Seasonal Variation in Chemical Composition of Size-Segregated Aerosols Over the Northeastern Arabian Sea

Water-soluble species constitute a significant fraction (up to 60–70%) of the total aerosol loading in the marine atmospheric boundary layer (MABL). The “indirect” effects, that is, climate forcing due to modification of cloud properties depend on the water-soluble composition of aerosols. Thus, the characterization of aerosols over the MABL is of greater relevance. Here, we present 1-year long aerosol chemical composition data of PM₁₀ and PM_2.5 at a costal location in the northeastern Arabian Sea (Goa; 15.45°N, 73.20°E, 56 m above the sea level). Average water-soluble ionic concentration (sum of anion and cation) is highest (25.5 ± 6.9 and 19.6 ± 5.8 μg·m⁻³ for PM₁₀ and PM_2.5, respectively) during winter season and lowest during post-monsoon (17.3 ± 9.1 and 14.4 ± 8.1 μg·m⁻³ for PM₁₀ and PM_2.5, respectively). Among water-soluble ionic spices, SO₄^2- ion was found to be dominant species in anions and NH₄⁺ is dominant in cations, for both PM₁₀ and PM_2.5 during all the seasons. These observations clearly hint to the contribution from anthropogenic emission and significant secondary inorganic species formation. Sea-salt (calculated based on Na⁺ and Cl⁻) concentration shows significant temporal variability with highest contribution during summer seasons in both fractions. Sea-salt corrected Ca²⁺, an indicator of mineral dust is found mostly during summer months, particularly in PM₁₀ samples, indicates contribution from mineral dust emissions from arid/semiarid regions located in the north/northwestern India and southwest Asia. These observations are corroborated with back-trajectory analyses, wherein air parcels were found to derive from the desert area in summer and Indo-Gangetic Plains (a hot spot for anthropogenic emissions) during winter. In addition, we also observe the presence of nss-K⁺ (sea-salt corrected), for PM_2.5, particularly during winter months, indicating influence of biomass burning emissions. The impact on aerosol chemistry is further assessed based on chloride depletion. Chloride depletion is observed very significant during post-monsoon months (October and November), wherein more than 80 up to 100% depletion is found, mediated by excess sulfates highlighting the role of secondary species in atmospheric chemistry. Regional scale characterization of atmospheric aerosols is important for their better parameterization in chemical transport model and estimation of radiative forcing.

Introduction

Atmospheric aerosols, derived from continental regions, undergo long-range transport and supply significant amount of nutrients as well as toxicants to remote and coastal oceanic region (Jickells et al., 2005; Paytan et al., 2009; Kumar et al., 2010; Jordi et al., 2012; Baker et al., 2013; Srinivas and Sarin, 2013a; Powell et al., 2015; Kumar et al., 2020). In addition, aerosols emitted from marine region (mainly sea salt) can impact on the chemical composition of aerosols over the continent and, thus, play a vital role in atmospheric chemistry (Quinn et al., 2004; Keene et al., 2007; Kumar and Sarin, 2009; Sarin et al., 2011). The availability (and lability) of nutrients/toxicants significantly depend on the ambient atmospheric chemistry (Baker and Croot, 2010; Kumar and Sarin, 2010a), which eventually undergo dry (Arimoto et al., 2003; Srinivas and Sarin, 2013a; Baker et al., 2017) as well as wet (Measures and Vink, 1999; Chance et al., 2015; Powell et al., 2015) deposition. Post deposition, they can modulate surface water biogeochemical processes (Mahowald et al., 2005; Guieu et al., 2019; Anderson, 2020) which have impact on carbon cycling and eventually on Earth’s climate (Ramanathan et al., 2001; Barnett et al., 2005; Rana et al., 2019).

The lifetime of atmospheric aerosols ranges from few hours to several days, and thus, they display large temporal variability (Pöschl, 2005). In addition, varying emission intensity and their source region also contribute to their temporal as well as spatial variability. The impact of aerosols can be assessed more quantitatively by having information on their chemical composition as well as their emission sources. For example, aerosols with more acidic composition have more processed labile Fe content (Baker and Croot, 2010; Kumar et al., 2010) and can impact significantly on primary productivity of the oceanic region where it will be deposited. Similarly, aerosols characterized with enriched Fe/Al ratio (Kumar and Sarin, 2010a; Srinivas and Sarin, 2013a; Srinivas and Sarin, 2013b) will supply more Fe which can contribute to enhanced primary productivity. Furthermore, the abundances and atmospheric reactivity of acidic species (nitrate and sulfate) largely depend on the size distribution (Sullivan et al., 2007), which have potential to enhance the bioavailability of nutrients (Baker and Croot, 2010; Kumar and Sarin, 2010a). Thus, one of the major limitations of current models relates to the lack of data on size-dependent chemical composition of atmospheric aerosols and the associated spatiotemporal variability. Considering the importance of chemical composition, efforts have been made in the past to chemically characterize aerosols over continental (Wang and Shooter, 2001; Tare et al., 2006; Williams et al., 2007; Ng et al., 2011; Sahai et al., 2011; Sun et al., 2012; Jain et al., 2014; Tiwari et al., 2014; Petit et al., 2015) as well as marine regions (Siefert et al., 1999; Johansen and Hoffmann, 2003; Kumar et al., 2008a; Kumar et al., 2008b; van Pinxteren et al., 2015; Budhavant et al., 2017; Pan et al., 2018; Aswini et al., 2020a; Cvitešić Kušan et al., 2020).

Owing to high population density and rapid industrialization, the Indian subcontinent is considered as one of the major hot spots for anthropogenic emissions (Streets et al., 2003; Srivastava et al., 2012; Tiwari et al., 2014; Sen et al., 2017; Ningombam et al., 2020; Ojha et al., 2020) which further make the adjoining marine region vulnerable to these emissions (Kumar and Sarin, 2010a, Kumar and Sarin, 2010b; Srinivas and Sarin, 2013b; Srinivas et al., 2019; Rastogi et al., 2020). In view of this, several field observations, long term (Williams et al., 2007; Kumar and Sarin, 2009; Jain et al., 2014; Tiwari et al., 2014; Kishore et al., 2019) and campaign based (Venkataraman et al., 2002; Kumar et al., 2008a; Kumar et al., 2008b; Sahai et al., 2011; Aswini et al., 2020a) as well as remote sensing (Woo et al., 2003; Venkataraman et al., 2006; Srivastava et al., 2012; Mehta et al., 2016; Sen et al., 2017; Thomas et al., 2019) and modeling studies (Pant and Harrison, 2012; Michael et al., 2013; Moorthy et al., 2013; Michael et al., 2014; Govardhan et al., 2015; Mukherjee et al., 2018) have been reported from India to understand chemical and physical properties of aerosols. However, major focus has been so far on the urban (Rastogi and Sarin, 2005; Jain et al., 2014; Tiwari et al., 2014; Sharma et al., 2016; Budhavant et al., 2017; Kishore et al., 2019) rural (Lawrence and Taneja, 2005; Balakrishnan et al., 2018; Gautam et al., 2020) and remote high-altitude (Shrestha et al., 1997; Kumar and Sarin, 2010b; Chatterjee et al., 2010; Saxena et al., 2016; Gautam et al., 2018; Ganguly et al., 2019) locations, leaving a large scope for studies on aerosol chemical composition near and/or over the marine region adjoining the Indian subcontinent. There have been very limited studies reported from coastal locations around India (Madhavan et al., 2008; Agnihotri et al., 2015; Aswini et al., 2020a; Yadav et al., 2020), which are mainly based on the bulk composition and provide a snapshot picture of aerosols composition. Recently, Thomas et al., 2019 have highlighted an increasing trend of aerosol optical depth over the coastal Arabian Sea compared to the Bay of Bengal during winter months. These observations further suggest the need for long-term field-based observations of chemical composition of aerosols over the coastal Arabian Sea.

Here, we report a comprehensive dataset on chemical composition of PM₁₀ and PM_2.5 collected for around one year at a coastal location in the northeastern Arabian Sea. Our main objective is to assess seasonal variability of chemical constituents in PM₁₀ and PM_2.5 and to identify major sources impacting aerosol composition at the study site. In addition, we have also assessed the role of various chemical processes impacting on the chemical composition of ambient aerosols collected at our study site. Our detailed study involving use of chemical constituents and their ratios as diagnostic tracers, in conjunction with meteorological parameters, is an important contribution toward understanding of surface water biogeochemical processes operative in the coastal Arabian Sea.

Materials and Methodology

Study Area

The observational site is located (Figure 1) in Goa (15.45°N, 73.20°E) on the west coast of India in the campus of CSIR-National Institute of Oceanography (CSIR-NIO). The sampling location is surrounded by the Arabian Sea on the west and Western Ghats on the eastern side (Aswini et al., 2020b; Kumar et al., 2020). Being in the tropical zone and near the Arabian Sea, Goa has a warm, humid climate for most of the year. Sampling site is 500 m away from the Arabian Sea and also can be considered as a representative site for the northeastern Arabian Sea (Kumar et al., 2020).

FIGURE 1

FIGURE 1. Location of bulk aerosol sampling at the roof of National Institute of Oceanography, Goa (at a height 56 m above sea level; 15.45°N, 73.20°E).

Methodology

Atmospheric aerosols with 50% cutoff aerodynamic diameter of 10 microns (PM₁₀) and 2.5 micron (PM_2.5) were collected on PALLFLEX™ Tissuquartz filters (8” × 10”) by using high-volume sampler (TISCH Environmental) at an average flow rate of 1.1 m³·min⁻¹. Few samples, between April 2018 and May 2018, were collected on Whatman-41 rectangular cellulose filters (20 × 25 cm) using same high-volume samplers. The sampler was set up on the terrace (15.45°N, 73.20°E) of NIO building at a height of 56 m from the ground level. Typically, each sample was collected by operating the sampler for 24-h with a sampling frequency of two samples per week. A total of 224 PM₁₀ and PM_2.5 samples (more details are in Table 1) were collected from December 2017 to February 2019. Prior to sample collection, quartz filters were conditioned in an oven at a temperature of 200°C for 3–4 h.

TABLE 1

TABLE 1. Details of number of samples, sampling days in each month, and type of filter used for PM collection.

A piece (1/8th) of the filter sample was cut from the sampled area using a ceramic scissor under a clean laminar flow bench. This filter piece along with deionized water (Milli-Q; specific resistivity >18.2 MΩ-cm) was transferred into 50 ml Savillex bombs and subjected to ultrasonication for 30 min. The water extracts were subsequently filtered through a PVDF syringe filter with pore size 0.2 µm and then transferred to a preconditioned polypropylene bottle. This filtered solution was analyzed for cations (Na⁺, NH₄⁺, K⁺, Mg²⁺, and Ca²⁺) and anions (Cl⁻, NO₃⁻, and SO₄²⁻) using a thermo scientific ion chromatography system (Dionex ICS 5000). The cations (Na⁺, NH₄⁺, K⁺, Mg²⁺, and Ca²⁺) were analyzed using the analytical column IonPac CS12A, 4 × 250 mm, and guard column IonPac CG12A, 4 × 50 mm, using CSRS 300 as a suppressor. Methyl sulfonic acid has been used as an eluant for the cation analysis with a run time of 15 min. The anions (Cl⁻, NO₃⁻, and SO₄^2-) were analyzed using the analytical column IonPac AS16, 4 × 250 mm, and guard column IonPac AG16, 4 × 50 mm, using ASRS 300 as suppressor. 22 mM NaOH solution has been used as eluant for the anion analysis with the run time of 10 min. Merck multielement standard was used for standard preparation by suitably diluting it. Daily calibrations were done before starting the analyses, and fresh standards were prepared before analyses. Along with samples, field blanks were also extracted and analyzed using similar methodology as that of aerosol samples. The concentrations of ionic species were corrected for procedural blanks. Based on blank concentrations and average volume of air filtered (∼2000 m³), the detection limits for the water-soluble ionic species in aerosols were ascertained (18, 18, 20, 18, 25, 10, 30, and 30 ng·m⁻³ for Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺,Cl⁻, NO₃⁻, and SO₄²⁻, respectively). The reproducibility in the analytical data for the measured concentrations is within 5% based on the repeat analysis of a number of samples and standards.

Meteorological data were obtained using an automatic weather system (AWS) installed at the roof top (∼56 m above the sea level) of the CSIR-National Institute of Oceanography (details of AWS are given in Mehra et al., 2005). In our study, we classified three different seasons 1) winter (WIN) during December–February; 2) summer (SUM) during March–May; and 3) post-monsoon (POM) during September–November, based on the previous studies (Agnihotri et al., 2015; Yadav et al., 2020) at this site. Using this classification, we computed 7-day air mass back trajectory (AMBT) ending at our study site for each sampling day (Figure 2). The AMBTs were computed using HYSPLIT-4 model and GDAS dataset of the National Oceanic and Atmospheric Administration Air Resource Laboratory (NOAA) at 500 m altitude (Stein et al., 2015). Supplementary Figures S1 and S2 depict the seasonal variation of temperature and relative humidity (RH), respectively. In winter, the average temperature was found to be 25°C with average RH of 72%, in summer average temperature was 28°C with average RH of 82%, and during post-monsoon average recorded temperature was 26°C with average RH of 90%. Although, we did not observe significant variation in temperature and relative humidity during different seasons, however, significant changes in wind trajectories were observed and discussed in detail in the next section.

FIGURE 2

FIGURE 2. 7-day back trajectories at 500 m for sampling days ending at our sampling site. AMBTs were computed using the HYSPLIT-4 model and GDAS dataset of the National Oceanic and Atmospheric Administration Air Resource Laboratory (NOAA) at 500 m altitude (Stein et al., 2015) for this study.

Results and Discussion

Back-Trajectory Analyses

The AMBTs corresponding to different seasons, for sample collection days, are shown in Figure 2 with different colors. A distinct wind pattern is observed for WIN and SUM months; however, mixed winds are seen during POM season. Winds are mostly derived from marine region as well as Middle East deserts during SUM seasons, with several trajectories crossing over the coastal region of western India, Pakistan, and Iran–Pakistan border region. In contrast, winds during WIN season are mostly derived from continental locations in the Indian subcontinent, particularly from the Indo-Gangetic Plains (one of the hot spots for anthropogenic emissions during WIN; Thomas et al., 2019). Those derived during WIN are also observed to pass over the coastal region of eastern India as well as over the Bay of Bengal (Figure 2). After the southwest monsoon withdrawal, wind origin is observed from marine as well as continental locations, with few winds derived from coastal northwest Africa and far eastern part of the Bay of Bengal. In order to identify major contribution from emission sources during POM, we have done cluster analyses (Stein et al., 2015); all AMBTs computed during this period. Our analyses indicate the dominance of continental sources (more than 65%) as compared to marine sources (Supplementary Figure S3). It is interesting to note here that majority of winds at our study site indicate long-range transport trajectories possibly bringing aerosols derived from variety of sources including natural arid/semiarid desert and marine regions as well as polluted anthropogenic locations. Thus, it is apparent; meteorology may play an important role in impacting ambient aerosol composition leading to a strong seasonal variability at this location.

Seasonal Variability of Water-Soluble Ionic Composition in PM₁₀ and PM_2.5

In this study, a total of 224 aerosol samples were collected, which includes 112 PM₁₀ and 112 PM_2.5 samples (detail of number of samples, sampling days in each month, and type of filter used for PM collection is tabulated in Table 1). Water-soluble ionic composition (WSIC) is calculated by adding mass concentration (µg m⁻³) of all cations (Na⁺, NH₄⁺, K⁺, Mg²⁺, and Ca²⁺) and anions (Cl⁻, NO₃⁻, and SO₄^2-) measured in this study. The charge balance (in equivalent units) between total cations (∑⁺) and total anions (∑⁻) is used to assess reliability and quality of the WSIC data (Kumar et al., 2008a, Kumar et al., 2008b). A correlation plot between ∑⁺ and ∑⁻ is shown in Figure 3, wherein we observe most of the data are falling on or near to the 1:1 line for both PM₁₀ and PM_2.5 samples. We observe significant correlation between ∑⁺ and ∑⁻ for all PM₁₀ and PM_2.5 samples (r² is shown in Figure 3), except for some PM₁₀ and PM_2.5 (Figure 3) samples, particularly during SUM months. The equivalence ionic ratio (Σ⁻/Σ⁺) for PM₁₀ varied from 0.80 to 1.18 (mean: 1.00 ± 0.07) in WIN, 0.69 to 1.48 (mean: 0.99 ± 0.11) in SUM, and 0.91 to 1.26 (mean: 1.06 ± 0.07) in POM seasons. PM_2.5 samples show similar ratios ranging from 0.82 to 1.20 (mean: 0.98 ± 0.07), 0.64 to 1.16 (mean 0.97 ± 0.11), and 0.80 to 1.19 (mean: 1.01 ± 0.11) during WIN, SUM, and POM seasons, respectively. We observed some of the SUM samples (PM₁₀ and PM_2.5) are falling away from the 1:1 line toward the cation axis. Such deviation indicates anion deficit which can be attributed to the lack of measurement of bicarbonate ions which are mainly contributed by alkaline dust (Kumar et al., 2008a). The wind trajectories during SUM are mostly derived from the arid/semiarid desert region which can potentially bring mineral dust to our sampling site. Similar anion deficit has been reported by Kumar et al. (2008b) over the Bay of Bengal region during March–April 2006. In addition, several studies have suggested lack of measurement of organic anions which can contribute to the anion budget of ambient aerosols (Kulshrestha et al., 1998; Momin et al., 1999; Venkataraman et al., 2002; Rastogi and Sarin 2005).

FIGURE 3

FIGURE 3. Seasonal correlation plot between total cation (sum of Na⁺, NH₄⁺, K⁺, Mg²⁺, and Ca²⁺) and total anion (sum of Cl⁻, NO₃⁻, and SO₄²⁻).

The seasonal variation of WSIC mass concentration for PM₁₀ and PM_2.5 is shown in Figure 4 using a box–whisker plot. WSIC concentration is found to be highest for WIN samples in PM₁₀ (mean: 25.5 ± 6.9; range: 10.4–50.0 μg m⁻³) followed by SUM (mean: 21.0 ± 4.6; range: 12.8–30.4 μg m⁻³) and POM (mean: 17.3 ± 9.1; range: 6.9–41.1 μg m⁻³) seasons. Similarly, WSIC in PM_2.5 is high for WIN (mean: 19.6 ± 5.8; range: 10.1–39.9 μg m⁻³); however, it is relatively higher during POM (mean: 14.4 ± 8.0; range: 2.3–29.0 μg m⁻³) than SUM (mean: 12.9 ± 7.3; range: 0.8–31.6 μg m⁻³) months. Compared to WIN and SUM, we observed large variability in WSIC during POM months, suggesting significant variability in sources, which is also supported by variation in AMBTs (Figure 2) during this period. Agnihotri et al. (2015) have reported on bulk aerosol chemical composition for WIN and SUM months during 2009–2011, wherein they observed high WSIC during SUM compared to WIN months. Another study by Yadav et al. (2020) have also observed higher WSIC for SUM than that in WIN for total suspended particulate (TSP or bulk) aerosol samples. However, they observed high WSIC in WIN compared to SUM for PM₁₀ and PM_2.5 samples during the year 2013 similar to our observation. The presence of sea salts, which are abundant in coarser fraction, contributes more during SUM (also supported by AMBTs) in the bulk samples as our sampling site is very near to coast. These coarser sea salts are relatively less collected on filters, while sampling using PM₁₀ and PM_2.5 inlets, as compared to bulk sampling. Thus, we (and previous studies) have observed higher WSIC for bulk during SUM than those collected with inlet of lower cutoff aerodynamic diameter.

FIGURE 4

FIGURE 4. Seasonal variability of water-soluble ionic composition of PM₁₀ and PM_2.5 during the study period at Goa.

In order to assess the relative contribution of PM_2.5 WSIC to PM₁₀ WSIC, a scatterplot between PM₁₀ and PM_2.5 WSIC is shown in Figure 5 for all three seasons. It is evident that majority of SUM samples are falling away from the 1:1 line toward the PM₁₀ axis; however, POM and WIN samples are relatively near and/or on the 1:1 line. This suggests dominant contribution of fine mode WSIC toward water-soluble composition of PM₁₀ during WIN and POM months. However, during summer months, PM₁₀ mass could be dominated by coarser sea salt and mineral dust particles (more detail on their abundance in different size fractions is discussed in later section), which contribute relatively less to the PM_2.5 mass. This is also evident from the ratio of WSIC PM_2.5/WSIC PM₁₀, which is found to be relatively low (0.59 ± 0.24) during SUM compared to WIN (0.78 ± 0.14) and POM (0.76 ± 0.18) months. Our observation is consistent with those reported by Agnihotri et al. (2015), which highlighted predominance of the sea salt and mineral dust during SUM months compared to WIN season.

FIGURE 5

FIGURE 5. Scatter plot among PM_2.5 WSIC and PM₁₀ WSIC showing relative contribution of WSIC in PM_2.5 to PM₁₀ WSIC.

The percentage contribution of an individual water-soluble species to the total WSIC (of PM₁₀ and PM_2.5) is represented as pie charts (Figure 6) for the three different seasons. Sulfate (SO₄^2-) and ammonium (NH₄⁺) ions are dominant (contributing around 80% of WSIC for all season samples except for PM₁₀ SUM month) among anions and cations, respectively, in both fractions (Figure 6). A decrease in SO₄^2- contribution is observed for SUM samples (∼47%) in PM₁₀ compared to WIN and POM seasons; however, no significant variation is observed in SO₄^2- fraction for PM_2.5 samples (Figure 6). Similarly, no significant variation is observed for NH₄⁺ percentage in PM_2.5; however, a gradual increase in NH₄⁺ contribution to PM₁₀ is observed with lowest during SUM (10%), followed by POM (13%) and highest in WIN (16%). Apart from SO₄^2- and NH₄⁺, we observe significant contribution of Ca²⁺, Na⁺, Cl⁻, and NO₃⁻ to PM₁₀, particularly during SUM months, and K⁺ in PM_2.5 samples. These observations suggest contribution of primary emission (mainly from sea salt and mineral dust) is significant in PM₁₀, while those from secondary aerosol formation (formed by gas to particle conversion or multiphase chemistry) may be dominant in PM_2.5, which is consistent with previous studies from the Indian subcontinent (Kumar and Sarin, 2010b; Ram et al., 2012; Rastogi et al., 2015).

FIGURE 6

FIGURE 6. Pie chart showing relative contribution of individual ionic species to the total WSIC in PM₁₀ and PM_2.5 during (A) winter, (B) summer, and (C) post-monsoon (see text for season description).

Chemical Species of WSIC and Their Variation in PM₁₀ and PM_2.5

Sea Salt (Na⁺ and Cl⁻)

Sea salts are mainly derived from breaking of sea waves in the marine region (Anguelova and Webster, 2006). By using Na⁺ and Cl⁻ concentration in ambient aerosols, sea-salt concentration can be estimated, presuming that Na⁺ and Cl⁻ ions solely derived from seawater, following this relation (Quinn et al., 2007; Kumar et al., 2008a):

$\text{Sea Salt}=\left[{\text{Cl}}^{-}\right]\left(\text{μg}{\text{m}}^{-3}\right)+1.47\ast \left[{\text{Na}}^{+}\right]\left(\text{μg}{\text{m}}^{-3}\right).$

Here, 1.47 is the ratio of (Na⁺+ K⁺+Mg²⁺+ Ca²⁺+SO₄^2- + HCO₃⁻)/Na⁺ in seawater (Holland, 1978; Quinn et al., 2004). This approach excludes the contribution of nss-K⁺, Mg²⁺, Ca²⁺, SO₄², and HCO₃⁻ in the sea-salt mass and allows for the loss of Cl⁻ due to its depletion through chemical reaction. The monthly average trend of sea-salt concentration is shown in Figure 7, as the box–whisker plot and their season average concentration are reported in Tables 2 and 3. A large variation is observed for PM₁₀ samples with higher concentration during SUM season (3.73 ± 1.7 μg·m⁻³) than WIN (1.89 ± 0.87 μg·m⁻³) and POM (1.70 ± 1.29 μg·m⁻³) period. On the other hand, no significant variation is found for PM_2.5 samples except few higher episodic values at the beginning of summer months (March 2018 and February 2019). These episodic higher values may be attributed to stronger winds from marine region bringing coarse and fine sea salts to our sampling site, which is also evident from higher sea-salt concentrations in PM₁₀ for March 2018 and February 2019 (Figure 7). An increase in sea-salt concentration depends on higher wind speed as well as wind direction and oceanic waves (de Leeuw et al., 2000; Niedermeier et al., 2014). During this study, we have observed relatively higher wind speed during summer months than rest of the year. In addition, the winds are mainly derived from marine region during summer, as evident from the AMBTs (Figure 2). Thus, the higher values of sea salt can be attributed to the meteorological condition prevailing at our study site. It is important to mention here that sea salts also contribute to other ions (eg, K⁺, Mg⁺², Ca²⁺, and SO₄^2-), and thus, sea-salt correction is necessary to assess their temporal variability. The non–sea-salt (nss) components have been calculated by using the following equations:

$\text{nss}\mathrm{-}\text{X}={\left[\text{X}\right]}_{\text{total}}\mathrm{-}{\text{Rx}}_{\text{sea}\mathrm{-}\text{salt}}\text{*}{\left[{\text{Na}}^{+}\right]}_{\text{measured}},$

where [X]_total is measured concentration of specific ion, Rx_sea-salt is the weight ratio of particular ion to Na⁺ in seawater, and [Na⁺] _measured is measured Na⁺ concentration in ug m⁻³. Rx_sea-salt for K⁺, Ca²⁺, Mg²⁺, and SO₄^2- is 0.037, 0.0373, 0.12, and 0.252, respectively (Keene et al., 1986; Kumar and Sarin, 2009; Sarin et al., 2011).

FIGURE 7

FIGURE 7. Box–whisker plots representing the monthly mean (solid line) and different percentile levels (95th, 75th, 25th, and 5th) of sea-salt concentrations during the study period. Different seasons are on the top x-axis.

nss-Ca²⁺ and nss-Mg²⁺

The presence of nss-Ca²⁺ and nss-Mg²⁺ ions in the ambient aerosol samples typically indicates the contribution of crustal mineral dust (Rastogi and Sarin, 2006; Kumar and Sarin, 2010b). Monthly average concentration and its variation for nss-Ca²⁺ and nss-Mg²⁺ are shown in the box–whisker plot (Figure 8), and their seasonal average for PM₁₀ and PM_2.5 is reported in Tables 2 and 3. A general increasing trend can be observed in PM₁₀ particles for nss-Ca²⁺ concentration (in μg·m⁻³) while moving from WIN (mean: 0.93 ± 0.64; range: 0.36 – 3.7) to SUM (mean: 1.31 ± 0.72; range: 0.36 – 2.88) season, and then, it again starts decreasing in POM (mean: 0.67 ± 0.4; range: 0.11 – 0.14) with an average comparable value with winter month of 2019. No such significant temporal variability is observed for PM_2.5. However, relatively lower values (mostly below detection (BD) were found for nss-Mg²⁺ (Table 3), with no significant temporal variability as observed for that of nss-Ca²⁺. Higher concentration of sea-salt corrected calcium during SUM can be attributed to contribution from the arid/semiarid desert region in the Middle East (Arabian Peninsula, region bordering Afghanistan–Iran–Pakistan, Sistan Basin) as well as desert region in northwestern India (Thar Desert). Air mass back trajectories are clearly observed to transverse over these regions (Figure 2) during the SUM months. In contrast, we observe mixed air masses with dominant contribution from the continent (based on cluster analyses; Supplementary Figure S3) during POM, as well as air mass from northern/northeastern India during WIN, can transport mineral dust contributing to nss-Ca²⁺. In contrast to other locations over India (Kumar and Sarin, 2010b; Ram et al., 2010), we did not observe significant variation in nss-Ca²⁺ during the annual seasonal cycle. For example, over Mt. Abu, a high-altitude site located in western India, increase in summer nss-Ca²⁺ is observed by a factor of two or more than those observed in monsoon and winter seasons (Kumar and Sarin, 2010b). Similarly, Ram et al. (2010) have observed an order of magnitude increase in nss-Ca²⁺, over Kanpur. However, there are occurrence of dust storms in the Middle East and southwest Asia region (Aswini et al., 2020b; Kumar et al., 2020) which can also significantly contribute to nss-Ca²⁺ over our study site. Similar kind of trend for Ca²⁺ was also reported in Agnihotri et al. (2015) (winter: 2.3 ± 1.4) and in Yadav et al. (2020) (WM: 1.45 ± 0.57; SIM: 2.79 ± 2.22).

FIGURE 8

FIGURE 8. Box–whisker plots showing monthly variation of nss-Ca²⁺ and nss-Mg²⁺ during the study period.

TABLE 2

TABLE 2. Seasonal average concentration (in μg m⁻³) of water-soluble PM₁₀ components during the present study.

TABLE 3

TABLE 3. Seasonal average concentration (in μg m⁻³) of water-soluble PM_2.5 components during the present study.

Secondary Inorganic Species (NH₄⁺, NO₃⁻, and nss-SO₄²⁻)

Secondary inorganic species are mainly formed via multiphase chemistry from the precursor gases (ammonia and oxides of sulfur and nitrogen) which are emitted mostly from anthropogenic (fossil fuel and biomass burning) emissions (Seinfeld and Pandis, 2006; Zheng et al., 2020). These secondary species play a vital role in influencing several atmospheric processes acid uptake, enhancing aerosol Fe solubility (Rastogi and Sarin, 2006; Kumar and Sarin, 2010b) impacting on the overall climate in short-term and long-term timescales (Zheng et al., 2020). The monthly distribution of all secondary species is shown as box–whisker plots in Figure 9 and their seasonal average concentration with minimum and maximum values in PM₁₀ and PM_2.5 are detailed in Tables 2 and 3, respectively. The NH₄⁺ and nss-SO₄²⁻ ions were found to covary throughout the year with almost similar concentration in PM₁₀ and PM_2.5. This indicates a significant and dominant contribution of inorganic ions in PM_2.5, which is also evident from the plot shown in Supplementary Figures S4 and S5, respectively. Majority of data are falling on or near to 1:1 line, toward PM_2.5, which indicate their dominance in PM_2.5. Furthermore, we observed higher values during WIN (PM₁₀ NH₄⁺ mean: 4.15 ± 1.67; range: 0.95–7.48, PM_2.5 NH₄⁺ mean: 4.37 ± 1.54; range: 1.51–8.08, PM₁₀ nss-SO₄^2- mean: 14.98 ± 4.74; range: 5.06–30.7, and PM_2.5 nss-SO₄²⁻ mean: 12.72 ± 4.00; range: 5.55–25.13) which gradually decreases in SUM (PM₁₀ NH₄⁺ mean: 2.24 ± 1.33; range: 0.29 – 5.40, PM_2.5 NH₄⁺ mean: 2.70 ± 1.75; range: 0.04 – 6.77, PM₁₀ nss-SO₄^2- mean: 9.30 ± 4.50; range: 1.66 – 19.79, and PM_2.5 nss-SO₄²⁻ mean: 7.77 ± 4.52; range: 0.55 – 18.20) to lowest value (in May 2018) and start to increase from October 2018 onward in POM to higher values in WIN 2019. Thus, a clear seasonality can be witnessed from this; however, long-term observation can provide a more robust picture on seasonal variability of these species. Higher concentration of sulfate and nitrate during WIN months can be attributed to local anthropogenic activities as well as winds from the northeastern region of India and/or Indo-Gangetic Plains (IGP), which are relatively more polluted regions, particularly during winter months (Ram et al., 2010). The AMBTs are observed to be derived from the emission regions located in the IGP. However, relatively lower concentration in summer is due to dilution of local anthropogenic emission by relatively pristine air parcel emanating from the marine region (Figure 2), leading to lower concentrations. In post-monsoon season, initially, the concentration is less but gradually increases in October and November months. This is due to winds which are coming from the southwest during monsoon period were getting reversed to northeast winds as evident from the air mass back trajectories shown in Figure 2. This high contribution of nss-SO₄²⁻ during winters suggest a significant component of secondary inorganic aerosols resulting from emissions of SO₂ by a variety of combustion sources using sulfurous fuels, such as coal and oil which is long range transported by winds from the continental region. However, in summer, although the concentration is very low, still most of the winds are coming from the marine region which depicts that nss-SO₄²⁻ may be derived from oxidation of DMS (dimethyl sulfide).

FIGURE 9

FIGURE 9. Box–whisker plots showing monthly variation of secondary ionic species (NH₄⁺, NO₃⁻, and nss-SO₄²⁻) ions in the present study.

In contrast to sulfate and ammonium ions, NO₃⁻ is found almost negligible in PM_2.5 as compared to PM₁₀ (Figure 9; Tables 2 and 3; Supplementary Figure S6). Moreover, no significant seasonal variability is found for NO₃⁻ and similar seasonal average observed for WIN (mean: 2.25 ± 1.44; range: 0.64 – 8.67) and SUM (mean: 2.93 ± 1.51; range: 0.61 – 5.60) which are relatively higher than POM (mean: 1.16 ± 0.72; range: 0.36 – 3.5) for PM₁₀ samples. NO₃⁻ is generally formed by the oxidation of NO_x (Wang et al., 2006). This formation of NO₃⁻ involves the gas-to-particle conversion of NO_x in the ambient atmosphere. Ooki and Uemastu (2005) reported that nitrate is the dominant constituents associated with the mineral dust rather than the nss-SO₄^2-. Relatively higher abundance of nitrate is observed for PM₁₀ than PM_2.5 (shown in the box–whisker plot; Figure 9) during this study and may be attributed to their association with coarser dust as well as sea-salt particle. However, it is not possible to decouple the relative contribution of sea salt and dust in neutralizing nitrates in the coarse mode using our chemical composition data. Yadav et al. (2020) have reported higher abundance of nitrate in PM₁₀ and bulk samples, as compared to PM_2.5, at coastal site on eastern (Vizag) and western (Goa) North Indian Ocean. Similar enhanced abundance has been found by several studies at high-altitude pristine sites (Kumar and Sarin, 2010b) and polluted the Indo-Gangetic Plains (Ram et al., 2010).

Biomass Burning Species (nss-K⁺)

Studies have indicated that concentration of sea-salt corrected potassium (nss-K⁺) in fine mode aerosols can serve as a diagnostic tracer for biomass burning source (Andreae, 1983; Andreae and Marlet, 2001). However, its contribution from sea salts and dust sources is highly variable for regional case studies with its dominance in the coarse fraction. Galanter et al. (2000) estimated the geographical distribution of biomass burning to be fairly uniformly distributed over the Indian subcontinent with a maximum in the northeastern parts. They also estimated that the biomass burning in India takes place mainly during winter and summer (January–May). The monthly average trend for nss-K⁺, in both size fractions, is shown in Supplementary Material S3. In this study, it was found that nss-K⁺ ion contribution was found to be maximum during WIN (Supplementary Figure S7), that is, ranged from 0.16 to 2.71 μg·m⁻³ (Mean: 0.95 ± 0.38 μg·m⁻³) and minimum during SUM ranged from BD to 1.28 μg·m⁻³ (Mean: 0.34 ± 0.26 µg·m⁻³) (Supplementary Figure S7). During post-monsoon season, nss-K⁺ ion ranged from BD to 1.47 (Mean: 0.54 ± 0.38 µgm⁻³) in PM₁₀ particles. Similarly, in PM_2.5 its abundance is maximum in winters, that is, ranged from 0.48 to 2.59 μg·m⁻³ (Mean: 1.04 ± 0.31 μg·m⁻³) and minimum in summer 0.02 to 1.36 μg·m⁻³ (Mean: 0.45 ± 0.35 μg·m⁻³). It is important to highlight here that the relative contribution of nss-K⁺ of fine fraction is more than 90% to total PM₁₀ nss-K⁺. This is evident from the PM_2.5-nss-K⁺/PM₁₀-nss-K⁺ ratio, which is found to be 0.94 ± 0.12, 0.96 ± 0.17 and 0.99 ± 0.03, during WIN, SUM, and POM seasons. This observation further suggests the dominance of biomass burning tracer in fine fraction as compared to coarser one, particularly during WIN and POM seasons, when relatively higher concentrations are found as compared to SUM period. This further points to contribution from long-range transport of aerosols derived from polluted IGP region impacting at the coastal region of the Arabian Sea and corroborated by back trajectories during WIN and POM seasons.

Major Sources of Aerosols Over Goa

We have observed seasonal variability of various chemical species, and their variations have been largely attributed to the distinct wind patterns, primarily discerned by the air mass back trajectories, during the whole year. In this section, we will further attempt to assess the sources of different chemical species and its relations with meteorology (mainly AMBTs). The NO₃⁻/nss-SO₄²⁻ ratio typically gives an idea about the contribution from the mobile sources (vehicular emissions) vis-à-vis stationary sources (industrial emissions) (Arimoto et al., 1996; Kumar and Sarin, 2010b; Yadav et al., 2020). In this study, the consistently lower ratio (<1) reveals that major contribution of anthropogenic activities is from stationary sources during all the seasons for both the particle classes PM₁₀ and PM_2.5. It is noteworthy that during WIN in PM_2.5, the mean ratio is 0.05 ± 0.04 which is almost 3 times lower than the ratio in PM₁₀ 0.17 ± 0.0.16. This is due to increase in concentration of nss-SO₄²⁻ in the fine mode aerosol by long-range transport from the Indo-Gangetic Plains (IGP) region which is also supported by our back-trajectory analysis. On the contrary during SUM, the mean ratio was 0.61 ± 0.29 for PM₁₀ which is almost 7 times higher than that for PM_2.5 (0.09 ± 0.10). This is due to increase in NO₃⁻ concentration in PM₁₀ and its uptake by coarser mineral dust and sea-salt particles. An enhanced transport of sea salt and mineral dust is evident from the increase observed in Na⁺ and nss-Ca²⁺ concentration during summer season, which is further supported by the AMBTs (Figure 2) deriving from desert and marine regions. These observations are found to similar to those reported by Kumar and Sarin (2010b) highlighting the role of long-range transport of nss-SO₄²⁻ associated with NH₄⁺ in fine mode and NO₃⁻ (associated with crustal species sea salts). The scatterplot between mass concentrations of nss-SO₄²⁻ and NH₄⁺ (Supplementary Figure S8) shows a significant correlation for all season, particularly in PM_2.5. This clearly suggests the favorable association between secondary sulfate and ammonium ions. We also observed a better correlation between NO₃⁻ and nss-Ca²⁺ + nss-Mg²⁺ + Na⁺ (Supplementary Figure S9), which establishes association of nitrates with dust and sea salt, particularly in coarser fraction.

The AMBTs during SUM and POM were mostly derived from the desert region, which are sources of mineral dust; a concomitant increase in nss-Ca²⁺ concentration has been observed during SUM and POM, particularly in PM₁₀ fraction. However, no such variability is found for nss-Mg²⁺. Moreover, very low concentration of nss-Mg²⁺ is found for PM_2.5 throughout the year (Table 2). A scatterplot between nss-Ca²⁺ and nss-Mg²⁺ in PM₁₀ (Supplementary Figure S10) exhibits relatively poor correlation, highlighting different sources of Ca²⁺ and Mg²⁺ ions. Similar observations were reported by Yadav et al. (2020) for aerosols collected at Goa and suggested distinct sources of these ion, which is in contrast to those observed at a semiarid location in western India (Kumar and Sarin, 2010b). Such poor correlation can be also attributed to relative contribution of different minerals (calcite vs dolomite) and their chemical processing during long-range transport (Rastogi and Sarin, 2006). Apart for the ions discussed above, the nss-K⁺ (typically considered as tracer for biomass burning emissions) concentration is found to be high during end of POM and WIN months (Supplementary Figure S7). We noticed a sharp increase in its concentration from September to October and further increase in November. This increase in nss-K⁺ is also associated with change in wind trajectories from southwesterly to northeasterly as southwest monsoon recedes and northeast monsoon picks up. During October–November months, crop residue burning is very frequent in the northwestern regions (Punjab, Haryana, and western Uttar Pradesh) of India. The emissions from these sources spread in all directions through long-range transport mechanisms, depending upon the meteorological conditions (Sarkar et al., 2018). Due to change in wind regime, we observe a significant increase in nss-K⁺. This observation further attests to the fact that meteorology plays an important role in impacting aerosol concentration and composition at our sampling site.

Interaction Between Primary and Secondary Aerosols in PM₁₀ and PM_2.5

Chloride Deficit

Sea salt undergoes heterogeneous phase reaction while interacting with acidic gases and/or secondary aerosols (eg, HNO₃ and H₂SO₄) causing removal of nascent chlorine via supersaturation of HCl, and this is referred as Cl⁻depletion (Sarin et al., 2011). This is significant over the marine or coastal regions due to the impact of polluted air mass interacting with sea salts (Struges and Shaw, 1993; Johansen et al., 1999; Kumar et al., 2008a, Kumar et al., 2008b). Such depletion and emission of reactive chloride (as a free radical) is considered to be an important intermediate in the oxidation reactions associated with the removal of light hydrocarbons and ozone in the atmosphere (Singh and Kasting 1988; Vogt et al., 1996). Cl⁻ depletion (%) can be calculated by the following relation (Sarin et al., 2011):

${\text{Cl}}^{\mathrm{-}}\text{depletion }\left(\text{\%}\right)=100\left[\left(1\text{.}80\times {\text{Na}}^{+}\right)\mathrm{-}{\text{Cl}}_{\text{m}}^{\mathrm{-}}\right]/\left(1\text{.}80\times {\text{Na}}^{+}\right),$

where 1.80xNa⁺ is the Cl⁻ concentration (µg m⁻³) expected from the sea and Cl⁻_m is the measured Cl⁻ concentration (µg m⁻³) in the sample. In the present study, we observed lower Cl⁻/Na⁺ ratio than bulk seawater ratio of 1.80 indicating the Cl⁻−depletion process, which is observed in all seasons for both PM₁₀ and PM_2.5. This is also evident from the scatterplot between mass concentrations (in μg m⁻³) of Na⁺ and Cl⁻, shown in Figure 10 for all seasons. We observe large scatter in the dataset, showing significant variability in the Cl⁻ depletion process at our study site. Majority of PM₁₀ data are falling near to the seawater line, particularly during the SUM season; however, large deviation (from seawater line) is observed for WIN and POM samples. Similar distribution is found for PM_2.5 data, although the sea-salt concentration is relatively lower than those observed for PM₁₀ (see Section Sea Salt salt (Na⁺and Cl⁻)). The Cl⁻/Na⁺ ratio for PM₁₀ was found to be lowest (mean: 0.60 ± 0.30) during WIN as compared to those in POM (mean: 0.75 ± 0.44) and SUM (Mean: 0.82 ± 0.64). However, in PM_2.5, we observe lowest ratio in POM (0.41 ± 0.28) compared to almost similar ratio in WIN (1.02 ± 0.48) and SUM (1.09 ± 0.56). This indicates relatively high Cl⁻-depletion in PM₁₀ (mean = 68.2 ± 14.3%) compared to PM_2.5 (mean = 59.1 ± 21.6%) during WIN, almost similar in both fractions (PM₁₀ mean = 56.2 ± 32.4% and PM_2.5 mean = 58.4 ± 22.5%) during SUM, and high in PM_2.5 (mean = 85.8 ± 10.3%) compared to PM₁₀ (mean = 61.5 ± 18.8%) during POM. This is also evident in Figure 11, showing average monthly trend of Cl⁻ depletion. Similar variations (39–78%) in Cl⁻ deficit have been reported by Venkataraman et al. (2002) at an urban site (Mumbai, 19°23′N and 72°50′E) on west coast of India in two different sampling periods during January–March. In contrast to such variable Cl deficit, Sarin et al. (2011) have observed very high deficit (ranging from 82 to 98%) during continental outflow season over the Bay of Bengal. However, large variability (12–100%) has been reported by Kumar et al. (2008a) over the Arabian Sea. Such observation clearly demonstrates the role of continental outflow in removing Cl⁻ from sea salt which has significant implication toward atmospheric chemistry in the marine atmospheric boundary layer. The released chlorine have potential as an oxidant (Solomon, 1999), which may have role in oxidation of dimethyl sulfide (DMS; Keene et al., 1986) as well as in enhancing tropospheric ozone due to their interaction with volatile organic compounds (Raff et al., 2009). In comparison to such high deficit, several studies (Savoie and Prospero 1982; Kerminen et al., 1998; Johansen et al., 1999) have observed relatively lower values (ranging from 20 to 30%) in open oceanic regions and have attributed to chemical uptake of nss-SO₄²⁻ derived from DMS sources in the open ocean. In this study, we have not measured the methane sulfonate ions; thus, we could not estimate the DMS derived (or biogenic sourced) sulfate.

FIGURE 10

FIGURE 10. Scatterplot between Na⁺ and Cl⁻ mass concentration in three seasons at coastal sampling station, Goa. The data point falling away from the seawater line indicates the Cl⁻ depletion process.

FIGURE 11

FIGURE 11. Variation of monthly average chloride deficit (%) in PM₁₀ and PM_2.5 during the study period.

As discussed previously, acidic species (nitrate and sulfate) play vital role in controlling this process; we will assess their contribution in different size fractions. A scatterplot between equivalent concentrations of (Cl⁻ + NO₃⁻) and Na⁺ is shown in Figure 12A, to understand the role of nitrate in Cl⁻ removal following Sarin et al. (2011). In contrast to the observation of Sarin et al. (2011) over the Bay of Bengal, we observed scatter of data on either side of 1:1 line, suggesting the role of both sulfate and nitrate in the Cl⁻ removal process. The concentration of sulfates is 3–10 times higher than nitrates in PM₁₀ and order of magnitude is higher in PM_2.5, which suggests sulfate ions are mostly responsible for the Cl⁻ removal process. However, we noticed majority of PM₁₀ samples during all season to fall toward the (Cl⁻+NO₃⁻), suggesting contribution from nitrates in controlling this process. The role of nitrate in removing sea-salt chloride is well documented in various studies (Wu and Okada, 1994; Quinn and Bates, 2005; Hsu et al., 2007), particularly in coarser fraction compared to fine mode (Pio and Lopes, 1998). In this study, we observed relatively higher ratios of [Cl⁻+NO₃⁻] to Na⁺ for WIN (mean: 0.91 ± 0.21) and POM (mean: 0.79 ± 0.24) in PM₁₀ than their PM_2.5 counterpart (0.79 ± 0.21 and 0.59 ± 0.20, respectively). However, during SUM, relatively higher ratio is observed for PM_2.5 (mean: 0.94 ± 0.31) than PM₁₀ (mean: 0.74 ± 0.30). These observations suggest contribution from nitrate in removing Cl⁻ from sea salt, as the overall ratios are higher than those observed for the Bay of Bengal (0.37) (Sarin et al., 2011) and the subtropical South China Sea (0.17) (Hsu et al., 2007), where the role of nss-SO₄²⁻ is dominant in controlling this process. We further examined the relationship between NH₄⁺ and nss-SO₄²⁻ (in equivalent units; Fig. 12b), where we observed most of the PM₁₀ and PM_2.5 data are falling on or nearby 1:1 line, indicating a complete neutralization by ammonium ions. The NH₄⁺/nss-SO₄²⁻ ratio was found to be greater than one in PM_2.5 during WIN and SUM (1.47 ± 0.14 and 1.06 ± 0.60, respectively) as well as in PM₁₀ (1.32 ± 0.23 and 1.09 ± 0.29, respectively). This indicates formation of (NH₄)₂SO₄ as compared to (NH₄)HSO₄ in PM₁₀ as well as in PM_2.5 during WIN and SUM seasons. In contrast, relatively lower ratios were observed for POM (0.81 ± 0.47 for PM₁₀ and 0.92 ± 0.31 for PM_2.5), which suggest the abundance of (NH₄)HSO₄ instead of (NH₄)₂SO₄ or relatively more acidic nature of aerosols controlled by sulfates. We have also observed higher Cl⁻deficit during POM than SUM and WIN, which can thus be attributed to this excess sulfates over and above that is neutralized by NH₄⁺. Based on cluster analyses (Supplementary Figure S3), we have observed AMBTs are mostly derived from the continental sources during POM, and the presence of excess nss-SO₄²⁻ can impact other processes which include chemical processing of iron present in ambient aerosols. This has significant implication to the supply of labile Fe, which can act as micronutrients, to coastal waters of the Arabian Sea.

FIGURE 12

FIGURE 12. Scatterplot between (A) Cl⁻ + NO₃⁻ vs. Na⁺; (B) NH₄⁺ vs nss-SO₄²⁻ (in equivalence unit), highlighting the role of secondary species in removal of chloride from sea-salt particle.

Neutralization Factor

Neutralization factor (NF) is an indicator of aerosol acidity, and it can be estimated from the ratio of molar concentrations of cations and anions (Wu et al., 2017; Yadav et al., 2020). In this study, considering the significant contribution of nss-Ca²⁺, nss-Mg²⁺, and nss-K⁺, we have used equation for calculation of the NF following Yadav et al. (2020), which is

$\text{NF }=\left(\left[{\text{NH}}_4^{\mathrm{+}}\right]+\left[{\text{Na}}^{+}\right]+2\text{*}\left[{\text{nss}-\text{Ca}}^{2+}\right]+2\text{*}\left[{\text{nss}-\text{Mg}}^{2+}\right]+\left[\text{nss}-\text{K}+\right]\right)/\left(2\text{*}\left[{\text{nss}-\text{SO}}_4^{2\mathrm{-}}\right]+\left[{\text{NO}}_3^{\mathrm{-}}\right]+\left[{\text{Cl}}^{\mathrm{-}}\right]\right).$

Here, all the ionic concentrations are in µeq m⁻³. A value of NF < 1 indicates acidic nature of an ambient aerosol and/or incomplete neutralization; however, a value of NF > 1 indicates that cations are involved in neutralizing the acidic species of aerosol (Wu et al., 2017). We observed the NF averaging around 1 for PM_2.5 (1.00 ± 0.20) except for those collected during POM (0.97 ± 0.11), suggesting a complete neutralization of acidic species in ambient aerosols during WIN and SUM months. Being NH₄⁺, the dominant cation, it plays a key role in neutralizing the acidic species during WIN and SUM in PM_2.5. However, either due to lack of ammonium or excess nss-SO₄²⁻, we observe an incomplete neutralization during the POM season. Similarly, near neutrality of ionic species is found for PM₁₀ during WIN (0.96 ± 0.07) and SUM (0.97 ± 0.12) months. However, relatively lower NF (0.90 ± 0.07) is shown by POM samples, highlighting the presence of excess acid (more than neutralized by alkaline species). This excess acid can significantly impact other atmospheric processes including Cl⁻-depletion. It is interesting to note here that we have observed relatively higher Cl⁻ deficit in PM₁₀ and PM_2.5 during the POM season, which is corroborating our previous interpretation of high Cl deficit (see Section Chloride Deficit). We further compared our results with Yadav et al. (2020), wherein they have reported near neutrality for both PM₁₀ and PM_2.5 and lower NF for TSP suggesting acidic composition at Goa. These results further highlights on increase in neutralization process with decrease in particle grain size (Wu et al., 2017), which is consistent with our observation as well.

Conclusion

We present here a one-year long aerosol chemical composition data of size-segregated aerosols (PM₁₀ and PM_2.5) at a costal location in the northeastern Arabian Sea (Goa; 15.45°N, 73.20°E, 56 m above the sea level). Following are the major outcomes of our study:

1. A uniform dominance of nss- SO₄²⁻ and NH₄⁺ is observed in both PM₁₀ and PM_2.5 in all three seasons, with highest abundance of both species during winter months.

2. A significant temporal variability is observed in sea-salt concentration in PM₁₀ with highest values observed during summer, while no significant temporal variability is found for PM_2.5 composition.

3. Significant increase in secondary aerosols (SO₄²⁻ and NH₄⁺) is observed in PM_2.5, during winter associated with higher values of nss-K⁺ indicating long-range transport of secondary aerosols from the north/northwestern India (IGP). This observation is corroborated with air mass back-trajectory analyses.

4. A remarkable contribution by excess of acidic ions is observed being responsible for significant chloride depletion which is highest during post-monsoon months for fine mode aerosols.

5. Neutralization factor is found to be lowest during post-monsoon; however, near neutrality is observed for rest of the year.

In addition to confirming the results of previous studies based on shorter measurement periods, this study has regional as well as global importance as it provides quality dataset on aerosols water-soluble ionic composition at a regional location in the northeastern Arabian Sea. Such dataset is helpful in constraining output of the climatic models and gives more realistic future projections.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

AnK: methodology, investigation, and writing-original draft. AsK: conceptualization, investigation, writing-original draft, resources, project administration, and funding acquisition. MA: methodology and data analyses. PP: methodology and data analyses. GS: methodology and data analyses. NG: Review and Editing.

Funding

Funding for this research is supported by MoES PMN project. The Department of Science and Technology funded AsK, Govt. of India, under the SPLICE Program (Grant No. DST/CCP/Aerosol/85/2017(G)).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model (http://www.ready/noaa.gov) used in this publication. We thank K. Suresh and Udisha Singh for the support during analyses of water soluble ionic species. NIO contribution no. is 6662.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2020.619174/full#supplementary-material.

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