We present highly time-resolved (30 to 120 min)
measurements of size-fractionated (PM10 and PM2.5) elements in two
cities in Asia (Delhi and Beijing) and Europe (Krakow and London). For most
elements, the mean concentrations in PM10 and PM2.5 are higher in
the Asian cities (up to 24 and 28 times, respectively) than in Krakow and
often higher in Delhi than in Beijing. Among European cities, Krakow shows
higher elemental concentrations (up to 20 and 27 times, respectively) than
London. Hourly maximum concentrations of Pb and Zn reach up to 1 µg m-3 in Delhi, substantially higher than at the other sites. The
enrichment factor of an element together with the size distribution allows
for a rough classification of elements by major source. We define five
groups: (1) dust emissions, (2) non-exhaust traffic emissions, (3) solid fuel
combustion, (4) mixed traffic/industrial emissions, and (5)
industrial/coal/waste burning emissions, with the last group exhibiting the
most site-to-site variability. We demonstrate that the high time resolution
and size-segregated elemental dataset can be a powerful tool to assess
aerosol composition and sources in urban environments. Our results highlight
the need to consider the size distributions of toxic elements, diurnal
patterns of targeted emissions, and local vs. regional effects in
formulating effective environmental policies to protect public health.
Introduction
The percentage of the global population living in urban areas with more than
1 million inhabitants has been steadily increasing over the last decades
(Krzyzanowski et al., 2014). Air pollution in these cities is a major
contributor to the global disease burden (Lim et al., 2012), with more
than 96 % of the population in these cities exposed to PM2.5 (particulate matter with an aerodynamic diameter below 2.5 µm)
concentration above World Health Organization (WHO) air quality standards
(Krzyzanowski et al., 2014). Smaller particles are likely more toxic since
they can penetrate deep into the lungs (Miller et al., 1979). Particle
toxicity depends also on PM composition (Kelly and Fussell, 2012), with
identified toxic constituents including elemental and organic carbon and
metals. Transition metals such as Fe, V, Ni, CrVI, Cu, and Zn are of
particular concern due to their potential to produce reactive oxygen species
(ROS) in biological tissue (Manke et al., 2013). Moreover, metals such as
Pb and Cd and the metalloid As accumulate in body tissue and contribute to many
adverse health effects, such as lung cancer, cognitive deficits, and hearing
impairment (Jaishankar et al., 2014). Elements are also recognized as
effective markers for source apportionment (SA), especially for
anthropogenic emissions in urban areas (e.g., traffic, industry, and power
production). Emissions from these sources vary on timescales of a few hours
or less, and such rapid changes cannot be resolved by conventional 24 h filter measurements. The vast majority of elemental SA studies in the
literature are limited by the time resolution of the input samples
(Dall'Osto et al., 2013; Pant and Harrison, 2012). Highly time-resolved and
size-segregated measurements are thus required for the determination of
elemental PM sources and health effects within urban areas under varying
meteorological conditions.
Efforts in European and Asian countries to tackle poor air quality include
the EURO norms (EEA, 2018) in European cities to control vehicular
emissions, odd–even traffic regulations in Delhi (Kumar et al., 2017) and
Beijing (An et al., 2019), and the “Stop Smog” program in Poland (Shah,
2018). In addition, strict emission control measures were implemented in
China (Gao et al., 2016) in September 2013, by lowering the fraction of coal
in energy production from 24 % in 2012 to 10 % in 2017. Evaluation and
optimization of such programs require elucidation of the sources and
processes governing PM abundance and composition. This remains challenging
and may strongly differ from site to site depending on local environmental
conditions. To assess this, we present high time resolution PM10 and
PM2.5 metal and trace element concentrations in four Asian and European
cities: Delhi, Beijing, Krakow, and London. A simple conceptual framework
allows for the characterization of major sources, site-to-site similarities, and
local differences and the identification of key information required for
efficient policy development. Moreover, when the aim of the analysis is not
to obtain quantitative information, this method is proved particularly
useful since it does not require a full SA analysis (presented elsewhere for
London and Delhi; Visser et al., 2015a; Rai et al., 2020), which is complex
and time-consuming, and which can be challenging to compare across sites due
to differences in source definitions.
Materials and methodsDescription of the sampling sites
The sampling site (40.00∘ N, 116.38∘ E) in Beijing was
located in a residential area north of the urban core, near the Olympic Park,
without any nearby industrial sources. It is a typical urban site in the
central zone of Beijing. It is located approximately 1.2 km away from the
west 3rd Ring Road and 2.7 km away from the north 2nd Ring Road.
Both ring roads are characterized by heavy traffic. Coal-based heating is a
major sector of coal consumption in northern China (Tian et al., 2015). The
measurements were performed from 6 November to 12 December 2017.
The sampling site (50.06∘ N, 19.91∘ E) in Krakow was
located in a residential area close to the city center. The major local
sources of pollution are municipal emissions, combustion, industry, and
traffic. Traffic in the city is dense with frequent traffic jams
(∼ 1 km away from sampling location). Factories (steel and
nonferrous metallurgical industries) are located at a distance of about 10 km from the sampling site. Additionally, a coal power plant is located in
the southern area of the city. Moreover, a zinc ore industry source is situated
about 50 km to the north of the city. The sources with the highest PM
emission rates are situated in the northeastern part of Krakow, i.e., Huta
Arcelor Mittal steel works, the Cementownia cement factory, and the EC Krakow
coal-fired power plant (Junninen et al., 2009). However, in Krakow, there
are numerous small coal-fired low-efficiency boilers (LE boilers)
distributed over the city. The measurements were performed from 11 to 23 October 2018. It is important to note that the sampling period in Krakow is
different from the rest of the sites.
The Delhi sampling location (28.54∘ N, 77.19∘ E) was
situated in a residential and commercial area in the south part of Delhi.
Roads with heavy traffic within 2–5 km surround the sampling location in all
directions. Many anthropogenic sources, such as traffic, agricultural residue
burning, waste burning, and a coal-based power plant, and various micro-, small-,
and medium-scale manufacturing and processing units, such as metal
processing, electroplating, and paint and chemical manufacturing for
the pretreatment of metals, might contribute to the low air quality of this
region. However, the coal-based power plant in the southeast direction (18 km) was shut down in October 2018, although emission of fly ash continued
during the study period. The measurements were performed from 15 January to
9 February 2019.
The London sampling location (51.52∘ N, 0.21∘ W),
classified as having an urban background, was within a school ground in a residential
area of North Kensington (NK). Long-term measurements of air pollutants at
NK have been described in detail in a previous study (Bigi and Harrison,
2010) and are considered as being representative of the background air quality
for most of London. NK is situated within a heavy traffic suburban area of
London. The measurements were performed from 6 January to 11 February 2012.
Instrumentation
In Beijing, Delhi, and Krakow, sampling and analysis were conducted with an
Xact 625i®Ambient Metals Monitor (Cooper
Environmental, Tigard, OR, USA) with an alternating PM10 and PM2.5
inlet switching system (Furger et al., 2020). Details of the Xact can be
found in previous studies (Cooper et al., 2010; Furger et al., 2017; Rai et
al., 2020; Tremper et al., 2018). The field measurements with the Xact were
performed with 1 h time resolution in Beijing and 0.5 h time resolution in
Krakow and Delhi. The instrument was able to detect 34 elements (Al, Si, P,
S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr,
Y, Zr, Cd, In, Sn, Sb, Ba, Hg, Tl, Pb, and Bi). However, some of the elements
were below the minimum detection limit (MDL) of the instrument (Table S1) for
certain periods of time. Therefore, we discarded the elements that were
below the MDL in PM10 and PM2.5≥80 % of the time.
In London, we deployed a rotating drum impactor (RDI) which sampled the following with 2 h time resolution in size-segregated stages: PM10-2.5 (coarse),
PM2.5-1.0 (intermediate) and PM1.0-0.3 (fine). Trace element
composition of the RDI samples was determined by synchrotron-radiation-induced X-ray fluorescence spectrometry (SR-XRF) at the X05DA
beamline (Flechsig et al., 2009) at the Swiss Light Source (SLS), Paul
Scherrer Institute (PSI), Villigen PSI, Switzerland, and at Beamline L at
the Hamburger Synchrotronstrahlungslabor (HASYLAB), Deutsches
Elektronen-Synchrotron (DESY), Hamburg, Germany (beamline dismantled in
November 2012). In total 25 elements were quantified (Na, Mg, Al, Si, P, S,
Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Sr, Zr, Mo, Sn, Sb, Ba, and Pb).
Details of the RDI-SR-XRF analysis were described in previous studies
(Bukowiecki et al., 2008; Richard et al., 2010; Visser et al., 2015b). Due
to the RDI's omission of particles smaller than 300 nm, the fine-mode
elemental data for London are less reliable as compared to the other sites.
While the comparison of size-resolved London data with the other sites
should therefore be interpreted with caution, we present London
PM2.5/PM10 ratios, group classification (in PM10 and
PM2.5), and their diurnal patterns (in PM2.5 and coarse
(PM10-PM2.5)) in the Supplement (Figs. S4, S5, and S8,
respectively).
Xact measurements of Cl and S were compared to the chloride and sulfate data
obtained from co-located aerosol mass spectrometer (AMS) measurements (Fig. S1). The AMS instruments consisted of a high-resolution long-time-of-flight
(HR-L-TOF) AMS deployed for online measurements of size-segregated mass spectra
of non-refractory (NR)-PM2.5 with 2 min resolution in Beijing and a
HR-TOF-AMS of NR-PM1 with 2 min resolution in Delhi. The scatter plots
exhibit a good correlation, which is reflected by a Pearson's R of 0.91
(Delhi) and 0.96 (Beijing) for S vs. sulfate, and 0.98 (Delhi) and 0.97
(Beijing) for Cl vs. chloride. The correlation resulted in a slope of 1.13
(Delhi) and 1.23 (Beijing) for sulfate and 1.03 (Delhi) and 1.9 (Beijing)
for Cl. The S measurements of the two instruments agree within the typical
uncertainties of such measurements (∼ 25 %; Canagaratna et
al., 2007; Furger et al., 2017). In addition, the Delhi measurements cover
different size fractions (PM2.5 for the Xact vs. PM1 for the
AMS).
The Xact/AMS ratio for Cl observed in Beijing likely occurs because the
relative ionization efficiency for AMS measurements of Cl was not determined
in Beijing (whereas calibrations with NH4Cl were performed in
Delhi). In addition, the Beijing measurements likely have a higher fraction
of other forms of Cl (e.g., ZnCl2, PbCl2, FeCl3), which are
not efficiently detected in standard AMS operation. High Cl concentrations
from November to March in Beijing are reported in previous studies, which are
believed to be associated with coal burning (Yao et al., 2002; Zhang et al.,
2019). The contribution from sea salt particles is less important because
the sampling site in Beijing is about 200 km from the sea. However, the
sea and/or road salt discussion would be strengthened by the measurement of Na,
which is an important tracer of sea and road salt in the form of NaCl. While Na
and Cl are good tracers for sea and road salt, the Cl/Na ratio in Beijing during
winter is reported to be much higher (2.3) than the ratio in seawater (1.17; Yao et al., 2002).
Crustal enrichment factor (EF) analysis
EF analysis was applied to determine the enrichment of a given element
relative to its abundance in the upper continental crust (UCC). For this
analysis Ti (Fomba et al., 2013; Majewski and Rogula-Kozłowska, 2016; Wei
et al., 1999) was selected as the reference element due to its stable and
spatially homogenous characteristics in the soil. The compilation of UCC
(Rudnik and Gao, 2014) was used to calculate EFs and crustal contributions
in elemental concentrations. For an element (X) in a sample, the EF relative
to Ti is given as
EF=XXTiTiSampleXXTiTiCrust.
The unexpectedly low EFs observed for Si (0.41–0.45) and compared to
previous studies (Majewski and Rogula-Kozłowska, 2016; Tao et al., 2013)
are likely due to self-attenuation issues in XRF analysis for lighter
elements (atomic number < 19), which may cause underestimation in
their concentrations (Maenhaut et al., 2011; Visser et al., 2015b).
Therefore, the measurements of Al and Si from Xact need to be treated with
caution. However, low EFs for Si is also probably due to crust–air
fractionation in the wind-blown generation of crustal aerosol particles
(Rahn, 1976). Given that Si is the only outlier across all measured
elements, a major anthropogenic contribution to Ti seems unlikely. However,
Ti emission is possible from non-exhaust traffic sources, measured in road
dust samples worldwide (Amato et al., 2009; Pant et al., 2015).
ResultsPM10el and PM2.5el concentration
Hourly average elemental PM10 (PM10el) and elemental PM2.5
(PM2.5el) concentrations were measured, for which Fig. 1a and b
summarize the results of 18 elements measured at all four sites. Total
measured concentrations at Delhi (54 µg m-3 in PM10; 32 µg m-3 in PM2.5) are 3 times higher than those at the
other sites, followed by Beijing (16.7; 5.2 µg m-3), Krakow (9; 4.3 µg m-3), and London
(1.9; 0.9 µg m-3; see Fig. S2a for average
value statistics). Although the measurement periods do not overlap, they
were all performed during the colder months of the year (partially true for
Krakow; see Sect. 2.1), and characteristic features of each site are
evident. For the four sites, PM10el diurnal cycles and PM10el and PM2.5el time series are shown in Figs. 4 and S2b, respectively.
The total PM10el and PM2.5el concentrations in Delhi show a strong
diurnal cycle, with high concentrations overnight and in the early morning
hours, followed by a sharp decrease during the day (Figs. 4 and S2b). In
contrast, Beijing experiences multi-day haze events, with only minor diurnal
cycling (Fig. S3). In Krakow and London, concentrations are mostly elevated
during rush hour and during daytime in general (from 08:00 until 18:00
local time (LT)).
(a) Averaged elemental concentrations and (b) fractions (%) of
elements in both size ranges at all four sites: Delhi (D), Beijing (B),
Krakow (K), and London (L). (c) Enrichment factors (using Ti as reference) of
the measured elements in PM10 (EF ∼ 10; solid line). (d)
Averaged elemental concentrations in PM10 normalized by those at
Krakow. Note that Rb, As, and Se are not included in (a) and (b) because of
absence in the London dataset, while all three are considered in (c) and (d)
for the comparison between the rest of the sites.
At all four sites, Si, Cl, Fe, S, Ca, and K account for > 95 % of PM10 (> 88 % without K) and > 94 % of PM2.5 (see Fig. 1b; Tables S2 and S3). Among elements with higher atomic numbers (Z=29–82), Zn and Pb are highest at all sites except
London (where Zn and Cu show the highest concentrations). Figure 1d presents
the mean PM10el concentrations normalized to those in Krakow. With rare
exceptions, element concentrations were highest in Delhi followed by
Beijing, Krakow, and London. The concentrations of toxic PM10el (Cr, Ni, Fe, Cu, Zn, As and Pb) in Delhi are higher than at any other site, such
as Cr (2 to 9 times), Ni (2 to 8 times), Mn (1 to 16 times), Cu (4 to 13
times), Zn (5 to 95 times), and Pb (12 to 205 times). However, the mean
concentrations of carcinogenic elements (Pb, Ni, As, and Cr; IARC, 2020)
fall below the US EPA-recommended inhalation reference concentrations (RfCs)
for resident air (200, 20, 15, and 100 ng m-3, respectively; USEPA, 2020), except for Pb in Delhi,
which exceeds the RfCs by more than a factor of 2. Individual exceedances of
the RfCs are relatively common in Delhi for Pb (52.8 % of data) and As
(34 %), indicating severe risks to human health. At other sites, RfC
exceedances are less common, comprising only 10 % of As data in Beijing,
and 1.76 % of Cr and 1.4 % of Ni in Krakow; no other RfC exceedances are observed.
Characteristic element groups
To evaluate the similarities and differences in element behavior across
sites, we investigate the PM10 EF for each element, where EFs
≫ 1 indicate strong anthropogenic influence, and
their corresponding PM2.5 to PM10 ratios. The mass ratio
PM2.5/PM10 for an element gives a rough indication of the particle
size distribution that reflects the corresponding emission processes and can
provide insight into specific sources. For example, abrasion processes
(e.g., mineral dust resuspension and brake/tire wear) result in coarse
particles, whereas combustion and industrial processes are more likely to
emit fine particles.
Figure 2 shows the PM10 EFs as a function of PM2.5/PM10 for
all elements measured at Delhi, Beijing, and Krakow (see Fig. S5 for
London). Each site is shown separately in Fig. 2 and overlaid in Fig. S5.
PM10 EFs for all sites and PM2.5/PM10 for Delhi, Beijing, and
Krakow are shown in Figs. 1c and 3 (see Fig. S4 for London together with
other sites), respectively. In general, EFs increase with increasing
PM2.5/PM10. From Fig. 2, we divide the measured elements into five
groups based on their position in the EF vs. PM2.5/PM10 space;
this framework provides insight into element sources and emission
characteristics. The classification for London is uncertain due to the lower
cutoff issue mentioned in Sect. 2.2, but some qualitative agreement with
the other sites is evident, with the largest differences related to the
PM2.5/PM10 ratio. Therefore, London is included in the group
classification below, although the data are shown in the Supplement for ease
of viewing. Figure 4 compares the PM10 diurnal cycles of representative
elements from the five groups for all four sites normalized to the mean
element concentration, while Fig. 5 compares the absolute concentrations of
PM2.5 and coarse diurnals for the same elements on a site-by-site basis
for Delhi, Beijing, and Krakow (see Fig. S8 for London). Diurnals of other
elements are shown in Figs. S6 and S7. The groups are discussed below.
Classification of the measured elements in five groups for Delhi,
Beijing, and Krakow based on their PM10 enrichment factor (EF) vs. PM2.5/PM10 values. PM10 EF vs. PM2.5/PM10 values and
PM2.5 EF vs. PM2.5/PM10 values for all four sites are shown in the Supplement (Fig. S5).
Box-and-whisker plots of the measured elemental PM2.5/PM10 ratios at Delhi, Beijing, and Krakow (see Fig. S4 for all four sites).
Box: first to third quartile range; -: median line; +: mean; whiskers:
10 %–90 % percentiles.
Diurnal patterns (means) of selected elements representative of
each group (G1: Group 1, G2: Group 2, G3: Group 3, G4: Group 4, G5: Group 5)
in PM10 normalized by the mean values of the elements in PM10 and
the total elemental PM10 (in µg m-3, bottom) at all
sites. Note that due to the time resolution of the original data, the London
data are 2 h averages, while the other data are 1 h averages.
Diurnal variations of elements representative of each group (G1:
Group 1, G2: Group 2, G3: Group 3, G4: Group 4, G5: Group 5) in PM2.5
and coarse size fractions (PM10-PM2.5) at Delhi, Beijing, and
Krakow (see Fig. S8 for London).
Group 1 consists of elements with the lowest EFs and the highest
fraction of coarse particles. It includes Ca, Si, and Ti at all three sites,
Sr at Delhi and Beijing, Fe in Delhi, and Zr in Beijing. Elements associated
with this group are typically of crustal origin, consistent with their
position in Fig. 2. In contrast, Zr and Fe have been linked to both brake
wear and mineral dust in urban environments (Moreno et al., 2013; Visser et
al., 2015b).
Si is selected as the Group 1 representative element. A strong traffic
influence (i.e., rush-hour peaks) on PM10 is evident at London, Krakow,
and Delhi, while a much flatter diurnal pattern with only small rush-hour
effects is evident in Beijing (Fig. 4). PM2.5 concentrations are very
low and in general not significant relative to PM10 (Fig. 5). These
diurnal patterns are consistent with vehicle-induced resuspension of the
dust deposited on the road surface, which in turn derives mostly from road
abrasion, vehicle abrasion, and airborne dust from construction activities or
agricultural soil (Thorpe and Harrison, 2008 and references therein).
Group 2 elements have low EFs but mean PM2.5/PM10 between 0.22 and 0.43. The increased PM2.5/PM10 value also corresponds to increased temporal variation in PM2.5/PM10, as shown by the larger interquartile range in Fig. 3. Group 2 includes Ba, Ni, and Mn at all three sites, Rb, Cr, Fe, and Zr at two sites, and Sr at a single site (Fig. 2). Several of these elements are associated with multiple sources, including coarse traffic emissions such as brake wear (e.g., Ni, Mn, Fe, Ba, and Zr; Bukowiecki et al., 2010; Srimuruganandam and Nagendra, 2012;
Visser, et al., 2015a) and other anthropogenic sources such as industrial
emissions or oil burning (Ni) or crustal material (Fe and Zr).
Because of these multiple sources, several Group 2 elements show significant
site-to-site variation, despite remaining in or near the group boundaries.
For example, Fig. 3 shows that Ni has a similar lower quartile for
PM2.5/PM10 across all sites, while the upper quartile is much
higher at Krakow. This is likely due to the strong influence of local
steel and nonferrous metallurgical industries (Samek et al., 2017a, b), whereas the other sites are more strongly influenced by
non-exhaust emissions and dust (Grigoratos and Martini, 2015; Pant and
Harrison, 2012; Yu, 2013). Such differences are also evident in the Ni
diurnals and time series (Figs. S6, S7, and S9), as Ni concentrations in
Krakow are driven by strong isolated plumes.
As an example of a typical Group 2 element, the diurnal patterns of Ba are
shown in Figs. 4, 5, and S8. Similar to Group 1, significant rush-hour peaks
are evident, although the trend is now also reflected in PM2.5. In the
Asian cities, high concentrations are also observed overnight. This is
likely related to heavy-duty vehicular activities, which in these cities
occur predominantly at night due to their ban during peak traffic hours
(07:30–11:00 and 17:00–22:00 LT and less dominant during daytime)
in Delhi (Rai et al., 2020) and the entire day in Beijing (Zheng et al.,
2015). As both non-exhaust traffic emissions (i.e., brake wear and dust
resuspension) are related to traffic activity, the time series of most
elements in Groups 1 and 2 are relatively well correlated, although not as
tightly as the Group 1 elements are among themselves due to their common
source. This is illustrated in the correlation matrices shown in Fig. S10,
where elements are sorted by group along each axis. Group 2 elements are
also relatively well correlated among themselves at all sites, with the
exception of Ni at Krakow for the reasons discussed above.
Group 3 includes K at all three sites and adds Rb at Krakow (Fig. 2). These elements show low EFs and high PM2.5/PM10, although
uncertainties are high for Rb at Krakow given that 86 % and 65 % data
points in PM2.5 and PM10, respectively, are below the MDL. Although
coarse-mode K can result from sea/road salt (Gupta et al., 2012; Zhao et
al., 2015) and mineral/road dust (Rahman et al., 2011; Rogula-Kozłowska,
2016; Viana et al., 2008), the high fraction of K observed in the fine mode
suggests solid fuel (coal and wood) burning as a larger source (Cheng et
al., 2015; Pant and Harrison, 2012; Rogula-Kozłowska, et al., 2012;
Rogula-Kozłowska, 2016; Viana et al., 2013; Waked et al., 2014). Further,
Delhi, Beijing, and Krakow are far from the ocean, and de-icing salt was not
used on the roads during the measurement periods. In London and Delhi, K was
attributed to solid fuel combustion via SA studies (Rai et al., 2020; Visser
et al., 2015a). The diurnals in Delhi and Krakow show elevated values in the
evening (Fig. 4), which is likewise consistent with solid fuel combustion
for domestic heating. However, in Beijing only PM2.5 exhibits such a
diurnal variation (Fig. 5), whereas the PM10 fraction is similar to the
other sites without a clear diurnal variation (Fig. 4). This corresponds to
a wider spread of PM2.5/PM10 at Beijing (with the lower quartile
approaching values typical of Group 1), suggesting a larger contribution
from dust.
Group 4 has somewhat higher EFs than Groups 1–3 and moderate
PM2.5/PM10. The group contains Cu at Beijing and Krakow, as well
as Sn at Beijing and Cr at Krakow. No elements are assigned to this group in
Delhi, although Cu is near the border. The EFs of these elements are
≫ 100 in PM2.5 and > 10 in
PM10 (Fig. S5), indicating strong anthropogenic influence. The Group 4
elements are typically emitted from both traffic (characteristic of Group 2)
and industrial or waste combustion sources (Group 5), and their position in
Fig. 2 reflects the combination of these different sources. For example, Cu
derives from brake wear in Europe (Thorpe and Harrison, 2008; Visser et al.,
2015a) and Asia (Iijima et al., 2007), while Cu and Sn are also emitted from
industry or waste burning (Chang et al., 2018; Das et al., 2015; Fomba et
al., 2014; Kumar et al., 2015; Venter et al., 2017). Cr has also been found
in the emissions from both traffic (Hjortenkrans et al., 2007; Thorpe and
Harrison, 2008) and oil burning in Krakow (Samek et al., 2017a).
The diurnal patterns of Cu are shown in Figs. 4, 5, and S8. London, Beijing,
and Krakow all show peaks during the morning and evening rush hours, mainly
due to the PM10 fraction. In Krakow, PM2.5 is approximately
correlated with the coarse fraction, although the morning peak appears
∼ 2 h later, while in Beijing PM2.5 Cu is instead
elevated at night. Delhi contrasts sharply with the other sites, which
probably is the reason why Cu in Delhi is not categorized in Group 4. Figure 3 shows that the PM2.5/PM10 medians and quartiles are similar, but
the mean (0.72 in Delhi and 0.46 in Beijing and Krakow) is substantially
higher in Delhi because the Cu time series (Fig. S11) is subject to a series
of high-intensity PM2.5 plumes from local industries and/or waste
burning. These plumes are tightly correlated with those of Cd, suggesting
emissions from Cd–copper alloy manufacturing plants (Vincent and Passant,
2006), electronic waste burning (Rai et al., 2020; Owoade et al.,
2015), and/or steel metallurgy (Tauler et al., 2009).
Group 5 elements have both the highest EF and highest
PM2.5/PM10 values. Similar to Groups 1–4, Group 5 includes
elements that are directly emitted in the particle phase (elements mainly
present in primary components) but differs by also including elements for
which the major fraction is likely emitted as gases and converted via
atmospheric processing to lower volatility products, which partition to the
particle phase (elements mainly present in secondary components; Seinfeld
and Pandis, 2006). Primary component elements include As, Zn, Se, and Pb
(Liu et al., 2017) at all three sites, Sn at Delhi and Krakow, and Cu in
Delhi, while secondary component elements comprise Cl, Br, and S (Zhang et
al., 2013) at all three sites. Although Cl and Br can in principle relate to
primary emission of sea or road salt, this is unlikely for the sites studied
(except London) due to the large distance from the sea, strong and regular
diurnal patterns inversely related to temperature, and correlation with
elements characteristic of coal combustion and industrial emissions. In
London, a major fraction of Cl was attributed to sea/road salt (Visser et
al., 2015a). Further, Xact S and Cl measurements show a strong correlation
with AMS-derived non-refractory SO42- and Cl-, respectively,
which is nearly insensitive to Cl from sea/road salt Cl (see Sect. 2.2).
The PM2.5/PM10 of these elements is among the highest recorded,
with the partial exception of Cl, which is probably due to fact that
secondary aerosol condensation is driven by surface area rather than volume.
In Delhi, Cl PM2.5/PM10 values are high, consistent with a high
fraction of NH4Cl. However, the interquartile range of Cl
PM2.5/PM10 at Beijing and Krakow is quite wide (0.5 to 0.9), with
the lower values approximately matching those of Zn and Pb and suggesting
that primary emissions of ZnCl2 and PbCl2 are not negligible at
these sites.
The primary component elements of Group 5 are strongly linked to various
industries and combustion of non-wood fuels. Pb was found to be present in
very high concentrations in Delhi with episodic peaks, and possible sources
include industrial emissions (Sahu et al., 2011), waste incineration (Kumar
et al., 2018), and small-scale Pb battery recycling units (Prakash et al., 2017). Additionally, burning of plastic and electronic waste can
contribute to Pb in Delhi. Zn and As are emitted from a variety of sources,
including industries, refuse burning/incineration, and coal combustion, but
Zn is also emitted from traffic and wood burning. In Beijing and Krakow,
coal burning from coal power plants (Samek, 2012; Yu, 2013), domestic
heating, and iron and steel industries (Samek et al., 2018; Yang et al., 2013)
are major sources of Zn, Se, As, and Pb. Cu and Sn also have industrial
sources, as discussed in connection with Group 4.
The set of potential sources discussed above for the primary Group 5
elements is complex and highly site-dependent, which corresponds to the
significant differences between sites evident in the Group 5 correlation
matrices (Fig. S10). In Beijing, Pb, Zn, Cl, Br, Se, and S are all tightly
correlated, consistent with coal burning emissions. Similar correlations are
observed in Krakow, with the exception of Zn and Pb, which are rather
correlated with each other, as well as Mn and Fe. The Zn and Pb time series
in Krakow contain high-intensity plumes (Fig. S12), with a strong peak at
∼ 11:00 LT in PM2.5 (Figs. 4 and 5), suggesting
industrial emissions (Logiewa et al., 2020). The correlation pattern in
Delhi is more complex than at the other sites, with several pairs of tightly
correlated elements (e.g., Br and Cl; Se and S) but few larger groupings.
This suggests plumes from a variety of point sources rather than a
regionally homogeneous composition.
The location-specific influences on primary component elements in Group 5
are also evident in the diurnal patterns. For example, as shown in Fig. 4,
the diurnal pattern of Pb is relatively flat in Beijing with a slight rise
in the evening, peaks at approximately 08:00–10:00 LT in London, peaks at
∼ 11:00 LT with a tail extending into the afternoon in Krakow,
and has a strong diurnal cycle with a massive pre-dawn peak in Delhi.
Site-to-site differences are also evident in the location of the elements
within the Group 5 box in Fig. 2 (and Fig. S5). Systematic shifts are
evident between Beijing (elements clustered to the lower left), Delhi
(elements clustered to the upper right; note that two of the elements in the
lower left are Cu and Zn, which require a significant shift towards the
upper right to even be included in Group 5), and Krakow (intermediate). It
is important to note that the y axes in Figs. 2 and S5 have a logarithmic
scale, while the x axes have a linear scale, which indicates that the
graphical vertical shifts represent higher differences than the same
graphical horizontal shifts. The mean (± standard deviation)
PM10el EFs for Group 5 elements in Delhi, Beijing, and Krakow are 1190 (± 1017), 384 (± 357), and 1021 (± 1425), respectively.
This site-dependent shift contrasts with Groups 1–3, for which no systematic
changes are evident. Interestingly, this appears to be a feature of
industrial emissions rather than anthropogenic emissions more generally, as
it is not evident in the traffic-dominated or biomass-combustion-dominated groups
(Groups 2 and 3).
Discussion and conclusions
The broad intercontinental comparison presented here demonstrates both the
large degree of similarity and crucial local differences in the PMel
concentration and composition in European and Asian cities. The combination
of PM10el EF and PM2.5/PM10 provides a robust and useful
framework for categorizing elements and assessing site-to-site differences.
Five groups are identified based on these metrics (see Fig. 2), with Groups 1–3 having low EFs with increasing PM2.5/PM10 and Groups 4–5 having
high EFs with increasing PM2.5/PM10. Broadly, Group 1 is related to crustal materials and road dust, Group 2 to non-exhaust traffic emissions,
Group 3 to biomass combustion, Group 4 to mixed industrial/traffic
emissions, and Group 5 to industrial emissions and coal/waste burning. On an
element-by-element basis, the group composition remains relatively
consistent across sites, although some reassignment of elements occurs
depending on local sources and conditions. Interestingly, we observe
systematic shifts within the EF vs. PM2.5/PM10 space only for
Group 5 (and perhaps in the sparsely populated Group 4) but not in Groups 2
or 3, despite these groups also being dominated by anthropogenic sources.
However, the consistent classification of elements into a particular group
regardless of site does not imply that the temporal behavior of these
elements is independent of local conditions or policies. For example, the
stagnant meteorological conditions frequently encountered in Beijing during
the colder season suppress diurnal variation regardless of element source,
while the multitude of strongly emitting point sources yielding individual
plumes in Delhi, coupled with rapid dilution as the boundary layer rises,
leads to systematic, intense pre-sunrise peaks in concentration but with a
composition that strongly varies on a day-to-day basis. The effects of air
quality policy are also evident, as the night / day concentration ratios of
resuspension-related elements (crustal material, road dust, and non-exhaust
traffic emissions) are significantly higher in Delhi and Beijing than in
Krakow and London, due to time restrictions on heavy-duty truck activity in
the Asian cities.
The diurnal patterns of the total PM10el concentrations (Fig. 4)
reflect many of the trends discussed above. Meteorological conditions yield
a relatively flat diurnal pattern for Beijing, while concentrations are
highest overnight and in the early morning (before rush hour) in Delhi due
to the combined effects of industrial emissions, burning of various solid
fuels, and a shallow boundary layer. Krakow and London instead have their
highest PM10el concentrations during the day, but features related to rush hour are more visible in Krakow, whereas the London diurnals are
similar to that of resuspended dust (Visser et al., 2015a). This may reflect
differences in the fleet composition, specifically a higher fraction of
older vehicles and vehicles with faulty catalytic converters or diesel
particulate filters in Krakow (Majewski et al., 2018).
The global similarities and local differences discussed above should be
considered in air quality policy formulation. Current practices focus mainly
on total PM mass reduction, neglecting its toxicity. As an example, the
carcinogenic elements represent a specific health concern. These elements
are not assigned to a single group by the EF vs. PM2.5/PM10 values, and
the group(s) to which they are assigned do not necessarily correlate with
total PM10el. While such policies may have significant ancillary
benefits, they may not efficiently address the most critical health risks.
In addition, the inhalability of potential toxins needs consideration; Pb
and As (which are more industry-related) have PM2.5/PM10 values
that are up to 3 times higher than those of Ni and Cr (which are more
traffic-related). If size dependence is not considered, inefficient or
ineffective regulatory priorities may result. Finally, this study
demonstrates that regulatory policy can affect not only overall
concentrations but also the timing of daily maxima (e.g., truck activity
restrictions in Delhi and Beijing). The above considerations highlight the
importance of time- and size-resolved measurements for policy formulation,
as well as the need to integrate these with daily human activities. Although
the method proposed in this work allows for a comparison of the
characteristics in different cities, a full SA analysis is necessary if more
quantitative information (e.g., source contributions) is desired.
Data availability
Data related to this article are available at
10.5281/zenodo.4311854 (Rai and Furger, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-717-2021-supplement.
Author contributions
PR and JGS wrote the paper with input from all co-authors. PR, MF, DB, YT,
VK, AKT, LW, SV, AS, and JN designed the study. GW designed ISS in Xact. YT
and ASHP analyzed AMS data. PR analyzed Xact data. SV, MF, and JGS provided
offline data for London. ASHP, JGS, MF, IEH, and UB were involved with the
supervision. ASHP, JGS, MF, and UB assisted in the interpretation of the
results.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We are grateful to Jamie Berg,
Krag Petterson, and Varun Yadav of Cooper Environmental Services for
instrument troubleshooting during field campaigns. We thank René Richter
of PSI for his tremendous support with building the Xact housing and inlet
switching system.
Financial support
This research has been supported by the Swiss National Science Foundation (grant nos. 200021_162448, 200021_169787, 200020_188624, BSSGI0_155846, and IZLCZ2_169986), the Swiss Federal Office for the Environment (FOEN), the National Science Foundation of China (grant no. 21661132005), the SDC Clean Air China Programme (grant no. 7F-09802.01.03), the SDC Clean Air Project in India (grant no. 7F-10093.01.04), the Department of Biotechnology (DBT), Government of India (grant no. BT/IN/UK/APHH/41/KB/2016-17), and the Central
Pollution Control Board (CPCB), Government of India (AQM/Source apportionment EPC Project/2017).
Review statement
This paper was edited by Willy Maenhaut and reviewed by three anonymous referees.
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