Diel variation in PM2.5-Hg
The chronological sequence of Hg stable isotope ratios, along with weather
conditions for the 56 PM2.5 samples, are presented in Fig. 2 (see also
Tables S1 and S2 for quantitative atmospheric data and Δ201Hg
values). The major features of this dataset include (i) large variations in
both MDF and odd-MIF of Hg isotopes, (ii) significant diel differences in Hg
isotope ratios, (iii) correlations of weather conditions and air mass
backward trajectories with Hg isotope signals, and (iv) detectable even-MIF.
Chronological sequence of MIF (Δ199Hg and Δ200Hg)
and MDF (δ202Hg) of the 56 samples collected
during daytime (D, red) and nighttime (N, blue), along with selected
weather data including cumulative hours of sunshine (Solar) and air mass
backward-trajectory directions.
The volumetric PM2.5 concentrations ranged from 4 to 158 µg m-3
with an average value of 52±40 µg m-3 (1 SD, n=61) and the highest values (> 100 µg m-3) were detected
during a severe haze event of 4–7 October. The mass-based Hg
contents ranged from 0.08 to 1.22 µg g-1 with a mean value of 0.40±0.21 µg g-1 (1 SD, n=61).
Hg isotope analysis showed that δ202Hg values varied from
-1.49 ‰ to 0.55 ‰ (mean =-0.53‰±0.40 ‰, 1 SD, n=56), with the lowest value found in sample Oct-2-N and the highest in
Oct-8-N. Significant odd-MIF of Hg isotopes was found and the
Δ199Hg values ranged from -0.53 ‰ to
1.04 ‰ (mean = 0.14‰±0.33 ‰) with the lowest (-0.53 ‰)
Δ199Hg value on Oct-5-D during the severe haze event and the
highest (1.04 ‰) Δ199Hg value on Sept-26-D
during a sunny day (without cloud) (Fig. 2). All samples also displayed
slight even-MIF, with Δ200Hg values ranging from
-0.02 ‰ to 0.21 ‰ (average
0.07‰±0.06‰, 1 SD, n=56),
which were significant compared to the detection precision of ±0.04 ‰. The overall variations in Hg isotope ratios for
these 12 h D / N PM2.5 samples are generally consistent with several
prior reports for the 24 h PBM samples (Rolison et al., 2013; Das et al.,
2016; Huang et al., 2016).
The t test results (Table S3) showed that diel variation was statistically
significant (p<0.05) for Hg contents, Δ199Hg, and
Δ200Hg values, as their p values are 0.005, 0.000 and 0.004
according to paired-sample t tests and are 0.003, 0.017 and 0.019
according to independent-sample t tests. For all samples, Hg contents for
D samples (0.32±0.14 µg g-1) were lower than those for N samples
(0.48±0.24 µg g-1), and Δ199Hg and
Δ200Hg values for D samples (mean of 0.26‰±0.40‰ and 0.09‰±0.06‰, respectively) were higher than those for N samples
(-0.04‰±0.22‰ and
0.06‰±0.05‰, respectively).
However, PM2.5 concentrations and δ202Hg had statistically
insignificant (p>0.05) diel variation, as their p values are
0.887 and 0.052 according to paired-sample t tests and are 0.909 and 0.053
according to independent-sample t tests.
Diel variation in odd-MIF of PM2.5-Hg independent of air mass
source
Many consecutive D–N sampling intervals had similar air mass backward trajectories (Table S1 and Fig. S1), suggesting that the dominant sources of
PM2.5-Hg did not vary over each such 24 h sampling period. For example,
pairs Sept-16-D and Sept-16-N, Sept-17-D and Sept-17-N, Sept-20-D and
Sept-20-N, Sept-21-D and Sept-21-N, Oct-1-D and Oct-1-N, Oct-2-D and Oct-2-N,
and Oct-4-D and Oct-4-N have similar air mass trajectories from the southwest,
and pairs Oct-8-D and Oct-8-N, Oct-9-D and Oct-9-N, Oct-10-D and Oct-10-N,
Oct-11-D and Oct-11-N, and Oct-12-D and Oct-12-N have similar air mass
trajectories from the northwest and north (Fig. S1). It is reasonable to
assume, therefore, that each of these D–N PM2.5 sample pairs had
identical dominant sources of PM2.5-Hg and to expect that they would
have very similar Hg isotope compositions. Instead, however, the data
presented in Table S2 and Fig. 2 revealed a unique and consistent pattern
of diel variation in Hg isotope ratios; specifically, each PM2.5
D sample had a statistically significantly higher positive
Δ199Hg value (up to +1.04‰) than its
consecutive PM2.5 N sample.
The more positive Δ199Hg values measured for the PM2.5 D
samples are highly unlikely to be uncharacteristic of known emission sources
of PM2.5-Hg. It is possible that PM2.5-Hg from different
emission sources may have different Hg isotope compositions. However, prior
studies showed that Δ199Hg values of the PBM from dominant
anthropogenic emission sources are generally negative or close to zero.
Schleicher et al. (2015) demonstrated that coal combustion is likely the
major source of PM2.5-Hg in Beijing. Huang et al. (2016) reported that
regional anthropogenic activities such as coal combustion
(Δ199Hg values from -0.30 ‰ to 0.05 ‰),
metal smelting (-0.20 ‰ to -0.05 ‰ ) and cement
production (-0.25 ‰ to 0.05 ‰), as well as biomass
burning (low to -0.53 ‰), were likely the dominant sources of
PM2.5-Hg at this study site. As shown in Fig. 2 and Table S1, the
PM2.5 D samples with high Δ199Hg values
(> 0.60 ‰) each had very different air mass backward
trajectories (Fig. S1). For instance, Sept-18-D (with Δ199Hg
value +0.90 ‰), Sept-26-D (with Δ199Hg value
+1.04 ‰) and Oct-3-D (with Δ199Hg value
+0.86 ‰) were associated with north, southwest and north–south
mixed air masses, respectively. A reasonable explanation of these
observations is that high positive Δ199Hg values measured for
D samples resulted from PM2.5-Hg transformation, specifically
photoreduction, during atmospheric transport. Indeed, the diel variation in
Δ199Hg for PM2.5 D–N sample pairs may well reflect
strong (D) versus less or no (N) influences of photochemical reactions under
time-variant local and regional weather conditions.
Photochemical reduction as a cause of odd-MIF in subset of daytime
PM2.5 samples
To detail the effects of photochemical reactions on the variation in Hg
isotope ratios for PM2.5-Hg, we regrouped our dataset into subsets
corresponding to day and night. We further regrouped our results into two
source-related subsets, southeast (S-E) and northwest (N-W), according to
the air mass backward trajectories during each sampling event (Fig. 1), and
two other subsets corresponding to sunny days within the S-E group (sunS-E)
and all sunny days (Sun), which includes sunS-E and N-W as N-W consisted
entirely of sunny days. The N-W subset of PM2.5 was associated with an
air mass that tracked from the north, northeast, northwest and west,
which are relatively less polluted areas, and is therefore representative of
long-range transport and relatively constant sources of PM2.5 and Hg
(Huang et al., 2016). The S-E subset was
associated with an air mass that tracked from the south, southwest,
southeast and east, which are heavily polluted and highly populated areas, and was
characterized by relatively high contents of PM2.5, likely from
industrial sources in the region (coal fired power plants, coking and steel
industries). Unlike the N-W arriving air mass which corresponded to all
sunny days during the entire sampling period, the S-E arriving air mass was
associated with a range of weather conditions including hazy, cloudy, rainy
and sunny days. According to our results (Table S2 and Fig. 2), PM2.5
concentrations of the N-W subset (23±19 µg m-3) were
significantly (p<0.05) lower than the S-E subset (69±40 µg m-3),
which is consistent with the fact that the N-W areas of
Beijing were less industrialized, less populated and less polluted than the
S-E areas. However, regardless of whether their associated air masses
originated from moderately or heavily polluted areas, both N-W and S-E
subset samples showed diel variations in their Hg contents and isotope
ratios (see Fig. 3 and discussion below). This, as discussed above,
indicates that air mass source was not a dominant factor producing the diel
variation in Hg isotope ratios in consecutive D–N PM2.5 samples.
Diel variations in Δ199Hg for PM2.5-Hg for the entire
dataset, the N-W subset, the S-E subset, and the S-E sunny days only subset.
Note that all days included in the N-W subset were sunny. Diel differences
within each subset were examined using the independent-sample t tests.
The observed diel difference in Δ199Hg values of PM2.5-Hg is
even more prominent and statistically robust within subsets of PM2.5
samples regrouped according to their air mass trajectories (i.e., PM2.5
source related) and sunny days (with greater extent of photochemical
reactions). As shown in Fig. 3, Δ199Hg values for N-W subset
samples collected during the day had a higher range
(0.04 ‰ to 0.90 ‰) and mean
(0.39‰±0.27‰ SD, n=10) compared to those (-0.07 ‰ to
0.32 ‰, mean = 0.09‰±0.13 ‰ SD, n=9) for N samples (p=0.02). Similarly, analysis of the sunS-E subset revealed a significant
difference in Δ199Hg values (p=0.03) between sunny days
and nights, but not when the entire S-E sample set (p=0.22), which
includes hazy, rainy and cloudy days, was considered. Since the N-W subset
was associated with less polluted areas and the S-E subset was associated
with heavily polluted and highly populated areas, the observation of
significant diel variation in Δ199Hg in PM2.5-Hg within each
subset (Fig. 3) is consistent with the above conclusion that such variation in PM2.5-Hg isotope ratios was not controlled by variation in Hg
emission sources. The highly positive Δ199Hg values observed
for daytime samples within the Sun subset (Fig. S2) further supports the
conclusion that PM2.5-Hg was strongly affected by photochemical
reactions on sunny days.
Linear correlations of Δ199Hg versus Δ201Hg for
all 56 PM2.5 samples (Fig. 4a) and three subsets, N-W (Fig. 4b), S-E
(Fig. 4c) and Sun (Fig. 4d), yielded slopes of 1.06±0.05 (1 SD,
r2=0.89), 1.06±0.12 (r2=0.81), 1.13±0.05
(r2=0.92) and 1.13±0.08 (r2=0.84), respectively
(Fig. 4). Such slopes are all indicative of photochemical reduction of
Hg2+ according to prior studies (Bergquist and Blum, 2007; Zheng and
Hintelmann, 2009). The photoreduction process is further evidenced by a
progressive increase in Δ199Hg from zero or slightly negative
values to positive values as the content of Hg in PM2.5 (CHg)
decreased in D samples (Fig. S1a). This trend is statistically more
significant (p<0.05) for D samples within the N-W and Sun subsets
(Fig. S1b and d). Similarly, for all sunny day samples, a positive
correlation (p<0.05) was also observed between Δ199Hg
and δ202Hg (Fig. S3), consistent with prior experimental
results (Bergquist and Blum, 2007; Zheng and Hintelmann, 2009).
Collectively, the Hg isotope results suggest that photochemical reduction is
an important process during the transport of PM2.5-Hg in the
atmosphere.
Correlations between Δ199Hg and Δ201Hg
for different subsets of PM2.5 samples: (a) all data, (b) northwest
(N-W), (c) southeast (S-E) and (d) all sunny days (Sun). The slope, intercept
and r-square of the line from simple linear regression. Vertical and
horizontal error bars correspond to 2 SD analytical precision.
Among all D samples, Δ199Hg is only weakly correlated with
sunshine duration (r2=0.20, p=0.02). However,
Δ199Hg values for all D samples collected on days with
sunshine durations >8 h are positive whereas half of the
Δ199Hg values for samples collected on cloudy or hazy days
with shorter sunshine durations are negative or near zero (Fig. S4). In
addition, a significant positive linear correlation between
Δ199Hg values and atmospheric ozone contents (PO3)
(r2=0.517, p<0.01) for all but four daytime samples was
obtained (Fig. S5). The four outliers (Sept-16-D, Sept-17-D, Oct-5-D and Oct-6-D) were collected on days with high
ozone (PO3 above 50 ppbv) and severe smog formation. Conversely,
no significant correlation (p>0.05) between
Δ199Hg and PO3 was found for the nighttime samples.
The increase in Δ199Hg of daytime PM2.5-Hg with sunlight
duration and ozone concentration indicates that the physical and
photochemical conditions of the atmosphere may affect the atmospheric
transformation of PM2.5-Hg. A prior experimental study showed that GEM
oxidation can produce negative Δ199Hg values in oxidized
Hg2+ with Δ199Hg/Δ201Hg ratios of 1.6 and 1.9
for Br and Cl radical initiated oxidations (Sun et al., 2016).
We can exclude the possible contribution of Hg0 oxidation to
PM2.5-Hg, given the fact that Δ199Hg/Δ201Hg
ratio was about 1.1 and most PM2.5-Hg samples collected during daytime when Br and Cl radicals could form had positive Δ199Hg
values. Thus it is highly unlikely that oxidation would have caused the diel
variation in Hg isotopes in PM2.5. However, the exception to the
observed relationships between Δ199Hg with sunlight duration
and ozone concentration show that in a highly oxidizing atmosphere (higher
PO3) such as occurs during extreme smog events, the odd-MIF of Hg
isotopes in PM2.5 may decrease or reverse. A possible explanation for
this effect may be the increased production of GOM and its collection with
PM2.5-Hg during such smog events. While PM2.5-Hg samples collected
on quartz fiber filters may include some GOM (Lynam and Keeler,
2002), this contribution was likely small in most of our D and N samples due
to the opposing diel trends in the concentrations of PBM and GOM in urban
air (Engle et al., 2010). GOM would therefore not have had
a major effect on the observed diel variations in Δ199Hg values
for PM2.5-Hg and may have in fact masked an even larger MIF signature
due to the photoreduction of PBM during the day.
Interestingly, negative Δ199Hg values in daytime PM2.5-Hg were
only observed during a rainy day and an extreme smog event. Since the Hg
emitted from local sources had close to zero and negative values of odd-MIF,
higher humidity (such as during rainy days) and heavy pollution (the extreme
smog) may enhance the effect of scavenging of locally produced gaseous or
particulate Hg during rain or smog events, which may therefore have
contributed to the reversal of the odd-MIF signature of Hg collected as
PM2.5 at these times. In addition, the negative Δ199Hg values
in PM2.5 may have resulted from the contribution of biomass burning with
limited photoreduction effect during periods of less sunshine (Fig. 2 and
Table S1) since plant foliage has negative Δ199Hg values
(Yu et al., 2016) and more negative Δ199Hg values
(down to -0.53 ‰) of PM2.5-Hg in Beijing were
related to biomass burning, a source of PM2.5-Hg south of Beijing in
autumn (Huang et al., 2016). This could further
explain the relatively lower Δ199Hg values in the majority of the
N samples (for example, Sept-28-N and Oct-5-N with Δ199Hg of
-0.46 ‰ and -0.51 ‰), even in
those collected under clear weather conditions. Indeed, each bulk sample
collected during night time was a mixture of the leftover PM2.5 (with
positive odd-MIF) from the previous daytime and the new PM2.5 input
from various sources including industrial emissions (with close to zero
Δ199Hg) and biomass burning (somewhat negative Δ199Hg)
(Huang et al., 2016) during nighttime.
A possible explanation of the observed effects of diel variation in
PM2.5-Hg would be the temperature-dependent gas–aerosol partitioning of
GOM (Rutter and Schauer, 2007; Amos et al., 2012), which favors more
adsorption of GOM on PM during nighttime when atmospheric temperature us
relatively lower than daytime. However, the magnitude of such adsorption is
also proportional to the GOM concentration in the atmosphere. An inverse
calculation exercise (in the Supplement) shows that the higher PM2.5-Hg
measured for our samples would require higher GOM concentrations during
nighttime, which contradicts prior findings that GOM concentrations are
significantly lower during nighttime than daytime as GOM is a product of
photo-oxidation processes (Poissant et al., 2005; Liu et al., 2007; Amos et
al., 2012). In addition, GOM gas–aerosol partitioning is considered a
chemisorption and desorption process (Rutter and Schauer, 2007), which is
unlikely to result in appreciable odd-MIF of Hg isotopes (Jiskra et al., 2012; Smith et al., 2015). Therefore, GOM partitioning would
have little or no effect on the observed diel variations in
Δ199Hg values for PM2.5-Hg.
Variation in atmosphere boundary layer height (ABLH) from 1000 to 1300 m
during daytime to less than 200 to 300 m during nighttime may have
contributed to the diel variation in Hg isotopic composition of
PM2.5-Hg (Quan et al., 2013). With a high ABLH
during daytime, relatively strong turbulence may help in mixing the
PM2.5-Hg from the surface to the upper free troposphere, where
photoreactions may be favored due to higher intensities of ultraviolet
radiation on clear days. In contrast, a lower ABLH at night may weaken the
vertical transport of PM2.5-Hg, but enhance the contribution from newly
produced PM2.5-Hg, possibly resulting in higher concentrations of
PM2.5-Hg with negative or close to zero Δ199Hg values from
emission sources and/or GOM. However, vertically resolved, day–night
measurements of Hg stable isotope ratios in PBM and GOM are needed to fully
evaluate the effects of various physical processes on diel variation in the
Hg isotopic compositions for the PM2.5.
While our results cannot exclude the effects of other possible processes,
such as oxidation, adsorption (and desorption) or gas–aerosol partitioning, and
precipitation, based on the limited previous studies (Jiskra et al., 2012;
Smith et al., 2015; Sun et al., 2016), these processes are not likely to be
important to the diel variation in odd-MIF of Hg isotopes in PM2.5-Hg we
observed.
Hg isotope ratios and contents in four subgroups of consecutive
pairs of day–night samples collected during periods of relatively constant
atmospheric conditions. Linear correlations between Δ199Hg and
δ202Hg (c), CHg (e) and the total cumulative daily solar
radiation on a horizontal surface (SH, MJ m-2) (f), and between CHg
and SH (g) were displayed. The uncertainty values for measurement of
Δ199Hg and δ202Hg of PM2.5 samples were
0.06 ‰ and 0.12 ‰ in 2 SD, respectively.