Felix Nieberding 1, Bettina Breuer1, Elisa Braeckevelt1, Otto Klemm1, Qinghai Song2, Yiping Zhang2

Climatology Working Group, University of Münster, 48149 Münster, Germany
Key Lab of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Yunnan 666303, China


Received: January 13, 2017
Revised: November 19, 2017
Accepted: November 27, 2017

Download Citation: ||https://doi.org/10.4209/aaqr.2017.01.0060  

  • Download: PDF

Cite this article:

Nieberding, F., Breuer, B., Braeckevelt, E., Klemm, O., Song, Q. and Zhang, Y. (2018). Fog Water Chemical Composition on Ailaoshan Mountain, Yunnan Province, SW China. Aerosol Air Qual. Res. 18: 37-48. https://doi.org/10.4209/aaqr.2017.01.0060


  • Fogwater Chemical Composition at Ailaoshan Mountain, Yunnan Province, SW China.
  • Acidic fog was found in rural mountainous SW China (median pH = 4.05).
  • The ions ammonium, nitrate and sulphate dominate the fog water chemical composition.
  • Acidification is mainly derived from emissions of SO2 and NOx.
  • Emissions of ammonia were responsible for partly neutralization of acidity.


Between December 2015 and March 2016, fog water was collected at the subtropical mountain cloud forest site Ailaoshan in SW China at 2476 m above mean sea level. An active fog collector was employed to collect 117 samples during more than 140 hours of fog, covering 6 major fog events. The chemical analysis included acidity and inorganic ion concentrations. The median pH values of the fog events varied between 3.7 and 4.2, characterizing the fog water as acidic (pH < 5.0) to very acidic (pH < 4.0). The ion composition was dominated by H+, NH4+, SO42– and NO3, which made up more than 86% of the total ionic concentration (TIC). The generally rather high ion concentration levels cannot be explained by nearby emission sources but rather by long-range transport of air pollutants from various sources in East Asia. During one event on February 8, 2016, it is evident that it was the emissions from a large coal-fired power plant in Myanmar that led to high concentrations of sulphuric acid. This pilot study of fog chemistry in rural, mountainous SW China should be complemented by studies spanning the moist summer monsoon season and covering more chemical species.

Keywords: Fog chemistry; Acidic fog; Rural China; Ion loadings; Trajectories.


Fog can “be viewed as clouds that are in contact with the Earth’s surface” (Seinfeld and Pandis, 2006). Physically, fog is made up of liquid or frozen hydrometeors dispersed in air that produce a horizontal visibility of less than 1000 m. Depending on the formation process and on the geographical location, several different types of fog exist, e.g., advection fog, radiation fog, sea fog, orographic fog, and more.

Because fog can hinder radiation fluxes, it can strongly affect human transport systems, the photosynthesis of vegetation and the radiation budget of the troposphere. It also plays a role in the hydrology of ecosystems and in the deposition of nutrients and pollutants to the vegetation. The chemistry of fog water is an important characteristic that can help us develop a full understanding of the role that fog plays in the atmospheric system.

Looking at fog chemistry from another angle, it may also be utilized as a tracer for air pollution in general. Fog in remote areas carries none or only few pollutants, while fog in areas of high air pollution levels exhibits high concentrations of ions, organic compounds and other pollutants. In some cases, the fog water chemistry is influenced by air pollution that was emitted into the atmosphere far away, upwind of the fog’s location, and was brought to the foggy site by long-range transport.

We therefore argue that the chemical composition of fog water should be studied ubiquitously. This study focusses on a rural region in Southwest China that is very foggy in some areas, but for which only little information is available on the chemistry of fog water. Therefore, this is a basic study that will provide information on fog chemistry in a remote region in mountainous SW China. There is only one earlier study from Xishuangbanna, which is located about 260 km to the southwest and at a much lower elevation than our mountainous sampling location. At their site, Liu et al. (2005) collected fog water for chemical analysis in 2001 and 2002 and found that the water showed only little contamination from industrial sources.

In a broader context covering East Asia, the chemical composition of fog water has been studied in Taiwan (Liang et al., 2009; Simon et al., 2016), Japan (Minami and Ishizaka, 1996; Aikawa et al., 2005; Watanabe et al., 2011), South Korea (Kim et al., 2006), on Mount Taishan in the North China Plain (Wang et al., 2011; Guo et al., 2012), on central Chinese Mount Heng (Sun et al., 2010), in Shanghai (Li et al., 2011), Nanjing (Tang et al., 2008; Lu et al., 2010; Yang et al., 2012), Eastern China (Desyaterik et al., 2013), and in Ji’an, Southeast China (Wang et al., 2014). Many of these studies showed that anthropogenic emissions strongly influence the fog chemistry. In particular, the pH values of fog water were often low in conjunction with the presence of sulphate. Only the study in tropical Xishuangbanna in SW China as mentioned before (Liu et al., 2005) showed rather low concentrations of ions, especially acidifying ions, and pH values above 5.6. We chose a relatively nearby mountain study site that should, on the one hand, contain clean fog water due to the absence of regional emission sources of pollutants. On the other hand, given that our site is high on a mountain, it may contain polluted and acidified fog from long-range transport of air masses from China and Indochina. To gain preliminary information on the chemical composition of fog at this site, we analyzed fog during the winter monsoon period 2015/2016.


Study Site

The study site is located at the Ailaoshan Station for Subtropical Forest Ecosystem Studies on Ailaoshan Mountain in Jingdong County, Yunnan Province, SW China (24.544656N, 101.027824E, elevation 2476 m above mean sea level, AMSL). Jingdong county has a population density of about 77 inhabitants per km2, and the nearest city is situated in a valley (about 1300 m AMSL), 20 km to the southwest. The surrounding villages are supported mainly by subsistence farming, tea production and, at a smaller scale, by the production of coffee beans. Traditional rice farming on small-scale terraces is ubiquitous in the area. Kunming, the capital of Yunnan province with approximately seven million inhabitants, is situated about 200 km to the northeast. Yunnan province borders Myanmar in the southwest and Laos and Vietnam in the south.

The study site is located within a main valley that is oriented from NW to SE, is 150 km long, 30 km wide, and covers an altitude range from 1500 m to over 2600 m ASL (Fig. 1(a)). It is characterized by subtropical mountain climate with an annual average temperature of 11.3°C and annual average precipitation of 1840 mm (Tan et al., 2011). There is a pronounced wet season from May through October (Fig. 2), during which the monsoon winds mainly occur from the SW. During the dry season from November to April, the main wind direction is SE. On a smaller scale, however, the site is situated in an orthogonal side valley, which is oriented from SW to NE, is 15 km long, 2.5 km wide, and covers an altitude range of 150 m (Fig. 1(b)). Therefore, the wind regime at the tower site (where fog samples were collected; see below for details) is subject to channeling of the flow and the air masses arrive predominantly from the SW (Fig. S1, supplement) throughout the year.

Fig. 1. Location of the study site in SW China with neighboring countries (a) and on Ailaoshan Mountain with contour lines of the surrounding valleys and hills (b). Elevation data provided through ETOPO1 1 Arc-Minute Global Relief Model (Amante and Eakins, 2009). The map was created with ArcGIS software (Esri, USA).Fig. 1Location of the study site in SW China with neighboring countries (a) and on Ailaoshan Mountain with contour lines of the surrounding valleys and hills (b). Elevation data provided through ETOPO1 1 Arc-Minute Global Relief Model (Amante and Eakins, 2009). The map was created with ArcGIS software (Esri, USA).

Fig. 2. Climate diagram of the Ailaoshan mountain research site, based on 5 years of data (2009–2013) only.Fig. 2Climate diagram of the Ailaoshan mountain research site, based on 5 years of data (2009–2013) only.

The Ailaoshan National Nature Reserve covers an area of 504 km2. It is an intensively studied old-growth subtropical evergreen broadleaved forest (Tang et al., 2007; Tang and Ohsawa, 2009; Song et al., 2017). The dominant plant species are Lithocarpus chintungensis, Rhododendron leptothrium, Vaccinium ducluoxii, Lithocarpus xylocarpus, Castanopsis wattii, Schima noronhae, Hartia sinensis, and Manglietia insignsis (Schaefer et al., 2009). A meteorological tower is situated within the side valley on a slope facing northwest, and the canopy height at the tower site is about 25 m above fog occurrences during the summer and fewer, but still significant, fog occurrences during the drier winter monsoon season (Fig. 3). Although no detailed analysis of the processes leading to foggy conditions at the site exist at this point, we presume that it is mostly orographic lifting that leads to foggy conditions at this high-altitude mountain cloud forest. 

Fig. 3. Fog frequency during the year 2015 as measured at the Ailaoshan study site. A day with fog is counted when the average visibility of one 30 minutes timespan is lower than 1000 m.Fig. 3. Fog frequency during the year 2015 as measured at the Ailaoshan study site. A day with fog is counted when the average visibility of one 30 minutes timespan is lower than 1000 m.

Experimental Setup

We placed an active strand fog collector on top of the meteorological tower at 29 m AGL. The collector pulls foggy air through 6 arrays of Teflon strands. The flow rate (FR) is 17.23 m3 min–1. The flow velocity is 5.65 m s–1. Fog droplets impact on the strands, combine with other impacted droplets to larger ones, and eventually run down along the strands and into a sample bottle. The 50% droplet collection efficiency cut size diameter is 2.5 µm according to fluid dynamic computations. The overall fog collection efficiency (FCE) is 88% with respect to the liquid water content (LWC) of the foggy air mass. For more details, see Degefie et al. (2015). Additionally, a Present Weather Detector (PWD11, Vaisala Oyi, Finland) was installed to measure horizontal visibility (as a proxy for fog density). Calibrated to the World Meteorological Organization’s definition of fog, the collector started sampling automatically whenever the visibility dropped below 1000 m (10 min average). At the same time, a radio alarm was set off to inform a nearby operator about the start of fog collection. The experimental setup was oriented towards the SW to ensure undisturbed airflow from the main wind direction.

Fog Collection

Fog samples were collected from December 2015 to March 2016 with a sampling resolution between 0.5 and 2 hours. Generally, the fog water samples were taken manually every 30 minutes. It was the goal to collect sequential fog samples throughout individual fog events in order to study the temporal development of the chemical composition of fog water. A volume of 30 mL fog water was required for the full ion analysis as described below. If the sample volume was less than 30 mL after 30 min and fog was still present, the sampling period was extended to 60 min. This procedure was repeated until either a sampling volume of 30 mL was reached, or fog ceased. When fog ceased and the volume of the last fog water was between 10 mL and 30 mL, only pH and electric conductivity were measured.

When the sample volume was below 10 mL, the sample was discarded. Exceptions from this routine occurred 4 times when a fog event lasted very long and the operator needed some rest. In these cases, the sampling period was extended accordingly. Otherwise, the described procedure was realized day and night. The water sample was divided between two 15 mL sampling bottles. All bottles were made from high density polyethylene (HDPE), and the samples were immediately deep frozen until the laboratory analysis was conducted.

The fog collector was cleaned by spraying deionized water into the sampling unit while it was running. This was performed every two days, or, if foreseeable, directly before a fog event. Two types of blank samples were taken during the investigation period: DI-water blanks and fog collector blanks. For DI-water blanks, deionized water was placed directly into sampling bottles to ensure the quality of the deionized water and the subsequent laboratory analysis. Fog collector blanks were taken directly after cleaning by spraying additional deionized water into the sampling unit while running the fog collector, and then collecting the DI water through the sample tube and into the sampling bottle. The collected fog water blank samples were treated like regular fog samples.

Chemical Analysis

The samples were weighed immediately after they were collected. Afterwards, pH and electrical conductivity were measured using a handheld WTW pH/Cond 3320 (Xylem Analytic, Germany) calibrated with pH 4 and pH 7 buffer solutions. Analysis of major ion concentrations took place in two different laboratories in China. The biogeochemical laboratory of the Xishuangbanna Tropical Botanical Garden (XTBG) in Kunming analyzed samples for ammonium (NH4+), chloride (Cl), nitrate (NO3) and sulphate (SO42–), whereas the central laboratory of the XTBG in Xishuangbanna analyzed the samples for calcium (Ca2+), potassium (K+), sodium (Na+) and magnesium (Mg2+) ions. For Cl and SO42–, a Dionex ICS-1600 (Thermo Fisher Scientific, USA) ion chromatograph was used, while an Auto Analyzer 3 (SEAL Analytical Inc., USA) continuous flow analyzer was used for NH4+ and NO3. For Ca2+ and Mg2+, ions were analyzed using an inductively coupled plasma atomic-emission spectrometer iCAP6300 (Thermo Fisher Scientific, USA). For K+ and Na+, an atomic absorption spectrometer 932 (GBC Scientific Equipment Pty Ltd, Australia) was used.

Statistical Analysis

Samples that did not have sufficient amounts of water for all analyses or did not meet analytical detection limits were excluded from further evaluation. Additionally, fog samples were tested for ion balance according to the proposed approach from the European Monitoring and Evaluation Programme (EMEP) for precipitation water (WMO, 2004).

To estimate the amount of SO42– in the air deriving from anthropogenic sources, the non-sea-salt sulphate (nss-SO42–) concentration was calculated from the measured SO42– and Naconcentrations and the average sodium/sulphate ratio in seawater (Eq. (1)) (Warneck and Williams, 2012). All concentrations in Eq. (1) are given in units µeq L–1):

[nss-SO42–] = SO42– – 0.12 [Na+]                                (1)

As shown in many previous studies, H2SO4 and HNO3 are the main acidifiers in fog water (e.g., Li et al., 2011; Degefie et al., 2015; Klemm et al., 2015; Simon et al., 2016). To measure the non-neutralized acidity of fog water, Hara et al. (1995) proposed the quantitative index pAi, which is defined as the negative decimal logarithm of the nss-SO42– concentration plus the NO3 concentration (Eq. (2), both ion concentrations in mmol L–1):

pAi = –log([nss-SO42–] + [NO3])                                 (2)

Furthermore, we calculated the fractional acidity (FA), as proposed by Daum et al. (1984), to represent the ratio of non-neutralized H+ in liquid water (Eq. (3)).


Square brackets indicate equivalent concentrations. Thus, as Lu et al. (2010) mentioned, if all hydrogen ions originate from the acidic input of H2SO4 and HNO3 and no neutralization has taken place, then FA = 1.

For each sample, the LWC (in units mg m–3) was calculated from the fog water collection rate (FWCR, in mg min–1) divided by the product of FCE and FR. The ion loads of ions i per volume of air (IL(i), in units µeq m–3) are calculated from the measured ion concentrations (IC(i) in units µeq L–1) and LWC using Eq. (4),


where (i) represents the selected ion species and ρ is the density of water (1000 kg m–3). 

Backward Trajectories

Calculating air mass backward trajectories from archived meteorological data is a common approach for deriving the origin of a certain air mass, and, thus, for identifying potential source regions of air pollutants (e.g., Yang et al., 2012; Klemm et al., 2015; Simon et al., 2015; Stein et al., 2015). For each fog sample, a 48-hour backward trajectory was calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Stein et al., 2015; Rolph, 2016) provided by the Air Resources Laboratory of the National Oceanic and Atmospheric Administration (NOAA) of the United States Department of Commerce (USA). The Global Data Assimilation System (GDAS) with 0.5 degree resolution was chosen and the vertical motion mode “Model vertical velocity” was applied. Because the topography by the meteorological model is too flat, the 

model underestimated the height AMSL of the fog collection site (Draxler and Hess, 1998). To take this limitation into account, the calculated trajectories were to arrive at 720 m above the model’s ground level (AGL), which is identical to the 2505 m AMSL. This corresponds to the actual height of the fog collector AMSL in the real world.


Fog Frequency and Quality Control

During our field campaign, a total of 134 fog samples were collected between December 26, 2015 05:42 and March 2, 2016 08:30 [UTC+08]. Most of the fog samples (91) were collected at intervals between 0.5 and 2 hours and only a few (10) at intervals of more than 5 hours. Due to freezing during fog events and technical difficulties, 4 samples could not be assigned to a starting or ending time.

Of the 134 samples, 3 had sample volumes between 10 mL and 30 mL and were analyzed for pH and electric conductivity only. These data were not used for further analysis. Eleven samples were blanks, and 3 were dismissed due to questionable ion concentrations (flagged by the laboratories). The electrical conductivities (ECs) of the DI-water blanks show that the water used to clean the sampler and the instruments was of overall good quality (EC < 10 µS cm–1). ECs from the fog collector blanks (EC < 20 µS cm–1) also indicate that there was no remarkable contamination from the fog collector. Of the remaining 117 fog water samples with full ion analyses, 98 were assigned to 6 different fog events lasting between 3 and 69 hours.

To examine the quality of the chemical analyses, all 117 samples were checked for data quality according to EMEP criteria for liquid precipitation samples (WMO, 2004). A total of 87 samples (74%) matched the criteria for ion balance closing. The 30 samples that did not meet this criteria had a positive ion balance, which indicates that they likely contained organic acids such as acetic or formic acid. Frequent biomass burning, which occurs throughout the region and especially during the dry season (Liu et al., 2005), can be a major source of these organic acids (Vet et al., 2014). However, these acids were not measured in our study’s protocol; so, even though these 30 samples did not meet the criteria for ion balance closing, there is a good chance that the ion analyses were of good quality. Therefore, instead of excluding the samples and losing valuable information, we followed the reasoning of Klemm et al. (2015), who suggest not excluding data points from further analysis under such conditions. As such, we considered all samples that had not been flagged by the laboratories during chemical analysis as valid.

Ionic Composition and EC

Table 1 shows the minimum, median and maximum measured ion concentrations, ion loadings, pH values and electrical conductivities, as well as the derived indicators for the 6 single events and for all samples taken. The median total ionic concentration (TIC) of the samples is 780 µeq L–1, which is high compared to the data from tropical Xishuangbanna, which is the only other site in rural SW China for which fog chemistry data is available (Liu et al., 2005). Conversely, the concentration level of fog samples at our site is lower than at sites in the highly polluted North China Plain (Wang et al., 2011) and in urban Shanghai (Li et al., 2011). This indicates that the fog water at our mountainous Ailaoshan site is strongly influenced by non-local emissions of air pollution, which reaches the site through long-range transport. Apparently, the long range transport of air pollutants leads to higher concentrations in high-mountain clouds and fog than in fog at lower altitudes ASL, such as in Xishuangbanna, which was presumably formed locally.

Table 1. Fog event statistics and chemical compositions of fog water on Ailaoshan Mountain, SW China. All ion concentrations in µeq L–1.Table 1. Fog event statistics and chemical compositions of fog water on Ailaoshan Mountain, SW China. All ion concentrations in µeq L–1.

The TICs were highly variable between individual fog events, with the median for Event 3 (1220 µeq L–1) being more than 5 times higher than for Event 6 (212 µeq L–1) (Fig. 4(d)). Because these two fog events considerably differ from each other and represent the upper and lower ends of the entire concentration ranges, they will be discussed in more detail below.

For all events, the dominant ion species were H+, NH4+, SO42– and NO3, accounting for more than 86% of the overall median TIC (92% of the median for Event 3, 82% of the median for Event 6). These ranges are consistent with other studies from China (e.g., Sun et al., 2010) and with studies from many other sites all over the world (Beiderwieden et al., 2005; Giulianelli et al., 2014; Herckes et al., 2015; Simon et al., 2016). For many sites in Southeast Asia, it has been reported that SO42– is the major acidifier in fog and rainwater (e.g., Aikawa et al., 2001; Kim et al., 2006; Wang et al., 2010). Its precursor gas, sulphur dioxide (SO2), is emitted mainly from the combustion of lignite and other sulphur-rich fossil fuels (Ohara et al., 2007). Usually some sulphate originates from the scavenging of sea-salt particles and thus may play an important role in the ion composition, especially in regions influenced by oceanic air masses. To differentiate between sulphate originating from sea salt and sulphate from anthropogenic sources, the non-sea-salt-sulphate-sulphate (nss-SO42–) concentration was calculated for each sample. The contribution of nss-SO42– to total SO42– was higher than 99.5% (median of all samples), showing that sulphate from sea salt is negligible at our study site, which is located about 700 km away from the closest ocean coast and at almost 2500 m AMSL. We therefore neglect the contribution of nss-SO42– from here on. We also replaced the nss-SO42– concentration in Eqs. (2) and (3) with the total SO42– concentration as measured in the samples in order to calculate pAi and FA, respectively.

Sulphate had a median contribution of 26 ± 6% (average ± 1 standard deviation of equivalent concentration) to the TIC and was the major anion in all events. The median SO42– concentration of 185 µeq L–1 is low compared to other studies in Southern, Northern and Eastern China (Lu et al., 2010; Li et al., 2011; Wang et al., 2011), but higher than rural Xishuangbanna (Liu et al., 2005).

The second most abundant anion was NO3 with a median contribution of 9.6 ± 3.2% to TIC. The NO3 originates mainly from nitric acid (HNO3), which itself stems from the precursor gasses NO and NO2 (NOx) from vehicular transport emissions. Over the past 30 years, the NOx emissions have increased in China and Indochina due to the increase of vehicular traffic, causing the relative and absolute contributions of HNOto the acidification of precipitation (rain, and likely fog) to increase as well (c.f., Wang et al., 2008; Li et al., 2011). The NO3-to-SO42– ratios of our fog samples were typically below unity (median of all samples 39%, Table 1), indicating that the contribution of sulphate to TIC is high at our study site (Fig. 4(b)). The NO3/SO42– ratios of Event 3 were the lowest and also showed remarkably low variability (0.17 ± 0.002%). This likely indicates that the sulphate originated from one single source rather than from several diffusive emissions. For Event 6, the nitrate ion levels were higher than in Event 3. The median NO3/SO42– ratio was 0.36 ± 0.6%, and for two samples, this ratio was even larger than two.

Fig. 4. Boxplots of the different fog events and for all samples of pH (a), the nitrate/sulphate ratio (b), the sulphate concentration in µeq L–1 (c) and the total ionic composition (TIC) in µeq L–1 (d), which is the sum of the anion concentrations and the cation concentrations.Fig. 4Boxplots of the different fog events and for all samples of pH (a), the nitrate/sulphate ratio (b), the sulphate concentration in µeq L–1 (c) and the total ionic composition (TIC) in µeq L–1 (d), which is the sum of the anion concentrations and the cation concentrations.

With a median contribution of 34 ± 10% to the TIC, NH4was the predominant cation species during all events, except for Event 6, where H+ was the most abundant cation. In liquid precipitation, neutralization of the acidic components H2SO4 and HNO3 by ammonia is the major source for NH4+. Ammonia predominantly comes from decomposition of animal waste and the use of mineral fertilizers, but it also comes from biomass burning (Klimont, 2001). There is likely a considerable amount of ammonia near the study site, since it is situated in a rural area where subsistence farming with livestock is common, sewage treatment systems and municipal waste management are often unavailable, and biomass burning occurs frequently, especially during the dry season (Liu et al., 2005).

In many studies, calcium (Ca2+) has been found to be a major chemical component of fog water, mainly due to mineral dust from construction sites (Li et al., 2011) and land erosion (Liu et al., 2010; Klemm et al., 2015). However, in our study Ca2+ contributes only slightly to the TIC, with a median of 3.7 ± 4.2%. One sample with an exceptionally high Ca2+ concentration was collected at the beginning of Event 2 with a contribution to the TIC of almost 30%. Sodium (Na+) and chloride (Cl) usually originate from the droplet scavenging of sea salt. Another source of Cl is the combustion of fossil fuels and waste incineration (Li et al., 2011; Wang et al., 2011). At our study site, the median Cl/Na+ ratios ranged from 2.1 (Event 1) to 10.3 (Event 6) which is substantially higher than the average ratio in seawater (1.16, Warneck and Williams, 2012). Potassium (K+) is mainly emitted from biomass burning (Li et al., 2011) and was the highest in Event 4. Magnesium, which originates from sea salt as well as from resuspended road dust and long-range dust transport, only made a minor contribution to the TIC in our samples. Figs. S2 and S3 in the supplement show that courses of ion loads vary largely throughout single events. This variability is generally smaller though than the variability between events.

pH, pAi and Fractional Acidity

In the atmosphere, the natural equilibrium with background CO2 yields a pH of 5.6 in fog and rainwater. Naturally occurring acids like H2SO4, HNO3 (Galloway et al., 1976) and organic acids decrease the pH in rain and fog to around 5. A total of 44% of the samples in our study are very acidic with pH ≤ 4.0, whereas 54% may be considered acidic with 4.0 < pH ≤ 5.0; only two samples had a pH above 5.0. Again, Event 3 and Event 6 had the lowest and highest median pH values of 3.7 and 4.2, respectively (Fig. 4(a)).

We found that the median pAi values ranged from 3.3 (Event 3) to 4.2 (Event 6). For Event 6, the median pH was only 0.07 units larger than pAi, whereas for all other events the differences were at least 0.45 (Table 1Fig. 5(a)). Event 6 also had the highest median fractional acidity (FA) of 0.8, whereas for all other events, FA was below 0.4 (Fig. 5(b)). These data show that there was not much neutralization of acidity during Event 6. In concert with this finding, the contribution of the neutralizing agent NH4+ to TIC is low. The remaining acidity beyond that stemming from H2SO4 and HNO3 likely derived from anthropogenic HCl from waste incineration, which can be easily scavenged by droplets (Wang et al., 2011).

Fig. 5. Relations between the pH and pAi values (a) and pH and fractional acidity (FA) (b) for Event 3 and Event 6, as well as for the remaining samples.Fig. 5. Relations between the pH and pAi values (a) and pH and fractional acidity (FA) (b) for Event 3 and Event 6, as well as for the remaining samples.

Backward Trajectories and Sources of Pollutants

The chemical composition of fog is determined by the origin of its air mass and the by the incorporation of pollutants along the transport pathway. To identify the origin of the respective air masses, we computed 48-hour backward trajectories for each fog water sample taken. The air masses originated, on a regional scale, mainly from the south (sector WSW to SE), travelling along rural areas of Southeast Asia, mainly Myanmar, Laos, Vietnam and SW China.

High concentrations of ions in fog water may be associated with high levels of pollutants in the foggy air mass or from low concentrations of liquid water (LWC) in the air. An increase in the LWC will lead to dilution of the solutes in the samples and vice versa. We employed the ion loadings (ILs in units mg m–3) concept in addition to the sheer liquid-water ion concentrations (in units µeq L–1) to assess the chemical information in conjunction with air mass histories. If a high ion concentration in fog water would have occurred only because the LWC of the respective air mass was low, the overall ion loading of the air mass IL (which is the product of ion concentrations with LWC) would have been the same as that for an air mass with low fog water concentrations. Therefore, the IL is a good indicator of the total concentrations of ions in air. Event 3 had the highest ILs and Event 6 had the lowest (Table 1Fig. 6(b)). The ion concentrations in Event 3 were high, but not exceptionally so. Through multiplication with LWC, which was also high during Event 3, the IL in Event 3 is exceptionally high. This event carried the largest amount of ions per volume of air.

Fig. 6. Median ion concentrations of the major ions H+, NH4+, SO42–, NO3– (a) and median ion loadings of the major ions H+, NH4+, SO42–, NO3– (b) for the 6 different fog events and for all fog samples.Fig. 6. Median ion concentrations of the major ions H+, NH4+, SO42–, NO3 (a) and median ion loadings of the major ions H+, NH4+, SO42–, NO3 (b) for the 6 different fog events and for all fog samples.

The backward trajectories show that the air masses of the highly polluted and acidic Event 3 arrived from the low altitudes of eastern Myanmar (Fig. 7(a)). For the less unpolluted Event 6, some of the respective air masses arrived from high altitudes of the West (India, Bangladesh and Myanmar; 6 samples), and some arrived from lower altitudes of the Southeast (mainly Southern China and Northern Vietnam; 26 samples) (Fig. 7(b)).

Fig. 7. Backward trajectories of the last 48 hours before arrival at the study site for 5 samples of Event 3 (February 8th 2016 13:00–15:30)Fig. 7. Backward trajectories of the last 48 hours before arrival at the study site for 5 samples of Event 3 (February 8th 2016 13:00–15:30) (a) and 30 samples of Event 6 (February 24th 2016 19:00–February 27th 2016 09:00) (b). The location of the only coal combusting power plant in Myanmar near Tigyit is marked in map (a) (96.703524 N, 20.431292 E). The maps were created with ArcGIS software (Esri, USA).


A total of 98% of our samples collected at the Ailaoshan high mountain site in SW China had a pH below 5.0, and the acidity was mostly driven by the conjugate base of sulphuric acid (sulphate). This seems surprising, considering that the site is remote and without any apparent influence from local or regional pollution sources. There is no mentionable emission source of atmospheric acids or their precursors in the region, so we conclude that it is SO2 emissions from China and Indochina and their long range transport to our research site that contributed largely to the acidity and ion composition of the respective air masses.

In one case (Event 3), sulphate dominated the anion load, and the pH was low (3.7) and the fractional acidity high, while the variation of fog water chemistry was remarkably low throughout the event. We conclude that it was very likely a single source that led to the concentration of sulphuric acid in fog at Ailaoshan. This is supported by the 48-hour backward trajectories, which showed that Event 3 air flows originated from the same area in the northeast region of Myanmar. The spatial proximity of the trajectories to Myanmar’s only coal combustion power plant near Tigyit suggests that emissions from this power plat are responsible for the high pollution level of Event 3. We believe that emissions from this power plant can easily be transported to our study site and lead to polluted fog water there, as long as there is no significant precipitation from the respective air masses that could lead to scavenging of pollutants.

During other fog events, HNO3 and its precursors (NOx), likely from vehicular sources, contributed to the acidity as well. The neutralization of acidity through NH4+, originating from agriculture and waste decomposition, was rather low at this rural mountain site. Therefore, the fog we collected during the 2015–2016 dry monsoon season can be considered to be acidic throughout (with a median pH of 4.05).

Further research should be performed to develop an understanding of the dynamics of air chemistry, including the composition of rainwater and fog water in SW China. We consider our contribution to be a pilot study that should be complemented as soon as possible by many more studies on fog chemistry, rain chemistry, and gas phase chemistry in SW China. For fog chemistry, it is essential that future studies both span the moist summer monsoon season and cover more chemical variables, such as metal ions, elements, organic acids, and anhydrosugars. Then, a more complete understanding of the processes impacting the composition of fog in SW China can be achieved.


The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www.ready.noaa.gov) used in this publication. Two anonymous reviewers helped to improve the quality of the manuscript by giving valuable comments on an earlier version. We thank C. Brennecka for help during language-editing of the manuscript. 


  1. Aikawa, M., Hiraki, T., Shoga, M. and Tamaki, M. (2001). Fog and precipitation chemistry at Mt. Rokko in Kobe, April 1997 March 1998. Water Air Soil Pollut. 130: 1517–1522. [Publisher Site]

  2. Aikawa, M., Hiraki, T., Shoga, M. and Tamaki, M. (2005). Chemistry of fog water collected in the Mt. Rokko area (Kobe City, Japan) between April 1997 and March 2001. Water Air Soil Pollut. 160: 373–393. [Publisher Site]

  3. Amante, C. and Eakins, B.W. (2009). ETOPO1 1 Arc-Minute Global Relief Model: Procedures, data sources and analysis. National Geophysical Data Center, Marine Geology and Geophysics Division, Boulder, Colorado.

  4. Beiderwieden, E., Wrzesinsky, T. and Klemm, O. (2005). Chemical characterization of fog and rain water collected at the eastern Andes cordillera. Hydrol. Earth Syst. Sci. 9: 185–191. [Publisher Site]

  5. Daum, P.H., Kelly, T.J., Schwartz, S.E. and Newman, L. (1984). Measurements of the chemical composition of stratiform clouds. Atmos. Environ. 18: 2671–2684. [Publisher Site]

  6. Degefie, D.T., El-Madany, T.S., Held, M., Hejkal, J., Hammer, E., Dupont, J.C., Haeffelin, M., Fleischer, E. and Klemm, O. (2015). Fog chemical composition and its feedback to fog water fluxes, water vapor fluxes, and microphysical evolution of two events near Paris. Atmos. Res. 164–165: 328–338. [Publisher Site]

  7. Desyaterik, Y., Sun, Y., Shen, X., Lee, T., Wang, X., Wang, T. and Collett, J.L. (2013). Speciation of “brown” carbon in cloud water impacted by agricultural biomass burning in eastern China: “BROWN” CARBON SPECIATION IN CLOUD WATER. J. Geophys. Res. 118: 7389–7399. [Publisher Site]

  8. Draxler, R.R. and Hess, G.D. (1998). An overview of the HYSPLIT_4 modeling system for trajectories, dispersion, and deposition. Aust. Meteorol. Mag. 47: 295–308. 

  9. Galloway, J.N., Likens, G.E. and Edgerton, E.S. (1976). Acid precipitation in the northeastern United States: pH and acidity. Science 194: 722–724. [Publisher Site]

  10. Giulianelli, L., Gilardoni, S., Tarozzi, L., Rinaldi, M., Decesari, S., Carbone, C., Facchini, M.C. and Fuzzi, S. (2014). Fog occurrence and chemical composition in the Po valley over the last twenty years. Atmos. Environ. 98: 394–401. [Publisher Site]

  11. Guo, J., Wang, Y., Shen, X., Wang, Z., Lee, T., Wang, X., Li, P., Sun, M., Collett Jr., J.L., Wang, W. and Wang, T. (2012). Characterization of cloud water chemistry at Mount Tai, China: Seasonal variation, anthropogenic impact, and cloud processing. Atmos. Environ. 60: 467–476. [Publisher Site]

  12. Hara, H., Kitamura, M., Mori, A., Noguchi, I., Ohizumi, T., Seto, S., Takeuchi, T. and Deguchi, T. (1995). Precipitation chemistry in Japan 1989-1993. Water Air Soil Pollut. 85: 2307–2312. [Publisher Site]

  13. Herckes, P., Marcotte, A.R., Wang, Y. and Collett, J.L. (2015). Fog composition in the Central Valley of California over three decades. AtmosRes. 151: 20–30. [Publisher Site]

  14. Kim, M.G., Lee, B.K. and Kim, H.J. (2006). Cloud/fog water chemistry at a high elevation site in South Korea. J. Atmos. Chem. 55: 13–29. [Publisher Site]

  15. Klemm, O., Tseng, W.T., Lin, C.C., Klemm, K.I. and Lin, N.H. (2015). pH control in fog and rain in East Asia: Temporal advection of clean air masses to Mt. Bamboo, Taiwan. Atmosphere 6: 1785–1800. [Publisher Site]

  16. Klimont, Z. (2001). Current and future emissions of ammonia in China. 10th International Emission Inventory Conference -"One Atmosphere, One Inventory, Many Challenges", Denver, CO, May 1–3, 2001.  

  17. Li, P., Li, X., Yang, C., Wang, X., Chen, J. and Collett, Jr., J.L. (2011). Fog water chemistry in Shanghai. Atmos. Environ. 45: 4034–4041. [Publisher Site]

  18. Liang, Y.L., Lin, T.C., Hwong, J.L., Lin, N.H. and Wang, C.P. (2009). Fog and precipitation chemistry at a mid-land forest in central TaiwanJ. Environ. Qual. 38: 627. [Publisher Site]

  19. Liu, D.Y., Pu, M.J., Yang, J., Zhang, G.Z., Yan, W.L. and Li, Z.H. (2010). Microphysical Structure and evolution of a four-day persistent fog event in Nanjing in December 2006. Acta Meteorol. Sin. 24: 104–115.

  20. Liu, W., Fox, J.E.D. and Xu, Z. (2002). Nutrient fluxes in bulk precipitation, throughfall and stemflow in montane subtropical moist forest on Ailao Mountains in Yunnan, south-west China. J. Trop. Ecol. 18: 527–548. [Publisher Site]

  21. Liu, W.J., Zhang, Y.P., Li, H.M., Meng, F.R., Liu, Y.H. and Wang, C.M. (2005). Fog- and rainwater chemistry in the tropical seasonal rain forest of Xishuangbanna, Southwest China. Water Air Soil Pollut. 167: 295–309. [Publisher Site]

  22. Lu, C., Niu, S., Tang, L., Lv, J., Zhao, L. and Zhu, B. (2010). Chemical composition of fog water in Nanjing area of China and its related fog microphysics. AtmosRes. 97: 47–69. [Publisher Site]
  23. Minami, Y. and Ishizaka, Y. (1996). Evaluation of chemical composition in fog water near the summit of a high mountain in Japan. Atmos. Environ. 30: 3363–3376. [Publisher Site]

  24. Ohara, T., Akimoto, H., Kurokawa, J.I., Horii, N., Yamaji, K., Yan, X. and Hayasaka, T. (2007). An Asian emission inventory of anthropogenic emission sources for the period 1980–2020. Atmos. Chem. Phys. 7: 4419–4444. [Publisher Site]

  25. Rolph, G.D. (2016). Real-time Environmental Applications and Display sYstem (READY) Website (http://ready. arl.noaa.gov). NOAA Air Resources Laboratory, Silver Spring. Silver Spring MD. [Publisher Site]

  26. Schaefer, D.A., Feng, W. and Zou, X. (2009). Plant carbon inputs and environmental factors strongly affect soil respiration in a subtropical forest of southwestern China. Soil Biol. Biochem. 41: 1000–1007. [Publisher Site]

  27. Seinfeld, J.H. and Pandis, S.N. (2006). Atmospheric chemistry and physics: from air pollution to climate change, 2nd ed. Wiley, Hoboken, N.J.

  28. Simon, S., Klemm, O., El-Madany, T., Walk, J., Amelung, K., Lin, P.H., Chang, S.C., Lin, N.H., Engling, G., Hsu, S.C., Wey, T.H., Wang, Y.N. and Lee Y.C. (2016). Chemical composition of fog water at four sites in Taiwan. Aerosol Air Qual. Res. 16: 618–631. [Publisher Site]

  29. Song, Q.H., Braeckevelt, E., Zhang, Y.P., Sha, L.Q., Zhou, W.J., Liu, Y.T., Wu, C.S., Lu, Z.Y. and Klemm, O. (2017). Evapotranspiration from a primary subtropical evergreen forest in Southwest China. Ecohydrology 10: e1826. [Publisher Site]

  30. Stein, A.F., Draxler, R.R., Rolph, G.D., Stunder, B.J.B., Cohen, M.D. and Ngan, F. (2015). NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96: 2059–2077. [Publisher Site]

  31. Sun, M., Wang, Y., Wang, T., Fan, S., Wang, W., Li, P., Guo, J. and Li, Y. (2010). Cloud and the corresponding precipitation chemistry in south China: Water-soluble components and pollution transport. J. Geophys. Res. 115: D22303. [Publisher Site]

  32. Tan, Z.H., Zhang, Y.P., Schaefer, D., Yu, G.R., Liang, N.S. and Song, Q.H. (2011). An old-growth subtropical Asian evergreen forest as a large carbon sink. Atmos. Environ. 45: 1548–1554. [Publisher Site]

  33. Tang, C.Q., Li, T. and Zhu, X. (2007). Structure and regeneration dynamics of three subtropical midmontane moist evergreen broad-leaved forests in southwestern China, with special reference to bamboo in the forest understories. Can. J. For. Res. 37: 2701–2714. [Publisher Site]

  34. Tang, C.Q. and Ohsawa, M. (2009). Ecology of subtropical evergreen broad-leaved forests of Yunnan, southwestern China as compared to those of southwestern Japan. J. Plant Res. 122: 335–350. [Publisher Site]

  35. Tang, L., Niu, S. and Xu, X. (2008). Observational study of content of heavy metals in fog water relative to air pollution in suburbs of Nanjing. 2008 International Workshop on Geoscience and Remote Sensing. ETT and GRS 2008. pp. 384–387. 

  36. Vet, R., Artz, R.S., Carou, S., Shaw, M., Ro, C.U., Aas, W., Baker, A., Bowersox, V.C., Dentener, F., Galy-Lacaux, C., Hou, A., Pienaar, J.J., Gillett, R., Forti, M.C., Gromov, S., Hara, H., Khodzher, T., Mahowald, N.M., Nickovic, S., Rao, P.S.P. and Reid, N.W. (2014). A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus. Atmos. Environ. 93: 3–100. [Publisher Site]

  37. Wang, S.H., Lin, N.H., OuYang, C.F., Wang, J.L., Campbell, J.R., Peng, C.M., Lee, C.T., Sheu, G.R. and Tsay, S.C. (2010). Impact of Asian dust and continental pollutants on cloud chemistry observed in northern Taiwan during the experimental period of ABC/EAREX 2005. J. Geophys. Res. 115: D00K24. [Publisher Site]

  38. Wang, X., Chen, J., Sun, J., Li, W., Yang, L., Wen, L., Wang, W., Wang, X., Collett, J.L., Shi, Y., Zhang, Q., Hu, J., Yao, L., Zhu, Y., Sui, X., Sun, X. and Mellouki, A. (2014). Severe haze episodes and seriously polluted fog water in Ji’nan, China. Sci. Total Environ. 493: 133–137.  [Publisher Site]

  39. Wang, Y., Wai, K., Gao, J., Liu, X., Wang, T. and Wang, W. (2008). The impacts of anthropogenic emissions on the precipitation chemistry at an elevated site in North-eastern China. Atmos. Environ. 42: 2959–2970. [Publisher Site]

  40. Wang, Y., Guo, J., Wang, T., Ding, A., Gao, J., Zhou, Y., Collett, J.L. and Wang, W. (2011). Influence of regional pollution and sandstorms on the chemical composition of cloud/fog at the summit of Mt. Taishan in northern China. Atmos. Res. 99: 434–442. [Publisher Site]

  41. Warneck, P. and Williams, J., 2012. The atmospheric chemist’s companion. Springer Netherlands, Dordrecht. [Publisher Site]

  42. Watanabe, K., Honoki, H., Iwama, S., Iwatake, K., Mori, S., Nishimoto, D., Komori, S., Saito, Y., Yamada, H. and Uehara, Y. (2011). Chemical composition of fog water at Mt. Tateyama near the coast of the Japan Sea in central Japan. Erdkunde 65: 233–245. [Publisher Site]

  43. WMO (2004). Manual for the GAW precipitation chemistry programme. Guideline, Data Quality Objectives and Standard Operating Procedures. Allan, M.A. (Ed.), GAW Precipitation Chemistry Science Advisory Group, European Space Agency, (ESA), World Meteorological Organization. No. 160.

  44. Yang, J., Xie, Y.J., Shi, C.E., Liu, D.Y., Niu, S.J. and Li, Z.H. (2012). Ion composition of fog water and its relation to air pollutants during winter fog events in Nanjing, China. Pure Appl. Geophys. 169: 1037–1052. [Publisher Site]

Aerosol Air Qual. Res. 18 :37 -48 . https://doi.org/10.4209/aaqr.2017.01.0060  

Share this article with your colleagues 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

77st percentile
Powered by
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.