Vásquez and Holtzapple [86], the average carbon number (ACN) of the total of hydrocarbons can be represented by the average of the values obtained by multiplying the total sum of the molar percentage of the chemical compounds (type of hydrocarbon) with the same carbon number by that carbon number and dividing by one hundred, which is the sum of all percentages. For instance, Equation (1), derived from the one presented in Mull and Rommens [87], calculates the ACN as follows:

$${ACN}_{Total}={\displaystyle \frac{\sum _{CN}CN\times {\sum }_{TH}{\%M}_{TH,CN}}{\sum _{CN}{\sum }_{TH}{\%M}_{TH,CN} }}$$

(1)

where CN is the carbon number, TH is the type of hydrocarbon, and %M is the molar percentage that depends on two variables, TH and CN (the type of hydrocarbon corresponding to a specific carbon number).

Since the concentrations of the oxygenated compounds were mostly zero, to represent the calculated ACN data, only the rest of the hydrocarbon types mentioned in the previous section were reported. However, to calculate the ACN of each type of hydrocarbon (ACN_TH), it is necessary to adjust by rearranging some terms of Equation (1), thus having Equation (2). In Equation (1), to perform the calculation, it is necessary to multiply the sum of the molar concentrations of all types of hydrocarbons by each carbon number and divide by the double sum of all the obtained concentrations. Besides, for Equation (2), the numerator is shortened to only the sum of the product of the molar concentration by each carbon number of a single hydrocarbon type.

$${ACN}_{TH}={\displaystyle \frac{\sum _{CN}\left(CN\times {\%M}_{TH,CN}\right)}{\sum _{CN}{\%M}_{TH,CN}}}$$

(2)

PIONA analysis and calculation of the two types of ACN are detailed in the Supplementary Materials for Extra gasoline (Supplementary Materials File S3-Results Summary Extra and for Súper gasoline (Supplementary Materials File S4-Results Summary Súper), respectively.

2.6. Statistical Analysis

Each data set was subjected to normality tests to determine the appropriate analysis method. After determining that the data did not follow a normal distribution (Supplementary Materials File S5-Minitab Files), nonparametric tests such as Mann–Whitney and Kolmogorov–Smirnov for two-sample comparisons, and Mood Median and Kruskal–Wallis with Bonferroni’s adjustment for multisample comparisons (Supplementary Materials File S6-Statgraphics Files) were used.

3. Results

3.1. Determination of Research Octane Number

Table 3. Research octane numbers of Extra and Súper gasoline analyzed at the LACBAL laboratory.

N° Sample	Extra Gasoline	Súper Gasoline
1	85.5	93.5
2	85.7	93.0
3	85.9	90.8
Mean ± Standard Deviation	85.7 ± 0.2	92.4 ± 1.4

presents the results of the research octane number conducted at LACBAL of Escuela Politécnica Nacional.

As we can verify in Table 3, these RON values align with those analyzed by Druet Rodríguez and Vera Castro [14], which approximate 84–86 octane for Extra gasoline and 91–93 octane for Súper gasoline, respectively. According to Thomas [91], lead compounds are used in gasoline to increase the octane number by about 5–10 units [92], as unleaded gasoline is more expensive to produce or import and, therefore, is less available in developing countries. A study conducted in 2022 found that gasoline commercialized in Ecuador still contains measurable quantities of lead [34], even though in 1996, Ministerial Agreement 112 [92] approved the elimination of lead from gasoline sold in Ecuador since 1998. Also, according to Dell et al. [93], tetraethyl lead was added to retard the onset of auto-ignition, thus obtaining satisfactory compression ratios with lower octane fuel [94]. These findings explain why Ecuadorian gasoline still contains high levels of lead, suggesting that the actual octane ratings of the fuel are even lower than those required by Ecuadorian law and the ones set by the global standards [81,82,84] mentioned in Table 2.

3.2. PIONA Analysis

Figure 2 shows the average total molar concentration of each of the hydrocarbon types present in the Extra gasoline and Súper gasoline samples. These values were calculated with correction and normalization factors. Similar to the previous section, by comparing the molar concentrations of olefins and aromatics obtained from Extra gasoline and Súper gasoline, it can be inferred that they are not very different from those reported by Rocha-Hoyos et al. [95]. In fact, it can be shown that the use of correction factors to calculate and adjust the values obtained from the chromatograms performed was a well-executed approach. Furthermore, according to the analyses reported by Ali and Aboul-Fotouh [96], after having characterized several gasoline blends, they conclude that if a blend has among its highest components isoparaffins, aromatics, and naphthenes, it is very likely that the RON would be higher than 80. It can be observed in Table 3 that the two types of gasoline analyzed meet this premise. Likewise, Ali and Aboul-Fotouh reported the percentages of the later-mentioned hydrocarbons, and they vary between 38 and 39%, 28–31%, and 15–16%, respectively [96]. On the other hand, the addition of significant amounts of aromatics, oxygenates (such as tetraethyl lead “TEL” or methyl tert-butyl ether “MTBE”), and olefins have been repeatedly criticized for their detrimental effects on the environment and human health [97,98,99]. These compounds are primarily responsible for influencing the mass of exhaust emissions composed of HC, CO, unburned total hydrocarbon content (THC), and NO_x, as well as for favoring the formation of ozone in the environment and toxic air pollutants such as benzene, 1,3-butadiene, formaldehyde, and acetaldehyde [100,101]. For this reason, new types of additives have been investigated over time. Currently, some researchers have demonstrated the benefits of using additives containing alcohols (ethanol [102], propanol [103], butanol [104], and others) or ethers (ethyl tert-butyl ether, “ETBE” [101]) by providing an increase in gasoline octane number [105] and a reduction in THC, CO, and particulate matter emissions [106,107]. Therefore, based on the bibliographical data presented in Table 1 and Figure 2, it can be inferred that the composition of Ecuadorian gasoline samples that meet international standards is low.

These arguments could explain why Extra gasoline has a higher concentration of oxygenates (0.02%) compared to Súper gasoline, which is reflected in a lower average RON value. The use of oxygenates can have a significantly counterproductive effect on gasoline quality [14]. For example, from one point of view, it enhances RON [26] by improving acceleration performance; however, it increases pollutant emissions [108]. Perdih and Perdih [109] further assert that a structural comparison of n-alkanes vs. branched alkanes shows that low RON fuel-air mixes may knock because alkanes with a significant number (3 to 7) of adjacent CH₂ groups may create a small number of gem-diperoxydiol groups. Furthermore, Speight states that aromatic hydrocarbons (benzene and toluene), highly branched iso-paraffins (iso-octane), and olefins (di-isobutylene) tend to have high antiknock values. Normal paraffins, such as n-heptane, have a low antiknock value, while low-branched iso-paraffins and naphthenic hydrocarbons, such as cyclohexane, rank in an intermediate position [110]. In refineries, olefins are developed and blended with the final oil to raise octanes; they do not occur naturally in crude oil [71,111]. This has allowed countries that do not have strict regulations on fuel composition to add up to 20% of total olefins, as is the case in Ecuador [112]. Considering all these facts, in comparison with Extra gasoline, Súper gasoline exhibits higher amounts of branched alkanes (isoparaffins), linear olefins, and aromatics, whereas it has lower levels of paraffins (n-alkanes) and naphthenes, which overall grants it a higher RON. Nevertheless, Szybist and Splitter [113] emphasized that it is necessary to analyze the other physicochemical properties of gasoline. Because they, together with a private company, were able, through trial and error, to blend four types of hydrocarbons (aromatic, saturated, olefins, and ethanol) in different proportions to create three distinct gasolines with a nominal RON of 98 ± 0. Simultaneously, these formulations exhibited different octane sensitivity values (S = RON − MON) of 1.3, 10.5, and 10.8, respectively.

On the other hand, when comparing the concentrations obtained from the gasoline samples analyzed in this study with the values reported by various sources described in Table 1, it is more likely that the gasoline sold in Ecuador adheres to the requirements stated for the Latin American (reference country: Brazil) market, given the climatic and environmental laws of the area [83]. As mentioned above, gasoline blends are produced according to the market where they are sold, and therefore, the blends have concentrations different from the standards of the main international markets.

Complementarily, Figure 3 shows the distribution of hydrocarbon types by carbon number, more commonly called the carbon distribution plot, for the average Extra gasoline (Figure 3a) and Súper gasoline (Figure 3b) samples. This graph is required because the fractions of distillates in gasoline tend to overlap. With this distribution, it is easier to identify at which carbon number the maximum molar concentration of a specific hydrocarbon is found [114]. This will contribute to the shape of the distribution of the total composition by carbon number. For Extra gasoline, Figure 3 shows the concentration of isoparaffins and branched olefins contribute most to the shape of the final composition distribution, whereas for Súper gasoline, the concentration of isoparaffins and aromatics are the main components. In addition, for Extra gasoline and Súper gasoline, the carbon distribution ranges from carbon number C4 to C12, just like the graph presented by Altin and Eser for a typical gasoline [115]. Both a/b graphs in Figure 3 show similar types of hydrocarbons. However, according to the carbon number in which a higher concentration is visualized, a higher content of isoparaffins and aromatics can be seen in the Súper gasoline samples, as well as a lower content of branched olefins and naphthenes. These variables are similar to the data presented in the literature cited above (see Table 2) and would explain why Súper gasoline samples have a higher-octane value (see Table 3).

In terms of composition and carbon number arrangement, Pitz et al. [116], have established that both paraffins and isoparaffins are mostly at C5–C7, with isoparaffins being selectively included as they increase the octane number. Naphthenes, which come from intermediate “residues” of the refining process, are cyclic alkanes with isomers ranging from C6 to C7. For olefins, branched olefins are preferred to linear olefins since there is a higher number of C5–C7 isomers (better octane number), but a lower concentration of C4 species. Therefore, the concentration of branched olefins is higher than that of linear olefins, such as methyl butenes and methyl pentenes. On the other hand, naphthenes and olefins range from C7 and above. For aromatics, benzene (C6) concentration is extremely limited due to its carcinogenic effects; therefore, toluene (up to 35%) is possibly added. Oxygenates are added to the final blend up to approximately 15%; hence, they are not naturally present in crude oil. In addition, according to Castillo et al. [117], Súper gasoline (higher octane number) has a higher content of short-branched isoparaffins and olefins, which can be corroborated in the comparison of Figure 3a with Figure 3b.

3.3. Analysis of Average Carbon Number

The results obtained from the calculation of the total average carbon number (ACN_Total) of each of the samples of Extra gasoline (Figure 4a and Figure 5a) and Súper gasoline (Figure 4b and Figure 5b) and the one calculated according to the type of hydrocarbon (ACN_TH) (see Figure 6) are presented below. Figure 4 shows that the ACN histogram varies by less than one unit for 99% of all samples. The distribution of the average carbon number value of the Extra gasoline samples is monomodal, with a greater number of values concentrated in the ranges of 6.76 to 7.09 with a total of seventy-seven samples. The remaining twenty-eight analyses were classified according to higher ranges (7.09–8.44) with lower frequencies. Meanwhile, the values of Súper gasoline samples show a bimodal distribution with a wider concentration of samples across four ranges from 6.58 to 6.95 with a number of twenty, twenty-four, sixteen, and fifteen samples, respectively. Additionally, across the range of 6.95 to 7.50, eighteen samples are distributed with lower frequencies.

For Extra gasoline, Figure 5 shows that the ACN_TH is 7.0 ± 0.3, and for Súper gasoline, it is 6.8 ± 0.2. As it can be concluded, the standard deviation is higher in the population of Extra gasoline samples. Extra gasoline samples have components that are more likely to evaporate in more unfavorable environmental conditions [117], i.e., actual conditions that are found at a gas station, and as a result, their concentration values change in greater proportion. This directly affects the calculated ACN, which has a larger standard deviation. The average ACN of the Extra gasoline samples is also higher because of the elevated concentration of branched olefins (Figure 2). There seems to be an inverse correlation between the ACN and RON numbers of the two types of gasoline analyzed. Therefore, the higher the octane number, the lower the average carbon number.

Figure 6 shows the ACN by hydrocarbon type and indicates that the number of carbons is no longer so unequal in value when compared to the bar graph data of the molar concentrations in Figure 2. However, several statistical tests have been performed to determine if they differ significantly. Figure 6 shows that an average ACN has been calculated from the ACNs calculated by type of hydrocarbon. This calculus was useful to refute the equality in obtaining the ACN_Total by Equation (1) instead of an average of the ACNs calculated with Equation (2). On the other hand, the ACN_Total has been compared with the value provided in the research of Altin and Eser [115], where the reported ACN is 6.8 calculated from a mass composition. The values calculated in our research are close to this proposed value, where we have 7.0 ± 0.3 and 6.8 ± 0.2 for Extra gasoline and Súper gasoline, respectively. Further statistical analysis of all variables is given in Section 3.4.

3.4. Statistical Analysis

Table 4. p-values resulted from the comparison between Extra and Súper gasoline samples regarding variables of molar composition and ACN.

Type of Variable	p-Value Comparing Extra Gasoline and Súper Gasoline	Type of Variable	p-Value Comparing Extra Gasoline and Súper Gasoline
Molar % of paraffins	0.18 MW 0.32 KS	ACN of paraffins	1.26 × 10−3 MW 1.05 × 10−4 KS
Molar % of Isoparaffins	0.00 MW 0.00 KS	ACN of Isoparaffins	0.27 MW 0.02 KS
Molar % of Linear Olefins	1.47 × 10−5 MW 2.84 × 10−5 KS	ACN of Linear Olefins	0.26 MW 1.84 × 10−5 KS
Molar % of Branched Olefins	3.82 × 10−12 MW 0.00 KS	ACN of Branched Olefins	1.29 × 10−7 MW 0.00 KS
Molar % of Naphthenes	0.00 MW 0.00 KS	ACN of Naphthenes	0.00 MW 0.00 KS
Molar % of Aromatics	2.17 × 10−11 MW 0.00 KS	ACN of Aromatics	2.39 × 10−10 MW 0.00 KS
Molar % of Oxygenates	0.35 MW 0.00 KS
		ACNTotal	7.68 × 10−12 MW 0.00 KS

^MW p-value calculated with the Mann–Whitney test. ^KS p-value calculated with the Kolmogorov–Smirnov test. p-value < 0.05 (significance level) means that the null hypothesis is rejected.

shows the p-values of the non-parametric tests used to determine whether or not there was equality or non-equality between the different data sets. The null hypothesis is rejected when the true p-value is less than 0.05 (significance level), indicating that the samples are not equal or are not within an interval that allows them to be considered similar. Within the same variable or category, the average carbon number of the Extra gasoline and Súper gasoline sample sets was compared. Additionally, two distinct numbers of the Mann–Whitney (MW) and Kolmogorov–Smirnov (KS) tests have been computed for each group. While the MW test compares medians, the KS test analyzes the distributions of the two samples under analysis.

Table 4 compares the molar concentrations of each type of hydrocarbon between Extra gasoline and Súper gasoline; only the molar concentration of paraffins meets the null hypothesis. In both tests, the median and type of distribution of the data are similar. For Extra and Súper gasolines, Figure 3 shows a rather analogous distribution that is observed only for paraffins. However, when the ACNs are compared between isoparaffins and linear olefins, the medians of both types of hydrocarbons are similar or equal, but the shape of the data distribution is not. Similarly, the same phenomenon occurs with the comparison of the molar concentration of the oxygenates in both gasoline grades. When comparing the medians or the shape of the data distribution in the rest of the variables, neither the variables nor their shapes are similar since both markers are less than 0.05. Therefore, it can be argued that the calculated ACN does not follow the same trend as the molar composition of each gasoline.

However, when the multiple comparison methods for the ACNs were calculated, the p-values were equal to zero, meaning that all medians are different according to the Mood median and the Kruskal–Wallis test performed with the statistical software. As previously indicated, Extra gasoline and Súper gasoline were compared, considering each type of hydrocarbon. Significant differences between each pair of sample medians were obtained using the Kruskal–Wallis Bonferroni correction, shown in

Table 5. Differences between the medians determined by the Kruskal–Wallis test for the average carbon number (ACN) variable for each of the types of hydrocarbons present in the Extra gasoline and Súper gasoline samples.

Type of Hydrocarbon per Type of Gasoline		Extra Gasoline						Súper Gasoline
		Paraffins	Isoparaffins	Linear Olefins	Branched Olefins	Naphthenes	Aromatics	Paraffins	Isoparaffins	Linear Olefins	Branched Olefins	Naphthenes	Aromatics	Total ACN
Extra gasoline	Paraffins	N/A	--	--	--	--	--	--	--	--	--	--	--	--
	Isoparaffins	−329.55 *	N/A	--	--	--	--	--	--	--	--	--	--	--
	Linear Olefins	−364.84 *	−35.29	N/A	--	--	--	--	--	--	--	--	--	--
	Branched Olefins	−703.58 *	−374.03 *	−338.74 *	N/A	--	--	--	--	--	--	--	--	--
	Naphthenes	−685.96 *	−356.41 *	−321.12 *	17.62	N/A	--	--	--	--	--	--	--	--
	Aromatics	−1140.27 *	−810.71 *	−775.42 *	−436.69 *	−454.31 *	N/A	--	--	--	--	--	--	--
	ACNTotal	−897.96 *	−568.41 *	−533.12 *	−194.38 *	−212.00 *	242.31 *	−941.24 *	−608.12 *	−585.63 *	−425.84 *	−523.67 *	190.74	148.75
Súper gasoline	Paraffins	−43.28	−372.83 *	−408.12 *	−746.86 *	−729.24 *	−1183.54 *	N/A	--	--	--	--	--	--
	Isoparaffins	289.85 *	−39.71	−74.99	−413.74 *	−396.12 *	−850.42 *	−333.12 *	N/A	--	--	--	--	--
	Linear Olefins	312.33 *	−17.22	−52.51	−391.25 *	−373.63 *	−827.94 *	−355.61 *	−22.48	N/A	--	--	--	--
	Branched Olefins	472.13 *	142.57	107.28	−231.46 *	−213.84 *	−668.14 *	−515.40 *	−182.28	−159.80	N/A	--	--	--
	Naphthenes	374.29 *	44.74	9.45	−329.28 *	−311.67 *	−765.98 *	−417.57 *	−84.45	−61.96	97.83	N/A	--	--
	Aromatics	1088.70 *	759.15 *	723.86 *	385.12 *	402.74 *	−51.57	−1131.98 *	−798.86 *	−776.37 *	−616.58 *	−714.41 *	N/A	--
	ACNTotal	−749.22 *	−419.66 *	−384.37 *	−45.64	−63.25	391.05 *	−792.50 *	−459.37 *	−436.89 *	−227.09 *	−374.93 *	339.48 *	N/A

* and font in red: Indicates a significant difference. N/A means not applicable for comparing the same sample. -- repeated value specified in subsequent rows.

. If there is a highly significant difference between the two variables, it has also been shown in the data displayed using an asterisk next to each value and changing the font color to red to have a better visualization of the results. The positive or negative limits for each pair of variables were used to validate these differences. These limits vary depending on the pair of variables considered, but most commonly they fluctuate under three ranges: ±190.876, ±196.937, and ±202.817. Any value that exceeds these thresholds in any direction (positive or negative integers), determines the variables as significantly different.

Table 5 shows the ACN of the same type of hydrocarbon between the Extra and Súper gasoline samples and determines that the ACNs of paraffins, isoparaffins, linear olefins, aromatics, and the ACN_Total are equivalent, whereas the ACNs of branched olefins and naphthenes is not equivalent. Only for the calculated ACN_Total can it be deduced that there would be no difference if this value were calculated from a molar or mass concentration, as shown by the similarity between our graphs and the ones exhibited by Altin and Eser [115], who utilized the mass concentration in their calculus to obtain the distribution plots. Alfin and Eser, as many other authors mentioned above, also affirmed that fuels, instead of achieving a certain distribution of hydrocarbons by classes or sizes, are often manufactured to fulfill the allowed limits imposed by industry requirements and regulations. From this point of view, Bonferroni corrections are adequate to determine that both types of gasoline are equal if only the ACN_Total is taken into consideration. For Extra and Súper gasolines, when comparing different types of hydrocarbons, the ACN of the isoparaffins of Extra gasoline is similar to that of linear and branched olefins and naphthenes of Súper gasoline. Linear olefins of Extra gasoline are similar to isoparaffins, branched olefins, and naphthenes of Súper gasoline; the branched olefins and naphthenes of Extra gasoline have the same ACN_Total as Súper gasoline. The ACN_Total of Extra gasoline is similar to the aromatics of Súper gasoline. Now, when comparing the types of hydrocarbons in the same group of gasoline, for Extra gasoline, we have similarities between isoparaffins-linear olefins and branched olefins-naphthenes; for Súper gasoline, equivalence is among isoparaffins-linear and branched olefins-naphthenes.

4. Conclusions

This paper explores the average carbon number and type of hydrocarbon analysis of the two most common commercial gasolines sold in Quito, Ecuador (Extra and Súper gasoline), using the GC-MS method to determine qualitatively and quantitatively their composition. The research octane number (RON) values for Extra and Súper gasoline were found to be consistent with previous studies, indicating an average RON of 85.7 for Extra gasoline and 92.4 for Súper gasoline. Additionally, for Súper gasoline, the average carbon number was ~6.8, whereas for Extra gasoline, the average carbon number was ~7. Therefore, gasoline with slightly shorter-chain hydrocarbons has higher octane numbers.

The PIONA analysis revealed that the molar concentrations of olefins and aromatics in the Ecuadorian gasoline samples are comparable to those reported in other studies. The use of correction and normalization factors was validated as an effective method for calculating these concentrations. For Extra and Súper gasoline, high-octane aromatic carbon distribution ranges from C6 to C9, whereas isoparaffin carbon distribution ranges from C5 to C8. Both isoparaffins and aromatics have high octane ratings, which increases the overall octane numbers of both gasolines studied. The most abundant hydrocarbons found in Extra and Súper gasoline, going from the highest to the lowest percentage of composition, are isoparaffins, aromatics, branched olefins, paraffins, naphthenes, and linear olefins. However, the average carbon number for each type of hydrocarbon varies depending on the type of hydrocarbon. For instance, the ACN of paraffins is about 5.2; meanwhile, the ACN of aromatics is about 8.3.

On the other hand, a statistical analysis was performed with the fifty-four and twenty-three samples of Extra gasoline and Súper gasoline, respectively. This analysis confirmed significant differences between Extra and Súper gasoline in terms of molar concentrations and average carbon number (ACN) values. The non-parametric tests, including Mann–Whitney and Kolmogorov–Smirnov, indicated that the samples are not equal, supporting the conclusion that Ecuadorian gasoline has unique characteristics influenced by local production and import practices.

Finally, this paper provides a new insight into the relationship between the composition analysis of gasoline and octane ratings. Compositional analysis includes average carbon number analysis and the type of hydrocarbon distribution. The concept of average carbon number is new and introduced in this study and could potentially serve as a tool to characterize fuels in the future.