Jong-Uk Park1, Sang-Woo Kim This email address is being protected from spambots. You need JavaScript enabled to view it.1, Patrick J. Sheridan2, Alastair Williams3, Scott D. Chambers3

School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Korea
NOAA, Global Monitoring Laboratory, Boulder, CO 80305, USA
Australian Nuclear Science and Technology Organisation (ANSTO), Lucas Heights, NSW 2234, Australia


 

Received: November 18, 2019
Revised: February 24, 2020
Accepted: April 3, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.2019.11.0599  


Cite this article:

Park, J.U., Kim, S.W., Sheridan, P.J., Williams, A. and Chambers, S.D. (2020). Long-term Variability of Aerosol Optical Properties at Mauna Loa. Aerosol Air Qual. Res. 20: 1700–1711. https://doi.org/10.4209/aaqr.2019.11.0599


HIGHLIGHTS

  • We studied the aerosol scattering/absorption properties at the Mauna Loa Observatory.
  • We explored the diurnal variation of these properties in free troposphere conditions.
  • The seasonal variation and property trends are analyzed based on the airmass origin.
 

ABSTRACT


We investigated the variability of the aerosol scattering (σsp; 1974–2015) and absorption (σap; 2000–2015) coefficients at the Mauna Loa Observatory using surface in situ measurements. Although σsp decreased during the morning (1.85 ± 3.43 Mm–1 at 550 nm, 8–11 local standard time [LST]), it increased during the afternoon (3.72 ± 7.63 Mm–1 at 550 nm, 14–17 LST) due to the development of thermally induced boundary layer winds. No distinct diurnal variation was observed in σap. The obvious increase in σsp and σap during the spring under free troposphere conditions (8–11 LST) is attributed to long-range-transported aerosols from Asia, especially dust and pollution aerosols from Northeast Asia and biomass burning aerosols from Southeast Asia. Accordingly, σsp increased from 1974 till 2015 (at 1.89% year–1), whereas no significant trend was noted for either σsp or σap from 2000 till 2015. An increasing trend for σsp prevailed in air masses originating in Northeast Asia (+0.51 Mm–1 decade–1).


Keywords: Aerosol in-situ measurement; Aerosol scattering coefficient; Aerosol absorption coefficient; Mauna Loa.


INTRODUCTION


Optical and radiative properties of atmospheric aerosols depend on their chemical compositions, shapes, and particle size distributions (Haywood and Ramaswamy, 1998; Delene and Ogren, 2002; Jacobson, 2002). These properties exhibit high spatial and temporal variations because of the relatively short lifetime and uneven geographical distribution related to emissions, chemical processes in the atmosphere, and weather patterns (Delene and Ogren, 2002; Andrews et al., 2011; Boucher et al., 2013; Collaud Coen et al., 2013; Park et al., 2019). Even though the space-based and ground-based remote sensing measurements allow the quantification of aerosol optical properties (AOPs) at increased spatio-temporal resolutions, they still have limitations retrieving sufficiently accurate AOPs other than the aerosol optical depth (AOD). Surface in situ measurements of AOPs play a crucial role (Hansen et al., 1995) in the reduction of the uncertainty by providing essential information in a more direct way (Andrews et al., 2011; Park et al., 2019).

Continuous, long-term measurements of aerosols, especially in the free troposphere (FT), are needed to understand their long-range transport, trends, and global or regional climate effects (Laj et al., 2009). Aerosols in the FT are spatially more representative than observations within the boundary layer because the lifetime of atmospheric aerosols lifted into the FT can be extended up to several weeks (Kent et al., 1998), and they can travel much faster and further due to the strong prevailing winds (McKendry et al., 2001; Wandinger et al., 2002; Liu et al., 2003; Mattis et al., 2008; Uno et al., 2009).

Measurements of AOPs at the Mauna Loa Observatory (MLO; 19.54°N, 155.58°W, 3397 m above mean sea level) were conducted by the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) Global Monitoring Division (GMD), as part of the NOAA Federated Aerosol Network (NFAN; Andrews et al., 2019). The MLO has been considered as an ideal location to monitor the FT background aerosol properties because of its geographical location (Lee et al., 1994; Ryan et al., 1997; Perry et al., 1999; Andrews et al., 2019). However, several model simulations and in situ measurements revealed that MLO is affected by both long-range-transported (LRT) aerosols, and aerosols entrained from the local planetary boundary layer (PBL; Mendonca, 1969; Shaw, 1980; Bodhaine et al., 1981; Miller, 1981; Darzi et al., 1982; Merrill et al., 1989; Harris et al., 1990; Bodhaine et al., 1995; 1996; Ryan, 1997; Perry et al., 1999; Takemura et al., 2002; Eck et al., 2005; Sharma and Barnes, 2016). To investigate the FT background of aerosol characteristics at MLO, it is necessary to deconvolute the influences from LRT and the local PBL.

In this study, we investigate the aerosol scattering and absorption properties at MLO from surface in situ measurements. We explore the diurnal variation of AOPs and determine the FT conditions (i.e., by excluding local influences) using Rn-222 concentrations. Seasonal variations and the trend of AOPs are then analyzed according to airmass origin.


METHODS


Hourly mean aerosol scattering coefficient (σsp) measured for total suspended particles without size cuts (January 1974–April 2000), and for sub-10 µm particles (April 2000–December 2015) with nephelometers at MLO, were used in this study. This is because the aerosol impactor system, which switches every 6 minutes for measuring sub-10 µm and submicron particles, was installed in April 2000 (Sheridan et al., 2001; Delene and Ogren, 2002). The aerosol absorption coefficient (σap) for sub-10 µm particles measured with filter-based absorption photometers (i.e., particle soot absorption photometer [PSAP] and continuous light absorption photometer [CLAP]) from April 2000 were analyzed. Both σsp and σap were measured under low relative humidity (RH; < 40%; Sheridan et al., 2001) and were corrected to standard temperature and pressure (STP; i.e., 273.15 K and 1013.25 hPa). All σsp and σap data (Level 2) were downloaded from NOAA/ESRL/GMD (ftp://ftp.cmdl.noaa.gov/aerosol/mlo/). Detailed descriptions of instruments, data periods, data corrections, and associated uncertainties are listed in Table 1


Intensive AOPs, such as single-scattering albedo (SSA), scattering Ångström exponent (SÅE), and absorption Ångström exponent (AÅE , were derived from σsp and σap to examine more detailed aerosol radiative and physical characteristics (Delene and Ogren, 2002). In this study, SSA was calculated at 550 nm (Eq. (1)). Herein, σap was adjusted to a wavelength (λ) of 550 nm by using the 1/λ dependence of aerosol light absorption (van der Hulst, 1957; Bergstrom et al., 2002).

Single Scattering Albedo (550 nm) = 

SÅE was calculated from σsp at 450 and 700 nm wavelengths:

 

These intensive AOPs were calculated only if σsp ≥ 1 Mm–1 and σap ≥ 0.1 Mm–1 to avoid substantial relative uncertainties which were induced when σspap) was close to the detection limit.

Hourly mean Rn-222 volume concentrations (mBq m–3) recorded at MLO since 2003 enabled us to identify the time of the day when the observatory is least perturbed by local influences. Rn-222 is a naturally occurring radioactive gas with a relatively short half-life (3.82 days; Turekian et al., 1977). Additionally, the main influx of Rn-222 to the atmosphere is attributed to the land surface, which is 2–3 orders higher than the oceanic flux (Schery and Huang, 2004). Rn-222 remains in a gaseous state in the atmosphere and is known to be removed solely by its radioactive decay due to its hydrophobicity and nature as a noble gas (Turekian et al., 1977). Therefore, Rn-222 can be considered as an ideal tracer for identifying terrestrial (soil) influences (Chambers et al., 2011, 2013, 2016).

Airmass backward trajectories (BTs) over 10-day periods (240 h), calculated using the NOAA Air Resources Laboratory’s Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (v4.0; Draxler and Hess, 1998) from the location of MLO, were used to identify the source regions of aerosols at the MLO in FT conditions. National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data (.gbl) was utilized as an input meteorological field (e.g., horizontal and vertical winds; Kalnay et al., 1996). Each BT was constructed every hour and was paired with hourly mean AOPs to identify the relationship between the AOPs and the origin of the airmass.

The trends of σsp and σap with their significance over the study period were examined with the Theil-Sen slope method and the Mann-Kendall (MK) test. The Theil-Sen method calculates the slope of the possible trend with a nonparametric approach. It uses the median value of the calculated slopes from all possible pairs of data synched with time information. The Mann-Kendall test is a nonparametric statistical test which is well suited to identify small but monotonic trends. These are typically used because of their insensitivity to missing values and outliers in a time series (Mann, 1945; Kendall, 1975; Gilbert, 1987; Collaud Coen et al., 2013).


RESULTS AND DISCUSSION


The median value of σsp at 550 nm based on measurements collected during a 42-year period (1974–2015) was 0.94 Mm–1, and the median value σap at 550 nm during the period of 2000–2015 was 0.13 Mm–1. The overall mean values of σsp and σap at 550 nm were 2.47 Mm–1 and 0.28 Mm–1, respectively, with relatively large standard deviations (4.99 Mm–1 and 0.45 Mm–1). This implies that MLO is operating under the pristine conditions most of the time, but it is intermittently affected by highly aerosol-loaded airmasses.


Diurnal Variation of AOPs and Determination of FT Condition

Fig. 1 shows the diurnal variation of in situ AOPs and Rn-222 concentrations at MLO. σsp exhibited a distinct diurnal variation, with increases in the afternoon hours (14–17 LST) and decreases during the morning hours (8–11 LST). High aerosol loadings, as indicated by σsp, during the afternoon hours, can be explained by the prevailing thermally induced (anabatic) winds up the flanks of Mauna Loa Mountain. Elevated Rn-222 concentration—which represents how recently the air mass was in contact with the land surface (Chambers et al., 2011, 2013)—is usually associated with the upslope wind which develops along the ridge of the mountain during the afternoon hours, whereas the upslope wind is also responsible for the influx of the PBL aerosols to MLO (Ryan et al., 1997). Meanwhile, no significant diurnal variation was observed in σap. The transport of scattering-dominant maritime aerosols from PBL to MLO by the aforementioned upslope wind is thought to be the reason for the elevated SSA in the afternoon, since MLO is located on the Big Island of Hawaii, where no particular industrial activities are held (DBEDT, 2019). 


Fig. 1. Diurnal variation of the aerosol optical properties (AOPs; σsp, σap, SÅE and SSA) and 222Rn concentration at the Mauna Loa Observatory (MLO). (a) Box-and-whisker plot, whereby the whiskers represent the 10th and 90th percentiles, and the horizontal lines in boxes represent the 25th, 50th, and 75th percentiles of the hourly values. The mean values are denoted with red dots. (b) Annual cycle of the diurnal variability of AOPs. Variables are normalized with the maximum hourly average value of the month. The least locally influenced (LLI) period (8–11 LST) is denoted by the white dotted line.Fig. 1. Diurnal variation of the aerosol optical properties (AOPs; σsp, σap, SÅE and SSA) and 222Rn concentration at the Mauna Loa Observatory (MLO). (a) Box-and-whisker plot, whereby the whiskers represent the 10th and 90th percentiles, and the horizontal lines in boxes represent the 25th, 50th, and 75th percentiles of the hourly values. The mean values are denoted with red dots. (b) Annual cycle of the diurnal variability of AOPs. Variables are normalized with the maximum hourly average value of the month. The least locally influenced (LLI) period (8–11 LST) is denoted by the white dotted line.

Interestingly, Rn-222 concentrations typically decreased between approximately 8 and 11 LST, which is a transition period between dominant, thermally driven katabatic (downslope) and anabatic (upslope) winds (Ryan et al., 1997; Chambers et al., 2013). In this study, we designated these hours of the day (8–11 LST) as Least Locally Influenced (LLI) hours to examine AOPs in FT conditions. A summary of the values of σsp and σap over 24-h and LLI-h periods is listed in Table 2. Both daily mean and median σsp values were approximately 34% and 32% higher than those of the LLI hours, respectively, whereas the σap values corresponding to the 24-h and LLI-h periods were not significantly different. Compared to the daily mean, the slightly lower SSA (0.87 ± 0.12) and higher SÅE (1.35 ± 1.24) values during the LLI h (see the white dashed boxes in Fig. 1(b)) suggest that the aerosols in FT conditions are more absorbing and slightly larger. 



Seasonal Variation of FT Aerosols


Air Mass Origins

Fig. 2 shows the aerosol optical depth (550 nm) from 13-year Moderate Resolution Imaging Spectroradiometer (MODIS) measurements with selected air mass source regions, and the monthly variation of percentages of air mass origins estimated based on BTs constructed over 10-day periods. The largest fraction of airmasses that reached MLO was from the Pacific Ocean (PO; 43.5%). Approximately 24.7% and 13.1% of airmasses originated from Northeast Asia (NE Asia) and Southeast Asia (SE Asia), respectively, the largest emission sources of natural (dust, biomass burning) and anthropogenic aerosols, as indicated by the MODIS-derived AOD. Several studies reported that aerosol properties at MLO were largely affected by Asian outflow (Bodhaine et al., 1981; Bodhaine, 1995; 1996; Perry et al., 1999; Eck et al., 2005). It is noteworthy that the AOPs at MLO were not much influenced by airmasses that originated from other regions (18.7%; Central America, continents in North Pacific Ocean, North America and continents in the Southern Hemisphere).

MLO is more frequently influenced by Asian airmasses (> 50%) from December to April due to the southward shift of the Intertropical Convergence Zone (ITCZ; Henderson-Sellers and Robinson, 1991; Schneider et al., 2014). By contrast, airmasses from Asia substantially decrease during June–September due to weakening FT westerlies along the Hawaiian Islands by the northward shift of the ITCZ. Instead, airmasses from North and Central America increase, even though the MLO is typically located above the trade wind inversion (TWI) layer. This is attributed to the weakened TWI due to the increased thermal instability together with an updraft induced from large-scale circulation (Hastenrath, 1991). 


Fig. 2. (a) Source region designation for quantification of their contributions on the free troposphere (FT) σsp and σap values measured at the MLO. The background color contour is a composite of monthly averaged Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua dark target aerosol optical depth (AOD) from 2003 to 2015 (Level 3, 1 × 1 resolution). (b) Monthly variations of air mass source regions. Monthly frequencies of air masses from the Pacific Ocean (PO; blue), Northeast Asia (NE Asia; red), Southeast Asia (SE Asia; green), and other regions (OR; gray).Fig. 2. (a) Source region designation for quantification of their contributions on the free troposphere (FT) σsp and σap values measured at the MLO. The background color contour is a composite of monthly averaged Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua dark target aerosol optical depth (AOD) from 2003 to 2015 (Level 3, 1° × 1° resolution). (b) Monthly variations of air mass source regions. Monthly frequencies of air masses from the Pacific Ocean (PO; blue), Northeast Asia (NE Asia; red), Southeast Asia (SE Asia; green), and other regions (OR; gray).


Aerosol Optical Properties

Monthly variations of σsp, σap, SÅE, and SSA are shown in Fig. 3. The distinct springtime peaks of σsp and σap are apparent in both the 24-h and LLI-h periods. Both the σsp and σap values at 550 nm during the spring months (March–May) were 4.52 ± 7.35 Mm–1 and 0.49 ± 0.59 Mm–1, respectively. These were almost twice as large as the annual mean (σsp: 2.47 ± 4.99 Mm–1; σap: 0.28 ± 0.45 Mm–1). Similarly, the values of σsp and σap in FT conditions (i.e., during LLI hours) during the spring exhibited values approximately twice as large (3.55 ± 4.79 Mm–1 and 0.49 ± 0.52 Mm–1, respectively) as their annual mean values (σsp: 1.85 ± 3.43 Mm–1; σap: 0.29 ± 0.42 Mm–1). Enhanced σsp and σap values in FT conditions during the spring can be explained by the FT transport of aerosols, particularly from the Asian continent. 


Fig. 3. Monthly variation of AOPs (σsp, σap, SÅE and SSA) observed at the MLO. The whiskers represent the 10th and 90th percentiles, and the horizontal lines in boxes represent the 25th, 50th, and 75th percentiles of monthly data. Monthly averages are denoted as dots. Percentiles of 24-h measurements are shown in red, while percentiles of LLI-h data are shown in blue.Fig. 3. Monthly variation of AOPs (σsp, σap, SÅE and SSA) observed at the MLO. The whiskers represent the 10th and 90th percentiles, and the horizontal lines in boxes represent the 25th, 50th, and 75th percentiles of monthly data. Monthly averages are denoted as dots. Percentiles of 24-h measurements are shown in red, while percentiles of LLI-h data are shown in blue.

SÅE was relatively low in the spring compared with summer and autumn. This can be explained by the relatively coarse dust particles from NE Asia. Monthly mean SSA at 550 nm ranged between 0.83 and 0.88 from October to April, while it remained > 0.9 during the summer. Observations of SSA smaller than 0.8 during the autumn can be attributed to preferential scavenging of light-scattering aerosols by clouds, fog and/or precipitation at low-σsp and -σap conditions (Andrews et al., 2011). Frequent transport of light-absorbing aerosols from NE and SE Asia is responsible for low SSA values from January to April.

Figs. 4 and 5 show the monthly variations of σsp and σap in FT conditions according to the airmass origin, and the contributions of each airmass source region on σsp and σap. We note that the contribution of the airmass origin (Cσi,j) to σspap) was normalized for the ith source region and jth month:

where N is the number of events. Elevated σsp and σap values were apparent in the spring with prevailing airmass transportation from NE and SE Asia. It should be noted that monthly variations of σsp and σap do not always coincide with the frequency of airmass source regions. Compared to the spring, more airmasses from NE and SE Asia reached the MLO in the winter. However, Asian airmasses contribute more to elevated σspap) values in the spring. For example, the contributions of transported aerosols over long ranges from NE Asia (i.e., pollution and Asian dust particles) and from SE Asia (i.e., biomass burning aerosols which is listed in parenthesis) in March were estimated to be 45% (26%) for σsp and 40% (25%) for σap. This temporal discrepancy between AOPs and the frequency of airmass impacting MLO is attributed to atmospheric conditions at the source regions as discussed below.

Although pollution emission is generally maximized during the winter in NE Asia, an increased atmospheric stability over NE Asia during the wintertime due to the development of the Siberian High inhibits the entrainment of aerosols from the boundary layer to FT (Cai et al., 2017). However, more favorable atmospheric conditions for the entrainment to FT occur in the spring, including the more frequent occurrence of frontal lifting (Bey et al., 2001). In SE Asia, large-scale biomass burning frequently occurs from January to April. These biomass burning aerosols can be entrained from a boundary layer to FT by atmospheric convection, and then further transported to downwind regions (Garreaud, 2001; Liu et al., 2003; Lee et al., 2017; Nam et al., 2018; Park et al., 2019). Airmasses in the mid-troposphere tend to move westward from the late spring due to the influences from the Tibetan High (Liu et al., 2003), so the transport of aerosols from SE Asia to MLO is subsequently decreased.  Interestingly, airmasses which travel only within the PO during the spring yield higher σsp and σap values compared to other months (Fig. 4). This is because the Asian aerosols, which are extensively distributed over the Pacific, can also reach MLO. More investigations on aerosol loadings and associated aerosol optical and radiative properties over broad Pacific regions during the spring are thus needed (Brock et al., 2019; Katich et al., 2018). 


Fig. 4. Monthly variations of σsp and σap for the air masses from the PO, NE Asia, and SE Asia regions. Cross lines in boxes represent the 25th, 50th, and 75th percentiles, and whiskers represent the 10th and 90th percentiles. Mean values of each month are denoted by asterisks. Data for σsp and σap from the period of 2000–2015 are utilized.Fig. 4. Monthly variations of σsp and σap for the air masses from the PO, NE Asia, and SE Asia regions. Cross lines in boxes represent the 25th, 50th, and 75th percentiles, and whiskers represent the 10th and 90th percentiles. Mean values of each month are denoted by asterisks. Data for σsp and σap from the period of 2000–2015 are utilized. 


Fig. 5. Monthly variation of source region contributions on σsp and σap at the MLO. PO, NE Asia, SE Asia, and other regions (OR) are denoted with blue, red, green, and gray colors, respectively.Fig. 5. Monthly variation of source region contributions on σsp and σap at the MLO. PO, NE Asia, SE Asia, and other regions (OR) are denoted with blue, red, green, and gray colors, respectively.


Systematic Relationships Among AOPs

We investigated the relationships among AOPs for three major contributing source regions (PO, NE Asia, and SE Asia) to explore the aerosol characteristics, such as their types, sources, and processes (Andrews et al., 2011; Sherman et al., 2015; Schmeisser et al., 2017). Statistical comparisons of σsp and σap for three source regions are listed in Table 3. The highest σsp and σap values were apparent in the NE Asian airmass, whereas the PO airmass yielded the lowest values. 


As σsp increases, σap also increases in all three regions (Fig. 6(a)). The higher slope between σsp and σap for the airmass from SE Asia suggests that the aerosols from SE Asia have a lower SSA than others, as shown in Fig. 6(b). Similarly, SSA also gradually increases as σsp increases. Selective scavenging of larger scattering aerosols is possible given that the removal of larger particles generally result in low aerosol concentration with higher absorption parts over the total extinction (Sellegri et al., 2003; Andrews et al., 2011). Contrary to the PO and SE Asia, SÅE gradually decreases with increasing σsp for the airmass from NE Asia (Fig. 6(c)). This relationship can be explained by the transport of coarse dust particles from arid and desert areas in NE Asia (Lee et al., 2012). An increasing SÅE with increasing σsp values for SE Asian airmasses is likely attributed to the fine-mode biomass burning aerosols (Toledano et al., 2007; Andrews et al., 2011; Schmeisser et al., 2017). SÅE and AÅE between NE and SE Asia are similar, but slightly higher SÅE and lower AÅE were observed in PO airmass (not shown). 


Fig. 6. Systematic relationship between the FT AOPs at the MLO analyzed according to the source region. Red, green, and blue colors respectively indicate the NE Asia, SE Asia, and PO regions. (a–c) Systematic relations among σsp and other AOPs, such as σap, single-scattering albedo (SSA), and scattering Ångström exponent (SÅE). The average values of variables correspond to each σsp bin, which is divided in 2 Mm–1 intervals, are denoted by filled diamonds with the respective colors used for each source region. Horizontal lines in boxes represent standard errors. Bins with more than 20 valid measurements are analyzed. Linear regression lines over each source region are denoted with dotted lines using the respective colors for the studied regions.Fig. 6. Systematic relationship between the FT AOPs at the MLO analyzed according to the source region. Red, green, and blue colors respectively indicate the NE Asia, SE Asia, and PO regions. (a–c) Systematic relations among σsp and other AOPs, such as σap, single-scattering albedo (SSA), and scattering Ångström exponent (SÅE). The average values of variables correspond to each σsp bin, which is divided in 2 Mm1 intervals, are denoted by filled diamonds with the respective colors used for each source region. Horizontal lines in boxes represent standard errors. Bins with more than 20 valid measurements are analyzed. Linear regression lines over each source region are denoted with dotted lines using the respective colors for the studied regions.


Inter-annual Trend of Aerosol Scattering and Absorption Coefficients

The trends of σsp and σap in FT conditions were calculated for three major source regions (NE Asia, SE Asia, and PO). Fig. 7(a) shows the time series of the annual mean σsp and σap values over the study period. A linear trend and its significance calculated with the Theil-Sen and Mann-Kendall methods is presented in Fig. 7(b). The value of σsp increased by approximately +1.89% per year during the period of 1974–2015. The highest increasing trend of σsp since 1974 appeared in the PO air mass (+2.18% year–1), followed by NE Asia (+2.09% year–1) and SE Asia (+1.22% year–1). However, the magnitude of the increasing trend adhered to the order of a) NE Asia (+0.51 Mm–1 decade–1), b) PO (+0.32 Mm–1 decade–1), and c) SE Asia (+0.27 Mm–1 decade–1). These first two trends (a, b) are significant at a 99% confidence level, and the last trend (c) at a 95% confidence. Both the σsp and σap values yield positive trends in all three regions during the period of 2000–2015, but both are insignificant at a 95% confidence level. 


Fig. 7. (a) Time series of annual average σsp and σap values according to the source region. Each source region is depicted in blue (PO), red (NE Asia), and green (SE Asia) colors. (b) Trends of σsp and σap values according to the air mass origins. Trends and their statistical significances are calculated with the Theil-Sen slope method and the Mann-Kendall test, respectively. Significant trends at 99% (95%) confidence levels are denoted as circles (triangles), while crosses denote insignificant trends at a 95% confidence level.Fig. 7. (a) Time series of annual average σsp and σap values according to the source region. Each source region is depicted in blue (PO), red (NE Asia), and green (SE Asia) colors. (b) Trends of σsp and σap values according to the air mass origins. Trends and their statistical significances are calculated with the Theil-Sen slope method and the Mann-Kendall test, respectively. Significant trends at 99% (95%) confidence levels are denoted as circles (triangles), while crosses denote insignificant trends at a 95% confidence level.

Overall, σsp and σap at MLO is experiencing greater influence from LRT aerosol plumes. Especially, aerosol transport from NE Asia, associated with increasing anthropogenic emission due to the economic growth (and with some natural variation) was the most prominent contributing source for the increasing trend of the extensive AOPs (Liu et al., 2003; Guo et al., 2011; Kim et al., 2011; Chen and Wang, 2015). The positive trend in PO airmass is attributable to enhanced outflow of pollution aerosols from NE Asia, which finally reaches MLO. This is supported by the concurrent peak of σsp and σap found during boreal spring when the source of air mass is PO, while the only possible FT source of aerosols are FT transport originating from NE and SE Asia.


CONCLUSIONS


We investigated diurnal, monthly, and inter-annual variations in the aerosol scattering coefficient (σsp; 1974–2015) and the absorption coefficient (σap; 2000–2015) at the MLO using surface in situ measurements. The major findings of this study are summarized below:

  • The value of σsp decreased during the hours of 8–11 LST (1.85 ± 3.43 Mm–1 at 550 nm) but increased during the afternoon (3.72 ± 7.63 Mm–1 at 550 nm; 14–17 LST) due to the development of upslope boundary layer winds. No distinct diurnal variation was observed in σap.
  • The highest σsp and σap values appeared when the air masses originated in NE Asia (σsp: 2.46 Mm1; σap: 0.36 Mm1), followed by SE Asia (σsp: 2.24 Mm1; σap: 35 Mm1) and the PO (σsp: 1.46 Mm1; σap: 0.23 Mm1).
  • NE Asia and SE Asia were the most prominent sources of air masses during the winter, but their contributions to σsp and σap values peaked during the spring.
  • A distinct increase in the values of σsp and σap during the spring under FT conditions (8–11 LST) was attributed to long-range-transported dust and pollution aerosols from NE Asia and biomass burning aerosols from SE Asia.
  • The largest increasing trend for σsp after 1974 was attributed to air masses from the PO (+2.18% year–1), followed by NE Asia (+2.09% year–1) and SE Asia (+1.22% year–1). However, the increasing trend’s magnitude adhered to the order of a) NE Asia (+0.51 Mm–1 decade–1), b) the PO (+0.32 Mm–1 decade–1), and c) SE Asia (+0.27 Mm–1 decade–1). Both the σsp and σap values showed positive trends for all three regions over the period of 2000–2015, but these values were insignificant at a 95% confidence level.

Long-term, continuous climate-relevant aerosol measurements at the MLO are needed in the future to better estimate the direct aerosol radiative effects related to emissions from Asia. In particular, simultaneous measurements of aerosol chemical components will be very helpful in identifying the aerosol sources.


ACKNOWLEDGEMENTS


This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B06032548) and the Korea Meteorological Administration Research and Development Program under Grant KMI2018-01111. Authors are also thankful to operators and technicians at Mauna Loa Observatory for supporting the measurements by conducting maintenance and calibrations of instruments.


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Aerosol Air Qual. Res. 20 :1700 -1711 . https://doi.org/10.4209/aaqr.2019.11.0599  


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