Emission characteristics and chemical composition of particulate matter emitted by typical non-road construction machinery

Abstract

According to the latest China Construction Machinery Industry Yearbook 2017, forklifts, loaders and excavators are three types of construction machinery that comprise the largest proportion of construction machinery in China. To study the emission characteristics of non-road mobile machinery, three types of non-road construction machinery used in the city were selected for testing and sampling. A Portable Emission Measurement System (PEMS) was used to obtain instantaneous exhaust emissions, and a portable particulate sampling system was used to collect PM2.5 samples from three types of construction machinery. The elements, water-soluble ions (WSIs), organic carbon (OC)/elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) were analysed. The China III standards for non-road construction machinery are stricter than the China II and China I construction machinery standards, indicating that strict regulations can help reduce PM emissions from non-road construction machinery. The results show that the fraction of the chemical components including elements, WSIs, OC/EC and PAHs to particulate matter is from 67.30% to 64.10%. The SO4²⁻/NO3⁻ ratio of each test vehicle is less than 1. The CE/AE values are all greater than 1, and the exhaust particles are alkaline. The estimated concentrations of secondary organic carbon (SOC) in the particulates discharged from loaders, excavators and forklifts are 3.73 mg/m³, 0.25 mg/m³ and 0.42 mg/m³, respectively, indicating the presence of secondary aerosols. The LMW-PAHs and MMW-PAHs were the major PAH species in the particulate matter, whereas a low concentration of HMW-PAHs is considered to be more harmful to human health than the other PAHs. All the PAH BaPeq values are higher than the BaP concentrations recommended by the World Health Organization. Compared with previous study, an increase in the emission standards for non-road mobile machinery can lead to a decline in pollutant emissions. In addition, the driving conditions, engine parameters, and the age of the vehicle and of non-road construction machinery, as well as the experimental methods used to study these variables, may affect the composition of exhaust emissions.

Full text

HOSTED BY
Contents lists available at ScienceDirect
Atmospheric Pollution Research
journal homepage: www.elsevier.com/locate/apr
Original Article
Emission characteristics and chemical composition of particulate matter
emitted by typical non-road construction machinery
Qijun Zhang, Lei Yang, Chao Ma, Yanjie Zhang, Lin Wu, Hongjun Mao
∗
Tianjin Key Laboratory of Transport Emission Control Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
ARTICLE INFO
Keywords:
Non-road construction machinery
Particulate matter
Chemical composition
PEMS
ABSTRACT
According to the latest China Construction Machinery Industry Yearbook 2017, forklifts, loaders and excavators
are three types of construction machinery that comprise the largest proportion of construction machinery in
China. To study the emission characteristics of non-road mobile machinery, three types of non-road construction
machinery used in the city were selected for testing and sampling. A Portable Emission Measurement System
(PEMS) was used to obtain instantaneous exhaust emissions, and a portable particulate sampling system was
used to collect PM
2.5
samples from three types of construction machinery. The elements, water-soluble ions
(WSIs), organic carbon (OC)/elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) were ana-
lysed. The China III standards for non-road construction machinery are stricter than the China II and China I
construction machinery standards, indicating that strict regulations can help reduce PM emissions from non-road
construction machinery. The results show that the fraction of the chemical components including elements,
WSIs, OC/EC and PAHs to particulate matter is from 67.30% to 64.10%. The SO
42−
/NO
3
−
ratio of each test
vehicle is less than 1. The CE/AE values are all greater than 1, and the exhaust particles are alkaline. The
estimated concentrations of secondary organic carbon (SOC) in the particulates discharged from loaders, ex-
cavators and forklifts are 3.73 mg/m
3
, 0.25 mg/m
3
and 0.42 mg/m
3
, respectively, indicating the presence of
secondary aerosols. The LMW-PAHs and MMW-PAHs were the major PAH species in the particulate matter,
whereas a low concentration of HMW-PAHs is considered to be more harmful to human health than the other
PAHs. All the PAH BaPeq values are higher than the BaP concentrations recommended by the World Health
Organization. Compared with previous study, an increase in the emission standards for non-road mobile ma-
chinery can lead to a decline in pollutant emissions. In addition, the driving conditions, engine parameters, and
the age of the vehicle and of non-road construction machinery, as well as the experimental methods used to
study these variables, may affect the composition of exhaust emissions.
1. Introduction
Since 2013, the source analysis of PM
2.5
in the Beijing-Tianjin-
Hebei, Yangtze River Delta and Pearl River Delta cities have been stu-
died by the Ministry of Ecology and Environment(MEE). The results
showed that mobile sources are the primary source of PM
2.5
pollution in
Beijing, Hangzhou, Guangzhou, Shenzhen and Shanghai(MEE, 2018).
Generally, mobile sources can be divided into road mobile sources
(motor vehicles) and non-road mobile sources (aircrafts, ships, railway
diesel locomotives and non-road mobile machinery). In recent years,
many researchers have conducted relevant research on vehicle pollu-
tant emissions(Cheng et al., 2010;Hao et al., 2019;Zhang et al., 2016;
Zheng et al., 2018;Zhou et al., 2019). Compared with motor vehicle
research, the research on non-road mobile sources is scarce.
Non-road mobile machinery generally uses a diesel engine and
Particulate matter (PM), nitrogen oxides (NOx), hydrocarbons (HC) and
carbon monoxide (CO) are the primary pollutants. The NOx and PM
emitted from diesel engines accounts for close to 70% and 90% of the
total vehicle emissions in China, respectively(MEE, 2018). Based on the
study of non-road vehicle emission inventories in the Pearl River Delta
region, diesel-based non-road vehicles have become the third largest
source of NOx emissions in the region. The emissions inventory issued
by the European Environmental Agency in 2019 shows that non-road
mobile source emissions contribute significantly to the European at-
mospheric environment, which contributes to 17% of PM emissions
(EEA, 2019). Yan et al. predicted that non-road mobile sources will
https://doi.org/10.1016/j.apr.2019.12.018
Received 5 October 2019; Received in revised form 22 December 2019; Accepted 24 December 2019
Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.
∗
Corresponding author.
E-mail address: hongjunm@nankai.edu.cn (H. Mao).
Atmospheric Pollution Research 11 (2020) 679–685
Available online 30 December 2019
1309-1042/ © 2020 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
T

gradually surpass motor vehicles as the main source of mobile pollution
after 2030 and will become the largest source of CO and HC emissions
in Asia by 2050(Yan et al., 2014). The results of air quality model si-
mulated by Dunker show that the contribution of mobile sources to
organic aerosol emissions in the Greater Houston area of the United
States in 2013 was 43%, and non-road mobile sources only contributed
to up to 18%(Dunker et al., 2019).
The particulate matter composition is important for the human
health effects research and estimation of emissions from non-road
mobile sources. Due to the need for atmospheric source analysis and
concerns about the toxicity of particulate matter in non-road diesel
engines, studying the chemical composition of particulate matter dis-
charged from non-road mobile sources (including elements, water-so-
luble ions, elemental carbon and organic carbon, polycyclic aromatic
hydrocarbons, etc.) has also become a hot research topic. Gerald et al.
used bench test with various exhaust gas treatment devices to study the
emission of PAHs from non-road engine exhausts(Z Liu et al., 2015).
Nyström et al. tested the distribution of carbonaceous particulate
matter and polycyclic aromatic hydrocarbons in a non-road diesel en-
gine using different fuels. It was found that the total mass fraction of
polycyclic aromatic hydrocarbons in the particles of low-sulfur diesel
engine was 3.12 μg/g(Nyström et al., 2016). Muresan et al. measured
the content of polycyclic aromatic hydrocarbons in non-road en-
gineering machinery particles to be 3.4 ± 0.9 μg/g, and the content
increased with increasing HC emissions(Muresan et al., 2015). Green
et al. compared the carbon content of non-road and diesel vehicles and
found that the EC content in the non-road vehicles was 23.7 ± 11.9%,
which was lower than the EC content of 45.5 ± 12.9% of road diesel
vehicles(Green et al., 2013).
Most of these studies were based on fuel consumption calculations
and bench test to determine the emission factors and establish factors
estimation models. However, due to the operating conditions of non-
road construction machinery, the emissions measured by the engine test
bench cannot accurately represent the emissions obtained by actual
operating conditions. Due to the lag of emission control standards, long
operating periods, and low maintenance rate for non-road mobile ma-
chinery, it is difficult to obtain emissions from non-road mobile ma-
chinery. In order to obtain accurate emissions from non-road con-
struction machinery under actual conditions, a portable emission
measurement system (PEMS) was used to obtain non-road mobile
source emissions. In recent years, Ge Yunshan and Chen Yingjun have
been used PEMS to study the emissions of non-road mobile sources and
obtained localized emission factors(Fu et al., 2012;Zhang et al., 2015).
According to the latest China Construction Machinery Industry
Yearbook 2017(CCMA, 2017), the number of construction machinery is
approximately 7 million. Forklifts, loaders and excavators are three
types of construction machinery and representing the largest proportion
of construction machinery in China (79%). Therefore, this work studies
three types of machinery, loaders, forklifts and excavators, which are
basically the most common types of construction machinery used in
China. To study the emission characteristics of non-road mobile ma-
chinery, three types of non-road machinery used in cities were selected
for testing and sampling. A PEMS was used to obtain instantaneous
exhaust emissions, and a portable particulate sampling system was used
to collect PM
2.5
samples from three types of construction machinery (1
excavator, 1 loader, and 1 forklift) operated under different working
conditions. The elements, WSIs, OC/EC and PAHs were analysed. The
chemical composition of the construction machinery emissions was
studied to provide fundamental data used to control emissions from
non-road construction machinery in China.
2. Experimental method
2.1. Tested machinery and fuel
In this study, typical non-road mobile machinery (1 excavator, 1
loader, 1 forklift) were selected for testing(Table 1). During the ex-
periment, the on-line analysis was performed on vehicle exhausts and
sample collections of particulate matter obtained under three working
conditions: idling, moving and working. At the same time, a camera
was used to record the machinery working. Each working condition
lasted at least 10–20 min, and the test were repeated 3–6 times for each
non-road construction machinery. The fuel used in this study was China
V diesel. The test fuel composition information was shown in Table 2.
2.2. Measurement system
Based on our previous tests, a portable emission measurement
system (PEMS) was used to collect the data and samples(Fig. 1)(Zhao
et al., 2019). This method can be used to obtain emission data for CO,
HC, NOx and PM in emissions produced by real world activities. The
SEMTECH-DS and SEMTECH EFM (Sensor, USA) was used to collect the
real time exhaust emissions data and recorded the exhaust gas flow. In
addition, a GPS module was used for measuring the speed, longitude,
latitude and altitude, and a temperature and humidity sensor was in-
cluded to record the ambient temperature and humidity. A portable
particle matter collection sampler (HY–100WS particle sampler,
Qingdao Hengyuan) was simultaneously used to collect the exhaust
particulate matter. The PM was collected using a quartz filter (90 mm,
2500 QAT-UP, Pall Life Sciences) and a Teflon filter (90 mm, Whatman,
America) to study the chemical composition of the particulate matter,
and the weight loss of these filters was ignored by strictly controlling
the sampling quality. As shown in Fig. 1, due to the high temperature of
the exhaust gas discharged from the non-road mechanical exhaust
pipes, the heating tube was used to sample particulate matter for pre-
venting the effects of condensation. After the flowmeter, the exhaust
enters the high-flow particulate matter sampler through the heating
tube.
Table 1
Non-road construction machinery parameters.
Vehicle Type Fuel type Rated power Displacement Cylinder number Year Emission standard
forklift diesel 86 kW 3.76 L 4 2016 China III
excavator diesel 103 kW 6.494 L 6 2016 China III
loader diesel 125 kW 6.75 L 6 2016 China III
Table 2
Test fuel properties.
Parameter Value
Density (20 °C) (kg/m
3
) 835.2
Viscosity (20 °C) (m
2
/s) 5.794
Flash point (°C) 83.5
Sulfur content (ppm) 113
Nitrogen content(mg/kg) 25
Carbon content(%) 86.09
Hydrogen content(%) 12.67
Oxygen content(%) 0.6
Vanadium content(mg/kg) < 1
Iron content(mg/kg) < 1
Calcium content(mg/kg) < 1
Zinc content(mg/kg) < 1
Nickel content(mg/kg) < 1
Q. Zhang, et al. Atmospheric Pollution Research 11 (2020) 679–685
680

2.3. Chemical composition analysis
The sampling filters obtained in this study were subjected to ele-
mental analysis, water-soluble ion analysis, carbon component analysis
and organic component PAH analysis. Inductively coupled plasma mass
spectrometry (ICP-MS, Agilent 7500A, Agilent, USA)was used for the
elemental analysis, in which seventeen elements present in the filters
were analysed. Water-soluble ions such as Na
+
,NH
4+
,K
+
,Mg
2+
,
Ca
2+
,Cl
−
,NO
2
−
,NO
3
−
, and SO
42−
were tested by an ion chromato-
graphy analyser (ICS3000 ion chromatograph, American Diane). The
organic carbon (OC) and elemental carbon (EC) were analysed with a
DRI MODEL2001 Thermal-optical carbon analyser developed by the
Desert Research Institute of the United States. In the thermo-optical
reflection carbon analysis protocol, eight parameters (OC1, OC2,OC3,
OC4, EC1, EC2, EC3, Organic carbon Pyrolysis (OCP)) are measured
during the analysis process. OC is defined as
OC1+OC2+OC3+OC4+OPC, and EC is defined as EC1+EC2+EC3-
OCP. The polycyclic aromatic hydrocarbons (PAHs) were detected
based on the EPA TO-13A standard, and gas chromatography-mass
spectrometry (GC-MS, Agilent 7890b-5977A, Agilent, USA) was used to
analyse 18 kinds of PAHs present in the samples, which included
Naphthalene (NA), Acenaphthylene (ACL), Acenaphthene (AC),
Fluorene (FL), Phenanthrene (PHE), Anthracene (AN), Fluoranthene
(FA), Pyrene (PY), Benzo (a) anthracene (BaA), Chrysene (CHR), Benzo
(b) fluoranthene (BbFA), Benzo (k) fluoranthene (BkFA), Benzo (e)
pyrene (BeP), Benzo (a) pyrene (BaP), Dibenzo (a,h) anthracene
(DBahA), Benzo (g,h,i) perylene (BghiP), Indeno (1,2,3-cd) pyrene (IP),
Coronene (COR).
2.4. Data processing
2.4.1. Fuel-based emission factor
The fuel-based emission factors for each operation mode and
average fuel-based emission factor for the construction machinery were
calculated using the ratio of the PM to the sum of excess CO
2
and CO.
These ratios are related to the amount of fuel consumed, and the ac-
tivity time is represented as(Fu et al., 2012;Hans et al., 2003)
=−′
∑++
−
E
FCMF mm
ρρρ(0.273 0.429 0.866 )
PM AVE diesel PM PM
tCO CO HC2(1)
where EF
PM-AVE
(gkg
−1
fuel) is the average emission factor from each
type of construction machinery. The ρ
CO2
,ρ
CO
,ρ
HC
are the concentra-
tions (g/s) of the CO
2
, CO, and HC in the exhaust, respectively. The
m’
PM
is the mass of filter before sampling and m
PM
is the mass of filter
after sampling. CMF
diesel
is the carbon mass fraction of diesel, in which
CMF
diesel
= 86.6%(Zhu et al., 2011). CO and CO
2
typically account for
more than 99% of the carbon emitted in engine exhaust(Yanowitz et al.,
2000).
2.4.2. Anion and cation equivalent concentration ratio (CE/AE)
The ratio of cation equivalent concentration (CE) to anion equiva-
lent concentration (AE) is used to describe the acidity of PM
2.5
. The CE/
AE greater than 1 indicates that the particles are alkaline, and CE/AE
less than 1 indicates that the particles are acidic(Cheng et al., 2011;
Xiang et al., 2017).
⎜⎟
⎜
⎟
=⎛
⎝+++ +
⎞
⎠
⎛
⎝+++
⎞
⎠
+++++
−−−−
CE AE NNH KMg Ca
SO NO Cl F
/[a]
23
[]
18
[]
39
[]
12
[]
20
/[]
48
[]
62
[]
35.5
[]
19
422
4
23
(2)
2.4.3. POC and SOC
The organic carbon consists of two parts: primary organic carbon
(POC) and secondary organic carbon (SOC). Since it is difficult to col-
lect and measure secondary organic carbon, indirect methods are used
to estimate the POC and SOC. The POC and SOC of the particles were
estimated by the minimum OC/EC ratio method(Viana et al., 2008;
Watson et al., 2008). The calculation method is as follows:
=∗
⎛
⎝⎞
⎠
P
OC EC OC
EC mi
n
(3)
=−SOC OC PO
C
()
tot (4)
where EC is the observed value of elemental carbon (ug/m
3
), (OC/EC)
min is the minimum ratio of OC/EC, and (OC)tot is the total organic
carbon obtained from the analysis (ug/m
3
).
2.4.4. Benzo[a]pyrene equivalent concentration
The benzo[a]pyrene equivalent (BaPeq) concentration is used to
assess the health risks of PAHs. BaPeq concentrations could be calcu-
lated using Equation (1), in which Ci is the mass concentration of the
individual PAHs, and TEFi is the toxic equivalency factor recommended
by Nisbet and Lagoy(Nisbet and Lagoy, 1992).
∑
=∗BaPeq TEF C
ii
(5)
2.5. Quality control
Portable emission measurement systems are highly accurate, and
the experimental pipeline should be sealed before the experiment is
performed to ensure the accuracy of the exhaust gas flow test. The in-
strument was warmed up 1 h before the start of the experiment. Before
and after the experiment, pure N
2
was used to calibrate the instrument
with zero calibration, and a gas mixture was used to perform the SPAN
calibration to determine the accuracy of the data obtained during the
experiment.
The pre-blank quartz filters were baked in a muffle furnace at 800 °C
for 2 h to remove the influence of the carbon components in the
membrane on the analysis of the organic matter and were then placed
in a constant temperature (25 ± 1 °C) and humidity (35 ± 1%)
chamber. After 48 h equilibration, it was weighed with a METTLER
millionth scale and weighed three times in a row (each weighing in-
terval is 1 h). The difference between the three weights should be no
more than ± 5 μg, and the average value was considered as the weight.
After sampling, the weighing method is the same, and the filters were
put in a refrigerator (4 °C) for analysis. For the instruments used in the
Fig. 1. A schematic drawing of the sampling setup.
Q. Zhang, et al. Atmospheric Pollution Research 11 (2020) 679–685
681

off-line analysis of the filter, the analytical instruments are calibrated
and maintained according to the instrument operating procedures be-
fore and after each test cycle, and the operating specifications are
strictly followed during the analysis to minimize error. The sample pre-
treatment, chemical analysis and QA/QC procedures were described in
detail in our previous work and other related studies(Fang et al., 2017;
Li et al., 2017;Liu et al., 2016;Luo et al., 2018).
3. Results and discussion
3.1. PM
2.5
emission factor
The fuel-based average emission factors of PM
2.5
for tested equip-
ment were summarized in Table 3. The EF
PM-AVE
for the forklift
(1.25 g kg
−1
fuel) and loader (1.64 g kg
−1
fuel) examined in this study
was lower than that determined by previous research(Cui et al., 2017;
Zequn, 2017). This result is reasonable because the emission standards
for the non-road construction machinery examined in this study are the
latest China III standards, which are stricter than the standards used by
previous studies. For the excavator, the EF
PM-AVE
(1.80 g kg
−1
fuel) in
this study is higher than that defined by the China II standard used in
the Cui's study(Cui et al., 2017), which is due to the poor operating
conditions and long running time of the excavator used in this study. In
general, the EF
PM-AVE
of China III non-road construction machinery is
typically lower than that of China II and China I construction ma-
chinery, indicating that strict regulations can help reduce PM emissions
from non-road construction machinery. Compared with the technical
guide by the MEE in 2014(MEE, 2014), all measured factors are dif-
ferent from the recommended values given in the technical guide.
Therefore, it is necessary to localize the relevant emission factors when
using technical guide for the calculating emission inventories of non-
road construction machinery.
3.2. Inorganic composition of PM
2.5
3.2.1. Elemental analysis
The chemical composition of the PM
2.5
obtained from each con-
struction machinery is shown in Fig. 2. In this study, Cd, Hg, Co, V, Ti,
Ni, Mn, Pb, Mg, Cu, K, Zn, Fe, S, Al, Na, and Ca were detected in the
PM
2.5
. Overall, the concentrations of Ca, Na, Al, S, Fe and Zn are the
highest, and the concentrations of Cd, Hg, Co, V, Ti and Ni are the
lowest. The differences in the individual elements present in the PM
2.5
samples may be affected by many factors, such as the engine rate
power, the gross vehicle weight, the complex reactions occurring in the
engine and the aftertreatment of the vehicle. Hu et al. (2009) classified
the total elements present in particle matter into the following groups:
oil additives (Ca and Zn), Fe, Cu, platinum group elements (PGE: Pt, Pd,
and Rh), V, Ti, mobile source air toxic (MSAT) metals (As, Cr, Pb, Mn,
and Ni), and other metals. In addition, sulfur is mainly from diesel fuel.
As the fuel quality increases, the concentration of sulfur in the exhaust
gas tends to decrease. The content of elements present in the samples
collected in this study is 2 times lower than the relevant elemental data
of China II excavator in Cui's research(Cui et al., 2017).
3.2.2. Water-soluble ions (WSIs)
As shown in Fig. 2,NO
3
−
,SO
42−
and NH
4
+have the highest con-
centrations of all the water-soluble ions detected in this study. The
SO
42−
ions in the exhaust gas mainly come from the sulfur in the fuel
oil. In this study, the SO
42−
/NO
3
−
ratio is 0.117–0.266, which is less
than 1, meaning that these ions are derived from mobile source pollu-
tion(Liu et al., 2014).
In this study, the ratio of the water-soluble ions to PM
2.5
is
0.99%–1.11%. Popovicheva et al. (2015) determined that the water-
soluble ions (F
−
,Cl
−
,NH
4+
,SO
42−
) are emitted by non-road diesel
engines at a steady state and account for 0.98% of PM
2.5
. The SO
42−
is
also the main component, which is similar to the results of this study.
The CE/AE values are all greater than 1, and the particles are alkaline.
This is similar to the previous research on vehicle source emissions.
3.3. Organic composition of PM
2.5
3.3.1. OC/EC
According to Han's research, the EC can be divided into Char-EC and
Soot-EC based on the analytical instruments used in different cracking
processes(Yongming et al., 2007). Char-EC is a residue formed by the
incomplete combustion of carbonaceous materials and retains the
structural characteristics of the original fuel. Soot-EC is a gaseous
precursor formed by high temperature combustion, is highly con-
centrated and contains refractory spherical carbon particles formed by a
condensation process. Char-EC = EC1-OPC, Soot-EC = EC2+EC3. The
mean values of Char-EC/Soot-EC ratios in the PM
2.5
of the excavator,
loader and forklift exhausts are 0.23, 0.5 and 0.12, respectively, which
are less than 1.0. This is consistent with the compositional distribution
of the source emissions and is also consistent with the research per-
formed by Chow (Chow et al., 2004).
Liu et al. showed that the proportion of the OC and EC emitted by
non-road-source diesel engines is related to the engine load, fuel sulfur
content, and sampling temperature(Zifei et al., 2005). The OC/EC ratio
obtained in this study ranged from 1.9 to 3.4 with an average of 2.3.
This result is similar to that of the Chow et al. study, in which the value
of the OC/EC ratio was greater than 2, and secondary organic carbon
generation was identified.
Organic carbon consists of two parts: primary organic carbon (POC)
and secondary organic carbon (SOC)(Anita et al., 2010;Oen et al.,
2006). POC is generally considered to be from a single emission source,
while SOC is primarily produced by VOCs and gas-particle conversion.
Many studies have been conducted on SOC concentrations emitted by
vehicles(Deng et al., 2017;May et al., 2014;Roth et al., 2020). EC is
mainly derived from the incomplete combustion of fossil fuels and
biomass, and elemental carbon is also one of the sources of CO
2
in the
long-term carbon cycle(Campa et al., 2009). In our study, the OC/EC
ratio of the particulate matter was greater than 2, indicating the pre-
sence of a secondary aerosol. Based on the experimental analysis re-
sults, the (OC/EC)
min
was 1.9 in this study. The estimated concentra-
tions of SOC in the particulates discharged from the loaders, excavators
and forklifts are 3.73 mg/m
3
, 0.25 mg/m
3
and 0.42 mg/m
3
, respec-
tively.
3.3.2. PAHs
18 kinds of PAHs were detected in the particulate matter evaluated
in this study, of which fluorene, phenanthrene, anthracene, fluor-
anthene, pyrene, benzo(b)fluoranthene and benzo(g,h,i)perylene ac-
counted for a higher proportion of approximately 77%–88% of PAH
emissions. For the loader, the average of the BaA/(BaA + CHR), IP/
(IP + BghiP), and FA/(FA + PY) ratios were 0.47, 0.53, and 0.33,
respectively. The ratios of the PAHs in the loader exhaust obtained in
this study are similar to those reported by Liu et al. and Cui et al.(Cui
Table 3
Average fuel-based emission factors on different emission standards (g·kg
−1
fuel).
EF
PM-AVE
China I China II China III
Forklift 2.4
a
3.5
a
1.25
b
Excavator 2.32
c
1.05
c
1.8
b
Loader 2.9
a
2.9
a
1.64
b
Technical guide
d
3.16 1.36 1.12
a
Zequn (2017).
b
This study.
c
Cui et al. (2017).
d
Technical guide for estimating inventories of non-road mobile pollution
sources, MEE.
Q. Zhang, et al. Atmospheric Pollution Research 11 (2020) 679–685
682

et al., 2017;ZLiu et al., 2015).
Based on the number of rings, the PAHs can be divided into lower
molecular weight PAHs (LMW-PAHs) containing 2 rings and 3 rings,
middle molecular weight PAHs (MMW-PAHs) containing 4 rings and
higher molecular weight PAHs (HMW-PAHs) containing 5 rings, 6 rings
and 7 rings. As shown in Fig. 2, LMW-PAHs and MMW-PAHs were the
major PAH species (collectively representing approximately
77.3%–88.3% of the total PAHs found in the particulate matter). The
results of this study and those of Li's research on diesel engine parti-
culate matter PAHs are mainly concentrated in the results of LMW and
MMW(Li, 2013). Although the concentration of HMW-PAHs is low, due
to their lipophilicity, HMW-PAHs are considered to be more harmful to
human health than the other PAHs(Tsai et al., 2004).
The Benzo [a] pyrene equivalent (BaPeq) concentration value of
PAHs was shown in Fig. 3. The total BaPeq for loader, excavator and
forklift were 0.20, 0.24 and 0.15 μg BaP/m
3
. All the PAH BaPeq values
calculated in this study for the loader, excavator and forklift are higher
than the BaP concentrations recommended by the World Health Orga-
nization (1 ng/m
3
). Due to the adverse effects and health hazards of
carbonaceous compositions, elements and PAHs, the PM emissions from
non-road construction machinery require urgent control.
Fig. 2. The chemical composition of the PM
2.5
obtained from each construction machinery. (a) elements, (b) WSIs, (c) OC/EC and (d) PAHs.
Fig. 3. The Benzo [a] pyrene equivalent concentration value of PAHs.
Q. Zhang, et al. Atmospheric Pollution Research 11 (2020) 679–685
683

3.4. Comparisons with previous study
The average source profiles of PM
2.5
obtained from the construction
machinery evaluated in this study are summarized in Table 4, and the
average source profiles previously reported are also summarized for
comparison. As shown in Table 4, the average fractions of the total
chemical components of the tested loader, excavator, and forklift
samples collected in this study are lower than those determined by Cui's
research, whereas the total fraction is higher than that determined for
fishing ship engine(Wen et al., 2018). This mainly occurred because the
emission standard of the non-road construction machinery evaluated in
this study is the China III stage, and the Cui research used a China II
stage standard excavator. The use of more strict emission standards for
non-road mobile machinery can lead to a decline in pollutant emissions.
In comparison, the total elements and carbonaceous and PAHs
fractions determined in this study are lower than those reported by Cui,
whereas the WSI fraction determined in this study is higher than that
reported by Cui. The proportion of WSIs measured in this study was
significantly lower than those emitted from the fishing ship engine. The
ship engine emissions contained a high concentration of sulfate species,
which could be the result of the different sulfur components in the
diesel fuels. The OC and EC fractions of the PM evaluated in our study
are higher than those reported by Wen(Wen et al., 2018), which might
be the result of the poor fuel quality and engine operational condition
of the fishing ship. Marine diesel may be directly discharged without
sufficient combustion, thus resulting in the ship exhaust having a lower
concentration of EC; however, black smoke is still clearly emitted by
ships. In addition, the driving conditions, engine parameters, and ve-
hicle age of non-road construction machinery and the experimental
methods used to study emissions may affect the composition of exhaust
emissions.
4. Conclusion
Three typical non-road construction machinery were tested to study
on the PM emissions and chemical components(elements, WSIs, OC/EC
and PAHs). The results indicated that strict regulations can help reduce
PM emissions from non-road construction machinery. Compared with
previous reports, all measured EFs were different from the re-
commended values given in the technical guide from MEE. More at-
tention needed to be paid to the localization of EFs for the calculating
emission inventories.
The chemical composition of non-road construction machinery ex-
haust particles was similar to that of diesel vehicle exhaust particles.
The fraction of the chemical components including elements, WSIs, OC/
EC and PAHs to particulate matter is from 67.30% to 64.10%. The OC/
EC ratio of the particulate matter was greater than 2, indicating the
presence of a secondary aerosol. The estimated concentrations of SOC
in the particulates discharged from the loaders, excavators and forklifts
were 3.73 mg/m
3
, 0.25 mg/m
3
and 0.42 mg/m
3
, respectively. All the
PAH BaPeq values were higher than the BaP concentrations of World
Health Organization (1 ng/m
3
). The PM emissions from non-road
construction machinery required urgent control for the harmful effects
and health hazards of carbonaceous compositions, elements and PAHs.
Finally, the operating conditions, engine parameters, and vehicle
age of non-road construction machinery and the experimental methods
may affect the composition of exhaust emissions. At the same time,
these chemical components played an important role in atmospheric
source analyses and air quality impact studies. More studies must be
conducted to clarify each non-road construction machinery type's
emission characteristics in real-world conditions.
Credit author statement
Qijun Zhang: Conceptualization, Methodology, Writing-Original
draft preparation, Writing-Reviewing and Editing. Lei Yang: Data
curation, Software. Chao Ma: Visualization, Investigation. Yanjie
Zhang: Validation. Lin Wu: Supervision. Hongjun Mao: Funding ac-
quisition, Methodology, Writing-Reviewing and Editing.
Acknowledgement
This study was supported by the Tianjin Education Commission
Research Project (2017KJ053), Natural Science Foundation of Tianjin
(18JCYBJC23700, 16JCYBJC22600), Opening Foundation of Ministry
of Education Key Laboratory of Pollution Processes and Environmental
Criteria (2017-01) and Tianjin Chengjian University Doctoral
Foundation Project.
References
Anita, L., Lucyna, F., Dominika, M., Dorota, P., Dorota, B., Magdalena, B.D., 2010.
Elemental and organic carbon in aerosols over urbanized coastal region (southern
Baltic Sea, Gdynia). Sci. Total Environ. 408, 4761–4769.
Table 4
Comparison of the average chemical composition of PM
2.5
obtained from dif-
ferent diesel vehicles (%).
Loader Excavator Forklift Excavator
(China II)
(Cui et al.,
2017)
Fishing
ship
(Wen
et al.,
2018)
Element Al 0.0475 0.1635 0.1242 0.852
Ca 0.2391 0.2615 0.3671 0.241 0.317
Cd 0.0001 0.0001 0.0001 0.000
Co 0.0002 0.0001 0.0001 0.001
Cu 0.0000 0.0000 0.0000 0.042 0.107
Fe 0.1630 0.2006 0.2323 0.815 0.267
Hg 0.0276 0.0701 0.0540
K 0.0227 0.0225 0.0229 0.197 0.037
Mg 0.0129 0.0322 0.0285 0.106 0.053
Mn 0.0019 0.0028 0.0026 0.013 0.006
Na 0.0533 0.0586 0.0560 0.245 0.082
Ni 0.0006 0.0003 0.0001 0.015 0.001
Pb 0.0041 0.0034 0.0033 0.011 0.000
S 0.0327 0.0498 0.0322
Ti 0.0004 0.0011 0.0010 0.008
V 0.0002 0.0002 0.0001 0.001 0.0001
Zn 0.0244 0.0292 0.0237 0.027 0.013
WSIs Cl
−
0.0290 0.0680 0.0150 0.098
NO
2
−
0.0990 0.1140 0.1090
NO
3
−
0.3870 0.4020 0.2720 0.278 0.254
SO
42-
0.0450 0.1070 0.0500 0.193 1.770
Na
+
0.0280 0.0490 0.0320
NH
4+
0.3810 0.3050 0.3410 0.044 0.446
K
+
0.0280 0.0060 0.0480
Mg
2+
0.0050 0.0120 0.0120
Ca
2+
0.1020 0.1200 0.1100
Carbonaceous OC 50.7690 42.4400 41.7020 39.2 35.5
EC 14.7600 21.9500 20.4280 33.3 6.6
PAHs NA 0.0002 0.0001 0.0002 0.008
ACL 0.0001 0.0001 0.0001 0.005
AC 0.0012 0.0001 0.0002 0.001
FL 0.0017 0.0009 0.0015 0.002
BghiP 0.0016 0.0007 0.0013 0.003
IP 0.0018 0.0006 0.0012 0.001
DBahA 0.0003 0.0000 0.0002 –
BbFA 0.0019 0.0014 0.0013
COR 0.0006 0.0000 0.0002
PHE 0.0075 0.0052 0.0068 0.005
AN 0.0015 0.0014 0.0012 0.001
FA 0.0037 0.0046 0.0013 0.026
BaA 0.0008 0.0009 0.0007 0.007
CHR 0.0010 0.0009 0.0007 0.008
PY 0.0074 0.0135 0.0115 0.028
BaP 0.0002 0.0003 0.0004 0.0004
BeP 0.0006 0.0004 0.0004
BkFA 0.0003 0.0002 0.0002 0.001
Q. Zhang, et al. Atmospheric Pollution Research 11 (2020) 679–685
684

Campa, A.M.S.D.L., Pio, C., Rosa, J.D.D.L., Querol, X., Alastuey, A., González-Castanedo,
Y., 2009. Characterization and origin of EC and OC particulate matter near the
Doñana National Park (SW Spain) ☆. Environ. Res. 109, 671–681.
CCMA. China Construction Machinery Industry Yearbook, 2017. China Construction
Machinery Association 2017.
Cheng, Y., Lee, S., Ho, K., Chow, J., Watson, J., Louie, P., et al., 2010. Chemically-spe-
ciated on-road PM2. 5 motor vehicle emission factors in Hong Kong. Sci. Total
Environ. 408, 1621–1627.
Cheng, S., Yang, L., Zhou, X., Xue, L., Gao, X., Yang, Z., et al., 2011. Size-fractionated
water-soluble ions, situ pH and water content in aerosol on hazy days and the in-
fluences on visibility impairment in Jinan, China. Atmos. Environ. 45, 4631–4640.
Chow, J.C., Watson, J.G., Kuhns, H., Etyemezian, V., Lowenthal, D.H., Crow, D., et al.,
2004. Source profiles for industrial, mobile, and area sources in the big bend regional
aerosol visibility and observational study. Chemosphere 54, 185–208.
Cui, M., Chen, Y., Feng, Y., Li, C., Zheng, J., Tian, C., et al., 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.
Deng, W., Hu, Q., Liu, T., Wang, X., Zhang, Y., Song, W., et al., 2017. Primary particulate
emissions and secondary organic aerosol (SOA) formation from idling diesel vehicle
exhaust in China. Sci. Total Environ. 593, 462–469.
Dunker, A.M., Koo, B., Yarwood, G., 2019. Source apportionment of organic aerosol and
ozone and the effects of emission reductions. Atmos. Environ. 198, 89–101.
EEA, 2019. European Union Emission Inventory Report 1990-2017. European
Environment Agency.
Fang, X., Bi, X., Hong, X., Wu, J., Zhang, Y., Feng, Y., 2017. Source apportionment of
ambient PM 10 and PM 2.5 in Haikou, China. Atmos. Res. 190, 1–9.
Fu, M., Ge, Y., Tan, J., Zeng, T., Liang, B., 2012. Characteristics of typical non-road
machinery emissions in China by using portable emission measurement system. Sci.
Total Environ. 437, 255–261.
Green, M.C., Chow, J.C., Chang, M.C.O., Chen, L.W.A., Kuhns, H.D., Etyemezian, V.R.,
et al., 2013. Source apportionment of atmospheric particulate carbon in Las Vegas,
Nevada, USA. Particuology 11, 110–118.
Hans, M., Claudio, M., Barber, P.W., Kuhns, H.D., Keislar, R.E., Watson, J.G., 2003. On-
road measurement of automotive particle emissions by ultraviolet lidar and trans-
missometer: instrument. Environ. Sci.; Technol. 37, 4971–4978.
Hao, Y., Gao, C., Deng, S., Yuan, M., Song, W., Lu, Z., et al., 2019. Chemical character-
isation of PM2. 5 emitted from motor vehicles powered by diesel, gasoline, natural
gas and methanol fuel. Sci. Total Environ. 674, 128–139.
Hu, S., Herner, J.D., Shafer, M., Robertson, W., Schauer, J.J., Dwyer, H., et al., 2009.
Metals emitted from heavy-duty diesel vehicles equipped with advanced PM and NO
emission controls. Atmos. Environ. 43, 2950–2959.
Li, d, 2013. Emission Characteristics of Semi-volatile Organic Compounds from Non-road
Diesel Engine. Joumal ofTianjin University(Science and Technology.
Li, Z., Ji, Y., Ma, H., Peng, Z., Zeng, X., Liu, S., et al., 2017. Characterization of inorganic
elements within PM2.5 and PM10 fractions of fly ashes from coal-fired power plants.
Aerosol; Air Qual. Res. 17, 1105–1116.
Liu, J., Li, J., Zhang, Y., Liu, D., Ding, P., Shen, C., et al., 2014. Source apportionment
using radiocarbon and organic tracers for PM2.5 carbonaceous aerosols in
Guangzhou, south China: contrasting local- and regional-scale haze events. Environ.
Sci.; Technol. 48, 12002–12011.
Liu, B., Na, S., Dai, Q., Mei, R., Sui, B., Bi, X., et al., 2016. Chemical composition and
source apportionment of ambient PM 2.5 during the non-heating period in Taian,
China. Atmos. Res. 170, 23–33.
Liu, Z.G., Wall, J.C., Ottinger, N.A., McGuffin, D., 2015. Mitigation of PAH and nitro-PAH
emissions from nonroad diesel engines. Environ. Sci.; Technol. 49, 3662–3671.
Luo, Y., Zhou, X., Zhang, J., Yan, X., Zhe, W., Yang, Z., et al., 2018. PM 2.5 pollution in a
petrochemical industry city of northern China: seasonal variation and source ap-
portionment. Atmos. Res. 212, 285–295.
May, A.A., Nguyen, N.T., Presto, A.A., Gordon, T.D., Lipsky, E.M., Karve, M., et al., 2014.
Gas-and particle-phase primary emissions from in-use, on-road gasoline and diesel
vehicles. Atmos. Environ. 88, 247–260.
MEE, 2014. Technical Guide for Estimating Inventories of Non-road Mobile Pollution
Sources. Ministry of ecology and environment of the people's Republic of China.
MEE. China Vehicle Environmental Management Annual Report, 2018. Ministry of
Ecology and Environment of the People's Republic of China 2018.
Muresan, B., Capony, A., Goriaux, M., Pillot, D., Higelin, P., Proust, C., et al., 2015. Key
factors controlling the real exhaust emissions from earthwork machines. Transp. Res.
D Transp. Environ. 41, 271–287.
Nisbet, I.C., Lagoy, P.K., 1992. Toxic equivalency factors (TEFs) for polycyclic aromatic
hydrocarbons (PAHs). Regul. Toxicol. Pharmacol. Rtp 16, 290–300.
Nyström, R., Sadiktsis, I., Ahmed, T.M., Westerholm, R., Koegler, J.H., Blomberg, A.,
et al., 2016. Physical and chemical properties of RME biodiesel exhaust particles
without engine modifications. Fuel 186, 261–269.
Oen, A.M.P., Schaanning, M., Ruus, A., Cornelissen, G., Källqvist, T., Breedveld, G.D.,
2006. Predicting low biota to sediment accumulation factors of PAHs by using in-
finite-sink and equilibrium extraction methods as well as BC-inclusive modeling.
Chemosphere 64, 1412–1420.
Popovicheva, O., Engling, G., Lin, K.T., Persiantseva, N., Timofeev, M., Kireeva, E., et al.,
2015. Diesel/biofuel exhaust particles from modern internal combustion engines:
microstructure, composition, and hygroscopicity. Fuel 157, 232–239.
Roth, P., Yang, J., Peng, W., Cocker III, D.R., Durbin, T.D., Asa-Awuku, A., et al., 2020.
Intermediate and high ethanol blends reduce secondary organic aerosol formation
from gasoline direct injection vehicles. Atmos. Environ. 220, 117064.
Tsai, P.J., Shih, T.S., Chen, H.L., Lee, W.J., 2004. Assessing and predicting the exposures
of polycyclic aromatic hydrocarbons (PAHs) and their carcinogenic potencies from
vehicle engine exhausts to highway toll station workers. Atmos. Environ. 38,
333–343.
Viana, M., Kuhlbusch, T., Querol, X., Alastuey, A., Harrison, R., Hopke, P., et al., 2008.
Source apportionment of particulate matter in Europe: a review of methods and re-
sults. J. Aerosol Sci. 39, 827–849.
Watson, J.G., Antony Chen, L.-W., Chow, J.C., Doraiswamy, P., Lowenthal, D.H., 2008.
Source apportionment: findings from the US supersites program. J. Air Waste Manag.
Assoc. 58, 265–288.
Wen, J., Wang, X., Zhang, Y., Zhu, H., Feng, Y., 2018. PM 2.5 source profiles and relative
heavy metal risk of ship emissions: source samples from diverse ships, engines, and
navigation processes. Atmos. Environ. 191, 55–63.
Xiang, P., Zhou, X., Duan, J., Tan, J., He, K., Yuan, C., et al., 2017. Chemical char-
acteristics of water-soluble organic compounds (WSOC) in PM 2.5 in Beijing, China:
2011–2012. Atmos. Res. 183, 104–112.
Yan F, Winijkul E, Streets DG, Lu Z, Zhang Y. Global emission projections for the trans-
portation sector using dynamic technology modeling. Atmos. Chem. Phys.,14,
11(2014-06-10) 2014; 13: 23373-23419.
Yanowitz, J., McCormick, R.L., Graboski, M.S., 2000. In-use emissions from heavy-duty
diesel vehicles. Environ. Sci.; Technol. 34, 729–740.
Yongming, H., Junji, C., Chow, J.C., Watson, J.G., Zhisheng, A., Zhangdong, J., et al.,
2007. Evaluation of the thermal/optical reflectance method for discrimination be-
tween char- and soot-EC. Chemosphere 69, 569–574.
Zequn, X., 2017. Study on Characteristics of Gaseous and Particulate Emission.from Non-
road Diesel Vehicles on Real World Conditions.
Zhang, F., Chen, Y., Tian, C., Lou, D., Matthias, V., 2015. Emission factors for gaseous and
particulate pollutants from offshore diesel engine vessels in China. Atmos. Chem.
Phys. Discuss. 15, 6319–6334.
Zhang, Q., Wu, L., Yang, Z., Zou, C., Liu, X., Zhang, K., et al., 2016. Characteristics of
gaseous and particulate pollutants exhaust from logistics transportation vehicle on
real-world conditions. Transp. Res. D Transp. Environ. 43, 40–48.
Zhao, J., Zhang, Y., Wang, T., Sun, L., Yang, Z., Lin, Y., et al., 2019. Characterization of
PM2.5-bound polycyclic aromatic hydrocarbons and their derivatives (nitro-and oxy-
PAHs) emissions from two ship engines under different operating conditions.
Chemosphere 225, 43–52.
Zheng, X., Wu, Y., Zhang, S., He, L., Hao, J., 2018. Evaluating real-world emissions of
light-duty gasoline vehicles with deactivated three-way catalyst converters. Atmos.
Pollut. Res. 9, 126–132.
Zhou, Z., Tan, Q., Liu, H., Deng, Y., Wu, K., Lu, C., et al., 2019. Emission characteristics
and high-resolution spatial and temporal distribution of pollutants from motor ve-
hicles in Chengdu, China. Atmos. Pollut. Res. 10, 749–758.
Zhu, D., Nussbaum, N.J., Kuhns, H.D., Chang, M.C.O., Sodeman, D., Moosmüller, H.,
et al., 2011. Real-world PM, NO, CO, and ultrafine particle emission factors for
military non-road heavy duty diesel vehicles. Atmos. Environ. 45, 2603–2609.
Zifei, L., Mingming, L., Eileen B, M., Keener, T.C., Soon-Jai, K., Fuyan, L., 2005.
Variations of the particulate carbon distribution from a nonroad diesel generator.
Environ. Sci.; Technol. 39, 7840.
Q. Zhang, et al. Atmospheric Pollution Research 11 (2020) 679–685
685