Assessing the Impact of Locally Produced Aerosol on the Rainwater Composition at the Gosan Background Site in East Asia

2. 1 Study Area

At Gosan Station (33.292°N, 126.162°E), located on top of a ~70 m high cliff on the western coast of Jeju Island in Korea, several atmospheric monitoring devices have been installed in and on the rooftops (3-4m above the ground) of shipping container buildings and on two tower platforms 10 m and 15 m high (Fig. 1). The mean annual precipitation is about 1,100 mm, half of which falls during the summer season. This coastal site is windy with a mean speed of 7 m s^-1. Land use around the site is primarily agricultural with scattered grassland and trees. There are two small villages near the site, 0.5 km to the southeast and 2 km to the northeast, with less than 600 residents in each and without any large fossil fuel-based industry.

Fig. 1.

The location of the wet-only rain collector and the wind sector division (I to VIII) at Gosan Station in Jeju Island, Korea. Additional rain samples were collected at four sites (T1, T2, F1 and F2) all within 35 m of each other. The inset shows the rainwater sampling sites in Korea mentioned in the text.

2. 2 Rainwater Chemistry Database and Meteorological Data

The rainwater chemistry data set for Gosan Station was provided by the Korea Meteorological Administration (KMA). It includes precipitation (mm), electrical conductivity (μS cm^-1), pH and concentrations (μmol L^-1) of major ions (F^-, Cl^-, NO₃^-, SO₄^2-, Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺). The data set covers the period from October 1998 to December 2011, during which time 540 individual rain samples were collected using a wet-only collector on a daily basis at 9:00 a.m. The position of the collector is marked on Fig. 1. The mouth of the collector is ~1 m above ground. The major ions were analyzed using ion chromatography and validated according to the guidelines of the World Meteorological Organization Global Atmosphere Watch Precipitation Chemistry Programme (KMA, 2012).

The mean wind direction and speed during the rain events were calculated using the automatic weather station (AWS) data archived by KMA from October 1999 to present. The AWS data set includes the 10-minute mean wind direction (degree) and speed (m s^-1), daily cumulative precipitation (mm) and 10-minute interval rain sensing signal (whether it is raining or not). Since the wet-only rain collector opens its lid only while the rain sensor is actuated, we averaged the wind direction and speed only for the ‘raining’ intervals. This averaging was carried out for the 493 (out of 540) samples for which AWS data were available.

The air mass back trajectory analysis was conducted using the NOAA/HYSPLIT (National Oceanic and Atmospheric Administration/HYbrid Single-Particle Lagrangian Integrated Trajectory) model (Draxler and Hess, 1997). The NCEP/GDAS (National Centers for Environmental Prediction/Global Data Assimilation System) data for the years 2004-2011 were used for the HYSPLIT model calculation.

2. 3 Simultaneous Rain Sampling

To test the effect of locally produced aerosols on the meter-scale variability of rainwater composition, two rain events on April 22 and 27, 2008 (GS22 and GS27, hereafter) were sampled simultaneously at four locations within the Gosan Station: on two tower platforms of 10m(T1) and 15m(T2) heights, and at two ground sites less than 20 m away from the tower platforms (F1, F2) (Fig. 1). Identical bulk samplers were installed 1-2m above the surfaces of tower platforms and ground as a measure against splashed rain input. The samples were immediately frozen and transferred to the laboratory at Seoul National University (SNU). In the lab, the samples were melted and filtered through 0.2 μm Nylon syringe filters. After discarding the first 0.5 mL, the filtered rainwater was stored in pre-cleaned HDPE bottles. Microbial growth was prevented by adding thymol to the final concentration of 0.4 μg L^-1, which did not affect the determination of major ion concentrations (Granat et al., 2001). Samples were then stored in a refrigerator until analysis. Sample handling was performed in laminar flow clean benches/ hoods to minimize contamination. The sampling procedure is also described in Soyol-Erdene et al. (2011).

Major ion concentrations were determined by ion chromatography (IC, ICS2000, Dionex) at SNU. Cation (Na⁺, K⁺, Ca²⁺, Mg²⁺) contents were also measured using inductively coupled plasma atomic emission spectrometer (ICP-AES, Optima-4300 DV, Perkin- Elmer) at Korea Basic Science Institute. The internal precision for all measurements was less than 10% (relative standard deviation, RSD). Accuracy was tested with two certified reference materials, BCR-CRM 408 (simulated rainwater, low contents) and SLRS-4 (river water), and we obtained results within 10% of the certified values. The ICP-AES results agreed with the IC results within 8.5% except one very low potassium datum (17.6% for sample GS27-T1). The IC and ICP-AES data were averaged.

2. 4 Comparison to Aerosol Data

To compare the rainwater and aerosol compositions, previously reported data were compiled for water-soluble components (Na⁺, NO₃^-, SO₄^2- and NH₄⁺) of total suspended particulate matter (Table 1). At Gosan Station, there were data obtained at 3 and 6 m above ground.

Table 1.

Water-soluble ion concentrations of total suspended particulates at Gosan Station.

3. 1 Ion Ratios as Proxies for the Local Influence of Terrestrial Sources

We analyzed the 14-year rainwater composition data as a function of wind direction and speed to investigate the local influence. The NO₃^-/Na⁺, nss-SO₄^2-/ Na⁺ and NH₄⁺/Na⁺ ratios (all are molar ratios hereafter) were chosen to represent the terrestrial/marine sources. The time series of NO₃^-/Na⁺ ratio along with precipitation and averaged wind direction and speed is given in the Appendix. Since marine aerosols are depleted in nitrate (NO₃^-/Na⁺<10^-4; Parungo et al., 1987), an easterly wind delivering terrestrial aerosols would increase the NO₃^-/Na⁺ ratio, and the NO₃^-/Na⁺ ratio will vary with the prevailing wind direction during rainfall events. To test this, the wind direction was divided into eight sectors of 45°each (I-VIII; Fig. 1), taking into consideration the positions of adjacent buildings and structures that can act as windbreaks. Then the NO₃^-/Na⁺ ratios of rainfall events during the 14 years were averaged for each wind sector. As expected, the NO₃^-/Na⁺ ratio varied with the wind direction, with higher ratios (0.63-0.75) for the inland sectors (III-V) compared with the seaward sectors (VIVIII) (0.23-0.53) (Fig. 2a). Another statistical way of displaying the wind-directional variability is to identify the prevailing wind direction associated with anomalously high NO₃^-/Na⁺ ratios. If we designate the outliers (>mean plus 3σ (1.30)) as “peak” events, the inland sectors had more frequent NO₃^-/Na⁺ peak events than the seaward sectors (Fig. 2b; Appendix). We can conclude that there is a local source of nitrate in the east.

Fig. 2.

(a) The molar ratios of NO3-/Na+, nss-SO42-/Na+ and NH4+/Na+ averaged for each wind sector. The roman numerals indicate the wind sectors, and the number of samples for each wind sector is listed underneath. The error bars correspond to the standard error. (b) The frequencies of peaks (>mean+3σ) for NO3-/Na+, nss-SO42-/Na+ and NH4+/Na+ ratios.

Fig. 3 shows how the NO₃^-/Na⁺ ratio varies with the wind speed. Regardless of the wind sectors, the NO₃^-/Na⁺ ratio decreased with increasing wind speed, reflecting the greater contribution of local marine aerosols under stronger wind conditions. For the seaward sectors, the NO₃^-/Na⁺ ratio exhibited only a slightly decreasing trend with increasing wind speed (slope=-0.02 m s^-1). In contrast, the inland sectors displayed a more significant decreasing trend (slope=-0.08 m s^-1; Fig. 3), due to the prominent NO₃^-/Na⁺ peaks below wind speeds ~7 m s^-1. When wind is stronger, it seems that the contribution of local marine aerosols exceeds that of terrestrial sources, as is expected for island sites surrounded by the sea.

Fig. 3.

The relationship between the NO3-/Na+ ratio and wind speed. Note the change in scale (logarithmic to linear) at NO3-/Na+=1. The shaded interval marks the wind speed above which the predominant source of the local aerosol changes from terrestrial to marine.

Air mass back trajectory analysis was performed for the NO₃^-/Na⁺ outliers to verify that these peak events represent local input and were not driven by transport from remote sources. For the 43 peak events (out of 48) for which GDAS meteorological data were available, we obtained 3-day back trajectories with starting heights of 50, 500 and 1,500 m above ground level at 3 hour intervals. The results are illustrated as frequencies of intersection between each map grid cell (0.25 ×0.25 degree) and air mass trajectories (Fig. 4). No remote source region or preferred air flow pathway could be identified (Fig. 4), supporting the local origin for the NO₃^-/Na⁺ peak events.

Fig. 4.

The frequency distribution map for trajectories computed for 43 peaks in NO3-/Na+ ratios, which are obtained at starting heights of 50, 500 and 1500 m above ground level.

We repeated the same analyses with nss-SO₄^2-/Na⁺ and NH₄⁺/Na⁺ ratios. Again, dependence on the wind direction was evident (Fig. 2). The coherence of those three ratios can be attributed to (1) the wind directional variation in marine aerosol input and (2) the supply of NO₃^-, nss-SO₄^2- and NH₄⁺ from eastern terrestrial sources. The local production of marine aerosols is evident (Fig. 3). However, the decreasing wind directional variability in the order of NO₃^-/NH₄⁺, nss-SO₄^2-/NH₄⁺ and NO₃^-/nss-SO₄^2- (Fig. 5) suggests that local terrestrial input of NO₃^- does exist and surpasses those of nss-SO₄^2- or NH₄⁺. This will be discussed further in Section 3.3.

Fig. 5.

The molar ratios of NO3-/nss-SO42-, NO3-/NH4+ and nss-SO42-/NH4+ averaged for each wind sector.

3. 2 Meter-scale Spatial Variability of Rainwater Composition

Two sets of concurrent samples (GS22 and GS27) with contrasting wind conditions and ion contents provide an opportunity to further examine the influence of wind direction on rainwater composition. During event GS22 wind blew from the seaward sectors (I and VIII), whereas winds from the inland sector (IV) prevailed during event GS27 (Fig. 6). GS27 had significantly higher ion content than GS22, with average total anionic charge of 956 μeq L^-1 and 254 μeq L^-1, respectively. The concentrations of the individual ions were also higher in GS27, especially for NO₃^- and SO₄^2-.

Fig. 6.

Wind diagram for samples GS22 and GS27 with the relative frequency (%) of wind direction. Each sample is characterized by a distinct wind direction.

A plot of nss-SO₄^2-/Na⁺ vs. NO₃^-/Na⁺ is useful for illustrating the relative contributions of the marine and terrestrial inputs (Fig. 7). This was verified by compiling rainwater composition monitored at various sites in Korea (Kim et al., 2006; Kang et al., 2003; Lee et al., 2000). In Fig. 7, the coastal sites plot near the origin due to the abundant input of marine aerosols, and an increase of the terrestrial contribution in the inland sites elevates those ratios along a line with a slope of ~1. The good linearity between nss-SO₄^2-/Na⁺ and NO₃^-/Na⁺ ratios qualifies the use of these ratios in assessing the relative contributions of the sea and land. In addition, the slope suggests that the primary source of nss-SO₄^2- and NO₃^- is characterized by a molar ratio ~1 in the Korean peninsula.

Fig. 7.

Plot of nss-SO42-/Na+ vs. NO3-/Na+ molar ratios for samples GS22 and GS27. Gray circles are literature values compiled from previous long-term rainwater composition monitoring conducted in Korea. Area near the origin is expanded in the inset.

As expected from the wind direction, the marine-influenced GS22 samples all plot near the origin (Fig. 7). GS27 samples have higher ratios and a wide range of both nss-SO₄^2-/Na⁺ and NO₃^-/Na⁺ ratios (Fig. 7), highlighting meter-scale variability of rainwater composition. This likely results from uneven contributions of locally produced aerosols to individual rain collectors due to different sampling conditions of the collectors - e.g., the collector height. The nss-SO₄^2-/Na⁺ and NO₃^-/Na⁺ ratios increase in the order of T2-T1-F1-F2 for rain event GS27. This means that the ground level sites (F1 and F2) are affected more by locally produced aerosols than tower level sites.

3. 3 Comparison of Rainwater and Aerosol Compositions

We can gain a more comprehensive understanding of the local aerosols by examining the published chemical composition of aerosols at two different heights (3 m and 6 m) (Hong et al., 2011; Kundu et al., 2010; Lee et al., 2010; Kawamura et al., 2004; Park, 2003; Carmichael et al., 1997). The ion contents for watersoluble components of aerosols were generally higher at 3 m than at 6 m (Table 1). For the three ion ratios used in the analysis of the rainwater data, NO₃^-/Na⁺ was higher at the 3m site, whereas nss-SO₄^2-/Na⁺ and NH₄⁺/Na⁺ were higher at the 6 m site (Fig. 8a). The NO₃^-/nss-SO₄^2- and NO₃^-/NH₄⁺ ratios were higher at the 3 m site (Fig. 8b), which implies that the increase of NO₃^- concentration toward the ground level was greater than those of nss-SO₄^2- and NH₄⁺. The higher relative abundance of NO₃^- compared to nss-SO₄^2- and NH₄⁺ at the 3m site can be explained by a greater association of NO₃^- with larger particles. Larger particles settle down faster than smaller particles and hence are more abundant near the surface. In another study for the aerosol size distribution of the water-soluble ions at Gosan Station, NO₃^-/NH₄⁺ and NO₃^-/nss-SO₄^2- ratios were shown to increase with particle size - 0.16 and 0.25 (PM_1.0), 0.25 and 0.34 (PM_2.5), and 0.48 and 0.69 (PM₁₀) (Lim et al., 2012), supporting the association of NO₃^- with larger particles. Local aerosols are produced from the ground surface and are more influential to the lower elevation site.

Fig. 8.

Molar ratios of water-soluble ions in total suspended particulates collected at 3 m and 6 m height at Gosan Station: (a) NO3-/Na+, nss-SO42-/Na+ and NH4+/Na+ and (b) NO3-/nss-SO42-, NO3-/NH4+ and nss-SO42-/NH4+. Open symbols indicate the rainwater composition obtained in this study.

There is a caveat to comparing rainwater concentration with aerosol data, because rainwater vertically integrates the aerosol composition above the sampling site whereas aerosols are monitored at a specific height. Nevertheless, the averaged ionic ratios of the rainwater plotted on extensions of the aerosol trends (Fig. 8) and showed that the local influence for NO₃^- exceeds those of nss-SO₄^2- and NH₄⁺ near the ground level. Additionally, the wind directional variation in NO₃^-/ NH₄⁺ supports the greater supply of nitrate from local terrestrial sources (Fig. 5).

3. 4 Quantification of the Local Influence on the Wet Deposition Flux

Quantification is necessary to evaluate how significant the local input is to the total wet deposition flux. Using the 14-year database, we estimated the local contribution of NO₃^-, SO₄^2- and NH₄⁺ in rainwater. The measured total wet deposition flux of an ionic species i (i=Na⁺, NO₃^-, SO₄^2- or NH₄⁺), F_i (mol m^-2, concentration [mol m^-3]×precipitation [m]), is the sum of the fluxes that originate from long-range transport (F_i,R) and from local sources (F_i,L): F_i=F_i,R+F_i,L. We define f as the fraction of the flux transported longrange: F_i=F_i,R / F_i. Then, the flux ratio reflecting the long-range transport of two different ionic species i₁ and i₂ can be expressed in terms of the ratios of f and total fluxes:

The total flux ratio (F_i1/F_i2) can be replaced by the measured concentration ratios i₁/i₂ - e.g., NO₃^-/Na⁺ when i₁ and i₂ are NO₃^- and Na⁺, respectively. We tried to estimate the F_i1,R /F_i2,R ratios given the measured F_i1/F_i2 ratios and optimizing for f_i1 and f_i2. To render the problem solvable, we assumed that the local and long-range inputs can be differentiated based on their wind-directional variability. That is, the concentration ratios of aerosols transported long-range (F_i1,R /F_i2,R) should remain constant whatever the local wind direction, but the concentration ratios of aerosols generated locally (F_i1,L /F_i2,L) are expected to carry more terrestrial signature if the local wind blows from inland.

The initial values for f_Na⁺, f_NO3^-, f_SO4^2- and f_NH4⁺ were set randomly between 0 and 1 and optimized using a genetic algorithm from the Matlab Optimization Toolbox 6.2. The flux ratios used were: $$ \frac{F_{{NO}_3^{-},R}}{F_{{Na}^{+},R}}$$, $$ \frac{F_{{nss-SO}_4^{2-},R}}{F_{{Na}^{+},R}}$$, $$ \frac{F_{{NH}_4^{+},R}}{F_{{Na}^{+},R}}$$, $$ \frac{F_{{NO}_3^{-},R}}{F_{{nss-SO}_4^{2-},R}}$$, $$ \frac{F_{{NO}_3^{-},R}}{F_{{NH}_4^{+},R}}$$ and $$ \frac{F_{{nss-SO}_4^{2-},R}}{F_{{NH}_4^{+},R}}$$. The mean long-range flux ratios were calculated in two different ways -(1)$$ \frac{1}{n}\sum \left(\frac{F_{i_1,R}}{F_{i_2,R}}\right)$$ and (2)$$ \frac{1}{n}\left(\frac{\sum F_{i_1,R}}{\sum F_{i_2,R}}\right)$$ where n is the number of records used in the calculation (n=493). The algorithm seeks the minimum f that makes the mean flux ratios (1) and (2) of the 8 different wind sectors coincide within a predefined range. We named this predefined range the natural wind directional variability, and we let it vary from 5% to 50% (the x-axis in Fig. 9). For example, a natural wind directional variability of 5% means that the resultant mean F_i1,R /F_i2,R ratios can have at most 5% difference among the 8 different wind sectors. The minimum f values thus calculated results in maximum F_i,R and therefore represents the lower limit of the local influence. The optimization result is shown in Fig. 9. When the natural wind directional variability was set to 5%, it was estimated that at least 10% of nitrogen (N; NO₃^- +NH₄⁺) and 12% of sulfur (S; nss-SO₄^2-) had local origin. When higher variability was permitted, local influence was reduced and more portions were assigned to long-range transport. However, it should be noted that these modeled values represent the minimum possible local influence. Furthermore, in order to retain the simplicity of the model calculation, we did not consider the particle size distribution of the ions and the effect of the wind speed which may add uncertainty to the model results.

Fig. 9.

Model results for quantification of the local influence. The lower limits of the local influence on the total nitrogen and sulfur deposition are estimated, assuming an a priori level of wind directional variability (see text for explanation).