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Abstract
Factors influencing atmospheric visibility (VIS) in coastal areas are more complex than those for inland and far oceans owing to the complex circulation and aerosol sources. This study analyzed the factors influencing VIS under sea–land breeze circulation (SLBC) for different external aerosol sources based on field survey data in southern Chinese coastal areas. First, SLBC characteristics observed during the experiment period showed that on SLBC days, sea breeze occurs more frequently (∼50%) than land breeze (∼27%), and the wind speed (WS) is generally small, with a mean sea and land breeze WSs of ∼2.18 m/s and ∼2.38 m/s, respectively. Then, analysis of factors influencing VIS was conducted for different land/sea breeze conditions and external aerosol source conditions indicated by the HYSPLIT4 model simulations. Results showed that the aerosol particle number concentration (PNC) and relative humidity (RH) both had negative correlations with VIS, while only very weak relationships between WS and VIS were found, possibly due to small WSs on SLBC days or because local aerosols were not pure marine aerosols. Further two-factor analysis of VIS showed that the power-law function relating VIS with PNC in each RH bin ranges from ∼-0.3 to ∼-1.5, and VIS exhibited sharper exponential decline with increasing PNC under high RH. A new method of retrieving aerosol-extinction hygroscopic growth factor (fext) with the measured VIS, RH, and PNC was developed to investigate the optical hygroscopic growth property of aerosols. Results show that aerosols in the study area have similar fext under different land/sea breeze and external aerosol source conditions; the deliquescence RH of aerosols is ∼60%, suggesting that mainly polluted marine aerosol was observed during experiments in this area.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Atmospheric visibility (VIS) is an important visual indicator of atmospheric opacity and air pollution, which affect human activities [1]. High levels of air pollution, indicated by low VIS, significantly affect the environment and climate changes; owing to these negative effects, air pollution remediation has garnered global attention [2]. Observation of VIS can be traced back to the 1960s. Previous studies have shown that VIS changes because of interactions between regional and local weather patterns as well as air pollution [3]. In coastal areas, air pollution and air pollutant transport are also related to specific meteorological conditions, such as tropical cyclones [4,5] and sea–land breeze circulation (SLBC) [3,6].
SLBC plays a vital role in local weather changes, and the transmission and diffusion of pollutants in coastal cities under weak synoptic forcing situations [7]. SLBC is a mesoscale circulation phenomenon unique to coastal areas and is caused by sea–land air thermal differences [8]. During daytime, land surface temperature is higher than sea surface temperature; therefore, near-surface warm air over land the rises, and cooler air flows in from the ambient sea; at 600–1000 m, air over the land flows back to the sea, which results in circulation [9]. In contrast, the air movement direction at night is opposite to that during daytime. SLBC causes accumulation of pollutants in offshore areas, thereby affecting the diffusion and transportation of air pollutants [10,11]. Land breeze (LB) prevailing at night transports pollutants to the coastal areas, and this transportation decreases VIS. Conversely, advection of clean air from the sea in the early stages of sea breeze (SB) formation continuously reduces pollutants and improves VIS [3].
The results of previously reported studies demonstrate that VIS is majorly affected by aerosol concentration, and the correlation between aerosol concentration and VIS exhibits regional differences. In some studies, negative correlations between pollutant concentrations and VIS were reported in the inland areas of China for Xi'an, Taishan, Jinan, Shijiazhuang, and Chengdu [12–16]. Furthermore, some recently reported results indicate a negative linear correlation between pollutant concentration and VIS at low aerosol concentrations (concentration of PM2.5 < 50 µg/m3); however, this negative linear correlation can change to a negative exponential relationship with increasing aerosol concentration [2]. The quantitative relationship between pollutant concentration and VIS also changes with variations of relative humidity (RH) [14,16,17]. At low RH or under low pollution, VIS is mainly affected by pollutant concentration [2,16]; that is, at low RH (i.e., RH < 60%), VIS is primarily affected by the concentration of fine aerosol particles; in contrast, at high RH (i.e., RH > 60%), the impact of pollutant concentration on VIS is weakened, and the influence of RH becomes significant; this phenomenon is more noticeable at low pollutant concentrations than at high concentrations [14,16].
The types of aerosols found in coastal areas are influenced by both inland and marine aerosols. The relationships between aerosol concentration, RH, and VIS in coastal areas could be complex and different from those in inland areas [3,6]. However, only a few observational analyses and studies, focusing on the factors influencing VIS at land–sea junction areas and the relationships between these factors, have been reported to date [5–8]. Moreover, in most of these studies, daily or monthly average data were used to analyze the long-term changes without considering the influence of typical weather conditions or identifying the potential aerosol sources. In this study, we analyze VIS characteristics and its influencing factors under SLBC using field data collected in Maoming, which is a typical coastal city in southern China’s Guangdong province.
2. Data collection and methodology
2.1 Experiments and data collection
From September 27 to October 08, 2018, a field campaign was conducted by Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, at Bohe Meteorological Station in Maoming (21.45 °N, 111.31 °E) to examine the meteorological and environmental characteristics of near-shore atmospheric boundary layer in Maoming. Maoming is located on the southeast coast of Guangdong province in China (Fig. 1) with fewer local pollution sources. This atmospheric condition is different from that of the urban agglomeration in the Pearl River Delta. The coastline of Maoming area extends approximately from east to west.
Fig. 1. Experiment station location of Maoming (red star), China. The map is provided by the official website of the Ministry of Natural Resources, PRC, NO: GS (2020)4388. The inset map is from Google Earth.
The near-surface meteorological and environmental data used in this study included VIS (collected by a forward scattering VIS meter), wind speed (WS) and RH (recorded by near-surface automatic weather station), and particle spectra (measured by an optical particle counter, OPC). The measurement details are as follows:
- 1) The near-surface meteorological data at 2 m above ground were collected using a near-surface automatic weather station WXT520 (Vaisala, Finland), which is a compact low-power device with six weather sensors. The specific performance of this device was analyzed in a previous study [18]. During our experiments, the weather sensors were mounted on a flux measuring tower, and the atmospheric pressure (P), atmospheric temperature (T), RH, wind direction (WD), WS, and precipitation were detected at intervals of 5 s. The WDs of 0°, 90°, 180°, and 270° represent north, east, south, and west, respectively.
- 2) VIS was measured by a 6220 forward scattering VIS meter (Belfort Instrument, USA). An infrared light-emitting diode was used to transmit light to the sampler, and the receiver collected the forward-scattered light before calculating the extinction of light to obtain VIS. The transmitter and receiver were equipped with hoods to reduce the influence of rainfall on the VIS meter. Before experiments, the sensors were calibrated using a 90001 calibration kit, following the method provided in the Visibility Sensor Manual published by Belfort Instruments [19]. The temporal resolution of the VIS data was 5 s.
- 3) Aerosol particle size distributions (PSDs) in the radius range of 0.1–5 µm were measured by a DLJ-92 multichannel OPC developed by the Hefei Institute of Physical Science, Chinese Academy of Sciences [20]. Utilizing Mie theory [21], the OPC used the light scattering characteristics of the particles to classify and quantify particle number concentration (PNC) and size spectrum distribution. The temporal resolution of the collected PNC data was 1 min. During experiments, the OPC was placed in an air conditioned room to decrease the influence of high humidity and high salinity in the coastal environment on the instrument. The outdoor ambient aerosol was extracted into the OPC through a pipe. The PNC per unit volume was calculated by integrating the spectrum over all particle sizes. It should be noted that the particle size cutoff radius of 0.1 µm may cause underestimation of the PNC. However, aerosol particles with radius < 0.1 µm occupy less than 7% of the PNC, which is estimated by lognormal fitting of the OPC-measured PSDs in our experiment, and are consistent with the observed results from a previous coastal aerosol study [22]. Therefore, the missing smaller particle sizes would have weak impacts on VIS evaluations in this study.
All experimental data were resampled to 1 min via moving average analysis to ensure data comparability. In total, 5462 groups of effective data observations were obtained. The geopotential value, temperature, wind velocity components (radial wind, u; zonal wind, v) at 850 hPa, and near-surface u and v values at 10 m above the ground were obtained from hourly ERA5 reanalysis data, which were also used to analyze the weather conditions and near-surface wind fields during the experimental period in Maoming area. ERA5 is the fifth-generation reanalysis data product of the European Centre for Medium-Range Weather Forecasts (ECMWF) and is based on the integrated forecasting system Cy41r2, which has been in operation since 2016 [23]. ERA5 reanalysis data have temporal resolutions of 1 h and horizontal resolutions of 0.25° × 0.25°, with 137 vertical pressure levels from the surface to 0.01 hPa [23,24]. The ERA5 reanalysis data can be downloaded from the Climate Data Store (https://cds.climate.copernicus.eu/#!/home). Local time coordinated (LTC) was the time unit used for all the experimental and reanalysis data in this study.
2.2 Definition of VIS
In previous studies, PM2.5 concentration and RH were used as the main factors controlling VIS [1,2,14,16]. In this study, we selected controlling factors based on a theoretical analysis of VIS. The relationship between VIS and extinction coefficient is expressed as follows [25]:
Combining Eqs. (2) and (3), we can modify Eq. (1) as follows:
According to Mie theory, the extinction coefficient can be determined using aerosol size and refractive index, both of which are related to the aerosol properties and RH [20,26–28]. If the aerosol source is relatively constant, then VIS should be negatively correlated with RH and aerosol PNC (N0). Consequently, we adopted RH and PNC as the factors that control VIS in this study. The near-surface WS is the main driving factor for sea-salt aerosol formation over the ocean [29] and is also closely related to pollution transport and diffusion [30]. Thus, WS was also considered as a potential factor influencing VIS in this study. Furthermore, the SLBC characteristics and aerosol trajectories were investigated prior to VIS influencing factor analysis to distinguish different aerosol sources.
3. Characteristics of SLBC in Maoming
3.1 Definition of SLBC day
Different geographical locations and environments produce different SLBCs [31,32]. Thus, establishing reasonable criteria to distinguish the wind patterns of SLBC from those of large-scale weather systems by combining multiple factors is crucial [33,34]. In this study, we combined the coastline wind patterns of Maoming station, observational data, and ERA5 reanalysis data to select the SLBC day.
First, we used the selection method of weak circulation weather condition days provided by Azorin–Molina [35] to exclude days with strong pressure gradients. A weak circulation weather condition day is determined by the threshold (threshold < 5 hPa) of the near-surface pressure (∼1000 hPa) difference between day and night. Thereafter, SLBC days were selected based on weak circulation weather days according to following criteria:
- 1) The surface temperature differences between land and sea were greater than 0 °C from sunrise to sunset on this day.
- 2) Three hours of continuous SB (onshore wind) from sunrise to 2 h before sunset and WS > 1 m/s.
- 3) Two hours of continuous LB (offshore wind) from 00:00 LTC to 1 h after sunrise and WS < 0.5 m/s.
3.2 Characteristics of SLBC
Based on the criteria noted above, the identified SLBC days for the duration of the Maoming experiments were September 30 to October 03, 2018 (marked as period_1) and October 07 to October 08, 2018, LTC (marked as period_2). Figure 2 shows the diurnal variations in WS (black line) and WD (red dotted line) measurements obtained with the WXT520 sensor (Fig. 2(a)). The near-surface wind field over Maoming and adjacent areas, derived from ERA5 10 m wind speed data on a typical SLBC day, are shown in Fig. 2(b) and 2(c). As shown in Fig. 2(a), no clear diurnal WD cycles were observed on non-SLBC days (from 14:00 on September 27 to 8:00 on September 30, 2018, and from 00:00 on October 04 to 00:00 on October 07, 2018, LTC), particularly from September 27 to September 30, 2018, when Maoming area was affected by Typhoon Trami. In contrast, on the SLBC days (period_1 and period_2), near-surface WD exhibited SLBC characteristics. The WD was in the range of 90°–180° (SB) during daytime and less than 90° at night (LB). Brief SB–LB turnovers occurred at around 09:00 and 23:00 on each day (Table 1); the SB–LB turnover is an atmospheric advection phenomenon caused by the thermal differences between the land and sea [8].
Fig. 2. (a) Changes in near-surface wind fields in Maoming from September 27 to October 08, 2018. The black line denotes WS, and the red dotted line represents WD. Wind field of SLBC on October 07, 2018, in Maoming (red star): (b) LB (offshore wind) at 02:00 LTC and (c) SB (onshore wind) at 16:00 LTC.
Table 1. Daily SB–LB turnover times (h).
The statistical results of frequencies and WS distributions of SB and LB on SLBC days are shown in Fig. 3. During the experimental period, SB frequency was higher (48.7% for period_1 and 54.8% for period_2) than that of LB (27.2% for period_1 and 27.7% for period_2), as shown in Fig. 3(a). WS on SLBC days was generally lower than 5 m/s (Figs. 2(a) and 2(b)). The mean WS of SB (∼ 2.18 m/s) was slightly lower than that of LB (∼ 2.38 m/s). The most frequent WS was ∼ 2 m/s for LB (46.5%) and ∼2 m/s for SB (∼ 40.6%). The observed SLBC characteristics were consistent with those in previously reported studies [36].
Fig. 3. (a) Frequencies of SB and LB in Maoming during the two marked periods; (b) frequency distributions of the WS under SB and LB conditions. The purple bar denotes SB, and orange bar denotes LB.
3.3 Pollutant trajectories and variations in aerosol concentrations
In this study, the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model, implemented in the meteorological analysis mapping software MeteoInfo with TrajStat plug-in, was used to analyze the aerosol sources in Maoming. The HYSPLIT4 model, jointly developed by the US Air Resources Laboratory and the Bureau of Meteorology, Australia, was employed for analysis of the transportation and diffusion trajectories of the pollutants and for simulation of the backward trajectories of air masses [37]. TrajStat displayed the air mass trajectories and was added in subsequent analyses [38,39].
In our simulation, the Bohe Meteorological Station was selected as the starting point, with two simulated heights of 500 and 1500 m above the ground surface [40]. The Global Data Assimilation System (GDAS) database was used as the input data to HYSPLIT4 to simulate the backward path of airflow for a duration of 48 h during period _1 and period_2; the time resolution was set to 1 h. We simulated 192 and 144 backward trajectories of the airflows at the two simulated heights during period_1 and period_2, respectively. Next, statistical clustering analysis was performed on the backward trajectory results. Figure 4 shows the backward trajectory results under land/sea breeze in the two periods, overlaid with the aerosol optical depth (AOD) obtained from the moderate resolution imaging spectroradiometer (MODIS; aboard the Aqua satellite). The variations in PNC, measured by OPC during the experimental period in Maoming, are shown in Fig. 5.
Fig. 4. Backward cluster grouped by airflow trajectory in Maoming (red star) under (a) SB and (b) LB during period_1, and (c) SB and (d) LB during period_2. AOD (colored) obtained from the MODIS sensor aboard the Aqua satellite is also overlaid.
During period_1, most airflow originated from the external land sources located in the northeast direction of Maoming. A high aerosol PNC (40–80 cm-3) was observed during the first two days (September 30–October 1) in period_1 because of the anthropogenic pollutants transported from the middle and lower reaches of the Yangzi River (blue trajectory in Fig. 4(a)). From October 2 to 4, the aerosols mainly originated from the adjacent clean region (Figs. 4(a) and 4(b)). A clear diurnal cycle of the PNC was observed during period_1 owing to the high background aerosol loading transported from inland and the dilution effects of daytime SB circulating clear air from the sea. Moreover, the enhanced nighttime LB PNC was mostly attributed to fine-sized aerosols (<1 µm) according to OPC-measured aerosol size distribution. During period_2, a large-scale heavy-pollution event (external aerosol sources) occurred. Therefore, although the main aerosol source (100% for SB and 83.33% for LB) in period_2 was the adjacent ocean area, the PNC was relatively high (approximately 40–60 cm-3). However, the diurnal cycle of the PNC during period_2 was not clear. Therefore, we observed that the aerosol traceability and daily variation in aerosol concentration were significantly different during period_1 and period_2. In the next section, we compare and analyze atmospheric VIS influencing factors under these two conditions.
4. VIS influencing factors under SLBC in Maoming
Field measurement data were used to analyze the relationships between VIS and selected influencing factors under different conditions, including SB and LB transporting aerosols from different external aerosol sources (in period_1 and period_2).
4.1 Single-factor analysis of VIS
Figure 6 shows the relationships between VIS and PNC, RH, and WS under different conditions. The first and second rows in this figure show the results for period_1 and period_2, respectively.
Fig. 6. Relationship between (a, b) VIS and PNC, (c, d) VIS and RH, and (e, f) VIS and WS during period_1 (row 1) and period_2 (row 2). The orange and blue dots represent the results of LB and SB conditions, respectively, and the orange and blue colored lines were derived from linear regression analysis under each condition; R represents the correlation coefficient.
As shown in Figs. 6(a) and 6(b), the PNC and VIS during the two periods showed negative correlations. In general, PNC exhibited better correlation with VIS under the LB condition (R = -0.78 and -0.57 for period_1 and period_2, respectively; R represents the correlation coefficient) than under the SB condition (R = -0.38 and -0.24 for period_1 and period_2, respectively); when both LB and SB conditions were considered in the analysis, we obtained R = -0.53 and -0.5 for period_1 and period_2, respectively.
As shown in Figs. 6(c) and 6(d), the relationships between VIS and RH under LB and SB conditions were significantly different, particularly during period_2. A large negative correlation between VIS and RH was observed under LB conditions, with R = -0.95 (period_1) and -0.99 (period_2). These values were lower (more negative) than those observed under SB conditions (-0.72 for period_1 and -0.46 for period_2). In contrast, VIS was weakly correlated with WS, as shown in Figs. 6(e) and 6(f), particularly under SB conditions. Although near-surface WS was the major driving source of sea salt particles, WS during the experimental period was generally low (< 5 m/s). According to previous studies, the relationships between near-surface WS and aerosol PNC and AOD are weak under low WSs [7,41]. Therefore, this could be one of the reasons for the weak relationship between VIS and WS observed in this study. Furthermore, based on the back-trajectory analysis in the previous section, aerosols from the ocean are not pure marine aerosols, which could be another reason for the weak correlations with WS.
4.2 Two-factor analysis of VIS
The single-factor analysis, presented in the previous section, showed that the major influencing VIS factors were PNC and RH. Next, we performed a two-factor analysis of VIS. As shown in Fig. 7, five RH bins were selected with intervals of 10% each, and the relationship between PNC and VIS in each RH bin was obtained via fitting. Owing to insufficient data samples, LB and SB conditions were not distinguished in the two-factor analysis. VIS data range could be used to obtain a crude distinction between these two conditions. According to Fig. 6, most data samples under SB conditions indicated relatively high VIS (VIS > ∼ 12 km).
As shown in Fig. 7 and Table 2, VIS followed a negative power-law relationship with PNC in each RH bin. During both periods, the power-law function relating VIS with PNC in each RH bin showed a large range from ∼-0.3 to ∼-1.5, decreasing with increase in RH. This indicates that VIS exhibits a sharper exponential decline with increasing PNC under high RH; this result was also reported in previous studies [16]. However, this sharp exponential decrease in VIS could also be due to the narrower range of PNC under low RH than that under high RH. The narrow PNC range under different RH conditions could introduce uncertainties in the log-fitting process in this study, which will be studied using long-term observations in our future study. Furthermore, the range covered by the PNC was the widest at RH = 60–70% (period_1) and 70–80% (period_2) among all RH bins. Under these two RH conditions with sufficient sampling ranges, the power-law function relating VIS with PNC showed a value of ∼-1, which was close to that predicted by Eq. (4). This implies that VIS should be inversely proportional to PNC if the mean extinction coefficient remains constant.
Table 2. Regression formulas for VIS and PNC in different RH bins during the two periods.
Furthermore, although the fitting parameters were different for period_1 and period_2 (Table 2), the fitted lines in the same RH bin, as shown in Fig. 7, were actually close to each other for these two periods. Obtaining the relationship between VIS and PNC similar to that predicted by the theoretical analysis suggests that the local aerosol properties at the observation site do not change significantly during the experimental period under different aerosol trajectories and SB–LB conditions.
5. Discussion
The aerosol trajectory analysis only provided information on potential aerosol sources, based on which the relationships between VIS, PNC, and RH were analyzed. These experimentally analyzed relationships were consistent with those predicted by theoretical calculations. In this section, we discuss the aerosol hygroscopic characteristics based on Eq. (4) to assess aerosol hygroscopic properties under different conditions.
Hydrophilic aerosols significantly influence atmospheric VIS under sub-saturated conditions [42] (i.e., RH < 100%). Aerosol hygroscopic characteristics can be described using the hygroscopic growth factor of the particle size and particle optical properties. The aerosol-extinction or scattering hygroscopic growth, used in remote sensing and radiative transfer model calculations [43–48], can be obtained using light detection and ranging, a nephelometer with humidity control devices [44,48–51], or via theoretical analysis by combining the aerosol model with Mie theory [21,52–54] and assuming uniform aerosol composition while ignoring variations in aerosol PNC [55]. In this study, we estimated the aerosol-extinction hygroscopic growth factor (${f_{ext}}$) from VIS, considering changes in PNC.
According to Eq. (4), ${\delta _{ext}}$ is estimated as follows:
Therefore, ${f_{ext}}$ can be obtained by examining the changes in ${\delta _{ext}}$ with RH, as shown in Fig. 8. Figure 8(a) shows the relationship between ${\delta _{ext}}$ and RH under different conditions. Evidently, the aerosols under different periods and SLBC follow quite similar laws between ${\delta _{ext}}$ and RH, suggesting that the aerosols have similar hygroscopic characteristics. Notably, some of the yellow points (Fig. 8(a)) in period_1 SB exhibited larger extinctions with ${\delta _{ext}}$ > 0.005 km-1 than the other points under other conditions when RH was in the range of 50–60%. All samples were collected during October 2–4, 2018, when the external aerosols originated from the adjacent clean regions. These results suggest that the aerosols in Maoming during the experimental period were mostly marine aerosols, either mixed locally (first two days during period_1) or transported from adjacent polluted coastal regions (during period_2). The mixed marine aerosols containing lower concentrations of the external aerosols (last three days in period_1) exhibited different optical properties and possibly different hygroscopic characteristics from those of the mixed marine aerosols containing high concentrations of the external aerosols.
Fig. 8. (a) Relationship between ${\delta _{ext}}$ and RH under different conditions. (b) ${f_{ext}}$ of the mixed marine aerosol.
As shown in Fig. 8(b), the ${f_{ext}}$ of the mixed marine aerosols were derived. First, the mean ${\delta _{ext}}$ and ${\delta _{ext}} - RH$ relationships were obtained at an RH interval of 2.5%. Next, we selected ${\delta _{ext}}$ as ${\delta _{ext,wet}}$ and the mean ${\delta _{ext}}$ as ${\delta _{ext,dry}}$ when the RH was in range of 40–50% to calculate ${f_{ext}}$. Furthermore, a nonlinear regression model for ${f_{ext}}$ (Eq. (6)) was constructed to calculate the ${f_{ext}}$ of the mixed marine aerosols in Maoming as follows:
As shown in Fig. 8(b), ${f_{ext}}$ increased monotonically with RH, and we also observed deliquescence, which is an important feature of ${f_{ext}}$, at approximately 60% RH. The ${f_{ext}}$ decreased slightly at RH < 60%. In contrast, at RH > 60%, the ${f_{ext}}$ increased rapidly with RH, indicating a stronger water uptake ability of the aerosols.
6. Conclusions
We analyzed the characteristics of SLBC in Maoming, along with identifying the factors influencing atmospheric VIS and correlations between VIS and these factors under different conditions, including SB and LB conditions for different external aerosol sources. The analyses were performed using field data and the ERA5 reanalysis dataset. Based on theoretical analyses, we proposed a novel method to obtain the aerosol-extinction hygroscopic growth factor using VIS and PNC data. Furthermore, an empirical formula with one free parameter (RH) was obtained based on the proposed method to represent the mixed marine-aerosol-extinction hygroscopic growth factor of Maoming area. The following conclusions were drawn from the analysis results:
- 1) WS on SLBC days in Maoming was generally less than 5 m/s, and the mean WS of SB (∼ 2.18 m/s) was slightly weaker than that of LB (∼ 2.38 m/s). LB flowed in from the north–east (WD: 0°–50°; frequency: 27%) under SLBC, while SB flowed in from the south–east (WD: 100°–140°; frequency: 50%). SB was more frequent than LB. Brief SB–LB turnovers occurred at around 09:00 and 23:00 LTC on each day.
- 2) PNC and RH both exhibited negative correlations with VIS; these relationships were significantly different under SB or LB conditions for different external aerosol sources. PNC exhibited a more negative correlation with VIS under LB conditions (R = -0.78 and -0.57 for period_1 and period_2, respectively) than under SB conditions (R = -0.38 and -0.24 for period_1 and period_2, respectively). Similarly, RH showed more negative correlations with VIS under LB conditions (R = -0.95 and -0.99 for period_1 and period_2, respectively) than under SB conditions (R = -0.72 and -0.46 for period_1 and period_2, respectively). We also determined a weak relationship between WS and VIS because of the low WS on SLBC days.
- 3) The two-factor analysis of VIS with PNC and RH showed that VIS followed a negative power-law relationship with PNC in each RH bin. With increasing RH, VIS exhibited a sharper exponential decline with increasing PNC. In the RH bins with sufficient data samples, VIS followed a power-law relationship with PNC, which is close to the relationship derived via theoretical analysis. This implies that VIS may be inversely proportional to PNC if the average aerosol extinction coefficient remains constant. Based on these results, we analyzed the possibility of calculating the aerosol-extinction hygroscopic factor theoretically using VIS and PNC.
- 4) We calculated the mixed marine-aerosol-extinction hygroscopic growth factor for Maoming area and obtained an empirical formula based on the proposed method. For this extinction hygroscopic growth factor, deliquescence was observed at approximately 60% RH. The extinction hygroscopic growth factor showed a slight increase at RH < 60%; at RH > 60%, the aerosol water uptake ability improved, and the extinction hygroscopic growth factor therefore increased rapidly with RH.
Funding
National Key Research and Development Program of China (2018YFC0213101); General Program of the National Natural Science Foundation of China (41875041); Natural Science Foundation for Distinguished Young Scholars of Anhui Province (2008085J19); HFIPS Director’s Foundation (YZJJ2022QN06).
Acknowledgments
The authors would like to thank NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT model, Dr. Y. Q. Wang for providing meteorological analysis mapping software MeteoInfo, and Copernicus Climate Change Service Climate Data Store (CDS) for providing the ERA5 data. The authors acknowledge the editor’s and referees’ efforts in improving this manuscript.
Disclosures
The authors declare no conflicts of interest.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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