Shao-En Sun1, Shih-Yu Chang2, Shuenn-Chin Chang3,4, Chung-Te Lee This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 Graduate Institute of Environmental Engineering, National Central University, Taoyuan 320, Taiwan
2 Department of Public Health, Chung Shan Medical University, Taichung 402, Taiwan
3 School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
4 Environmental Protection Administration, Taipei 100, Taiwan


Received: March 15, 2022
Revised: August 18, 2022
Accepted: August 18, 2022

 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.220132  


Cite this article:

Sun, S.E., Chang, S.Y. ,Chang, S.C., Lee, C.T. (2022). In-situ Measurement of Aerosol Water Content in an Urban Area Using a Sequential Aerosol-Water Measurement System (SAWMS). Aerosol Air Qual. Res. 22, 220132. https://doi.org/10.4209/aaqr.220132


HIGHLIGHTS

  • Aerosol water content (AWC) at 90% RH often exceeded ambient PM2.5 level.
  • Aerosol water-soluble inorganic ions contributed most AWC.
  • Measured and modeled AWC correlated well above 80% RH.
  • AWC helps nitrate formation based on the nitrogen oxidation ratio.
 

ABSTRACT


Aerosol water content (AWC) significantly affects secondary aerosol formation and atmospheric visibility. Most ambient AWC values are obtained from models because direct measurement is challenging. In this study, the sequential aerosol-water measurement system (SAWMS) was applied to measure AWC at the Xiaogang air-quality monitoring site of the Taiwan Environmental Protection Administration, located in an industrialized seaport city. The relative humidity (RH) was set at 90% in the SAWMS during measurement, and the PM2.5 AWC was 39.0 ± 14.3 µg m–3 on average, which was 140% higher than the monitored PM2.5 average concentration. Water-soluble inorganic ions (WSIIs) were analyzed offline and used in ISORROPIA II to model the AWC. The modeled and measured AWC was well-correlated (R2 = 0.85; n = 39, p < 0.05), indicating that WSIIs contributed the most to AWC. During high AWC periods, NO3 concentrations were relatively higher, suggesting that NO3 was the predominant species contributing to AWC. Humidographs were constructed to analyze the AWC values under varying RH levels during the humidification and dehumidification processes for the three selected samples. The humidification results revealed a significant difference between the measured and modeled AWC within 60–80% RH. This might be due to deviations of aerosol combination types and the mixing state from atmospheric conditions. The modeled AWC was close to the measured AWC when the RH was over 80%. The nitrogen oxidation ratio and the AWC were well-correlated (R2 = 0.60) throughout the sampling period, implying that the measured AWC was beneficial to NO3 formation in the urban area. In summary, significant differences between modeled and measured AWC appeared during the humidification and dehumidification processes when the RH was below 80%, indicating that direct measurement of AWC under varying RH levels is still necessary.


Keywords: Aerosol water measurement, Urban aerosol water content, Aerosol humidograph


1 INTRODUCTION


Atmospheric aerosols are crucial in affecting air quality, atmospheric visibility, and atmospheric radiative budgets (Coakley Jr et al., 1983; Janjai et al., 2011; Papadimas et al., 2012; Sporre et al., 2020). Hygroscopic aerosol chemical components, such as ammonium nitrate (AN), ammonium sulfate (AS), ammonium chloride (AC), and sodium chloride, easily deliquesce into a droplet in a humid environment, which increases the aerosol size and water content, enhances the aerosol scattering efficiency, and reduces atmospheric visibility (Tang and Munkelwitz, 1994; Dick et al., 2000; Wang et al., 2020). Moreover, the aerosol water content (AWC) can act as an incubator for secondary organic (SOA) and inorganic aerosol formations by promoting aqueous-phase and heterogeneous reactions (Wong et al., 2015; Faust et al., 2017; Rossich Molina and Gerber, 2019Zhang et al., 2021). AWC has been well-correlated to the sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), and has enhanced ambient secondary sulfate and nitrate formation (Zhang et al., 2021). In laboratory experiments, AS seed aerosol has been generated at low and high RH levels and exposed to SOA precursor gases (isoprene, α-pinene, toluene, and acetylene), where the deliquesced AS seed (high RH environment) yielded more SOA than the non-deliquesced seed (Wong et al., 2015; Faust et al., 2017). Although the AWC should be considered part of the aerosol mass, most methods (e.g., gravimetry, β attenuation, and oscillating microbalance) measure the dry aerosol mass. As a result, the aerosol mass concentration and impacts on atmospheric visibility might be underestimated in a high RH environment.

Several studies have measured particulate-bound water (PM-bound water) with the offline filter-based Thermal ramp Karl-Fisher (tr-KF) method (Canepari et al., 2013; Perrino et al., 2016; Widziewicz-Rzońca et al., 2020). In that method, Teflon filters are conditioned at 50% RH to investigate the PM-bound water of each chemical component (Perrino et al., 2016) and the characteristic strength (i.e., strongly or loosely) of the bound water (Widziewicz-Rzońca et al., 2020). Dai et al. (2021) modified the Dry-Ambient Aerosol Size Spectrometer (DAASS) (Stanier et al., 2004) to estimate the AWC in-situ through the aerosol size change at different RH levels. Tan et al. (2020) used aerosol backscatter coefficient from a polarization Lidar to derive that the AWC contributed more than half of the PM2.5 concentration. In addition to AWC measurements, Ding et al. (2019) used Humidified Tandem Differential Mobility Analyzer to measure the size distribution of urban aerosols and acquire the hygroscopicity parameter (κ) and the growth factor (GF) of different particle sizes. In an 80% RH environment, the accumulation mode aerosol GFs on polluted days were 1.3–1.45, whereas the GFs on clean days were 1.2–1.3; the GFs of other particle size modes were below 1.25 on either polluted or clean days (Ding et al., 2019). An alternative to measurement methods of AWC is thermodynamic equilibrium models, e.g., ISORROPIA II (Nenes et al., 1998; Fountoukis and Nenes, 2007; Zhang et al., 2021), E-AIM IV (Parworth et al., 2017), which require ambient ion concentrations and meteorological variables as input. However, ISORROPIA II provides only the AWC contributed by water-soluble inorganic ions (WSIIs) and ignores that from organics, which have been estimated to contribute as high as 32% of total AWC (Li et al., 2019). While all these methods can provide valuable information regarding the AWC, there has been no direct in-situ measurement of ambient AWC, which could reduce the inherent uncertainties in the offline, indirect, and simulation methods.

This study deployed the sequential aerosol-water measuring system (SAWMS), a direct in-situ filter-based method developed by Sun et al. (2021), in an industrialized seaport of an urban environment. The humidographs for the SAWMS Teflon filters were further acquired to focus on the deliquesced and crystallized properties of the collected aerosols. The modeled and measured AWC was compared, and the AWC was evaluated for its impact on secondary aerosol formation during the study.

 
2 METHODS


The location of the sampling site in this study was at the Taiwan Environmental Protection Administration (TEPA) Xiaogang (XG) air-quality monitoring station (22.57°N, 122.34°E) in Kaohsiung City (Fig. 1). The study was conducted from November 27th to December 5th, 2019. Samples were collected every 4 hours with a total of 39 samples (missing data were due to technical problems during site operation). Although the AWC was immediately measured after sampling, the AWC values in this study were re-analyzed offline to monitor the consistency of the result. The WSIIs were extracted from the filters after the AWC measurements and applied to a thermodynamic equilibrium model to estimate the AWC.

Fig. 1. The location and surrounding area of the TEPA XG air-quality monitoring site at Kaohsiung City in Taiwan (https://www.​google.com.tw/maps).Fig. 1. The location and surrounding area of the TEPA XG air-quality monitoring site at Kaohsiung City in Taiwan (https://www.​google.com.tw/maps).

 
2.1 Sampling Site and Instruments

The XG site has relatively high PM2.5 concentrations for Taiwan and is surrounded by a main traffic artery (5 lanes) 400 m to the north and the Kaohsiung International Airport 1 km to the north. A dense industrial area is located 1.2 km south of the site. The SAWMS was set inside the monitoring station mounted 15 m above the ground; the indoor temperature of the monitoring station was maintained at 23 ± 1°C. During the sampling period, the average ambient temperature, RH, and wind speed were 21.6°C, 67%, and 1.7 m s–1, respectively, and the prevailing wind direction was from the north.

The SAWMS has two parts: the sampling system and the measurement system. The sampling stream passes through a PM10 (particulate matter with an aerodynamic diameter below or equal to 10 µm) inlet and then a Very Sharp Cut Cyclone (VSCC, BGI Mesa Laboratories, Inc., Lakewood, CO, USA) for PM2.5 (particulate matter with an aerodynamic diameter below or equal to 2.5 µm) size selection followed by aerosol collection on the Teflon filter (PTFE, 2 µm; Ø = 25 mm; SKC Inc., Eighty Four, PA, USA). Teflon filters were selected for their hydrophobic property, reducing the background water signal in the system. Widziewicz-Rzońca et al. (2020) also showed that Teflon filters absorb less water than other filter materials. The flow rate in the sampling system was 16.7 L min–1. The measurement system rotates the sample filter to the AWC measurement position after aerosol collection. Meanwhile, a new filter is rotated in for aerosol collection, allowing continuous sampling and measurement. More descriptions of SAWMS can be found in Section 2.3 and Sun et al. (2021).

The PM2.5 mass concentration data were obtained from the Met One BAM1020 (Met One Instrument, Inc., Grants Pass, OR, USA) at the monitoring site. After the SAWMS measurement, WSIIs of PM2.5 on the filters were extracted with deionized water and analyzed by ion chromatography (IC, cations: Dionex Model ICS-1000, anions: Model Dionex Aquion, Dionex is now Thermo Fisher Scientific Inc., Waltham, MA, USA). The measured WSIIs included Na+, K+, NH4+, Mg2+, Ca2+, NO3, SO42, and Cl.

 
2.2 Aerosol Water Content

The SAWMS directly measures the total water content (Wtotal) and the system background water content (Wbackground); the AWC (Waerosol) is calculated by subtracting the Wbackground from the Wtotal as follows:

 

Wbackground includes the water mass retained in the filter, tubes, and filter holder. Sun et al. (2021) reported that the filter holder mainly contributed Wbackground in a 90% RH condition. However, Wbackground varies with different RH levels; thus, a calibration line (shown in Fig. S1 in Supplementary Material) for Wbackground in RH levels ranging from 30–90% RH was necessary.

 
2.3 Analytical Process

The 4-hour sampling time was set based on consideration of the detection limit of SAWMS and the general range of AWC expected in the sampling environment (Lee et al., 2021). Fig. 2 shows the analytical processes schematically. The collected aerosols were first dried with N2 (≈ 0% RH) by allowing N2 to flow through the tubes and filter holder. The purged water peak would appear in the GC-TCD (GC700, China Chromatography Co., Ltd., New Taipei, Taiwan) signal and return to the baseline in 10 min. After drying, the filter was conditioned with moist air at a selected RH for 30 min to measure the AWC in the humidification process and 90% RH moist air conditioning for 20 min and another 30 min conditioning at a selected RH for the dehumidification process. Conditioning times of 20 min and longer showed no difference for the AWC measurement (Sun et al., 2021); nevertheless, we chose a 30 min conditioning time for the humidification process. After the conditioning process, the absorbed aerosol water was purged out by N2 to a GC-TCD for measurement. A complete measurement process was about 40–60 min; a 4-hour aerosol collection period could afford 2–3 times of re-analysis to confirm the data reproducibility. The samples were then placed in a refrigerator (Temp. < 4°C) for storage before IC analysis of WSIIs. The analytical method for the AWC of atmospheric aerosols under varying RH levels during humidification and dehumidification processes was detailed by Sun et al. (2021).

Fig. 2. The schematic diagram of the analytical processes in SAWMS.
Fig. 2. 
The schematic diagram of the analytical processes in SAWMS.


2.4 Model and Data

The ISORROPIA II thermodynamic equilibrium model (Fountoukis and Nenes, 2007) was used to estimate the AWC based on the aerosol chemical composition, ambient temperature, and RH. The ISORROPIA II provides a “forward mode” and a “reverse mode” to run the model. In the “forward mode”, the input values of the WSIIs to the model should include gas and aerosol inorganic species that partition in and out of the aerosol, while the “reverse mode” only requires aerosol species data. In this study, the “reverse mode” was used as HNO3 and NH3 gases were not measured. In addition, ISORROPIA II offers options of “stable state” and “metastable state” suitable for modeling aerosol humidification and dehumidification processes. In the “stable state”, the aerosols can be solid, liquid, or both for modeling the deliquescence of aerosols in the humidification process. In contrast, the “metastable state” is optimal for liquid aerosols modeling water evaporation during dehumidification. In this study, the "stable state" was chosen for the humidification process and the "metastable state" for the dehumidification process in modeling.

The NOR and SOR were applied to evaluate the effects of AWC on secondary aerosol formation (Colbeck and Harrison, 1984; Zhang et al., 2021):

NO3 and SO42 concentrations were obtained from the filter analysis; NO2 and SO2 concentrations were from the monitoring data at the TEPA XG site. SOR and NOR represent the oxidation levels of sulfur and nitrogen, respectively. High SOR and NOR imply that the environment enhances the conversion of precursor gases to SO42 and NO3 (i.e., secondary aerosol formation), respectively.


3 RESULT AND DISCUSSION


 
3.1 Aerosol Water Content of the Urban Aerosol

The RH in the SAWMS was set at 90% to study the potential of aerosol water absorption in a humid environment. Fig. 3 shows the measured PM2.5 AWC (SAWMS) time series, modeled AWC (ISORROPIA), and the monitored dry PM2.5 mass concentrations averaged every four hours at the XG site from November 27th to December 5th, 2019 (the time series of the PM2.5 WSIIs are shown in Fig. S2). During the measurement process, the smart heater installed in the Met One BAM 1020 monitor was turned on to prevent interference from airflow moisture. In so doing, the acquired data were comparable with the weighed aerosol mass and considered as dry PM2.5. Although the PM2.5 and measured AWC variations were roughly similar, the linear relationship shown in Fig. S3 was poor, partly due to more considerable variations in the AWC than PM2.5 concentrations (37% vs. 25% relative standard deviation). This trend likely represented more variability in PM2.5 hygroscopic components than in dry PM2.5 mass. The average PM2.5 AWC was 39.0 ± 14.3 µg m–3 for the entire sampling period, and the AWC/PM2.5 ratio was 1.4 ± 0.4, suggesting that the measured dry PM2.5 mass concentration would underestimate the actual (dry + AWC) PM2.5 concentration by about 140% at an atmospheric RH of 90%. In the high RH environment, most AWC concentrations were higher than the dry PM2.5 mass concentrations monitored by TEPA, thus the added AWC likely impaired the visibility significantly (Ren et al., 2021). A relatively higher AWC appeared on the early mornings of December 1st, December 2nd, and December 4th (the light tinted areas in Fig. 3). These periods did not show a noticeable increase in PM2.5 concentrations, and the average RH (75 ± 6.5%) was only slightly higher than the other periods (68 ± 8.3%). However, the AWC (58.6 ± 10.3 µg m–3) was significantly greater (33.3 ± 9.0 µg m–3), indicating that most of the atmospheric RH were over the deliquescence relative humidity (DRH) of the hygroscopic PM2.5 components. Due to the "smart heater" used in the Met One BAM-1020 PM2.5 monitor, when the atmospheric RH is higher than 35%, semi-volatile species, such as AN and AC, can easily volatilize and result in an underestimation of the PM2.5 concentrations (Le et al., 2020). The NO3 concentration (8.3 ± 3.3 µg m–3) indeed was much higher in the high AWC periods (the light tinted areas) than in the other periods (3.5 ± 2.1 µg m–3). Since the DRH of AN is 60% (Tang and Munkelwitz, 1993; Sun et al., 2021), NO3 likely contributed large amounts of AWC in these periods.

Fig. 3. The time series of dry PM2.5 mass concentration, measured AWC (SAWMS), modeled AWC (ISORROPIA II), and RH. Light tinted areas indicate the periods with relatively high AWC. Fig. 3. The time series of dry PM2.5 mass concentration, measured AWC (SAWMS), modeled AWC (ISORROPIA II), and RH. Light tinted areas indicate the periods with relatively high AWC.

The PM2.5 WSIIs were applied to ISORROPIA II (Fountoukis and Nenes, 2007) to model AWC contributed by ionic species. Fig. 4 revealed that the time variations of the modeled and measured PM2.5 AWC were consistent (R2 = 0.85; n = 39, p < 0.05) with a linear slope of 0.76, and the absolute percentage difference was 18 ± 13% indicating that WSIIs contributed most of the measured PM2.5 AWC. Although the modeled AWC estimated by E-AIM IV (Parworth et al., 2017) was similar to the measured values by SAWMS (R2 = 0.83; n =39, p < 0.05 Fig. S4(b)), ISORROPIA II estimated AWC (SAWMS) was even slightly better than E-AIM IV. Compared to the model values, the slightly higher measured AWC may have been due to the differences in the aerosol mixing state as discussed below and the contribution of the organics to PM2.5 AWC. Jin et al. (2020) suggested that organics contributed ~22% of the total AWC on average in Beijing in the 90% RH condition, which was similar to the difference between the measured and modeled AWC (from WSIIs) in this study.

Fig. 4. The contrast between measured (SAWMS) and modeled (ISORROPIA II) AWC during the study period (the diagonal dash line shows a 1:1 relationship).Fig. 4. The contrast between measured (SAWMS) and modeled (ISORROPIA II) AWC during the study period (the diagonal dash line shows a 1:1 relationship).

 
3.2 The Humidographs and Mixing State of the Atmospheric Aerosols

This study selected three 4-hour samples from the front, middle, and rear periods to contrast the RH effect on AWC. The resulting AWC values at different RH levels during the humidification and dehumidification processes were plotted as humidographs. Fig. 5(a) shows the humidograph of the aerosols collected from 23:00 November 30th to 3:00 December 1st, 2019. During the humidification process, ISORROPIA II estimated that aerosol deliquesced around 47% RH, while the SAWMS measured this point as around 60% RH, a significant discrepancy between the two methods. However, the measured (SAWMS) and modeled (ISORROPIA II) AWC values were consistent after 80% RH. The deviation of modeled and measured aerosol deliquescence points explained the difference in the humidographs. Once the modeled and measured deliquescence points are identical, the humidographs will be similar between the two. ISORROPIA II indicated that AN (43.8%) was the major WSII contributor in this sample, followed by AS (28.2%), Na2SO4 (12.0%), and AC (8.4%). By summing up the modeled individual AWC contributions from salts shown in Fig. S5, the modeled AWC (additive) was very similar to the SAWMS measurement across most RH levels. However, for the dehumidification process, the measured and modeled AWC values were consistent at high RH; the dehydration rate of the measured AWC was faster than for modeled AWC below 68% RH. Figs. 5(b) and 5(c) show the humidographs of the samples collected on November 27th (9:00–13:00) and December 4th (10:00–14:00), 2019. Modeling results indicated that the major components of the two samples were AS (45%, 54%), AN (17%, 24%), Na2SO4 (9%, 4%), and AC (3%, 2%), respectively, in the total WSIIs. The values of AS were higher than AN in these two samples. Deliquescence occurred around 45% RH in both the modeling and measurement data, indicating the aerosols were affected by the mutual deliquescence relative humidity (MDRH) of the mixed aerosols and deliquesced earlier than the DRH of individual components. Jin et al. (2020) inferred that organics contributed to AWC before the deliquescence of water-soluble inorganic salts.

Fig. 5. The humidographs of the collected urban aerosols from (a) 23:00 November 30th to 3:00 December 1st, (b) 9:00 to 13:00 November 27th, and (c) 10:00 to 14:00 December 4th, respectively, at the XG site in 2019.Fig. 5. The humidographs of the collected urban aerosols from (a) 23:00 November 30th to 3:00 December 1st, (b) 9:00 to 13:00 November 27th, and (c) 10:00 to 14:00 December 4th, respectively, at the XG site in 2019.

The measured AWC (SAWMS) that appeared in low RH might respond to organic AWC. However, the measured AWC (SAWMS) values did not increase as rapidly as the modeled AWC (ISORROPIA II), but were more similar to the modeled AWC (ISORROPIA II, additive) results, which implied that parts of the aerosols were still not deliquesced. The result also explained why the measured AWC during humidification did not rapidly increase like the modeled values. However, the AWC significantly increased after 80% RH (Fig. 5(b)), which may have been affected by the deliquescence of AS. In the dehumidification process above 80% RH, the measured AWC dehydrated more rapidly than in the model; and below 80% RH had a similar dehydration rate as the model (Fig. 5(b)), while the measured AWC diverged from the model at 80% RH (Fig. 5(c)), again revealing more rapid dehydration.

Based on the results above, the assumption of aerosol combination types and the mixing state of the inorganic salts in the model likely differed from the atmospheric condition. Especially the range of 60–80% RH, which included the DRH of each inorganic salt, revealed the most prominent difference. The model assumed that all the aerosol species deliquesced simultaneously on MDRH, indicating an internal-mixing state, thus could not demonstrate the partial deliquescence of various species. Although the modeled AWC was close to the measured AWC in a high RH environment (above 80% RH), the results significantly differed in the low RH atmosphere. That is, as the hygroscopicity of aerosols is relevant from low to high RH levels, the measurement across a range of atmospheric RH is necessary.

 
3.3 The Time Variation of the AWC in the Atmosphere

The hourly values of AWC and the atmospheric RH are necessary to understand the hygroscopicity of the urban aerosols in the atmosphere. Fig. 6 shows the estimated value of AWC at the atmospheric RH that was added to the PM2.5 mass concentration within the three selected 4-hour samples in Section 3.2. The average AWC/PM2.5 of the second sample (during midnight from November 30th to December 1st) was 0.84 ± 0.1, which was ten times higher than AWC/PM2.5 of the other two samples (0.05 and 0.08, respectively). Note that the second sample had the lowest PM2.5 concentration, but the hourly RH values of the four hours were all over 77%, suggesting that some chemical components of the PM2.5 deliquesced. The ISORROPIA II result suggested that AN (7.5 µg m–3), Na2SO4 (2.0 µg m–3), and AC (1.4 µg m–3) all deliquesced and increased AWC significantly, with AN contributing 50% of the AWC. Tsai and Kuo (2005) used the Karl-Fischer method to measure urban and seaside AWC in southern Taiwan for a 60% RH (dehydration) environment, with the AWC contributing 24.9–30.5% and 20.9–34.2% of the PM2.5, respectively, in these two environments. The AWC of the three samples contributed 11%, 24%, and 15% of the actual (dry + AWC) PM2.5 concentrations at 60% RH (dehydration). As mentioned above, the AN concentration and RH were relatively high in the second sample; therefore, the AWC was high. This analysis indicated that the AWC would still significantly increase when the atmospheric RH surpassed the DRH of the predominant hygroscopic species even at relatively low PM2.5 concentrations. In other words, the measured dry PM2.5 mass concentration from air-quality monitoring stations would underestimate the actual PM2.5 mass concentration in a high RH atmosphere. The monitoring of dry aerosols is necessary because it is more stable than wet aerosols and thus optimal for air quality agencies and policymakers. However, hygroscopic aerosol mass highly depends on atmospheric RH, and the resulting AWC affects the particle size and mass. These changes then impact visibility in the atmosphere and particle deposition patterns in the lungs during inhalation (Davies et al., 2021; Groth et al., 2021; Ren et al., 2021). Our analysis demonstrated that aerosol chemical composition and RH affect AWC variability, while additional analysis would be required to link changing AWC with the aforementioned impacts.

Fig. 6. The hourly PM2.5, RH, and estimated AWC from (1) 9:00 to 13:00 November 27th, (2) 23:00 November 30th to 3:00 December 1st, and (3) 10:00 to 14:00 December 4th, 2019, respectively.Fig. 6. The hourly PM2.5, RH, and estimated AWC from (1) 9:00 to 13:00 November 27th, (2) 23:00 November 30th to 3:00 December 1st, and (3) 10:00 to 14:00 December 4th, 2019, respectively.

 
3.4 NOR and SOR

Zhang et al. (2021) showed that AWC was well-correlated to SOR and NOR, implying that AWC enhanced the oxidation of NO2 to NO3 and SO2 to SO42 (Liu et al., 2012; Seinfeld and Pandis, 2012; Gao et al., 2016). Fig. 7(a) illustrates the good linear correlation between AWC and NOR (R2 = 0.60, n = 39; p < 0.05), indicating the AWC enhancement of NO3 formation. A logarithmic correlation (R2 = 0.66, n = 39; p < 0.05) was fitted to investigate the effects of different AWC intervals on NO3 formation. At lower AWC values (< 30 µg m–3), the initial increasing AWC was beneficial to the quick formation of NO3. At medium AWC values (30–50 µg m–3), NOR increased from 0.6 to 0.8 and showed a linear correlation with AWC. In contrast, at higher AWC values (50–60 µg m–3), the NOR was confined in a smaller range of 0.8–0.9. The NOR then decreased as AWC values were higher than 60 µg m–3. AWC appeared to aid NO3 formation more obviously at lower and medium levels. In contrast, AWC and SOR showed no significant relationship (Fig. 7(b)), likely due to the low concentration of SO2 (3.2 ± 1.4 ppb) at the XG site. The large SOR values suggested that the SO42 had existed in the atmosphere for a while, possibly from regional sources. Regarding the study periods analyzed here, the AWC yielded from hygroscopic aerosols would support the formation of NO3 during an increase in atmospheric RH amidst abundant levels of NO2.

Fig. 7. The measured AWC values at 90% RH with (a) NOR and (b) SOR for the entire study period.Fig. 7. The measured AWC values at 90% RH with (a) NOR and (b) SOR for the entire study period.

 
4 CONCLUSIONS


Continuous measurements of urban AWC are rare. This study measured the ambient AWC at a Taiwan EPA air-quality monitoring station located in an industrialized seaport city. The results showed that the average PM2.5 AWC was 39.0 ± 14.3 µg m–3 at 90% RH; AWC/PM2.5 was 1.4 ± 0.4. Moreover, the measured AWC and modeled AWC (by ISORROPIA II) were well correlated (R2 = 0.85; n = 39, p < 0.05) at 90% RH, implying that WSIIs mainly contributed to the PM2.5 AWC. However, the measured AWC was usually higher than modeled AWC, which might be due to the differences in the aerosol mixing state or aerosol combination types in the atmosphere.

This study selected three samples to obtain humidographs during the humidification and dehumidification processes. The modeled and measured AWC values had relatively significant differences in the range of 60–80% RH during the humidification process, which might be because the model assumed all the components were internally mixed and deliquesced together at an MDRH. However, the modeled and measured AWC discrepancy suggested that parts of the aerosols might be externally mixed and deliquesced at the DRH of each salt. When the RH was over 80%, the modeled AWC was consistent with the measured AWC. Nevertheless, compared to the model, the relatively fast dehydration illustrated by the measured AWC is worth further research effort.

During high AWC periods, the NO3 concentration was higher than in other periods implying that AN contributed large amounts of the AWC. Although the PM2.5 concentrations were not high, the AWC significantly increased when the atmospheric RH was higher than the DRH of the predominant hygroscopic species and caused an underestimation of the atmospheric PM2.5 by the monitoring instrument. The AWC correlated well to NOR during the study, especially in the early stage of AWC increases, indicating that the AWC enhanced the NO3 formation. However, the low correlation of SOR with AWC may have been due to the low atmospheric SO2 concentrations and the predominance of sulfate from regional sources.

In summary, direct measurement of AWC is necessary for understanding hygroscopic aerosol variability in the atmosphere. Although the modeled AWC was close to the measured AWC when RH was higher than 80%, the differences in the humidification and dehumidification processes when comparing modeled and measured values highlighted the need to further these studies.

 
ACKNOWLEDGEMENT


This study was supported by the Ministry of Science and Technology in Taiwan under the grants MOST 107-2111-M-008-021, MOST 108-2111-M-008-023, and MOST 109-2111-M-008-020. The authors appreciate Dr. Stephen Miles Griffith for editing this manuscript.


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