Yuan-Chung Lin This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Ya-Ching Li1, Kassian T.T. Amesho1Feng-Chih Chou1, Pei-Cheng Cheng1

1 Institute of Environmental Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
2 Center for Emerging Contaminants Research, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan


 

Received: February 27, 2020
Revised: April 29, 2020
Accepted: May 1, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.2020.02.0081 


Cite this article:

Lin, Y.C., Li, Y.C., Amesho, K.T.T., Chou, F.C. and Cheng, P.C. (2020). Filterable PM2.5, Metallic Elements, and Organic Carbon Emissions from the Exhausts of Diesel Vehicles. Aerosol Air Qual. Res. 20: 1319–1328. https://doi.org/10.4209/aaqr.2020.02.0081


HIGHLIGHTS

  • We investigated MEs, and organic carbon emissions from the exhausts of diesel vehicles.
  • Metallic element compositions were in decreasing order of Ca > Zn > Al > K > Fe > Cr.
  • Metallic elements concentration in PM2.5 has also been reported.
  • EC had the highest concentration in PM2.5 than OC.
  • EFs of MEs in PM2.5 were studied in relationship to atmospheric PM2.5 composition.
 

ABSTRACT


Urban air pollution in the form of fine particulate matter (PM2.5) poses a substantial health threat to humans via inhalation. To assess the risks from this pollutant, we characterized and quantified the PM2.5 exhausted by 15 diesel vehicles ranging from 28,306 to 883,374 km (an average of 525,854 km) in total mileage and varying in manufacturer and model year (1988–2005). We applied inductively coupled plasma optical emission spectroscopy (ICP-OES) to analyze the metallic element constituents, among which Ca composed the largest fraction, followed by Zn, Al, K, Fe, Mg, and Cr. Measuring the carbonaceous content, we found that the total carbon (TC), the organic carbon (OC), and the elemental carbon (EC) accounted for 3461 µg m–3, 1410 µg m–3, and 2051 µg m–3 of the PM2.5 concentration, respectively. Of the metallic elements, Ca exhibited the highest emission factor (EF), between 45.3 and 259 µg L-fuel–1 (with an average of 132 µg L-fuel–1), whereas Zn and Cr displayed the lowest ones, averaging 13.1 µg L-fuel–1 and 1.91 µg L-fuel–1, respectively. We also investigated the relationship between the concentrations and the EFs of the metallic elements in the exhaust, and the ambient PM2.5 composition. These results help illustrate the contribution of PM2.5 emitted by diesel vehicles to metallic element concentrations in the natural environment and the consequent risks.


Keywords: PM2.5 emissions; Diesel vehicles; Metallic element concentrations; Organic carbon emissions.


INTRODUCTION


The pollution control for fine particulate matter (PM2.5) is one of the greatest challenging air pollution issues and is the highest priority pollutant in Kaohsiung City, Taiwan (Lin et al., 2015; Li et al., 2016). Vehicles, especially in-use diesel vehicles and heavy-duty diesel trucks (HDDTs), are considered to be among the main contributors of atmospheric PM2.5 (Liu et al., 2018b). Nonetheless, this issue indicates that we lack a comprehensive understanding of vehicular PM emission proportions in terms of the exact pollutant constituents. This information would be beneficial for defining secondary PM formation models, emission inventories (Agarwal et al., 2017; Al Hanai et al., 2019), and most importantly to examine the health effects of PM2.5 exposure (Badaloni et al., 2017; Chartres et al., 2019).

In order to examine automobile emissions, a chassis dynamometer testing system and on-board testing system with a portable emissions measurement system (PEMS) have been employed as universal standard methods (Zhang et al., 2015), and a large number of studies on vehicle emissions have been conducted in Taiwan’s neighboring countries such as China, among others, in recent years (Liang et al., 2019). Thus far, most of the studies on vehicle emission or quantifications have been centered around emission rate measurements, while the scope of the research on chemical characterization of PM2.5 emissions has been inadequate. Presently, there are insufficient studies on the investigation of filterable PM2.5, metallic element concentrations, and organic carbon emissions of PM2.5 from the exhausts of diesel vehicles with diverse accrued mileages, particularly for in-use diesel vehicles in Taiwan (Tsai et al., 2018) although several researchers established the emission inventories for black carbon (BC), organic carbon (OC), and other constituents found in PM2.5 using SPECIATE (U.S. EPA, 2014) and recounted BC emission factors (EFs) (Campagnolo et al., 2019). Numerous preliminary findings related to these factors have been conducted in other countries. For example, Jaiprakash et al. (2017) described the emissions from a diesel engine exhaust in India to determine the concentrations of metallic elements and the EC/OC component, and Oanh et al. (2010) developed a PM profile for the purpose of in-use diesel vehicles in Thailand with the help of a chassis dynamometer.

Carbon is among the most significant elements found in atmospheric PM, which constitute approximately 20–60% of PM2.5 concentration and largely occurs in a form of OC and EC. Atmospheric EC is released straight from primary anthropogenic sources (Cui et al., 2017), whereas OC could be emitted right from sources like primary particulates and secondary OC that may be produced from the products of chemical reactions in the atmosphere with low vapor pressure. OC and EC in PM play crucial roles with respect to the impact of global climate change, visibility degradation, and human health (Cui et al., 2017).

PM2.5 has a large surface area that has a potential to act as a transporter of metallic elements and heavy metals (Jiang et al., 2019; Sun et al., 2019), bacteria, and viruses that can also be inhaled by human beings via the respiratory system and absorbed by the alveoli or can enter other organs through lung aeration, possibly causing asthma, bronchitis, and cardiovascular diseases (Liu et al., 2016; McGuinn et al., 2019). Many studies on heavy metals as constituents of PM2.5 and their sources have been carried out in different parts of the world, including China, Denmark, Turkey and France (Hvidtfeldt et al., 2019; Jiang et al., 2019).

It has been reported by various studies that exposure to metallic elements is associated with lower lung function and higher occurrence of respiratory symptoms and diseases in adults (Achilleos et al., 2017; Akinwunmi et al., 2017). Suvarapu and Baek (2017) examined both the concentrations of metallic elements in PM2.5 (external) and in blood (internal) by acquiring inclusive statistics about metallic elements and examined the effect of metallic elements on human respiratory health (Grivas et al., 2018). Metallic elements have been proven to have harmful effects on human body, and metallic components attached to PM2.5 particles are typically found in the PM2.5 concentrations (Pervez et al., 2018). Thus, metallic elements can pose harmful effects on the heart, lungs, and nervous system, and people who are exposed to toxic metallic elements over a long period of time may be affected (Yang et al., 2019; Zhao et al., 2019). In this case, children are the most vulnerable to toxic environments and may suffer from learning disabilities, memory and attention deficits, or psychiatric complications (Lee et al., 2018; Liu et al., 2018a; 2019). As detailed in the introduction, metallic elements in the natural environment constitute the source of many diseases, and their concentrations and quantity impact health in different ways (Zhang et al., 2018; Turap et al., 2019). In Huzhou, China, Peng et al. (2017) assessed the significant source of contributions to PM2.5-bound metallic elements (i.e., Cr, Co, Ni, As, Cd, and Pb) and the order was as follows: secondary sources > vehicle sources > soil dust > coal combustion > cement dust. Cr is typically released to air mainly through processes of combustion and through metal industries. Other sources of Cr into the environment include cement-manufacturing plants (cement contains chromium), chromium-containing asbestos linings, chromium-based automotive catalytic converter pollution, and tobacco smoke (ATSDR, 2012; Tchounwou et al., 2012; Ryder et al., 2020). Cr exposure has been associated with various health conditions such as nasal septum atrophy and cancer (ATSDR, 2012).

To address this issue, we conducted a comprehensive study of filterable PM2.5, metallic element concentrations in PM2.5 and organic carbon emissions from the exhausts of 15 in-use diesel vehicles. The ultimate goal for this study was to investigate and determine the concentrations of metallic element concentrations in PM2.5 and organic carbon emissions from the exhausts of diesel vehicles by performing a chemical analysis (Fe, Zn, Mg, K, Ca, Al and Cr) with the help of inductively coupled plasma optical emission spectroscopy (ICP-OES).


MATERIALS AND METHODS



Sampling Methods


Diesel Vehicle Details and Engine Testing

15 vehicles were tested in this study to determine the concentrations of metallic elements in PM2.5 and organic carbon emissions from the exhausts of different diesel vehicle groups. The detailed specifications of each sampled vehicle, including the brand, model year, fuel type, displacement (L), emission standard classification, and mileages (km), among indices, are shown in Table 1. The samples from the 15 tested diesel vehicle exhausts were collected at a constant, measured flow rate, suitable for the precise testing procedure for the interval corresponding to the period of each mode of the driving phase. Therefore, an emissions rate (g kWh–1) for each technique of the cycle was achieved. The descriptions of the 15 experimented diesel vehicles are presented in Table 1, while the schematic illustration of the experiment is presented in Fig. 1



Fig. 1. Schematic diagram of the experimental system.Fig. 1. Schematic diagram of the experimental system.


Sampling Procedures for Pollutant Emissions

The emissions from gaseous pollutants, mainly PM2.5, in the tail-pipe exhaust were monitored and observed online by using a portable, convenient gaseous pollutant analyzer (Sprint V4; Telegan). This specific analyzer has a detection limit of 0–5000 ppm, with a 1 ppm resolution and a reaction time of 30 seconds. During the experiments, the air samples were uninterruptedly extricated from the tail exhaust pipe, and a fraction of the sample was directed to the analyzer to identify the pollutants of significance. The controller unit, which serves as a convenient, flexible gauging device for spot checks and on-site measurements, was furnished with a probe socket and a coordinated disproportion pressure test. The inclusive range of tests makes it appropriate for precise measurements of temperature, humidity, pressure, velocity, current, and voltage. The device provides consistent precision and reliability in research facility measurements in a compact unit that can quantify five significant constituents (PM10 and PM2.5, O3, SO2, NO2/NOx, and CO) in the field. It provides a quicker response time than the prevailing models and therefore, it is 20% lighter. Preparation interval has also been reduced by half to enable portable measurement. This instrument is simple to operate, and features are of innovative design that secures the unit from shocks and vibrations, features that improve its practicality in the field.


Sampling Procedures and Particle Component Analysis for EC/OC

During this study, Pallflex Tissuquartz filters (2500QAT-UP) were employed for the collection of particles, where each 0.5 cm2 filter area was used to quantify OC and EC. These filters were pre-baked at a temperature of 550°C for 6 h to remove all adsorbed OC before use. Both OC and EC were measured and estimated with a Thermal/Optical Carbon Analyzer (Model 2001A; Desert Research Institute, USA) in accordance with the NIOSH 5040 procedures (Zhang et al., 2015). With the organic vapor adsorption on the quartz filters, particularly volatile OC can produce positive artifacts. Further discussion on procedures and particle component analysis used in this section is provided in Section S3 of the supplementary material.


Analytical Procedures


Measurements for Atmospheric Metallic Element Concentration

The PM and further pollutant samples along with blank quartz fibers were analyzed to determine the chemical and isotopic composition via absorption. The PM2.5 samples along with the two blank quartz fiber filters were absorbed with a mixed acid of HF + HClO4 (10:1) and HNO3 for 24 h in securely sealed Teflon flasks on a hot plate at a temperature of 120°C. In order to minimize the chance of contamination, all acids used for the absorption process and solution procedure were comprised of ultra-pure reagents, and Milli-Q water was applied in their preparation and dilution. The removal of the supernatant was performed through filtration (0.45 µm membrane filter paper), acidified with a few drops of nitric acid and stored at 4°C. The chemical analyses and determination of metallic element concentrations of Fe, Zn, Mg, K, Ca, Al, and Cr were carried out with ICP-OES (Optima 7000 DV; PerkinElmer, USA).


RESULTS AND DISCUSSION



PM2.5 Concentration and Emission Factor

As presented in Table 2, the PM2.5 concentration (µg m–3) from 15 diesel vehicles was found to be in the range of 3875–16,458 µg m–3. The PM2.5 released from the exhausts of these 15 diesel vehicles was the main component, accounting for an average concentration of 7927 µg m–3 of PM2.5. The emission PM2.5 factors from the diesel exhausts (µg L-fuel–1) were analyzed as indicated in Table 2. PM2.5 concentrations within the diesel exhausts of the 15 diesel vehicles ranged from 3875–16,458 µg m–3, with the 1169–3881 µg L-fuel–1 emission factors accounting for the concentration average of 7927 µg m3 and 2006 µg L-fuel–1, as detailed in Table 2. It was further found that the concentration of PM2.5 from the vehicle exhausts was affected by various factors, including vehicle type, fuel quality, vehicle model year, and the vehicle speed (Jhang et al., 2018; Lin et al., 2019). The results further showed that the diesel vehicle emissions had a significant effect on the PM2.5 concentration. This additionally implied that PM2.5 emission factors could be minimized once the emission control policies are enforced by authorities like the Kaohsiung City Government. That is, the PM2.5 emission factors would in all probability be reduced to 20.4% through enforcement of Taiwanese EPA emission standards (TEPA, 2020).  



Carbonaceous Components in PM2.5

The largest quantity of PM2.5 under this investigation came from EC and OC. The organic material comprised several components such as engine lubrication oil, unburned fuel, and low levels of incomplete combustion and pyrolysis products (Chen et al., 2019). The OC portion varied from 426–2870 µg m–3 (with a concentration average of 1410 µg m3), as shown in Table 3. The EC fraction ranged from 824–5570 µg m–3 (with a concentration average of 2051 µg m3), while TC had a fraction ranging from 1250–8390 µg m3 (with a concentration average of 3461 µg m3). However, some of the 3461 µg m3 of TC was attributed to metallic species and other PM2.5 constituents such as the concentrations of the most common three major inorganic ions in PM2.5, SO42– (sulfate), NO3 (nitrate), and NH4+, (ammonium) (Jiang et al., 2019). Fig. 2 shows the main chemical components (contribution of carbonaceous components) in the PM2.5 emitted from different diesel vehicle groups in raw exhausts under most driving conditions. It was observed from the results that the contribution of carbonaceous components in PM2.5 resulted in an increase in TC, particularly in the case of the diesel vehicles that fell into the pre-Euro group, which contributed over 4500 µg m3 of the carbonaceous components in the PM2.5, followed by diesel vehicles in the Euro 1 group, with a fraction contribution of approximately 3000 µg m3 of carbonaceous components in the PM2.5. Diesel vehicles in the Euro 2 and Euro 3 groups contributed an equal fraction of more than 2500 µg m3 of carbonaceous components in the PM2.5 from raw exhaust under the various driving conditions. The diesel vehicles in the Euro 4 group contributed less than 1250 µg m3 of TC. The EC contribution of carbonaceous components in the pre-Euro diesel vehicles group was observed to be less than 2700 µg m3, whereas the diesel vehicles in the Euro 1 group had an EC contribution of less than 1600 µg m3, and the diesel vehicles in the Euro 2 and Euro 3 groups had EC contributions of more than 1600 µg m3. The EC contribution from diesel vehicles in the Euro 4 group continued to decline, with less than 850 µg m–3. The OC contribution was also observed to be declining in all pre-Euro to Euro 4 diesel vehicle groups, ranging from 2100 to 425 µg m3


 


Fig. 2. Main chemical components (contribution of carbonaceous components) in the PM2.5 emitted from different diesel vehicle groups.
Fig. 2. Main chemical components (contribution of carbonaceous components) in the PM2.5 emitted from different diesel vehicle groups.

Jhang et al. (2018) found that the driving cycle, engine age, and engine technology are the major issues causing mass emissions of PM from diesel engines, which was similarly projected to have a substantial effect on the chemical composition of PM emissions from diesel engines. A study by Wei et al. (2019) on ship emissions clearly validated that engine operating condition has a substantial effect on the fraction of EC to OC in the PM2.5 emissions from diesel-fueled vehicle working over an FTP cycle. It is clear that more investigations of this nature are needed for a better understanding of the components that influence the proportion of EC to TC in the emissions from diesel engines. Right now, only a restricted measurement of EC-OC for diesel engine emissions exists that exploits the NIOSH and IMPROVE EC-OC measurement techniques (Shen et al., 2018).

Zhang et al. (2014) reported a reduction of 32.8% from the pre-EURO (an average of 60.3%) to the EURO 1 (an average of 60.7%) EC emission factors that follows the implementation of progressively stricter emission criteria. Zhang et al. (2009) quantified PM2.5 emissions from HDDTs and attained carbonaceous emission factors. They revealed that the proportions of BC and OC in PM2.5 were 43% and 37%, respectively. Shah et al. (2004) reported an EC proportion of 66% and OC portion of 21% in their international diesel PM emission inventory, whereas Oanh et al. (2010) found EC to be 47% using chassis dynamometer testing method. The EC proportion in our study was marginally higher than in these investigations, yet at the same time was within the same range. In relation to these results, the proportion of EC in our test was approximately 18.3–40% higher for the diesel vehicles.


Variations in Metallic Elements Concentrations and Contribution (%) in PM2.5

The different metallic element concentrations and contributions (%) in PM2.5 under this investigation are reported in Table 4. The average concentration of metallic elements (MEs) in PM2.5 was recorded as follows: Ca had the highest concentration of 450.7 µg m3, followed by Mg with the second highest average concentration of 264.9 µg m3, Al with a concentration average of 153.3 µg m3, and K with a concentration average of 87.3 µg m3. The lowest concentration of metallic elements in the PM2.5 emissions (µg m3) was observed in Cr, with an average concentration of 6.8 µg m3, followed by Fe, with an average concentration of 40.7 µg m3 and Zn, with a concentration average of 42.3 µg m3. In all the metallic element concentrations in PM2.5 (µg m3), the average contribution of total MEs emitted from the 15 tested vehicles was 1046 µg m3. A total of 7 metallic elements in PM2.5 emitted from the 15 different vehicles were categorized. With the inclusive quantified metallic elements on the particle-bounded of PM2.5 concentrations (total concentration), the highest metallic element concentration in PM2.5 emitted from the 15 diesel vehicles was found to be in Ca (42.0%), followed by Zn, which accounted for an average of 25.0%, while Al had a contribution of 15.0%, and Cr accounted for an average of 0.695% as the smallest amount, as shown in Fig. 3



Fig. 3. Different metallic element contributions (%) in PM2.5 emitted from diesel engines.Fig. 3. Different metallic element contributions (%) in PM2.5 emitted from diesel engines.

Our results are compatible to the findings in the earlier literature by Ojekunle et al. (2018) who reported that metallic elements such as Cd, Cr, Ca, Cu, and Zn all had higher concentrations in PM2.5 exceeding the WHO criteria in a study conducted in Nigeria. In a study conducted in Great Britain by Harrison et al. (2012), nearly one third of the Zn in atmospheric dust was ascribed to vehicle emissions such as diesel vehicles. Comparably, a related study was carried out by Liu et al. (2018a) on the priority of emission control for PM2.5-bound metallic elements in different seasons in Beijing, China. They found that Ca was among the metallic elements identified in annual trends in higher emitted levels, among which resuspended dust modeled the uppermost mass contribution to PM2.5-bound metallic elements. The origin of metallic species and their effects on human health at concentrations of up to 30 atmospheric metallic elements can be determined concurrently in a single ICP analysis with an accuracy of ±5 ppm (Watson, 2010). Most of the metallic species commonly found in used oil come from two main sources: the lubricant additive package and that due to wear on engine components (Watson, 2010). Lubricant-derived ash is generally comprised of oxides, sulfates, and phosphates of Zn, Ca, and Mg (Suvarapu and Baek, 2017).

It is commonly acknowledged that there is no standard method for regulating the worn metal content in lubricating oils, although this evidence is believed to be beneficial by most vehicle operators and owners (Watson, 2010). There are two techniques commonly used for the determination of worn metallic species in lubricating oils (Cheng et al., 2018).

Spectrometric analysis provide data on the composition of metallic species emanating from wear on engine components and/or from contamination and also from the metallic salts’ concentration in additives. The additive elements in % m/m are as follows: Ca, Mg, P, Zn, Mo, B, and N. Their concentrations may be used to classify the lubricant in use or to suggest that the lubricant tested has been mixed or contaminated by another type, grade, or brand (Watson, 2010). Worn metal elements (in ppm) such as Fe, Cr, Al, Pb, Sn, or Cu might signify wear on bearings, piston rings, cylinder liners, and other engine components. Contamination (in ppm) by MEs such as Na, V, Al, Si, Cl, and Ni are potential indicators of fuel-derived contamination. Mg, B, Cl, and Na can be extracted from seawater; Na and B can be extracted from cooling water, and Si can also be found in dust. However, Si can also be present as an anti-foam additive (Watson, 2010).

In this study, the atmospheric metallic elements found in our investigation included Fe, Zn, and Cr. These MEs have been found to have substantial effects on human health. Fang et al. (2010), in their study on metallic element pollution in Asia from 2000–2007, found that exposure to individual MEs can be a source of various risks for humans. For instance, extended exposure to Fe may result in the development of pneumoconiosis, whereas prolonged exposure to Zn may result in arteriosclerosis, hypertension, and heart disease. Remarkably, they also found out that Cr(VI) is carcinogenic and has the potential to cause asthma, liver damage, and nasal septum perforation (Banu et al., 2017; Oruko et al., 2020). Furthermore, Ni may lead to nasal and lung cancer (Cheng et al., 2018). In most cases, Cr species can get into the eukaryotic system and cause impulsive reactions with the intracellular reductants, for instance, ascorbate and glutathione, causing the short-lived intermediates Cr(V) and/or Cr(IV), free radicals and the Cr(III) end-product (Oruko et al., 2020). The short-lived intermediates within the cytoplasm are oxidized to Cr(VI), which quickly combines with complexes of DNA proteins and alters their usual physiological roles (Sun et al., 2019) as well as destroying their DNA (Oruko et al., 2020), which has genotoxic and mutagenic consequences. These also block important functional groups, displace other metal ions or change the active conformation of biological molecules (Torkmahalleh et al., 2013; Nigam et al., 2015) which contributes to hepatic damage and pulmonary obstruction and causes skin inflammation, gastrointestinal problems leading to ulcer formation. Moreover, Cr(VI) species has the potential to accumulate in the placenta and damage the fetus development, leading to birth defects and a decrease in reproductive health (Banu et al., 2017; Oruko et al., 2020). Moreover, Cu can lead to health conditions such as interstitial fibrosis, lung cancer, and pulmonary granuloma, whereas exposure to Cd and Pb may cause anemia, blood poisoning, and itai-itai disease.

These MEs may enter the coastal waters and estuaries, thereby causing devastating problems that could result in extensive harm to the life and activities of aquatic organisms and can also cause huge fatality. Thus, the accumulation of MEs in marine ecosystems becomes a significant global concern. MEs contamination might have devastating impacts on the ecological balance of the environment and negatively affect the diversity of aquatic organisms (Oruko et al., 2020). 

Among animal species, fishes are the inhabitants that cannot escape from the harmful effects of these ME pollutants (Banu et al., 2017; Oruko et al., 2020).

Wang et al. (2019) studied the health risk assessment of PM2.5 in students’ dormitories at a university in China and found that MEs in the dormitory comprised a mixture of coal combustion and emissions from industrial activities (71.21%), vehicle exhaust and traffic-related road dust (11.26%), and fugitive emissions ascribed to student activities (5.68%). They also found that PM2.5-related MEs caused a greater health risk to college students in the dormitory environment than in the outdoor environment. However, the exposure environments of the college students in this study were limited to dormitory and outdoor environments. Wei et al. (2019) emphasized that exposure to pollutant emissions may result in the development of myopia. They highlighted that prolonged exposure to PM2.5 and NOx may intensify ocular surface soreness and later, retinal inflammation, which in turn increases the risk of development of myopia.


EFs of Atmospheric Metallic Species of PM2.5

Table 5 presents the emission factors for atmospheric metallic species of PM2.5 from the exhausts of diesel engine vehicles. The emission factors for the investigated atmospheric metallic species from 15 different diesel vehicles vary, as shown in Table 5. The results revealed that Ca was the highest EF among all the investigated atmospheric metallic elements. It was found within the range of 45.3–259 µg L-fuel–1 (with an average of 132 µg L-fuel–1). It was further observed that Mg and Al were the second highest emission factors, in the range of 76.8 µg L-fuel–1 and 48.6 µg L-fuel–1, respectively. Zn and Cr had the lowest EFs, accounting for a mean average of 13.1 µg L-fuel–1 and 1.91 µg L-fuel–1, respectively, as detailed in Table 5. These results were similar to the EF values for each atmospheric metallic element in PM2.5 found in other studies (Zhang et al., 2015; Liu et al., 2019) as well as in preceding studies led by Cheng et al. (2010) and Zhang et al. (2018). 


The uncertainties (i.e., standard deviations) as a result of uncontrolled elements such as random errors in sampling and analysis, model year, and vehicle manufacture (brand), are presented in the results for the average of PM2.5 compositions (Tables 2 and 3). One of the limitations of our study was that the small tested fleet of 15 diesel vehicles was arbitrarily selected, and only a limited number of diesel fuel vehicles was sampled under this investigation. Another limitation is we did not conduct a comprehensive analysis of chemical composition to determine the concentration of secondary inorganic aerosols (SIAs), such as SO42– (sulfate), NO3 (nitrate), and NH4+, (ammonium) in PM2.5. This study has not quantified the speciation fraction of Cr and thus cannot determine the toxicity of Cr emitted from the vehicle exhausts. This is another significant limitation of this study. In order to minimize these uncertainties, this study recommends that adequate representative vehicles should be tested. Furthermore, better knowledge of the vehicle details, such as engine model, the use for each vehicle (such as delivery van, cargo truck, bus, tractor, construction equipment, etc.) emissions control technologies, and a more rigorous experimental design with a large fleet of vehicles are necessary for future research.


CONCLUSION


We measured the metallic elements in vehicle-emitted PM2.5 to evaluate their relationship with the ambient PM2.5 composition. We found, in descending concentration, Ca, Zn, Al, K, Fe, Mg, and Cr in the emitted PM2.5, which was sampled from the exhaust of 15 diesel-powered vehicles. Additionally, the carbonaceous content in the emitted PM2.5 was primarily composed of EC, which exhibited a concentration of 824–5570 µg m–3, averaging 2051 µg m3; the OC and the TC ranged from 426 to 2870 µg m3 (averaging 1410 µg m3) and from 1250 to 8390 µg m–3 (averaging 3461 µg m3), respectively. Among the metallic elements, Ca displayed the highest EF (45.3–259 µg L-fuel–1, averaging 132 µg L-fuel–1), and Zn and Cr exhibited the lowest ones (averaging 13.1 µg L-fuel–1 and 1.91 µg L-fuel–1, respectively). Because metallic elements are present in the ambient PM2.5, the health effects arising from exposure to these substances should be given top priority. Although several of these elements are mainly associated with the combustion of heavy fuel oil, others, including Zn, Fe, K, and Cr, can be linked to non-exhaust traffic emissions. As studies have thus far focused on the filterable PM2.5, organic carbon, and PM2.5-bound metallic elements emitted in the exhaust of currently used diesel vehicles, further investigation is needed to obtain data about additional vehicle types or models powered by other fuels in order to confirm the heavy-metal concentrations in fine particulate matter.

Furthermore, health risk assessments should be conducted to determine the potential risks of carcinogenic heavy metals in this fraction.


ACKNOWLEDGEMENTS


We greatly acknowledge the help from Yu-Han Jiang for sampling.


REFERENCES


  1. Achilleos, S., Kioumourtzoglou, M.A., Wu, C.D., Schwartz, J.D., Koutrakis, P. and Papatheodorou, S.I. (2017). Acute effects of fine particulate matter constituents on mortality: A systematic review and meta-regression analysis. Environ. Int. 109: 89–100. [Publisher Site]

  2. Agarwal, A., Mangal, A., Satsangi, A., Lakhani, A. and Kumari, K.M. (2017). Characterization, sources and health risk analysis of PM2.5 bound metals during foggy and non-foggy days in sub-urban atmosphere of Agra. Atmos. Res. 197: 121–131. [Publisher Site]

  3. Agency for Toxic Substances and Disease Registry (ATSDR) (2012). Toxicological Profile for Chromium. U.S. Department of Health and Human Services, USA.

  4. Akinwunmi, F., Akinhanmi, T.F., Atobatele, Z.A., Adewole, O., Odekunle, K., Arongundade, L.A., Odukoya, O.O., Olayiwola, O.M. and Ademuyiwa, O. (2017). Heavy metal burdens of public primary school children related to playground soils and classroom dusts in Ibadan North-West local government area, Nigeria. Environ. Toxicol. Pharmacol. 49: 21–26. [Publisher Site]

  5. Al Hanai, A.H., Antkiewicz, D.S., Hemming, J.D.C., Shafer, M.M., Lai, A.M., Arhami, M., Hosseini, V. and Schauer J.J. (2019). Seasonal variations in the oxidative stress and inflammatory potential of PM2.5 in Tehran using an alveolar macrophage model; The role of chemical composition and sources. Environ. Int. 123: 417–427. [Publisher Site]

  6. Badaloni, C., Cesaroni, G., Cerza, F., Davoli, M., Brunekreef, B. and Forastiere, F. (2017). Effects of long-term exposure to particulate matter and metal components on mortality in the Rome longitudinal study. Environ. Int. 109: 146–154. [Publisher Site]

  7. Banu, S.K., Stanley, J.A., Sivakumar, K.K., Arosh, J.A., Taylor, R.J. and Burghardt, R.C. (2017). Chromium VI - induced developmental toxicity of placenta is mediated through spatiotemporal dysregulation of cell survival and apoptotic proteins. Reprod. Toxicol. 68: 171–190. [Publisher Site]

  8. Campagnolo, D., Cattaneo, A., Corbella, L., Borghi, F., Buono, L.D., Rovelli, S., Spinazzé, A. and Cavallo, D.M. (2019). In-vehicle airborne fine and ultra-fine particulate matter exposure: The impact of leading vehicle emissions. Environ. Int. 123: 407–416. [Publisher Site]

  9. Chartres, N., Bero, L.A. and Norris, S.L. (2019). A review of methods used for hazard identification and risk assessment of environmental hazards. Environ. Int. 123: 231–239. [Publisher Site]

  10. Chen, S., Cui, K., Zhu, J., Zhao, Y., Wang, L.C. and Mutuku, J.K. (2019). Effect of exhaust gas recirculation rate on the emissions of persistent organic pollutants from a diesel engine. Aerosol Air Qual. Res. 19: 812–819. [Publisher Site]

  11. Cheng, X., Huang, Y., Zhang, S.P., Ni, S.J. and Long, Z.J. (2018). Characteristics, sources, and health risk assessment of trace elements in PM10 at an urban site in Chengdu, southwest China. Aerosol Air Qual. Res. 18: 357–370. [Publisher Site]

  12. Cheng, Y., Lee, S.C., Ho, K.F., Chow, J.C., Watson, J.G., Louie, P.K.K., Cao, J.J. and Hai, X. (2010). Chemically-speciated on-road PM2.5 motor vehicle emission factors in Hong Kong. Sci. Total Environ. 408: 1621–1627. [Publisher Site]

  13. Cui, M., Chen, Y., Feng, Y., Li, C., Zheng, J., Tian, C., Yan, C. and Zheng, M. (2017). Measurement of PM and its chemical composition in real-world emissions from non-road and on-road diesel vehicles. Atmos. Chem. Phys. 17: 6779–6795. [Publisher Site]

  14. Fang, G.C., Huang, Y.L. and Huang, J.L. (2010). Study of atmospheric metallic elements pollution in Asia during 2000-2007. J. Hazard. Mater. 180: 115–121. [Publisher Site]

  15. Grivas, G., Cheristanidis, S., Chaloulakou, A., Koutrakis, P. and Mihalopoulos, N. (2018). Elemental composition and source apportionment of fine and coarse particles at traffic and urban background locations in Athens, Greece. Aerosol Air Qual. Res. 18: 1642–1659. [Publisher Site]

  16. Harrison, R.M., Jones, A.M., Gietl, J., Yin, J. and Green, D. (2012). Estimation of the contribution of brake dust, tire wear and resuspension to non-exhaust traffic particles derived from atmospheric measurements. Environ. Sci. Technol. 46: 6523–6529. [Publisher Site]

  17. Hvidtfeldt, U.A., Sørensen, M., Geels, C., Ketzel., M., Khan, J., Tjønneland, A., Overvad, K., Brandt, J. and Raaschou-Nielsen, O. (2019). Long-term residential exposure to PM2.5, PM10, black carbon, NO2, and ozone and mortality in a Danish cohort. Environ. Int. 123: 265–272. [Publisher Site]

  18. International Council on Clean Transportation (ICCT) (2015a). From laboratory to road. A 2015 update of official and 'real-world' fuel consumption and CO2 values for passenger cars in Europe, International Council on Clean Transportation, Washington DC, USA.

  19. Jaiprakash, and Habib, G. (2017). Chemical and optical properties of PM2.5 from on-road operation of light duty vehicles in Delhi city. Sci. Total Environ. 586: 900–916. [Publisher Site]

  20. Jhang, S.R., Chen, K.S., Lin, S.L., Lin, Y.C., Amesho, K.T.T. and Chen, C.B. (2018). Evaluation of the reduction in carbonyl emissions and ozone formation potential from the exhaust of a heavy-duty diesel engine by hydrogen-diesel dual fuel combustion. Appl. Therm. Eng. 132: 586–594.[Publisher Site]

  21. Jiang, F., Liu, F., Lin, Q., Fu, Y., Yang, Y., Peng, L., Li, X., Zhang, G., Bi, X., Wang, X. and Sheng, G. (2019). Characteristics and formation mechanisms of sulfate and nitrate in size-segregated atmospheric particles from urban Guangzhou, China. Aerosol Air Qual. Res. 19: 1284–1293. [Publisher Site]

  22. Lee, H., Myung, W., Cheong, H.K., Yi, S.M., Hong, Y.C., Cho, S.I. and Kim, H. (2018). Ambient air pollution exposure and risk of migraine: Synergistic effect with high temperature. Environ. Int. 121: 383–391. [Publisher Site]

  23. Li, T.C., Yuan, C.S., Huang, H.C., Lee, C.L., Wu, S.P. and Tong, C. (2016). Inter-comparison of seasonal variation, chemical characteristics, and source identification of atmospheric fine particles on both sides of the Taiwan Strait. Sci. Rep. 6: 22956. [Publisher Site]

  24. Liang, B., Li, X., Ma, K. and Liang, S.X. (2019). Pollution characteristics of metal pollutants in PM2.5 and comparison of risk on human health in heating and non-heating seasons in Baoding, China. Ecotoxicol. Environ. Saf. 170: 166–171. [Publisher Site]

  25. Lin, C.Y, Li, Y.C, Amesho, K.T.T., Chou, F.C, and Cheng, P.C. (2019). Characterization and quantification of PM2.5 emissions and PAHs concentration in PM2.5 from the exhausts of diesel vehicles with various accumulated mileages. Sci. Total Environ. 660: 188–198. [Publisher Site]

  26. Lin, Y.C., Tsai, C.J., Wu, Y.C., Zhang, R., Chi, K.H., Huang, Y.T., Lin, S.H. and Hsu S.C. (2015). Characteristics of trace metals in traffic-derived particles in Hsuehshan Tunnel, Taiwan: size distribution, potential source, and fingerprinting metal ratio. Atmos. Chem. Phys. 15: 4117–4130. [Publisher Site]

  27. Liu, C., Yang, C.Y., Zhao, Y.H., Ma, Z.W., Bi, J., Liu, Y., Meng, X., Wang, Y., Cai, J., Kan, H. and Chen, R. (2016). Associations between long-term exposure to ambient particulate air pollution and type 2 diabetes prevalence, blood glucose and glycosylated hemoglobin levels in China. Environ. Int. 92–93: 416–421. [Publisher Site]

  28. Liu, C., Li, Q., Yan, L., Wang, H., Yu, J., Tang, J., Yao, H., Li, S., Zhang, Y. and Guo, Y. (2019). The association between maternal exposure to ambient particulate matter of 2.5 µm or less during pregnancy and fetal congenital anomalies in Yinchuan, China: A population-based cohort study. Environ. Int. 122: 316–321. [Publisher Site]

  29. Liu, J., Chen, Y., Chao, S., Cao, H., Zhang, A. and Yang, Y. (2018a). Emission control priority of PM2.5-bound heavy metals in different seasons: A comprehensive analysis from health risk perspective. Sci. Total Environ. 644: 20–30. [Publisher Site]

  30. Liu, Y., Xing, J., Wang, S., Fu, X. and Zheng, H. (2018b). Source-specific speciation profiles of PM2.5 for heavy metals and their anthropogenic emissions in China. Environ. Pollut. 239: 544–553. [Publisher Site]

  31. McGuinn, L.A., Schneider, A., McGarrah, R.W., Ward-Caviness, C., Neas, L.M., Di, Q., Schwartz, J., Hauser, E.R., Kraus, W.E., Cascio, W.E., Diaz-Sanchez, D. and Devlin, R.B. (2019). Association of long-term PM2.5 exposure with traditional and novel lipid measures related to cardiovascular disease risk. Environ. Int. 122: 193–200. [Publisher Site]

  32. Nigam, H., Das, M., Chauhan, S., Pandey, P., Swati, P., Yadav, M. and Tiwari, A. (2015). Effect of chromium generated by solid waste of tannery and microbial degradation of chromium to reduce its toxicity: A review. Adv. Appl. Sci. Res. 6: 129–136. [Google Scholar]

  33. Oanh, N.T.K., Thiansathit, W., Bond, T.C., Subramanian, R., Winijkul, E. and Paw-armart, I. (2010). Compositional characterization of PM2.5 emitted from in-use diesel vehicles. Atmos. Environ. 44: 15–22. [Publisher Site]

  34. Ojekunle, Z.O., Jinadu, O.O.E., Afolabi, T.A. and Taiwo, A.M. (2018). Environmental pollution and related hazards at Agbara industrial area, Ogun State. Sci. Rep. 8: 6482. [Publisher Site]

  35. Oruko, R.O., Selvarajan, R., Ogola, H.J.O., Edokpayi, J.N. and Odiyo, J.O. (2020). Contemporary and future direction of chromium tanning and management in sub Saharan Africa tanneries. Ecotoxicol. Environ. Saf. 133: 369–386. [Publisher Site]

  36. Peng, X., Shi, G., Liu, G., Xu, J., Tian, Y., Zhang, Y., Feng, Y. and Russell, A.G. (2017). Source apportionment and heavy metal health risk (HMHR) quantification from sources in a southern city in China, using an ME2-HMHR model. Environ. Pollut. 221: 335–342. [Publisher Site]

  37. Pervez, S., Bano, S., Watson, J.G., Chow, J.C., Matawle, J.L., Shrivastava, A., Tiwari, S. and Pervez, Y.F. (2018). Source profiles for PM10-2.5 resuspended dust and vehicle exhaust emissions in Central India. Aerosol Air Qual. Res. 18: 1660–1672. [Publisher Site]

  38. Ryder, O.S., DeWinter, J.L., Brown, S.G., Hoffman, K., Frey, B. and Mirzakhalili, A. (2020). Assessment of particulate toxic metals at an Environmental Justice community. Atmos. Environ. 6: 100070. [Publisher Site]

  39. Shah, S., Cocker, D., Miller, J. and Norbeck, J. (2004). Emission rates of particulate matter and elemental and organic carbon from in-use diesel engines. Environ. Sci. Technol. 38: 2544–2550. [Publisher Site]

  40. Sun, Z., Duan, F., Ma, Y., He, K., Zhu, L. and Ma, T. (2019). Heavy particulate matter pollution during the 2014-2015 winter in Tianjin, China. Aerosol Air Qual. Res. 19: 1338–1345. [Publisher Site]

  41. Suvarapu, L.N. and Baek, S. (2017). Determination of heavy metals in the ambient atmosphere: A review. Toxicol. Ind. Health 33: 79–96. [Publisher Site]

  42. Taiwan Environmental Protection Administration (TEPA). (2020). Environment Resource Database, Taiwan (ROC). [Publisher Site]

  43. Torkmahalleh, M.A., Yu,C.H., Lin, L., Fan, Z., Swift, J.L., Bonanno, L., Rasmussen, D.H., Holsen, T.M. and Hopke, P.K. (2013). Improved atmospheric sampling of hexavalent chromium. J. Air Waste Manage. Assoc. 63: 1313–1323. [Publisher Site]

  44. Tsai, J.H., Yao, Y.C., Huang, P.H., Chiang, H.L. (2018). Fuel economy and volatile organic compound exhaust emission for motorcycles with various running mileages. Aerosol Air Qual. Res. 18: 3056–3067. [Publisher Site]

  45. Turap, Y., Rekefu, S., Wang, G., Talifu, D., Gao, B., Aierken, T., Hao, S., Wang, X., Tursun, Y., Maihemuti, M. and Nuerla, A. (2019). Chemical characteristics and source apportionment of PM2.5 during winter in the Southern Part of Urumqi, China. Aerosol Air Qual. Res. 19: 1325–1337. [Publisher Site]

  46. U.S. Environmental Protection Agency (U.S. EPA) (2014). Climate change indicators in the United States, 2014. Third edition. EPA 430-R-14-004. [Website Link]

  47. Wang, F., Wang, J., Han, M., Jia, C. and Zhou, Y. (2019). Heavy metal characteristics and health risk assessment of PM2.5 in students’ dormitories in a university in Nanjing, China. Build. Environ. 160: 106206. [Publisher Site]

  48. Watson, S.A. (2010). Lubricant-derived ash - in-engine sources and opportunities for reduction. Doctoral dissertation, Massachusetts Institute of Technology.

  49. Wei, W.C., Lin, H.J., Lim, Y.P., Chen, C.S., Chang, C.Y., Lin, C.J., Chen, J.J.Y., Tien, P.T., Lin, C.L. and Wan, L. (2019). PM2.5 and NOx exposure promote myopia: Clinical evidence and experimental proof. Environ. Pollut. 254: 113031. [Publisher Site]

  50. Yang, H.H., Dhital, N.B., Wang, L.C., Hsieh, Y.S., Lee, K.T., Hsu, Y.T. and Huang, S.C. (2019). Chemical characterization of fine particulate matter in gasoline and diesel vehicle exhaust. Aerosol Air Qual. Res. 19: 1439–1449. [Publisher Site]

  51. Zhang, J., Wu, L., Fang, X., Li, F., Yang, Z., Wang, T., Mao, H. and Wei, E. (2018). Elemental composition and health risk assessment of PM10 and PM2.5 in the roadside microenvironment in Tianjin, China. Aerosol Air Qual. Res. 18: 1817–1827. [Publisher Site]

  52. Zhang, Y., Yao, Z., Shen, X., Liu, H. and Kebin He, K. (2015). Chemical characterization of PM2.5 emitted from on-road heavy-duty diesel trucks in China. Atmos. Environ. 122: 885–891. [Publisher Site]

  53. Zhao, Y., Cui, K., Zhu, J., Chen, S., Wang, L.C., Cheruiyot, N.K. and Mutuku, J.K. (2019). Effects of retarding fuel injection timing on toxic organic pollutant emissions from diesel engines. Aerosol Air Qual. Res. 19: 1346–1354. [Publisher Site]

Aerosol Air Qual. Res. 20:1319-1328. https://doi.org/10.4209/aaqr.2020.02.0081 


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