Iron (Fe) in aerosol particles is a major external source
of micronutrients for marine ecosystems and poses a potential threat to
human health. To understand the impacts of aerosol Fe, it is essential to
quantify the sources of dissolved Fe and total Fe. In this study, we applied receptor modeling for the first time to apportion the sources of
dissolved Fe and total Fe in fine particles collected under five different
weather conditions in the Hangzhou megacity of Eastern China, which is upwind of the East Asian outflow. Results showed that Fe solubility (dissolved Fe to total Fe) was the largest on fog days (6.7
The deposition of atmospheric aerosols is a major external source of iron
(Fe) in the ocean (Li et al., 2017; Pinedo-González et al., 2020; Yang
et al., 2020). Fe is an essential micronutrient that can impact
phytoplankton primary productivity, thereby modulating marine ecosystems,
global carbon cycling, and climate (Jickells et al., 2005; Tagliabue et al.,
2017; Matsui et al., 2018; Lei et al., 2018). In addition, atmospheric
Fe-containing particles have an adverse effect on human health, by generating
reactive oxygen species (ROS; Abbaspour et al., 2014), and can convert
S(IV) to S(VI) by catalytic oxidation for atmospheric sulfate
(SO
There are two major processes that can significantly increase Fe solubility in atmospheric aerosols, including aerosol primary emissions and atmospheric acidification processes (Shi et al., 2012). Dissolved Fe can be derived from natural and anthropogenic sources, such as mineral dust, fossil fuel combustion, biomass burning, and traffic exhaust (Chen et al., 2012; Pant et al., 2015; Conway et al., 2019; Rathod et al., 2020; Ito et al., 2020). Although natural emissions have a high emission flux, their contribution to Fe solubility is less than 1 % (Schroth et al., 2009). Recent studies have highlighted anthropogenic sources due to their high contribution to Fe solubility. For example, Schroth et al. (2009) suggested that Fe solubility was less than 1 % of the iron in arid soils, while oil combustion emissions had a pronounced effect on Fe solubility (77 %–81 %); Oakes et al. (2012) studied Fe solubility in anthropogenic source emission samples and found that Fe solubility was 0.06 % in coal fly, 46 % in biomass burning, 51 % in diesel exhaust, and 75 % in gasoline exhaust. These results imply that an increase in relative amounts of aerosols from these mixed anthropogenic sources may be responsible for the increase in Fe solubility.
There are a number of atmospheric processes which can affect Fe solubility
in atmospheric aerosol particles. One of the most important processes is the
mobilization of Fe in an acidic solution on the surface of aerosol particles
because acidic pH can trigger faster Fe dissolution and increase Fe
solubility (Shi et al., 2011; Maters et al., 2017; Li et al., 2017; Zhou et
al., 2020). When ambient relative humidity (RH) is above 60 %, aerosol particles can take up
water and change the surface to a wet or liquid state (with liquid–liquid
separation or a homogenous state, depending on the composition and RH; Sun et al., 2018; Liu et al., 2017). The wet or liquid surface can take up acid gases (such as SO
The two major contributors mentioned above (aerosol primary sources and atmospheric acidification processes) to Fe solubility are associated with weather conditions, which can change the dispersion efficiency (such as boundary layer height, wind, and convection), dry/wet deposition and the chemical conversion loss rate (Leibensperger et al., 2008; Zhang et al., 2018), temperature, relative humidity, and solar radiation (Camalier et al., 2007). Recently, Shi et al. (2020) found that different levels of Fe solubility are closely related with different weather conditions in one coastal city. However, to our knowledge, studies that have attempted to investigate Fe solubility under different weather conditions in the megacity are still sparse in the world, even though the sources of aerosol Fe (such as coal combustion, vehicle emissions, and industry emissions) are densely distributed in megacities (Q. Zhang et al., 2019). Therefore, to better understand how aerosol primary sources and atmospheric acidification processing influence Fe solubility in the megacity, the planned studies should be conducted under different weather conditions.
In this study, we collected atmospheric fine particles (PM
The sampling site was located on the Zijingang Campus of the Zhejiang University in Hangzhou (120
PM
A Th-16a intelligent sampler (Wuhan Tianhong Environmental Protection Industry Co., Ltd., China) with a flow rate of 100 L min
Individual particle samples were collected four times, at 08:00, 12:00, 18:00
and 00:00 LT, on sampling days, except for rain days. The sampler is a single-stage cascade impactor with a 0.5 mm diameter jet nozzle and a flow rate of 1.0 L min
Element concentrations were determined by an energy dispersive X-ray
fluorescence (EDXRF) spectrometer (Epsilon 4; Malvern Panalytical Ltd). In this method, element concentrations on a given elemental map were measured. The measured values firstly divided by the elemental map area, then multiplied by the total sample area to obtain element concentrations of the sample. Because quartz filter contains a large amount of silicon (Si), the Si measured by EDXRF is not used in this study. Elements including Na, Mg, Al, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Sr, Ba, and Pb were measured. The National Institute of Standards and Technology (NIST) standard was used as reference material for standardizing the instrument. The analysis values of NIST standard are given in Table S3, showing that the relative errors between the measured and standard value for the standard samples were less than 10 %. The average element concentrations of field blank samples (
Chemical analysis of the dissolved Fe was conducted using the ferrozine
technique described by Viollier et al. (2000). Sample extraction and
analysis were on the basis of Majestic et al. (2006) and Oakes et al. (2012). We conducted the analysis as follows: (1) half of the sample filters
were placed in clean tubes with 20 mL ammonium acetate (0.5 mM; pH
Individual particle samples were analyzed with a JEM-2100 (JEOL Ltd.) transmission electron microscope (TEM) operated at 200 kV. Elemental composition was semi-quantitatively determined by an energy-dispersive X-ray spectrometer (EDS) that can detect elements heavier than carbon (C). Copper (Cu) was excluded from the analyses because the TEM grids are made of Cu. The relative percentages of the elements were estimated based on the EDS spectra acquired through the INCA software (Oxford Instruments, Oxfordshire, UK). The distribution of aerosol particles on TEM grids was not homogeneous; coarser particles occur near the center, and finer particles are on the periphery. Therefore, to be more representative, four areas were chosen from the center to the periphery of the sampling spot on each grid. The projected areas of individual particles were determined using iTEM software (Olympus Soft Imaging Solutions GmbH, Germany), which is the standard image analysis platform for electron microscopy.
The concentrations of water-soluble inorganic ions, including Na
A thermodynamic equilibrium model (E-AIM model II; Clegg et al., 1998) was
used to calculate the aerosol acidity (in situ acidity) and liquid water
content (available at
The U.S. Environmental Protection Agency (U.S. EPA) PMF 5.0 model was used to identify sources of dissolved Fe and total Fe. A detailed description about PMF 5.0 is given in the user manual (Norris et al., 2014). There are two input files required to initiate the model, where one contains the concentration values and one contains the uncertainty values for each species. Uncertainty was determined as follows (Polissar et al., 1998):
The average PM
The average concentrations of total Fe and dissolved Fe were 777.6
The box-and-whisker plot of the concentrations of total Fe
Percentage contributions of total Fe and dissolved Fe concentrations
to PM
Fe solubility in aerosols was calculated as dissolved Fe
In order to identify the sources of dissolved Fe and total Fe, a PMF model was used to apportion their sources. PMF was run for 5 (Fig. S2), 6 (Fig. 2),
and 7 (Fig. S3) factors for the evaluation of factor profiles. In Fig. S2,
factor 1 of the five-factor solution is represented by high contributions of
secondary inorganic ions (SO
Factor profiles deduced from the PMF model analysis.
As shown in Fig. 2, factor 1 was identified as dust, with relatively high
loads of undissolved Fe, K, Ca, and Ti (Marsden et al., 2019). Factor 2 was
identified as a source of combustion, considering its high loading of EC (Hou
et al., 2012). Since there was no contribution of SO
Contributions of identified sources to dissolved Fe, total
Fe, and PM
As shown in Fig. 3, traffic emissions contributed 10.6 %, 5.8 %, 18.9 %, and 13.8 % to dissolved Fe and 12.7 %, 7.4 %, 8.1 %, and 17.9 % to total Fe on haze, fog, dust, and clear days, respectively. Although Fe solubility is as high as 51 % in diesel exhaust and 75 % in gasoline exhaust (Oakes et al., 2012), total Fe content from engine exhaust particles is extremely low. It is more than likely that traffic emissions associated with non-exhaust particles have relatively low Fe solubility. Since traffic emissions are urban sources, which are closer to the sampling site, there is less time for them to be chemically processed in the atmosphere. These results may explain why the contribution of traffic emissions to dissolved Fe is relatively low.
Figure 3 also shows that, although industrial emissions (factors 5 and 6 or
industrial emissions 1 and industrial emissions 2) contributed less than
20 % to PM
The likely reason for the high contribution of industrial emissions 2 and the relatively low contribution of secondary sources to dissolved Fe is that PMF is unable to completely separate secondary sources of dissolved Fe (i.e., dissolved from insoluble Fe due to atmospheric processing) from primary sources. This means that some of the dissolved Fe due to atmospheric processing may still be assigned to its primary factors if there is a strong co-variation between the dissolved Fe and primary tracers. This suggests that the contribution of secondary sources to dissolved Fe is likely higher than that indicated by the PMF. It should also be noted that industrial emissions are outside the city, and thus, particles from these sources undergo long-range transport before reaching the sampling site. This provides more time for chemical processing in the atmosphere, leading to Fe solubilization. In the following, we further investigated the mixing of acidic species and Fe aerosols to provide further evidence for Fe solubilization from primary insoluble Fe aerosols.
A number of studies have considered atmospheric acidification processing as being a key factor influencing Fe solubility, in addition to direct emission of dissolved Fe from primary sources (Ito and Shi, 2016; Li et al., 2017; G. Zhang et al., 2019; Shi et al., 2020; Zhu et al., 2020; Liu et al., 2021a). As mentioned above, a proportion of dissolved Fe was associated with a PMF factor identified as secondary sources during haze, fog, dust, and clear days, thereby suggesting a contribution from atmospheric processing. To further support this result, a total of 688, 404, 580, and 311 individual particles on haze, fog, dust, and clear days were analyzed by TEM/EDS, respectively. On rain days, individual particle samples were not collected. TEM analysis showed two types of Fe-containing particles, i.e., Fe-rich and S-Fe particles. Figure 4 shows that Fe-rich particles usually contain aggregates of multiple spherical Fe particles. TEM/EDS also detected minor Fe, besides major elements (S, C, and O), in acidic secondary aerosols, and these were named S-Fe particles (Fig. 4). This is similar to that reported by Li et al. (2017), who confirmed that such Fe was presented as Fe sulfate from nanoscale secondary ion mass spectrometry (NanoSIMS) observations, indicative of acid dissolution. It should be noted that individual secondary sulfate particles in urban air normally contain nitrate, which has been confirmed in single particle mass spectrometry studies (Whiteaker et al., 2002; Li et al., 2016).
Typical TEM images and corresponding EDS spectra of
Fe-rich and S-Fe particles.
We further calculated the number contribution of S-Fe particles to
Fe-containing particles, with 76.3 % on haze days, 87.1 % on fog days, 78.3 % on dust days, and 81.8 % on clear days. The result suggested that Fe particles were mostly internally mixed with acidic secondary aerosol species. To further investigate the impact of aerosol acidification on Fe solubility, the correlation of aerosol acidity/total Fe with Fe solubility was calculated. Aerosol acidity was estimated by the E-AIM model. As shown in Fig. 5, aerosol acidity/total Fe and Fe solubility show a good correlation on fog (
Correlations between Fe solubility and aerosol acidity or total Fe under different RH.
Size distributions of Fe-rich (blue line) and S-Fe (green
line) particles under haze
Correlations between Fe solubility and liquid water
content on haze
On the other hand, particles with a wet surface can easily take up acidic
gases (such as SO
The average Fe solubility was the largest on fog days (6.7
Maher et al. (2016) and Lu et al. (2020) reported that, when the atmospheric
Fe
Fe-containing particles from the continent can be transported and further deposited to the ocean (Winton et al., 2015; Yoshida et al., 2018; Conway et al., 2019). Li et al. (2017) found large amounts of anthropogenic fine Fe-containing particles in the East China Sea. In this study, the prevailing winds during the sampling period were dominated by the westerly or northwesterly winds under haze, fog, and dust conditions, suggesting that Fe-containing particles were likely transported into the ocean. In the future, biogeochemical cycle model should consider Fe-containing particles from upwind continental areas of the ocean.
The data used in this study are available from the corresponding author upon request (liweijun@zju.edu.cn).
The supplement related to this article is available online at:
YZ, WL, and ZS designed the study. JZ, YZ, LL, and LX collected aerosol and individual particle samples. YZ and YW contributed the laboratory experiments. YZ, WL, ZS, and JX performed the data analysis. YZ and WL wrote the paper and prepared the material, with contributions from all the co-authors. JS, LS, PF, DZ and ZS commented on the paper.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank the Atmospheric Science Practice Center of the School of Earth Sciences, Zhejiang University, for sharing the meteorological data during the sampling period.
This research has been supported by the National Natural Science Foundation of China (grant nos. 41907186 and 42075096), the China Postdoctoral Science Foundation (grant no. 2019M652059), the Natural Science Foundation of Zhejiang Province (grant no. LZ19D050001), and the UK Natural Environment Research Council (grant nos. NE/N007190/1 and NE/R005281/1).
This paper was edited by Timothy Bertram and reviewed by two anonymous referees.