Pitakchon Ponsawansong1,2,3, Tippawan Prapamontol This email address is being protected from spambots. You need JavaScript enabled to view it.1, Kittipan Rerkasem1, Somporn Chantara4, Kraichat Tantrakarnapa5, Sawaeng Kawichai1, Guoxing Li6, Cao Fang3, Xiaochuan Pan6, Yanlin Zhang This email address is being protected from spambots. You need JavaScript enabled to view it.3

1 Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand
2 PhD Degree Program in Environmental Science, Environmental Science Research Center, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
3 Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science and Technology, Nanjing, China
4 Department of Chemistry, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
5 Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
6 Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China

Received: February 7, 2023
Revised: May 30, 2023
Accepted: July 4, 2023

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

Download Citation: ||https://doi.org/10.4209/aaqr.230030  

Cite this article:

Ponsawansong, P., Prapamontol, T., Rerkasem, K., Chantara, S., Tantrakarnapa, K., Kawichai, S., Li, G., Fang, C., Pan, X., Zhang, Y. (2023). Sources of PM2.5 Oxidative Potential during Haze and Non-haze Seasons in Chiang Mai, Thailand. Aerosol Air Qual. Res. 23, 230030. https://doi.org/10.4209/aaqr.230030


  • The first Thai evidence of PM2.5 and its source contributions to oxidative activity.
  • DTT assay shows PM2.5 chemical compositions' oxidative potential in Chiang Mai.
  • Biomass burning dominates PM2.5 and DTTv activity in haze episodes.


Dithiothreitol (DTT) assay is an acellular technique used to investigate the oxidative potential (OP) of chemical substances bound on PM, which may potentially lead to oxidative stress after exposure. In this study, the source contributions of 16 high priority polycyclic aromatic hydrocarbons (PAHs), designated by the United States Environmental Protection Agency (U.S. EPA), and 10 species of water-soluble inorganic ions bound on PM2.5 and their OP were investigated using DTT assay. The 24-hr ambient PM2.5 samples were collected throughout 2018–2019 and the analyzed OP was compared during haze episodes, which generally occurs in the dry season, and non-haze rainy season in the Chiang Mai-Lamphun basin. During haze episodes, DTTv activity was positively correlated with 4–5 rings PAHs including fluoranthene (Fla) pyrene (Pyr), benzo[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF) and benzo[k]fluoranthene (BkF) with coefficient ranging from 0.327 to 0.545, p = 0.002 to 0.009 (Pearson’s correlation). Inorganic ions, particularly NH4+, SO42–, and NO3, which are the tracers of secondary inorganic aerosol (SIA), were positively correlated with DTTv activity (r = 0.394 to 0.659. p = 0.000 to 0.047; Spearman’s correlation). Positive matrix factorization (PMF) indicated the biomass burning factor had the highest contribution (57.9%) to PM2.5 during haze episodes, followed by SIA (26.2%), and vehicle exhausts (7.8%). Furthermore, multiple linear regression (MLR) showed that biomass burning was the highest contributor to DTTv (43.0%). These results suggest that during haze episodes, higher levels of PM2.5 and its chemical compositions play a crucial role on OP, particularly DTTv activity, which may induce oxidative stress in human body.

Keywords: Air pollution, Biomass burning, Dithiothreitol assay, Oxidative potential, Particulate matter


In Northern Thailand, particularly in the Chiang Mai-Lamphun basin, the annual increase of ambient particulate matter with diameter less than 2.5 µm (PM2.5) occurs between January and April. This is primarily attributed to the intensive open burning of biomass from both local sources, such as forest fires and agricultural wastes, and regional transport sources (i.e., Chuang et al., 2016a; Pongpiachan, 2013; Punsompong and Chantara, 2018; Thepnuan et al., 2019; Zhang et al., 2020).

The chemical compositions of PM2.5 may vary depending on the emission sources, meteorological conditions, and photochemical oxidation. For instance, biomass burning has been found to be a major source of polycyclic aromatic hydrocarbons (PAHs) and soluble inorganic ions on the PM surface (Chansuebsri et al., 2022; Chantara et al., 2012; ChooChuay et al., 2020; Khamkaew et al., 2016b; Liang et al., 2017). The identification of unique and dominant chemical species on PM can serve as tracers to indicate the potential emission. For example, the diagnostic ratio of PAHs can be used to distinguish the origin source (Xing et al., 2022; Yin et al., 2021). High molecular weight PAHs, such as benzo[b]fluoranthene, benzo[a]pyrene, benzo[k]fluoranthene, indeno[1,2,3,c,d]pyrene, and benzo[g,h,i]perylene have been reported as the dominant species from gasoline and diesel engines (Li et al., 2020; Shimada et al., 2022; Wang et al., 2015a; Wu et al., 2020). In contrast, low molecular weight PAHs, such as phenanthrene and fluorene, were found to be dominant in PM from biomass burning (Wang et al., 2015a; Wu et al., 2020).

Inorganic ions such as K+ are considered as an indicator of biomass burning as it is a component of fertilizers and plant structure (Li et al., 2021a; Li et al., 2021b). Ca2+ is mostly derived from soil surfaces during burning (Jaafar et al., 2014). NH4+, SO42–, and NO3 are commonly used as tracers of the secondary inorganic aerosol (SIA) or aged particles that are converted from precursor gaseous pollutants such as SO2, NOx, and NH3 via photooxidation and oxidizing agents (Krzyszczak and Czech, 2021; Xiao et al., 2020; Xu et al., 2022; Zhang et al., 2022).

Positive matrix factorization (PMF) is a quantitative receptor models developed by U.S. EPA to identify and group the complex chemical compositions based on inter-source variability and intra-source similarity or source profiles (Cesari et al., 2019; Wang et al., 2015a; Wu et al., 2020). Studies conducted worldwide have reviewed that various source, such as coal combustion, traffic, biomass burning, SIA, metal-related sources, and soil and dust, contribute to PM2.5 pollution (Liu et al., 2014; Liu et al., 2018; Yu et al., 2019). Each of these sources contributes to a different air pollution background.

The chemical constituents that bind on PM also play a crucial role in inducing biochemical changes through the activation and induction of reactive oxygen species (ROS) and free radicals (Lai et al., 2017; Wang et al., 2015b; Wu et al., 2012; Yang et al., 2017). Due to the highly reactive capabilities of these compounds, they can interact with biomolecules such as proteins, lipids and DNA, resulting in acute and chronic diseases (Dabass et al., 2016; Dales et al., 2009).

To investigate the capability of PM2.5 and its chemical compositions to generate ROS, the DTT assay has been widely employed in in-vitro studies as a quantitative measurement of oxidative potential (OP) (Charrier and Anastasio, 2012; Cho et al., 2005). The DTT assay was initially developed by Kumagai et al. (2002) who demonstrated a redox reaction between 9,10-phenanthrenequinone (PQN) and DTT. This reaction was found to catalyze the transfer of electron from DTT to oxygen, ultimately generating O2. The redox reaction was reported to have the association with heme oxygenase-1 and glutathione depletion, both of which are linked to oxidative stress, indicating that OP measurement by the DTT assay may be a reliable indicator of cellular oxidative stress (Kumagai et al., 2002; Li et al., 2020). The OP determined by the DTT assay is expressed in terms of passing air volume during collection (DTTv) and PM2.5 mass (DTTm). Studies have demonstrated that the OP of PM2.5 is strongly correlated with its chemical species, including organic carbon and metals, further suggesting the potential ROS-forming ability of the chemical constituents on PM, which may lead to oxidative stress (Cesari et al., 2019; Pietrogrande et al., 2018; Yu et al., 2019). The association between OP and chemical components provides important insight into potential health impacts of PM2.5 exposure, highlighting the need for regulation and monitoring of PM2.5 to mitigate the risks of oxidative stress (Crobeddu et al., 2017; He et al., 2020; Paoin et al., 2021; Wang et al., 2018).

In northern Thailand, the evidence suggests that biomass burning is a major contributor to PM2.5, yet but there is limited information regarding the source contributions of OP. Thus, there is need to investigate the source contributions of OP. The present study aims to address this research gap by investigating the source contributions of PM2.5 and OP during haze episodes and non-haze rainy seasons in Chiang Mai Province, located in upper northern Thailand.


2.1 Sample Collection

Ambient PM2.5 samples were collected over the two distinct periods, namely haze episodes and non-haze rainy seasons, spanning the duration of 2018 to 2019. These two episodes were categorized based on the adherence to Thailand National Ambient Air Quality Standards (NAAQSs) whereby the 24-hour ambient PM2.5 concentration exceeded 50 µg m–3 for a minimum of three consecutive days in each week. The monthly occurrence of fire active hotspots was obtained from Geo-informatic and space technology development agency (public organization) through the website (https://fire.gistda.or.th/download-v1.html). During haze episodes, PM2.5 samples were collected every other day (collected 24-hr per day), amounting to a total of 36 samples collected between January 5, 2018, and April 30, 2018 , as well as 43 samples collected between November 1, 2018, and May 14, 2019 In contrast, during non-haze rainy seasons, PM2.5 samples were collected once a week, with a total of 41 sample collected between May 1, 2018, and October 31, 2018, and 12 samples collected between May 20, 2019, and December 15, 2019. The PM2.5 sampler was positioned on the second floor rooftop of the Saraphi Hospital (18°40′53.41′′N, 99°2′35.63′′E, 298 masl), Saraphi District, Chiang Mai Province, Thailand. This location is surrounded by a residential buildings, rice paddy fields, and a 6-lane-highway, situated to the south of Chiang Mai City (Fig. S1). The samples were collected using an active pump with flow rate 5 L min–1 (MiniVol Airmetrics, Springfield, Oregon, USA) onto a quartz filter (47 mm diameter, Whatman® quartz filter, Grade QM-H, Buckinghamshire, UK), pre-baked at 450°C prior use. After collection, the samples were stored in a –20°C freezer until analysis, at which point the filter samples were punched into three parts for subsequent analysis of ions, DTT and PAHs.

The PM2.5 mass was determined using gravimetric analysis. Briefly, a quartz filter was weighed 3 times before and after PM2.5 collection using a 5-digit balance (Mettler Toledo, Ohio, USA). The average mass of the filter after PM2.5 collection was calculated by subtracting the average weight before collection from post-collection weight. The PM2.5 mass was expressed in unit of µg. The mass of PM2.5 was further converted to µg m–3 using the operation manual (Rev 1.2) of Minovol™ Tactical Air Sampler.

2.2 Sample Analysis

2.2.1 PAHs analysis

The analysis was applied from Kawichai et al. (2020). Briefly, a filter sample (1220.2 mm2) was placed in a glass bottle, and 20 µL of mixed deuterated internal standards comprising [2H10]-acenaphthylene and [2H12]-pyrelene (Supelco, Bellefonte, Pennsylvania, USA) were spiked onto the filter sample. The extraction process was conducted twice with 10 mL of dichloromethane for each extraction in a sonicate bath for 10 minutes, while maintaining the temperature below 10°C using ice packs. The final extracted sample (approx. 20 mL) was then filtered through a 0.45 µm PTFE filter into a V-shaped glass tube and evaporated until nearly dry. The resulting sample was re-dissolved with 1 mL dichloromethane and transferred into an amber glass vial for GC-MS analysis.

Gas chromatography was used for analysis of the extracted sample aliquot. The GC system (Agilent, 7890A, Santa Clara, California, USA) consisted of a 30 m HP-5MS capillary column and a mass spectrometer detector (Agilent, 5975C Santa Clara, California, USA). The carrier gas used was helium (purity 99.999%, Gas intertrade Co., Ltd., Samutprakarn, Thailand) at a constant flow rate of 1 mL min–1. The injector temperature was maintained at 250°C, and the oven temperature was programmed from 70°C (2 min initial hold) to 270°C at a rate of 20°C min–1. Selective ions monitoring (SIM) mode was operated for the analysis of 16 PAHs, namely naphthalene (Nap), acenaphthylene (Acy), acenaphthene (Ace), fluorene (Flu), phenanthrene (Phe), anthracene (Ant), fluoranthene (Fla), pyrene (Pyr), benzo[a]anthracene (BaA), chrysene (Chr), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IcdP), dibenz [a,h]anthracene (DahA) and benzo[g,h,i] perylene (BghiP). A mixed standard for these compounds was purchased from Restex, Bellefonte, Pennsylvania, USA.

The quality assurance of the methods was tested by using standard reference materials (SRM; N = 9 with %RSD < 30%) (Urban Dust 1649b, NIST, Gaithersburg, Maryland, USA). The concentration of each compound was subtracted with concentration of field blank (as shown in Table S1) and converted to the unit of ng m–3.

2.2.2 Inorganic ions analysis

A filter sample with a diameter 16 mm (201.1 mm2) was extracted by mixing it with 5 mL of deionized water in a PET bottle and sonicated for 30 min under temperature control using ice packs. Subsequently, the extracted aliquot then was filtered through a 0.22 µm polyethersulfone filter (Sterlitech, Auburn, Washington, USA) to eliminate any insoluble materials prior to analysis.

The ions analysis was conducted using a ThermoFisher Scientific ICS-5000+ system (Waltham, Massachusetts, USA), which included a gradient pump, a conductivity detector/chromatography compartment, and an automated sampler. The cations (Na+, NH4+, Ca2+, K+, and Mg2+) were separated on an IonPac CS12A analytical column and an IonPac CG12A guard column using an aqueous methanesulfonic acid (MSA, 30 mM L1) eluent at a flow rate of 1 mL min−1. The anions (F, Cl, SO42–, NO2, and NO3) were separated on an IonPac AS11-HC analytical column and an IonPac AG11-HC guard column using a sodium hydroxide (NaOH) gradient elution at a flow rate of 1.5 mL min1 with the following gradient conditions: 0–3 min, 0.5 mM L1 ; 3–5 min, 0.5–5 mM L–1; 5–15 min, 5–30 mM L–1 ; 15–20 min, 0.5 mM L1. The ion concentrations were then converted to the unit of µg m–3. The %RSD and %Recovery are shown in Table S2.

2.2.3 DTT assay

The OP measurement on PM2.5 was conducted using the protocol previously described by Wang et al. (2019). Briefly, a punched filter sample with diameter 20 mm (314.3 mm2) was extracted with 5 mL of deionized water for 30 min. A 1.75 mL aliquot of the extracted sample with a concentration range of 3.0 to 246.5 µg L–1 was mixed with 4.55 mL potassium phosphate buffer (0.1 M, pH 7.4, treated with Chelex 100 resin). The mixture was then incubated at 37°C for 5 min, after which 0.7 mL of DTT (1 mM) was added to initiate the reaction, and incubated for 0, 10, 20, and 30 min. The reaction was terminated by transferring 0.5 mL of the mixture at each reaction time to a new tube preloaded with 0.5 mL TCA (10% W/V). The aliquot was then added to 2 mL of Tris-base with EDTA buffer (pH 8.9) and reacted with 50 µL DTNB (10 mM) to form the product, TNB (Wang et al., 2019). The absorbance of TNB was measured at wavelength 412 nm using a UV-Vis spectrophotometer (SPECTROstar® Nano BMG labtech, Geylang, Singapore). The blank was deionized water, and the positive controls were 0.24 µM phenanthrenequinone (PQN). The positive control was performed in each batch to achieve a coefficient of variation lower than 20%. The mean consumption rate of PQN was 0.90 ± 0.28 nmol min–1). The standard curve was generated using DTT concentration of 1, 5, 10, 25, 50, 75, and 100 µM.

The depletion rate of DTT was normalized to the unit of total volume of air passing through the filter (volume-normalized DTT activities, DTTv, nmol min–1 m–3), considered as human exposure, and the total mass of PM in the reaction (mass-normalized DTT activities, DTTm, nmol min–1 µg–1) as a measure of inherent OP in PM.

2.3 Source Apportionment and Statistical Analysis

In 2018 data, PMF version 5.0 was used to perform source apportionment based on data completeness, utilizing all 16 U.S. EPA PAHs and 10 species of inorganic ions (Cesari et al., 2019). Daily PM2.5 concentration or DTTv activity was categorized as total variables, while individual chemical species concentrations were directly introduced. Chemical species concentrations below the limit of detection (LOD) were replaced with LOD/2, and uncertainties were calculated using equation from a previous study (Liao et al., 2021). All input variables were classified as strong variables based on the signal-to-noise ratio (S/N ≥ 1.0) criteria (Shimada et al., 2022). The PMF model was run up to 20 times randomly, aiming to obtain a minimum Q value for 3 to 5 factors. Bootstrapping (bootstrap numbers = 100) was utilized to validate the factors’ separability. Finally, the factor solution that provided the most interpretable results was selected to identify the potential source contributions on PM2.5 in both haze episode and non-haze rainy season (Tsiodra et al., 2021; Wang et al., 2015a; Wu et al., 2020).

Next, the source contribution factors obtained from PMF were introduced to multiple linear regression (MLR). Briefly, the OP (DTTv) from different source contributions was estimated according to the Eq. (1).


where y stands for the DTTv activity, subscripts 1 to i represent the different sources of PM2.5 emission, independent variables x1 to xi denote the respective contribution of the individual emission source to the concentration of PM2.5, and β1 to βi indicate the regression coefficients corresponding to the individual independent variables (Yu et al., 2019).

The temporal variation of PM2.5 and each chemical composition were analyzed using T-test analysis. Seasonal stratification was classified into 2 seasons including haze episode and non-haze rainy season. The correlation of OP and PAHs (n = 132) were analyzed using Pearson’s correlation coefficients, while OP and inorganic ions (n = 77) was tested with non-parametric methods, Spearman’s correlation.


3.1 PM2.5 Levels

A total of 132 PM2.5 samples were collected throughout the year of 2018 and 2019 (N = 79 in haze episode and N = 53 in non-haze rainy season). PM2.5 levels were highly elevated during January to April, exceeding Thailand NAAQSs at 50 µg m–3 about 3 to 4 times. March had the highest monthly average levels of PM2.5, with recorded value of 111.1 ± 54.1 µg m–3 and 95.9 ± 75.2 µg m–3 in 2018 and 2019, respectively, as shown in Fig. 1. The data from active fire hotspots suggested that the primary source of PM2.5 emissions were biomass burning, particularly forest fires and agricultural waste burning in rural areas (Chansuebsri et al., 2022; Chuang et al., 2016a; Kawichai et al., 2021; Khamkaew et al., 2016a), along with transboundary transportation (Amnuaylojaroen et al., 2020; Chantara et al., 2012; Punsompong and Chantara, 2018). The PM2.5 levels gradually decreased from May to November due to the arrival of the rainy season (Chuang et al., 2016a; Khamkaew et al., 2016b; Punsompong and Chantara, 2018; Wiriya et al., 2013).

 Fig. 1. The monthly PM2.5 mass concentration (average ± SD; µg m–3) from the sampling site at Saraphi Hospital along with monthly cumulative numbers of active fire hotspots counts in Chiang Mai Province, 2018–2019 (source: Fire hotspot number obtained from Geo-informatic and space technology development agency (public organization).Fig. 1. The monthly PM2.5 mass concentration (average ± SD; µg m–3) from the sampling site at Saraphi Hospital along with monthly cumulative numbers of active fire hotspots counts in Chiang Mai Province, 2018–2019 (source: Fire hotspot number obtained from Geo-informatic and space technology development agency (public organization).

3.2 PAHs Levels

Chemical compositions bound on PM2.5 played a key role on adverse health effects, with particular emphasis on PAHs. The 16 individual PAHs were evaluated and categorized into non-carcinogenic PAHs (ncPAHs), carcinogenic PAHs (cPAHs) and total PAHs (tPAHs).

The concentration of PAHs was found significantly (p = 0.000) higher during haze episodes (5.15 ± 1.00 ng m–3 in 2018 and 5.42 ± 1.48 ng m–3 in 2019) compared to non-haze rainy season (1.29 ± 0.51 ng m–3 in 2018 and 3.65 ± 0.22 ng m–3 in 2019). March of 2018 and 2019 exhibited the highest concentration of ncPAHs (1.08 ± 0.35 ng m–3 and 2.09 ± 0.72 ng m–3, respectively) and cPAH (6.66 ± 1.25 ng m–3 and 5.27 ± 2.13 ng m–3, respectively), with cPAHs comprising 86% and 71.6% of tPAHs, respectively, as shown in Fig. 2 and Fig. S2. The cPAH/tPAH ratio was notably high during haze episodes (76–86%) compared to non-haze rainy season samples (about 50%), indicating that PM2.5 during haze episode composed of a high amount of cPAHs.

Fig. 2. The monthly average concentration of cPAHs (ng m–3; black bar), ncPAHs (ng m–3; grey bar) and PM2.5 (µg m–3; red line) in 2018–2019. Fig. 2. The monthly average concentration of cPAHs (ng m–3; black bar), ncPAHs (ng m–3; grey bar) and PM2.5 (µg m–3; red line) in 2018–2019. 

In terms of identified PAHs, it was found that three cPAHs, namely IcdP, BaP, and BaA, were the most abundant, accounting for 17%, 13%, and 10% of total PAHs during haze episodes, and 19%, 9%, and 9% during the non-haze rainy season, respectively, as shown in Fig. S3.

The concentration of BaP, a highly toxic cPAHs, ranged from 0.63–2.94 ng m–3 during haze episodes, with the average of 0.87 ± 0.42 ng m–3 in 2018 and 0.43 ± 0.11 ng m–3 in 2019, which did not exceed the annual European union guidelines (1.0 ng m–3). The BaP concentrations in this study were comparable to those reported in previous studies conducted in the same region, such as Chiang Mai and Lamphun city, where concentrations ranged from 0.54 to 1.33 ng m–3 (Kawichai et al., 2021; Pengchai et al., 2009; Pongpiachan, 2013; Thepnuan and Chantara, 2020). Moreover, the BaP concentrations observed in this study were also consistent with those reported in nine provinces in northern Thailand, which ranged from 0.007 to 0.77 ng m–3 (Pongpiachan, 2013; Pongpiachan et al., 2017).

3.3 Inorganic Ions Levels

A total of 77 samples collected in 2018 were analyzed for inorganic ions, with 36 samples collected during the haze episode and 41 samples during non-haze rainy season. The concentrations of some ions, such as NO3, SO42–, NH4+, and K+, were significantly higher during the haze episode in comparison to the non-haze rainy season (Fig. 3 and Table S3). Among these ions, SO42– exhibited the highest concentration, constituting 64%, followed by NO3 (13%) and NH4+ (10%). Consistent with these results, a previous study by Chantara et al. (2012) reported the highest concentrations of theses ions during haze episode. Conversely, Na+ was found to be the most abundant ion during the non-haze rainy season, accounting for 44%. The Spearman’s correlation coefficient was employed to examine the association between PM2.5 and inorganic ions, as shown in Table S5. The correlation coefficient between PM2.5 and ion compositions during the haze episode in 2018 was 0.487 (p = 0.003), whereas the corresponding coefficient during the non-haze rainy season was 0.434 (p = 0.005).

Fig. 3. Monthly average of inorganic ions bound on PM2.5 concentration, 2018.Fig. 3. Monthly average of inorganic ions bound on PM2.5 concentration, 2018.

Sulfate (SO42–) was identified as the primary contributor to ion concentrations during haze episode (64% of total ions; Fig. S4), which is consistent with previous studies conducted in northern Thailand (Chantara et al., 2012; ChooChuay et al., 2020; Khamkaew et al., 2016b; Pongpiachan, 2013; Thepnuan et al., 2019). This finding is supported by a 5-year study conducted by Chantara et al. (2012), which reported that SO42– was the dominant ion species in this region, and its concentration pattern was closely associated with PM2.5 levels.

Nitrate (NO3) was the second major contributor to ion concentrations (13% of total ions), and it is commonly associated with traffic emission (Hu et al., 2008; Pengchai et al., 2009) and biomass burning (Chantara et al., 2012; Thepnuan et al., 2019). During haze episode, the coefficient correlation between ambient PM2.5 and SO42– and NO3 concentrations were 0.665 and 0.607, respectively, and they also significantly correlated with K+ (biomass tracer), r = 0.773 and 0.767.

3.4 DTT Activity

During haze episodes, the DTTv showed significant correlation with Flu (r = 0.377), Fla (r = 0.327), Pyr (r = 0.420), Chr (r = 0.410), BbF (r = 0.380), BkF (r = 0.545), F (r = 0.472), NO3 (r = 0.424), SO42– (r = 0.556), NH4+ (r = 0.659). However, the DTTm barely showed significant correlation with chemical compositions. Furthermore, after seasonal stratification DTTm did not show any correlation with PM2.5 and chemical compositions, shown in Tables S7 and S8. This may be attributed to the relatively low activity of DTTm, which did not differ significantly (p = 0.091) between haze episodes (0.13 ± 0.22 nmol min–1 µg–1) and non-haze rainy season (0.22 ± 0.31 nmol min–1 µg–1), as shown in Table S3. This finding indicates a relatively high exposure risk and low intrinsic oxidative toxicity (Wang et al., 2019). Moreover, the toxicological investigation of chemicals is primarily reliant on their concentration, while the mass contributes less to their toxicity. Therefore, caution should be exercised when using it for modelling purposes (Borlaza et al., 2021; Weber et al., 2021). Thus, this study mainly discusses DTTv.

It should be noted that DTT activity during the non-haze rainy season was found to be undetectable in some samples with PM2.5 mass lower than 250 µg. This could be attributed to low concentration of ambient PM2.5, which ranged from 10.6 to 33.7 µg m–3 or from 90 to 250 µg. In comparison, during the haze episode, PM2.5 mass was found to be over 350 µg. Thus, it is likely that the low mass of PM2.5 (< 250 µg) was not sufficient for accurate DTT analysis. In this study, the results of DTT activity from PM2.5 samples that had a mass lower than 250 µg were excluded prior to statistical analysis.

The DTTv activity in this study was found to be higher than that reported in Hangzhou, China (0.62 nmol min–1 m–3) (Wang et al., 2019) and Lecce, Italy (0.24 nmol min–1 m–3) (Pietrogrande et al., 2018), but lower than those in Jinzhou (4.4 nmol min–1 m–3), Tianjin (6.8 nmol min–1 m–3), and in Yantai, China (4.2 nmol min–1 m–3) (Liu et al., 2018) as well as in Indo-Gangetic plain, India (3.8 nmol min–1 m–3) (Patel and Rastogi, 2018), as shown in Table S6. The high DTTv activity or OP of PM2.5 during haze episodes suggested that the PM2.5 particles have a greater potential to induce redox activity and may result in oxidative stress upon inhalation into the lung.

3.5 Source Apportionment of PM2.5

PMF, a receptor model for quantitative estimation of contributions from specific sources, has been widely used to apportion the potential sources of PM2.5 by determining its dominant chemical compositions (Jamhari et al., 2022; Liu et al., 2021; Wang et al., 2015a; Wu et al., 2020).

The PM2.5 concentration in year 2018 (36 samples in haze episode and 41 samples in non-haze rainy season) and the concentration of 25 chemical species (15 species of PAHs and 10 species of ions) were introduced to the model. After testing from 3 to 5 factors, a bootstrapping result of 4 factor solution showed a mapping rate of over 80% and was the most interpretable, as shown in Tables S9 and S10.

During the haze episode, the dominant sources of PM2.5 were biomass burning (57.9%), SIA (26.2%), vehicle exhaust (7.8%), and others (8.1%). However, during the non-haze rainy season, the contribution of biomass burning reduced to 25.1%, while vehicle exhausts increased to 18.3%. This could be due to reduced open burning activity. The SIA source during non-haze rainy season was confounded by crustal origin, as evidence by the presence of Na+, Ca2+, and Mg2+ (Ahmad et al., 2021; Cesari et al., 2019; Liu et al., 2018). The highest contribution during the non-haze rainy season was from sources that could not be clearly identified due to the lack of dominant species, as shown in Fig. 4. To improve the interpretation of source contributions, analysis of additional chemical species, such as OC, EC, and metals is recommended.

 Fig. 4. Emission source profiles of PAHs and ions bound on PM2.5 during (a) haze episode and (b) non-haze rainy season.Fig. 4. Emission source profiles of PAHs and ions bound on PM2.5 during (a) haze episode and (b) non-haze rainy season.

3.5.1 biomass burning

The contribution from biomass burning in this study was estimated based on the abundance of K+ and low molecular weight PAHs including Flu and Phe. The K+ is commonly used as an indicator of PM2.5 derived from biomass burning since it is the component of plant’s structure and the usage of fertilizer (ChooChuay et al., 2020; Li et al., 2021a). Furthermore, low molecular weight PAHs (≤ 4 rings) are involved in the incomplete combustion at low temperatures, which is associated with biomass burning (Wang et al., 2015a).

The contribution of biomass burning was the highest factor in haze episode (57.9%) which consistent with other studies in northern Thailand (Chansuebsri et al., 2022; Kawichai et al., 2021; Khamkaew et al., 2016a; Thepnuan and Chantara, 2020) but reduced to 25.1% in non-haze rainy season. This reduction may be attributed to a decrease in agricultural waste burning. Nonetheless, it is important to note that household biomass burning, such as wood and dried leaves, particularly in rural area, may still contribute to PM2.5 levels.

3.5.2 SIA species

SIA species were classified in accordance with the relative abundance levels of SO42–, NO3, and NH4+. These aforementioned ions serve as reliable tracers of SIA species. Their presence is indicative of the atmospheric generation of SIA, a process that is intensified by elevated temperatures and solar radiation during its transport from regions experiencing combustion activities (Chuang et al., 2016b; Li et al., 2014; Wang et al., 2016). The identification of this contributing source substantiates the proposition that the PM2.5 collected in this study predominantly originated from rural areas where it had an intensive biomass burning.

3.5.3 Vehicle exhausts

High molecular weight PAHs serve as effective tracers for vehicle exhaust. The high loading of Chr, BkF, and BaP is specifically linked to diesel engine emission, while the occurrence of PAHs with 5–6 ring is predominantly associated with gasoline emission (Shimada et al., 2022; Wang et al., 2015a; Wu et al., 2020). In the context of this study, the contribution of vehicle exhaust to PM2.5 levels was determined to be 7.8% during haze episode, increasing to 18.3% during non-haze rainy season. These findings likely reflect the prevailing air pollution background in this city under investigation, considering the sampling location’s proximity to 6-lane roads and a railway.

3.5.4 Other sources

Other factors were observed in the formation to PM2.5 during haze episode and non-haze rainy season, accounting for 8.1% and 38.8%, respectively. These factors could not be attributed to specific potential sources due to the absence of dominant species. However, the presence of a high percentage of low molecular weight PAHs suggests the involvement of incomplete combustion of biomass burning. It is important to acknowledge that in order to ascertain specific source contributions, the inclusion of additional chemical species, particularly metals, OC and EC, would be necessary within the analytical model. Nonetheless, the present study successfully discerned source contributions by utilizing the dominant species associated with each source, as summarized in Table S9. As such, the results obtained from this study can serve as preliminary findings in determining potential source contributions.

3.6 Source and Chemical Species Contribution to DTT Activity

3.6.1 Source specific contribution to DTT activity

The daily contribution of source factors were analyzed using MLR model and are presented in chronological order in Fig. 5(a). The statistical significance of each source was evaluated based on the corresponding p-values, as shown in Table 1. Among the considered sources, only biomass burning and SIA demonstrated a significant fit to the model. However, it should be noted that even though certain sources were deemed statistically insignificant, they were still included in the MLR model. This decision was based on previous reports that highlighted the significant contributions of vehicle exhausts (Cesari et al., 2019; Yu et al., 2019).

Fig. 5. (a) Time series of DTTv activity in 2018 contributed from specific sources and (b) seasonal source contributions on DTTv.Fig. 5. (a) Time series of DTTv activity in 2018 contributed from specific sources and (b) seasonal source contributions on DTTv.

Table 1. The MLR result of source contributions on DTTv activity.

Fig. 5 provides a comprehensive depiction of the time-series of source contributions to OP. The graph clearly illustrates the distinct and daily variation in source contributions to OP. Consistent with previous evidence, biomass burning emerged as the dominant source during haze episode. However, during the non-haze rainy season, source contributions to OP become more diverse, encompassing a mixture of SIA, crustal origin and vehicle exhausts.

3.6.2 Chemical compositions contribution to DTT activity

The contribution of chemical compositions on DTT activity was firstly conducted by Pearson’s correlation (for PAHs) and Spearman’s correlation (for inorganic ions). The OP of the chemical compositions bound on PM2.5 was assessed through DTT activity, which was measured using two units: DTTv (nmol min–1 m–3), representing the inhalation level, and DTTm (nmol min–1 µg–1), representing intrinsic properties.

Significant associations were observed between individual PAHs, particularly those with 4–5 rings (shown in Table S6), and DTT activity. The correlation coefficient ranged from 0.4 to 0.5, suggesting a moderate positive relationship between these PAHs and DTTv activity. Consistent with previous studies, higher molecular weight PAHs, including those with up to 5 rings, exhibited a stronger correlation with DTT activity (Janssen et al., 2014; Zhang et al., 2021). This finding supports the notion that PAHs with higher molecular weights tend to enhance DTT consumption, whereas PAHs with lower molecular weights displayed a negative association with DTT activity.

Seasonal stratification revealed robust correlation (p < 0.05) between several individual PAHs, including Flu (r = 0.377), Fla (0.327), Pyr (r = 0.420), Chr (r = 0.410), BbF (r = 0.380), and BkF (r = 0.545) with DTTv activity during haze episodes. However, these correlation lost statistical significance during the non-haze rainy seasons. This suggests that the high concentration of PAHs bound to PM2.5 during haze episodes may induce the DTTv activity. Nonetheless, it should be noted that the correlation between individual PAHs and DTTv activity in this study might be underestimated due to the fact that PAHs are not inherently redox-active compounds (Fu et al., 2012; Krzyszczak and Czech, 2021; Santiago-De La Rosa et al., 2021; Song et al., 2020). PAHs in atmosphere can undergo transformation into their derivatives (i.e., quinone, nitro-PAHs, oxo-PAHs, hydroxy-PAHs) through photooxidation. These derivatives-PAHs exhibit higher reactivity in redox reactions compared to the parent compounds. Therefore, it is essential to conduct further investigations on the oxidative potential of these derivative-PAHs, as they may play a more significant role in the observed redox activity. The correlation analysis between inorganic ions and DTT activity is depicted in Table S7. During haze episode, DTTv activity exhibited positive correlation with several ions, including F (r = 0.472), NO3 (r = 0.424), SO42– (r = 0.556), NH4+ (r = 0.659), and Ca2+ (r = 0.394). Notably, the highest correlation coefficients between DTTv activity and ions during haze episode were observed for NH4+, SO42–, and NO3. However, these correlation lost statistical significance during the non-haze rainy season. It has been well known that NH4+, SO42–, and NO3 serve as tracers of SIA. While they have been reported to be inactive in DTT assay (Patel and Rastogi, 2018), their formation is often accompanied by the presence of secondary organic species (Verma et al., 2009). The secondary formation of these species should not be overlooked, as they may have a significant impact on the associated DTT activity (Wang et al., 2019).

SIA species are formed through the conversion of gaseous precursors and activated by sunlight through photooxidation and oxidizing agents. Consequently, there can be variation in SIA concentrations between daytime and nighttime. A study of Han et al. (2020) found that the SO42– concentration exhibited a variation during the daytime, depending on the presence of sunlight or the occurrence of photochemical oxidation of SO2 (Han et al., 2020; Szigeti et al., 2015). Furthermore, Wang et al. (2019) reported that SIA species contributed significantly to DTT consumption during daytime compared to nighttime.

This study has provided evidence that the elevated levels of PAHs and inorganic ions associated with PM2.5 during haze episodes contribute to OP as determined by the DTT assay. These chemical compositions, upon entering the lung, have the potential to induce the generation of ROS, resulting in oxidative stress.

These findings significantly contribute to the understanding of the chemical compositions bound to PM2.5, including PAHs and inorganic ions, and their impact on OP. In addition, this study represents the first investigation to report the source contributions to PM2.5 and OP in the upper northern Thailand. However, it is important to acknowledge the limitations of this study. Firstly, the analysis focused on a limited number of chemical compositions, and there was a lack of the investigation into the diurnal variation of SIA due to photooxidation, particularly during periods of elevated temperature and haze. Additionally, important components were not included in this study such as organic carbon, elemental carbon and metals. To provide a more comprehensive understanding of source contributions in this region, future research should incorporate a wider range of chemical species in the source apportionment model.


The present study investigated the source contribution to PM2.5 and its oxidative potential during haze episode and the non-haze rainy season in the upper northern Thailand. The results revealed that highly elevated of PM2.5 levels during haze episode were predominantly attributed to biomass burning activity. The PMF model, combined with seasonal stratification of DTTv, indicated that PM2.5 originating from biomass burning exhibited the highest oxidative potential, especially in March when PM2.5 levels reached their peak. However, it was observed that the contribution of biomass burning to PM2.5 reduced by approximately half during the non-haze rainy season, suggesting a reduction of open burning activities during this period.

These finding imply that biomass burning, and SIA were the major sources contributing to both PM2.5 and oxidative potential during haze episodes in the upper northern Thailand. The higher DTTv activity observed during this season indicates the potential for oxidative stress when PM2.5 particles are inhaled to the lung. These findings underscore the importance of implementing mitigation measures and strategic policies to improve air quality in the region, with a primary focus on addressing biomass burning activities. By targeting and reducing emissions from biomass burning, the adverse health effects associated with increased oxidative potential and PM2.5 pollution during haze episodes can be effectively mitigated.


We would like to acknowledge Thailand Science Research and Innovation (TSRI, formerly the Thailand Research Fund, TRF) [grant numbers: RDG 6030019 to Tippawan Prapamontol]; PhD-Royal Golden Jubilee Scholarship [grant number: PHD/0150/2559 to Pitakchon Ponsawansong] and National Natural Science Foundation of China [grant number: 41761144056 to Yanlin Zhang]. We are grateful to the Environment and Health Research Unit, Research Institute for Health Sciences, Chiang Mai University, for laboratory and field research support. We also gratefully acknowledge the medical staff at Saraphi hospital for their support. We are grateful to Dr. Voravit Suwanvanichkij, Research Institute for Health Sciences, Chiang Mai University for kindly making valuable suggestions on English usage of this manuscript.


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