Fine Particulate Sulfate in Hanoi during 1999 - 2022. Impact of SO2 Regional Emissions and Monsoon Circulations

Fine Particulate Sulfate in Hanoi during 1999 - 2022. 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Impact of SO2 Regional Emissions and Monsoon Circulations Pham Duy Hien, Vuong Thu Bac, Ha Lan Anh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7743977/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the long-term trends and seasonal variations of fine particulate sulfate (PM 2.5 -SO 4 ²⁻) in Hanoi from 1999 to 2022, using 843 daily measurements obtained through ion chromatography and Proton-Induced X-ray Emissions analysis. Results show a distinct temporal pattern: sulfate concentrations increased until 2006, then declined sharply through 2012, consistent with regional sulfur dioxide (SO 2 ) emission trends and closely mirror the emission patterns and the underlying economic development in China, the dominant emitter in Asia during this period. Seasonal decomposition reveals that sulfate peaked during the northeast monsoon (October–December) in the early period but shifted to peaks in April and August in recent years. These seasonal shifts reflect changes in the dominant atmospheric transport pathways, with a growing influence from India and Southeast Asia via the northwest and southwest monsoons as China's emissions decreased. The strong correlation between Hanoi's sulfate levels and regional SO 2 emissions highlights the significant role of long-range transport, which accounts for over 60% of the observed sulfate. This study emphasizes the importance of regional emission controls and transboundary pollution transport in shaping local air quality, providing valuable insights for environmental policy and public health planning in Southeast Asia. PM2.5-SO42− long-term trend LRT SEA Hanoi Vietnam Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 HIGHLIGHTS PM 2.5 sulfate in Hanoi closely tracks the SO 2 regional emission trends. Sulfate concentrations rose until 2006, then declined following China's emission reduction policies. Seasonal sulfate peaks shifted from October - January to April and August after 2006. Long-range transport via monsoonal airflows is the dominant source of sulfate. 1. Introduction Fine particulate matter (PM 2.5 ), defined as airborne particles with aerodynamic diameters smaller than 2.5 µm, is a major urban air pollutant with well-documented impacts on human health and the environment. Among its constituents, water-soluble inorganic ions (WSIIs) - notably sulfate (SO 4 ²⁻), nitrate (NO 3 ⁻), and ammonium (NH 4 ⁺) - account for 30% to 70% of PM 2.5 mass in many urban areas (Dong et al., 2014; Pathak et al., 2003; Seinfeld & Pandis, 2016; Squizzato et al., 2013; Yang et al., 2011; Zheng et al., 2018; Hien et al., 2021). These secondary aerosols are formed primarily through the atmospheric oxidation of gaseous precursors such as sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and ammonia (NH 3 ), followed by gas-to-particle conversion processes involving complex reactions with ozone, hydroxyl radicals, volatile organic compounds (VOCs), and other reactive species (Ianniello et al., 2011). The main anthropogenic sources of SO 2 and NO x are fossil fuel combustion from power generation and transport, while NH 3 emissions predominantly stem from agricultural activities including fertilizer use and livestock operations - as well as vehicular traffic (Bouwman et al., 1997; Wang et al., 2020). These precursors undergo atmospheric transformation to produce sulfuric acid (H 2 SO 4 ) and nitric acid (HNO 3 ), which react with ammonia to form salts such as ammonium sulfate ((NH 4 ) 2 SO 4 ), ammonium bisulfate (NH 4 HSO 4 ), and ammonium nitrate (NH 4 NO 3 ). Since 1999, a long-term monitoring program in Hanoi has generated extensive datasets on 24-hour PM 2.5 concentrations of sulfate, nitrate, and ammonium (Hien et al., 2004; 2009; 2021 and 2024). In addition to these chemical analyses, many samples have also been examined for sulfur content using Proton Induced X-ray Emission (PIXE), allowing for the derivation of sulfate concentrations (see Section 3.1). Together, these efforts have produced a robust dataset of 843 daily sulfate measurements over 23 years, providing a valuable basis for assessing long-term temporal trends and seasonal dynamics in sulfate pollution in Hanoi. The primary objective of this study is to analyze these trends and seasonal variations in PM 2.5 sulfate concentrations in Hanoi between 1999 and 2022. Given that over 60% of sulfate in Hanoi originates from long-range transport (LRT) via regional monsoon systems (Hien et al., 2004; 2009 and 2021), observed sulfate levels are expected to reflect both the spatial-temporal patterns of regional SO 2 emissions and the prevailing atmospheric circulation pathways. As China, India, and Southeast Asia (SEA) have been the largest regional SO 2 emitters (Kurokawa & Ohara, 2020), this study further illustrates how ground-level sulfate observations can serve as an indirect indicator of broader regional emission trends and the underlying economic and environmental policies driving those trends. 2. Materials and methods 2.1 Data source For this study, the 24-h PM 2.5 sulfate concentration data were taken from the database of water-soluble inorganic ions (WSII), which have been determined by the ion chromatography (IC) method and used in many previous studies (Hien et al., 2004; 2009 and 2021). The PM 2.5 sampling location was in Hanoi's Cau Giay district, approximately 5 km northwest of the city center. Numerous PM 2.5 samples were also analyzed for the sulfur (S) content by the Particle-Induced X-ray Emissions (PIXE) method at ANSTO, Australia, and the results were made available for the countries participating in the IAEA-supported Asia-Pacific Regional Cooperation Projects on Air Quality Management (Atanacio et al., 2016). In the meantime, dozens of samples were analyzed using both analytical methods to facilitate the determination of the relationship between sulfur and sulfate contents (see subsection 3.2). This study analyzes 843 sulfur and sulfate measurements collected between 1999 and 2022. However, continuous measurements with PM 2.5 samples collected twice weekly were conducted only until April 2012. After that, data collection was suspended for four months, then resumed intermittently in 2013, 2015, and 2022. 2.2 Trend and Seasonal Index The 24-hour sulfate contents exhibit a clear seasonal pattern, with peaks occurring between October and December and troughs between June and July. To analyze this variation, we decompose the sulfate time series S into three components: the trend T , the sulfate seasonal index, S SI , and the irregular index IR, as follows: S = T × SSI × IR (1) In equation (1), T is the interannual trend, estimated using a 12-month moving average. The seasonal index captures recurring fluctuations, while the irregular index accounts for random variations. The SSIs are normalized to have an average value of 1. The components T and SSI in equation (1) are derived using the statistical procedures outlined by Pindyck and Rubinfeld (1998). 2.3 Trajectories of monsoon air masses To investigate the origins and transport pathways of air masses arriving in Hanoi, a 5-day backward trajectories ending at Hanoi 500 m above ground level during 2012 are calculated using online version 5.4.2 of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Stein et al., 2015). 3. Results 3.1 Relationship between sulfur and sulfate The analytical results for samples analyzed by both IC and PIXE techniques have shown that sulfate is proportional to sulfur (Figure 1): SO 4 = (3.01 ± 0.04) × S – (0.07 + 0.01) (2) The SO 4 /S ratio equals 3:1 if all the S is present in the aerosols as SO 4 . This was almost the case in this work with a slope of 3.01 ± 0.06, a close-to-zero intercept (0.08 ± 0.15), and a significant correlation between SO 4 and S ( R 2 = 0.97). Thus, 97% of sulfur was present in PM 2.5 as sulfate, consistent with findings in Watson et al. (2008) and Hien et al. (2021). 3.2 Sulfate time series The 24-h sulfate concentrations in 1999 - 2022 ranged from 310 to 37530 ng m -3 with a mean of 7980 ng m -3 (Figure 1). The sulfate monthly means ranged from 541 to 20620 ng m -3 , averaging (7760 ± 3615) ng m -3 . Sulfate levels in Hanoi are higher than in Ho Chi Minh City (Huy et al., 2020) but lower than in major South China cities such as Guangzhou, Nanjing, and Hong Kong (Jia et al., 2018; Cheng et al., 2011; Zhan et al., 2023). 3.3 Sulfate trends The trend and the seasonal index decomposed from the sulfate time series are shown in Figure 2 and Figure 4, respectively. Sulfate increased from 1999 to 2006, then decreased rapidly until 2012 (Figure 2). To determine whether these sulfate rising and declining trends are statistically significant, we employed the conventional linear regression and estimated the rate of change of sulfate monthly mean concentrations using the slope of the least-squared linear fit. The results, displayed in Figure 3, show that the rate of change with ± 99% confidence intervals (CI) of + 0.4 µg m -3 y -1 during 1999-2006 and -1.15 µg m -3 y -1 during 2006-2012. 3.4 SSI The SSIs show distinct patterns depending on the sulfate rising (Figure 4a) or declining (Figure 4b). In the former case, SSI is high from October to January and low from May to August. In the latter case, the SSI decreases markedly in winter but increases sharply in April-May and August, reflecting the rising contributions from the NW and SW air masses amid the declining contributions from the N NE counterparts (see Discussion in Section 4). 4. Discussion The sulfate trend identified above is generally in line with Asia's total sulfur dioxide (SO2) emissions (Kurokawa and Ohara, 2020 ), reaching a maximum of approximately 45 Gt/year in 2006 and declining thereafter. China was the largest emitter (approximately 30 Gt/year), followed by India (~ 8 Gt/year) and Southeast Asia (SEA) (~ 4 Gt/year). Japan contributes very little, as its SO 2 emissions declined rapidly after reaching a maximum of less than 10 Gt/year in 1970 (Kurokawa and Ohara, 2020 ). Lu et al. ( 2010 ) also found a pattern of SO 2 emissions in China, which reached a maximum of 33.2 Tg in 2006 and declined rapidly afterward. SO 2 emissions from fossil fuel combustion in power plants and industry are the primary sources of SO2 emissions, which increased rapidly to boost China's economic boom in the early 2000s. The SO 2 emissions, however, began to decrease after 2006 in response to a new policy set out by China's government in the country's 11th Five-Year Plan (2006–2010), which aimed to cut national SO 2 emissions by 10% relative to the 2005 level. To achieve this goal, emission reduction was strictly required, including abatement measures such as using low-sulfur fuels, flue-gas desulfurization techniques, and the phase-out of small high-emitting power generation units (Xu et al., 2009 ; Lu et al., 2010 ; Zheng et al., 2018 ; Kurokawa and Ohara, 2020 ). In India, SO 2 emission sources primarily came from power plants and the industrial sector. However, unlike in China, SO 2 emissions in India monotonously increased from 1950 to 2015. The alignment between sulfate concentrations and regional SO 2 emissions suggests that much of the sulfate observed in Hanoi originates from regional SO 2 emissions and is transported by monsoon air masses rather than local emissions. Indeed, source apportionment using the Positive Matrix Factorization (PMF) model indicates that over 60% of the sulfate detected in Hanoi is long-range transported (LRT) via regional monsoon airflows (Hien et al., 2004 ; Bac and Hien, 2009 ; Hien et al., 2021 ). Similar findings have been reported in sulfate studies in Hong Kong (Pathak et al., 2003 ; Nie et al,. 2013 and 2014). East Asian monsoon air masses largely drive the seasonal variations in air pollution levels in Hanoi. During the northeast (NE) monsoon from October to January, cold continental air from the Asiatic high-pressure system over northern China and Mongolia transports significant amounts of pollutants from heavily polluted regions of China. High-pressure conditions also suppress local atmospheric dispersion, intensifying the effects of in-situ emissions. In contrast, from July to August, the southwest (SW) and southeast (SE) monsoons bring hot maritime air and are associated with convective atmospheric conditions that result in lower pollution levels. During this period, fewer long-range transported (LRT) aerosols reach Hanoi from Southeast Asia (SEA) and the East Sea. The northwesterly (NW) monsoon, which predominates in the transitional period from April to May, brings dry air and moderate pollution levels from India and Myanmar (Fig. 5 ). These patterns are reflected in the SSI (Fig. 4 ). In the period of rising SO 2 emissions in China (1999–2006), sulfate levels were highest during the N-NE monsoons (October to January) and lowest during the SW and SE monsoons (June to August) (Fig. 4 a). However, since 2007, SO 2 emissions in China have declined, resulting in the SSI peaks in April, May, and August (Fig. 4 b). This shift highlights the growing influence of the NW and SW monsoons in transporting pollutants from India and SEA, respectively. In addition to coal-fired power plant emissions, intensive agricultural residue burning and forest fires in these regions are likely a significant contributor to the pollution carried by these monsoonal air masses (Lasko et al., 2018 ). Conclusion This study demonstrates that PM 2.5 sulfate concentrations in Hanoi from 1999 to 2022 were strongly influenced by regional SO 2 emissions and long-range atmospheric transport. Sulfate levels rose steadily until 2006, then declined sharply in tandem with China's emission reduction policies under the 11 th Five-Year Plan. Seasonal patterns also shifted: in the early 2000s, peak sulfate concentrations aligned with the northeast monsoon and pollution transport from northern China. In later years, peaks have occurred in April and August, reflecting increasing contributions from the northwest and southwest monsoons, which carry pollutants from India and Southeast Asia. These findings underscore the dominant role of transboundary transport in shaping Hanoi's air quality and highlight the effectiveness of regional emission controls. As energy use and economic development continue to evolve across Asia, coordinated international efforts to monitor and mitigate emissions are essential. Ground-level sulfate measurements, such as those used in this study, provide a valuable lens through which to assess the impact of regional policies and to inform future strategies for air pollution management in urban centers across Southeast Asia. Declarations Funding Declaration There was no funding for this manuscript. Credit authorship contribution statement Pham Duy Hien (P. D. Hien): Writing – original draft, review, and editing, Conceptualization, Data curation, Supervision. 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Phys. 18, 14095–14111. https://doi.org/10.5194/acp-18-14095-2018. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7743977","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533085781,"identity":"07bba84f-d081-41e7-8225-0bdcdb86b3b2","order_by":0,"name":"Pham Duy Hien","email":"","orcid":"","institution":"Vietnam Atomic Energy Institute (VINATOM)","correspondingAuthor":false,"prefix":"","firstName":"Pham","middleName":"Duy","lastName":"Hien","suffix":""},{"id":533085783,"identity":"0b4d6cac-1352-40e5-aa54-0be6db8bac87","order_by":1,"name":"Vuong Thu Bac","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYFACHiCuYGAwIF4HG0jLGZK1MLaRosXgfu/BDz/nHZY3Z28+wPCjYhsRWo7xJUv2bjtsuLPnWAJjz5nbhLVItvEYSDNuO8y44UaOATNjG3FajH8zzjlsT7wWfjYeM2nGhsOJpGjJS7PsOZaevOHMsYSDRPmFjfns4Rs/aqxtNxxvPvjgRwURWqCgGUweIFo9ENSRongUjIJRMApGGgAAA887wjbBw1cAAAAASUVORK5CYII=","orcid":"","institution":"Vietnam Atomic Energy Institute (VINATOM)","correspondingAuthor":true,"prefix":"","firstName":"Vuong","middleName":"Thu","lastName":"Bac","suffix":""},{"id":533085785,"identity":"61ccc91e-2643-4915-89f0-b90aec7eeeee","order_by":2,"name":"Ha Lan Anh","email":"","orcid":"","institution":"Vietnam Atomic Energy Institute (VINATOM)","correspondingAuthor":false,"prefix":"","firstName":"Ha","middleName":"Lan","lastName":"Anh","suffix":""}],"badges":[],"createdAt":"2025-09-29 16:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7743977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7743977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97140451,"identity":"f6a1d570-075f-421b-ae29-4280c7411b76","added_by":"auto","created_at":"2025-12-01 10:05:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26854,"visible":true,"origin":"","legend":"\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e sulfate versus total sulfur\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7743977/v1/51f617a252b050a1df121562.png"},{"id":97112020,"identity":"39a2e8fe-b2cc-4c89-98fe-5651a9cacf19","added_by":"auto","created_at":"2025-12-01 06:39:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113788,"visible":true,"origin":"","legend":"\u003cp\u003eTime series and trend of 24-h PM\u003csub\u003e2.5\u003c/sub\u003e sulfate concentrations observed in Hanoi during 1999 - 2022.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7743977/v1/0fe0897b5002daf4d69e8e16.png"},{"id":97112024,"identity":"d9871663-5214-4278-a2ee-e181d9e7621e","added_by":"auto","created_at":"2025-12-01 06:39:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59508,"visible":true,"origin":"","legend":"\u003cp\u003eSulfate monthly mean concentrations and the least-squared linear fit show the sulfate rising and declining trends before and after 2006, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7743977/v1/6cd6aba38b416162d33f9419.png"},{"id":97112021,"identity":"49e981fc-cf1c-48d6-9586-8d5dd1c59993","added_by":"auto","created_at":"2025-12-01 06:39:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63840,"visible":true,"origin":"","legend":"\u003cp\u003eSSI during the sulfate rising a) and declining b)periods.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7743977/v1/755bb150351f2834578622f6.png"},{"id":97140616,"identity":"a45cb5fd-93ed-42e7-aa17-e8b5461b2af9","added_by":"auto","created_at":"2025-12-01 10:05:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":182549,"visible":true,"origin":"","legend":"\u003cp\u003eTypical 5-day HYSPLIT backward trajectories of air masses arriving in Hanoi during October and January (a) and April and August (b).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7743977/v1/197f50d84325e863353d67d5.png"},{"id":99545054,"identity":"d04efef3-9792-4050-9272-c57bd9a81891","added_by":"auto","created_at":"2026-01-05 15:54:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7743977/v1/a95f66ae-feef-45dd-abef-1201fac40c13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fine Particulate Sulfate in Hanoi during 1999 - 2022. Impact of SO2 Regional Emissions and Monsoon Circulations","fulltext":[{"header":"HIGHLIGHTS","content":"\u003cul\u003e\n \u003cli\u003ePM\u003csub\u003e2.5\u003c/sub\u003e sulfate in Hanoi closely tracks the SO\u003csub\u003e2\u003c/sub\u003e regional emission trends.\u003c/li\u003e\n \u003cli\u003eSulfate concentrations rose until 2006, then declined following China's emission reduction policies.\u003c/li\u003e\n \u003cli\u003eSeasonal sulfate peaks shifted from October - January to April and August after 2006.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLong-range transport via monsoonal airflows is the dominant source of sulfate.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eFine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e), defined as airborne particles with aerodynamic diameters smaller than 2.5 µm, is a major urban air pollutant with well-documented impacts on human health and the environment. Among its constituents, water-soluble inorganic ions (WSIIs) - notably sulfate (SO\u003csub\u003e4\u003c/sub\u003e²⁻), nitrate (NO\u003csub\u003e3\u003c/sub\u003e⁻), and ammonium (NH\u003csub\u003e4\u003c/sub\u003e⁺) - account for 30% to 70% of PM\u003csub\u003e2.5\u003c/sub\u003e mass in many urban areas (Dong et al., 2014; Pathak et al., 2003; Seinfeld \u0026amp; Pandis, 2016; Squizzato et al., 2013; Yang et al., 2011; Zheng et al., 2018; Hien et al., 2021). These secondary aerosols are formed primarily through the atmospheric oxidation of gaseous precursors such as sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e), and ammonia (NH\u003csub\u003e3\u003c/sub\u003e), followed by gas-to-particle conversion processes involving complex reactions with ozone, hydroxyl radicals, volatile organic compounds (VOCs), and other reactive species (Ianniello et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe main anthropogenic sources of SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003ex\u003c/sub\u003e are fossil fuel combustion from power generation and transport, while NH\u003csub\u003e3\u003c/sub\u003e emissions predominantly stem from agricultural activities including fertilizer use and livestock operations - as well as vehicular traffic (Bouwman et al., 1997; Wang et al., 2020). These precursors undergo atmospheric transformation to produce sulfuric acid (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) and nitric acid (HNO\u003csub\u003e3\u003c/sub\u003e), which react with ammonia to form salts such as ammonium sulfate ((NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e), ammonium bisulfate (NH\u003csub\u003e4\u003c/sub\u003eHSO\u003csub\u003e4\u003c/sub\u003e), and ammonium nitrate (NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eSince 1999, a long-term monitoring program in Hanoi has generated extensive datasets on 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e concentrations of sulfate, nitrate, and ammonium (Hien et al., 2004; 2009; 2021 and 2024). In addition to these chemical analyses, many samples have also been examined for sulfur content using Proton Induced X-ray Emission (PIXE), allowing for the derivation of sulfate concentrations (see Section 3.1). Together, these efforts have produced a robust dataset of 843 daily sulfate measurements over 23 years, providing a valuable basis for assessing long-term temporal trends and seasonal dynamics in sulfate pollution in Hanoi.\u003c/p\u003e\n\u003cp\u003eThe primary objective of this study is to analyze these trends and seasonal variations in PM\u003csub\u003e2.5\u003c/sub\u003e sulfate concentrations in Hanoi between 1999 and 2022. Given that over 60% of sulfate in Hanoi originates from long-range transport (LRT) via regional monsoon systems (Hien et al., 2004; 2009 and 2021), observed sulfate levels are expected to reflect both the spatial-temporal patterns of regional SO\u003csub\u003e2\u003c/sub\u003e emissions and the prevailing atmospheric circulation pathways. As China, India, and Southeast Asia (SEA) have been the largest regional SO\u003csub\u003e2\u003c/sub\u003e emitters (Kurokawa \u0026amp; Ohara, 2020), this study further illustrates how ground-level sulfate observations can serve as an indirect indicator of broader regional emission trends and the underlying economic and environmental policies driving those trends.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this study, the 24-h PM\u003csub\u003e2.5\u003c/sub\u003e sulfate concentration data were taken from the database of water-soluble inorganic ions (WSII), which have been determined by the ion chromatography (IC) method and used in many previous studies (Hien et al., 2004; 2009 and 2021). The PM\u003csub\u003e2.5\u003c/sub\u003e sampling location was in Hanoi\u0026apos;s Cau Giay district, approximately 5 km northwest of the city center. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNumerous PM\u003csub\u003e2.5\u003c/sub\u003e samples were also analyzed for the sulfur (S) content by the Particle-Induced X-ray Emissions (PIXE) method at ANSTO, Australia, and the results were made available for the countries participating in the IAEA-supported Asia-Pacific Regional Cooperation Projects on Air Quality Management (Atanacio et al., 2016). In the meantime, dozens of samples were analyzed using both analytical methods to facilitate the determination of the relationship between sulfur and sulfate contents (see subsection 3.2). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study analyzes 843 sulfur and sulfate measurements collected between 1999 and 2022. However, continuous measurements with PM\u003csub\u003e2.5\u003c/sub\u003e samples collected twice weekly were conducted only until April 2012. After that, data collection was suspended for four months, then resumed intermittently in 2013, 2015, and 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Trend and Seasonal Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 24-hour sulfate contents exhibit a clear seasonal pattern, with peaks occurring between October and December and troughs between June and July. To analyze this variation, we decompose the sulfate time series \u003cem\u003eS\u003c/em\u003e into three components: the trend \u003cem\u003eT\u003c/em\u003e, the sulfate seasonal index, S\u003cem\u003eSI\u003c/em\u003e, and the irregular index \u003cem\u003eIR,\u0026nbsp;\u003c/em\u003eas follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eS = T \u0026times; SSI \u0026times; IR\u0026nbsp;\u003c/em\u003e(1)\u003c/p\u003e\n\u003cp\u003eIn equation (1), \u003cem\u003eT\u003c/em\u003e is the interannual trend, estimated using a 12-month moving average. The seasonal index captures recurring fluctuations, while the irregular index accounts for random variations. The SSIs are normalized to have an average value of 1. The components\u003cem\u003e\u0026nbsp;T\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;SSI\u0026nbsp;\u003c/em\u003ein equation (1) are derived using the statistical procedures outlined by Pindyck and Rubinfeld (1998).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Trajectories of monsoon air masses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the origins and transport pathways of air masses arriving in Hanoi, a 5-day backward\u003c/p\u003e\n\u003cp\u003etrajectories ending at Hanoi 500 m above ground level during 2012 are calculated using online version 5.4.2 of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Stein et al., 2015).\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results ","content":"\u003cp\u003e\u003cstrong\u003e3.1 Relationship between sulfur and sulfate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical results for samples analyzed by both IC and PIXE techniques have shown that sulfate is proportional to sulfur (Figure 1):\u003c/p\u003e\n\u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e = (3.01 \u0026plusmn; 0.04)\u003cem\u003e\u0026nbsp;\u0026times;\u0026nbsp;\u003c/em\u003eS \u0026ndash; (0.07 + 0.01) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (2) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe SO\u003csub\u003e4\u003c/sub\u003e/S ratio equals 3:1 if all the S is present in the aerosols as SO\u003csub\u003e4\u003c/sub\u003e. This was almost the case in this work with a slope of 3.01 \u0026plusmn; 0.06, a close-to-zero intercept (0.08 \u0026plusmn; 0.15), and a significant correlation between SO\u003csub\u003e4\u003c/sub\u003e and S (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.97). Thus, 97% of sulfur was present in PM\u003csub\u003e2.5\u003c/sub\u003e as sulfate, consistent with findings in Watson et al. (2008) and Hien et al. (2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Sulfate time series\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 24-h sulfate concentrations in 1999 - 2022 ranged from 310 to 37530 ng m\u003csup\u003e-3\u003c/sup\u003e with a mean of 7980 ng m\u003csup\u003e-3\u003c/sup\u003e (Figure 1). The sulfate monthly means ranged from 541 to 20620 ng m\u003csup\u003e-3\u003c/sup\u003e, averaging (7760 \u0026plusmn; 3615) ng m\u003csup\u003e-3\u003c/sup\u003e. Sulfate levels in Hanoi are higher than in Ho Chi Minh City (Huy et al., 2020) but lower than in major South China cities such as Guangzhou, Nanjing, and Hong Kong (Jia et al., 2018; Cheng et al., 2011; Zhan et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Sulfate trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trend and the seasonal index decomposed from the sulfate time series are shown in Figure 2 and Figure 4, respectively.\u0026nbsp;Sulfate increased from 1999 to 2006, then decreased rapidly until 2012 (Figure 2). To determine whether these sulfate rising and declining trends are statistically significant, we employed the conventional linear regression and estimated the rate of change of sulfate monthly mean concentrations using the slope of the least-squared linear fit. The results, displayed in Figure 3, show that the rate of change with \u0026plusmn; 99% confidence intervals (CI) of + 0.4 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e during 1999-2006 and -1.15 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e during 2006-2012.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 SSI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SSIs show distinct patterns depending on the sulfate rising (Figure 4a) or declining (Figure 4b). In the former case, SSI is high from October to January and low from May to August. In the latter case, the SSI decreases markedly in winter but increases sharply in April-May and August, reflecting the rising contributions from the NW and SW air masses amid the declining contributions from the N NE counterparts (see Discussion in Section 4).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe sulfate trend identified above is generally in line with Asia's total sulfur dioxide (SO2) emissions (Kurokawa and Ohara, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reaching a maximum of approximately 45 Gt/year in 2006 and declining thereafter. China was the largest emitter (approximately 30 Gt/year), followed by India (~\u0026thinsp;8 Gt/year) and Southeast Asia (SEA) (~\u0026thinsp;4 Gt/year). Japan contributes very little, as its SO\u003csub\u003e2\u003c/sub\u003e emissions declined rapidly after reaching a maximum of less than 10 Gt/year in 1970 (Kurokawa and Ohara, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) also found a pattern of SO\u003csub\u003e2\u003c/sub\u003e emissions in China, which reached a maximum of 33.2 Tg in 2006 and declined rapidly afterward. SO\u003csub\u003e2\u003c/sub\u003e emissions from fossil fuel combustion in power plants and industry are the primary sources of SO2 emissions, which increased rapidly to boost China's economic boom in the early 2000s. The SO\u003csub\u003e2\u003c/sub\u003e emissions, however, began to decrease after 2006 in response to a new policy set out by China's government in the country's 11th Five-Year Plan (2006\u0026ndash;2010), which aimed to cut national SO\u003csub\u003e2\u003c/sub\u003e emissions by 10% relative to the 2005 level. To achieve this goal, emission reduction was strictly required, including abatement measures such as using low-sulfur fuels, flue-gas desulfurization techniques, and the phase-out of small high-emitting power generation units (Xu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kurokawa and Ohara, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In India, SO\u003csub\u003e2\u003c/sub\u003e emission sources primarily came from power plants and the industrial sector. However, unlike in China, SO\u003csub\u003e2\u003c/sub\u003e emissions in India monotonously increased from 1950 to 2015.\u003c/p\u003e\u003cp\u003eThe alignment between sulfate concentrations and regional SO\u003csub\u003e2\u003c/sub\u003e emissions suggests that much of the sulfate observed in Hanoi originates from regional SO\u003csub\u003e2\u003c/sub\u003e emissions and is transported by monsoon air masses rather than local emissions. Indeed, source apportionment using the Positive Matrix Factorization (PMF) model indicates that over 60% of the sulfate detected in Hanoi is long-range transported (LRT) via regional monsoon airflows (Hien et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bac and Hien, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hien et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar findings have been reported in sulfate studies in Hong Kong (Pathak et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Nie et al,. 2013 and 2014).\u003c/p\u003e\u003cp\u003eEast Asian monsoon air masses largely drive the seasonal variations in air pollution levels in Hanoi. During the northeast (NE) monsoon from October to January, cold continental air from the Asiatic high-pressure system over northern China and Mongolia transports significant amounts of pollutants from heavily polluted regions of China. High-pressure conditions also suppress local atmospheric dispersion, intensifying the effects of in-situ emissions. In contrast, from July to August, the southwest (SW) and southeast (SE) monsoons bring hot maritime air and are associated with convective atmospheric conditions that result in lower pollution levels. During this period, fewer long-range transported (LRT) aerosols reach Hanoi from Southeast Asia (SEA) and the East Sea. The northwesterly (NW) monsoon, which predominates in the transitional period from April to May, brings dry air and moderate pollution levels from India and Myanmar (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese patterns are reflected in the SSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the period of rising SO\u003csub\u003e2\u003c/sub\u003e emissions in China (1999\u0026ndash;2006), sulfate levels were highest during the N-NE monsoons (October to January) and lowest during the SW and SE monsoons (June to August) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). However, since 2007, SO\u003csub\u003e2\u003c/sub\u003e emissions in China have declined, resulting in the SSI peaks in April, May, and August (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This shift highlights the growing influence of the NW and SW monsoons in transporting pollutants from India and SEA, respectively. In addition to coal-fired power plant emissions, intensive agricultural residue burning and forest fires in these regions are likely a significant contributor to the pollution carried by these monsoonal air masses (Lasko et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that PM\u003csub\u003e2.5\u003c/sub\u003e sulfate concentrations in Hanoi from 1999 to 2022 were strongly influenced by regional SO\u003csub\u003e2\u003c/sub\u003e emissions and long-range atmospheric transport. Sulfate levels rose steadily until 2006, then declined sharply in tandem with China's emission reduction policies under the 11\u003csup\u003eth\u003c/sup\u003e Five-Year Plan. Seasonal patterns also shifted: in the early 2000s, peak sulfate concentrations aligned with the northeast monsoon and pollution transport from northern China. In later years, peaks have occurred in April and August, reflecting increasing contributions from the northwest and southwest monsoons, which carry pollutants from India and Southeast Asia.\u003c/p\u003e\n\u003cp\u003eThese findings underscore the dominant role of transboundary transport in shaping Hanoi's air quality and highlight the effectiveness of regional emission controls. As energy use and economic development continue to evolve across Asia, coordinated international efforts to monitor and mitigate emissions are essential. Ground-level sulfate measurements, such as those used in this study, provide a valuable lens through which to assess the impact of regional policies and to inform future strategies for air pollution management in urban centers across Southeast Asia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePham Duy Hien (P. D. Hien):\u003c/strong\u003e Writing \u0026ndash; original draft, review, and editing, Conceptualization, Data curation, Supervision. Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVuong Thu Bac (V. T. Bac):\u003c/strong\u003e Writing \u0026ndash; original draft, review, and editing, Data curation, Project administration.\u0026nbsp;\u003cstrong\u003eORCID ID:\u0026nbsp;\u003c/strong\u003e0000-0001-8149-8856. Email: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHa Lan Anh (H. L. Anh):\u003c/strong\u003e Formal analysis, Data curation. Email: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eA. J. Atanacio, D. D. Cohen, B. A. Begum, B. Ni, G. G. Pandit, S. K. Sahu, M. Santoso, D.D.Lestiani, J. M. Lim, S. A. Rahman, M. S. Elias, D. Shagjjamba , A. Markwitz, S. Waheed, N. Siddique, P. C. Pabroa, F. L. Santos, M. C. S. Seneviratne, L. Handagiripathira, W. Wimolwattanapun, T. B. Vuong, A. Karydas (2016). The APAD and ASFID: Long-term fine and coarse ambient particulate matter and source fingerprint databases for the Asia-Pacific Region. Air Quality and Climate Change Volume 50 No.3. 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Trends in China\u0026apos;s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18, 14095\u0026ndash;14111. \u003cu\u003ehttps://doi.org/10.5194/acp-18-14095-2018.\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PM2.5-SO42− long-term trend, LRT, SEA, Hanoi, Vietnam","lastPublishedDoi":"10.21203/rs.3.rs-7743977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7743977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the long-term trends and seasonal variations of fine particulate sulfate (PM\u003csub\u003e2.5\u003c/sub\u003e-SO\u003csub\u003e4\u003c/sub\u003e²⁻) in Hanoi from 1999 to 2022, using 843 daily measurements obtained through ion chromatography and Proton-Induced X-ray Emissions analysis. Results show a distinct temporal pattern: sulfate concentrations increased until 2006, then declined sharply through 2012, consistent with regional sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) emission trends and closely mirror the emission patterns and the underlying economic development in China, the dominant emitter in Asia during this period. Seasonal decomposition reveals that sulfate peaked during the northeast monsoon (October–December) in the early period but shifted to peaks in April and August in recent years. These seasonal shifts reflect changes in the dominant atmospheric transport pathways, with a growing influence from India and Southeast Asia via the northwest and southwest monsoons as China's emissions decreased. The strong correlation between Hanoi's sulfate levels and regional SO\u003csub\u003e2\u003c/sub\u003e emissions highlights the significant role of long-range transport, which accounts for over 60% of the observed sulfate. This study emphasizes the importance of regional emission controls and transboundary pollution transport in shaping local air quality, providing valuable insights for environmental policy and public health planning in Southeast Asia.\u003c/p\u003e","manuscriptTitle":"Fine Particulate Sulfate in Hanoi during 1999 - 2022. Impact of SO2 Regional Emissions and Monsoon Circulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 06:39:28","doi":"10.21203/rs.3.rs-7743977/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d154ef30-f538-4675-afbb-41efdb1fcb70","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T15:54:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 06:39:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7743977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7743977","identity":"rs-7743977","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}