Overview of Aerosol and Air Pollution in South Eastern Asia Countries

Received: March 11, 2023
Revised: July 17, 2023
Accepted: August 8, 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.

Cite this article:

Wen, T.Y., Chantara, S., Jalaludin, J., Lestari, P., Syafei, A.D., Pham, T.V., Tsai, Y.I. (2023). Overview of Aerosol and Air Pollution in South Eastern Asia Countries. Aerosol Air Qual. Res. 23, 230055. https://doi.org/10.4209/aaqr.230055

HIGHLIGHTS

• This paper reviews the particulate matter (PM) related in Southeast Asia.
• This paper presents the effects of PMs on adult's and children's health.
• This paper shows the characteristics of PMs from vehicles and biomass burning.
• This paper discusses two PM removal solutions: clean stoves and electric filters.

Keywords: Aerosol, IOB, LOB, PAHs, Elements, Sodium hydroxyl, ESP

1 INTRODUCTION

Southeast Asian countries experienced a 2.27-fold increase in carbon dioxide (CO₂) emissions from the use of fossil fuels between 1990 and 2010, compared with 1.81-fold increase in South Asian countries and 12% in North America during the same period, indicating CO₂ emissions in Southeast Asia have grown more rapidly than in any other region of the world (Raitzer et al., 2015). This result shows that Southeast Asia is one of the regions with the fastest development of industrial activities and economic undertakings in the world, but there are also certain air pollution problems with the toxic potentials of high particulate matter (PM) and gaseous air pollutant loads (Hopke et al., 2008; Novakova et al., 2020; Santoso et al., 2020). PM is categorized into fine PM (PM_2.5), with a diameter smaller than 2.5 µm, and coarse PM (PM_2.5-10), with a diameter of 2.5 µm to 10 µm. In urban areas, vehicle and industrial emissions are the most important sources of air pollution, whilst in rural areas, biomass burning is the most dominant source of air pollution followed by vehicle emissions. In addition to the primary combustion-related sources, secondary aerosols contribute to the PM formation (Ahmad Mohtar et al., 2022). Chronic and severe haze events are a major issue and a recurring problem, further reducing air quality, with a deleterious effect on human health in the region (Abdul Jabbar et al., 2022; Chomanee et al., 2020; Chujit et al., 2020; Collado et al., 2023; Kunii et al., 2002; Kim Oanh et al., 2006, 2011; Pavagadhi et al., 2013; Silva et al., 2010; Thepnuan et al., 2019, 2020) and are originated by wide-spread biomass burning activities, including forest and peatland burning (Lin et al., 2013; Li et al., 2017; Pani et al., 2021; Siregar et al., 2022).

This paper first explores the exposure and health effects of air pollution, briefly illustrating the effects of exposure to PM_2.5 on respiratory health, inflammation, and DNA damage. This is followed by an account of metal concentrations and associated sources of atmospheric particulates in Tambak Osowilangun, Indonesia, and an investigation of the polycyclic aromatic hydrocarbons (PAHs) contributed by the size-fractionated atmospheric particles in the urban and rural areas of Chiang Mai in northern Thailand. This paper also discusses the reduction technology of air pollutants emitted by charcoal kilns in the Mekong Delta, Vietnam, as well as electrostatic precipitators that are commonly used for particle collection purposes in coal-fired power plants. Through the representative air pollution research in Southeast Asia and the improvement of local air pollution control technology, one can further understand the air pollution challenges faced by the rapidly developing regions of the world.

2 EXPOSURE AND HEALTH EFFECTS

2.1 General Effect

Air pollution is the leading environmental cause of human death, accounting for an estimated 9 million (16%) of all global deaths per year (Landrigan et al., 2018). The overwhelming evidence of the harmful effects of air pollution on health, particularly for vulnerable populations (Iyer et al., 2022; Landrigan et al., 2018), led the World Health Organization to lower the global thresholds for minimally acceptable levels of PM_2.5 from 10 µg m^–3 to 5 µg m^–3 (WHO, 2021). Exposure to PM has been identified as a cause of numerous health effects, including increased hospitalizations, emergency department visits, decreased lung function, respiratory symptoms, exacerbation of chronic respiratory and cardiovascular disease, and premature mortality (Chujit et al., 2020; Guaita et al., 2011; Kim et al., 2015; Nguyen et al., 2022; Perez et al., 2012; Suhaimi and Jalaludin, 2015). Older adults and children or people with heart (or lung) disease are at much greater risk from particulate matter than other people (Siregar et al., 2022).

2.2 Seasonal Effects

In Southeast Asia (SEA), smoke haze events caused by uncontrolled biomass burning, including forest and peat fires, continue to affect air quality and human health. The most concerning pollutant in this context is particulate matter, which is transported by transboundary winds across SEA and affects air quality in several regions, including Malaysia, Thailand, Singapore, Myanmar, Laos, Vietnam, and Cambodia (Lin et al., 2013; Li et al., 2017; Adam et al., 2021; Pani et al., 2021; Sresawasd et al., 2021; Mohd Napi et al., 2022; Siregar et al., 2022). Sumatra and Borneo in Indonesia are the primary sources of haze, affecting neighboring countries such as Malaysia and Singapore during the dry season (June–September). In Thailand and Vietnam, emissions from biomass burning are significant between February and April, the dry season in the region (Kim Oanh et al., 2018).

Studies on the association between increased exposure to particulate matter from biomass combustion and aggravation of respiratory symptoms were noted during the 2013 and 2015 haze periods in Singapore and Malaysia (Ho et al., 2014; Wan Yaacob et al., 2016; Yeo et al., 2014). Common respiratory and physical symptoms included skin irritation, breathing difficulty, nasal and eye discomfort, headache, chest congestion, and sore wheezing. A review study by Ramakreshnan et al. (2018) indicated a strong association between haze exposure and decreased lung function, which is likely responsible for various respiratory diseases. Meanwhile, Othman et al. (2014) found that the occurrence of haze was associated with an increase in hospitalizations of 2.4 per 10,000 population per year, a 31% increase over normal days in Malaysia.

In a case-crossover analysis of forest fire haze events and mortality in Malaysia between 2000 and 2007 in the Klang Valley, Malaysia, Sahani et al. (2014) showed that increases in PM concentrations were significantly associated with increased mortality risk, especially in children under 14 years of age after 2 days of PM exposure. Sulong et al. (2017) found that cancer risk was increased in haze compared to non-haze. Excess lifetime cancer risk (ELCR) was the highest among adults during a haze episode in Kuala Lumpur, Malaysia. Meanwhile, Betha et al. (2013) discovered that inhalation exposure to PM_2.5-bound carcinogenic trace elements originating from peat fires poses a serious health threat, as 4 or 5 out of 1000 people living near peat fires in Indonesia may be affected by cancer.

2.3 Respiratory Health

In Malaysia, several epidemiological studies demonstrated that exposure to PM was associated with respiratory symptoms such as phlegm, cough, wheezing and chest tightness among children in urban areas (Choo et al., 2015; Hisamuddin et al., 2020; Ismail et al., 2019; Mohd Nor Rawi et al., 2015; Nazariah et al., 2013; Yahaya and Jalaludin, 2014). The prevalence of respiratory symptoms was higher among children in urban areas than children in rural areas. In a study on traffic policemen who were regularly exposed to traffic air pollutants, specifically benzene, toluene, ethylbenzene, xylene (BTEX) and PM₁₀, Fandi et al. (2020) showed there was a higher risk of developing respiratory disorders, as evidenced by the increased prevalence of respiratory symptoms (Mohamad and Noor, 2018).

A recent study in the Bangkok metropolitan area found that increased PM₁₀ and PM_2.5 levels were associated with an increased risk of outpatient visits for respiratory illness in children (Thongphunchung et al., 2021). Many previous epidemiological studies in Thailand indicated that short-term exposure to ambient particulate matter was significantly associated with increased risk of hospitalization and mortality (Guo et al., 2014; Phosri et al., 2019; Taneepanichskul et al., 2018). In Vietnam, significant effects on daily hospital admissions for respiratory diseases were found for PM₁₀, PM_2.5, and PM₁ (Luong et al., 2017). An increase of 10 µg m^–3 PM₁₀, PM_2.5, or PM₁ was associated with a 1.4%, 2.2%, and 2.5% increase, respectively, in the risk of admission on the same day of exposure. The study showed that infants and young children in Hanoi were at increased risk of being admitted for respiratory diseases due to high airborne particle concentrations.

2.4 Lung Function

PM_2.5 is the second most important risk factor for respiratory disease (Soriano et al., 2020), and is linked to significant morbidity and mortality (Atkinson et al., 2014; Cohen et al., 2017). Children's respiratory systems are particularly vulnerable to PM_2.5 exposure (Gauderman et al., 2002; Gouveia et al., 2018) due to underdeveloped lungs and immature immune systems in childhood (Kajekar, 2007; Nicholas et al., 2017). One of the most important and measurable indices for assessing respiratory health is lung function, which can be used to identify disease in children (Pellegrino et al., 2005).

Several epidemiological studies have focused on the relationship between exposure to PM_2.5 and PM₁₀ and lung function. Suhaimi et al. (2022) found that there was an association between traffic-related PM and abnormalities of Forced Expiratory Volume in 1 second (FEV₁%) in children in Klang Valley, Malaysia. Other studies by Arifuddin et al. (2019) and Mohd Nor Rawi et al. (2015) conducted in Selangor, also reported a significant association between PM_2.5 and PM₁₀ and lower lung function in children in urban areas. The study by Asrul and Juliana (2017) also stated that PM_2.5 and PM₁₀ were significantly associated with lung function abnormalities. It is suggested that the tiny pollutants may penetrate deeper into the lungs of children and affect the normal function of the lungs in the gas exchange region (American Lung Association, 2022). The association between PM₁₀ and reduced lung function was also found in the study of urban traffic policemen in Malaysia (Fandi et al., 2020).

2.5 Inflammation

The coagulation cascade and inflammation are considered potential triggers for adverse cardiovascular events induced by exposure to particulate matter (Tang et al., 2020). A number of intermediate proinflammatory cytokines such as C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) as well as anti-inflammatory cytokines, including adiponectin, are reliable biomarkers of systemic inflammation and cardiovascular disease (Stoner et al., 2013). Studies in cell models and animals have shown that both short- and long-term exposure to high concentrations of particulate matter lead to increased systemic inflammation (Godleski et al., 2002; Li et al., 1996).

In Malaysia, several epidemiological studies have been conducted on exposure to PM and inflammation. Jalaludin et al. (2014) reported that a significant association was found between indoor PM_2.5 and PM₁₀ concentrations with TNF-α level among children in the urban area of Klang Valley. Another study by Suhaimi et al. (2017) found a significant correlation between PM_2.5 and a biomarker of inflammation, cysteinyl leukotrienes (CysLTs) (r = 0.242, p = 0.014) among children living in the industrial area of Terengganu, Malaysia. Meanwhile, Nazariah et al. (2013) found an association between PM_2.5 and PM₁₀ with the level of IL-6 among school children in Klang Valley.

In Singapore, George et al. (2020) discovered that Bronchial Epithelial Cell Line (BEAS-2B) cells released considerably more IL-6, IL-8, and TNF in response to haze PM as compared to non-haze PM. Their research provides experimental evidence for higher PM exposure during the haze, which may elicit oxidative stress and pro-inflammatory cytokine release from airway epithelial cells.

An interesting study in Indonesia found that PM_0.25 exposure from vehicle exhaust might affect cardiometabolic biomarkers changes, as reflected by the elevated level of biomarkers of inflammation such as total immunoglobulin E (total IgE), high-sensitivity C-reactive protein (hs-CRP), TNF-α, glucose, and lipid metabolism disorders in vehicle inspectors (Ramdhan et al., 2021).

2.6 DNA Damage

The International Agency for Research on Cancer (IARC) classified PM as carcinogenic to humans (Loomis et al., 2014). It is known that PM causes specific biological effects, such as inflammatory processes, oxidative stress and genotoxicity (Li et al., 2018; Sánchez-Pérez et al., 2009; Shao et al., 2018) which may directly or indirectly damage DNA, and this process is considered an initial event in cancer development. If the DNA is not properly repaired, this process may also lead to mutations (Jan, 2001). Fig. 1 shows the mechanism of PM-induced health effects associated with oxidative stress, inflammation, and cancer risk.

Fig. 1. Mechanism of PM-induced health effects associated with oxidative stress, inflammation and cancer risk (Espitia-Pérez et al., 2019).

Several epidemiological studies have focused on the relationship between exposure to PM and DNA damage. The study by Ismail et al. (2019) revealed that PM_2.5, PM₁₀ and ultrafine particles (UFP) were significantly associated with comet tail length (parameter of DNA damage) in children in Klang Valley, Malaysia. Similarly, Sopian et al. (2021) and Hisamuddin et al. (2022) found that PM_2.5-bound PAHs were associated with comet tail moment in children living in high-traffic and industrial areas, respectively. Sopian et al. (2020) suggest that exposure to PM₁₀ as well as NO₂ and SO₂ increases the formation of micronuclei for children residing in proximity to an industrial area, after controlling for confounding factors (e.g., demographic, socio-economic, and lifestyle factors, and exposure to tobacco smoke). In another study by Suhaimi et al. (2021), they found a positive weak correlation between epigenetic modification (histone H3 level) and concentrations of PM₁ (r = 0.35), PM_2.5 (r = 0.34), and PM₁₀ (r = 0.33). Meanwhile, Awang et al. (2020) revealed that PM_2.5 concentration was positively correlated with the formation of micronuclei in urban traffic policemen in Malaysia.

3 CHARACTERISTICS

 
3.1 Identification of Metal Pollutants in Ambient Air (Indonesia)

3.1.1 Introduction

PM is one indicator of air pollution produced from anthropogenic and natural sources (Robinson et al., 2011). PM contains heavy metals such as Al, As, Ba, Br, Ca, Cl, Co, Cr, Cu, Fe, Hg, I, K, Mg, Na, Ni, P, Pb, S, Sc, Si, Se, Sr, Ti, V, and Zn. Generally, the elements Pb and Zn are emitted by industry and traffic (Han et al., 2006; Kim et al., 2015; Santoso et al., 2011). These heavy metals can have harmful effects on human health, such as various respiratory diseases, anemia symptoms, decreased immune system strength, autism symptoms, lung cancer, and death (Mukhtar et al., 2013a). Research in major cities indicates that an increase of 10 µg m^–3 in PM₁₀ increases mortality by 0.5%, mostly caused by lung and heart disease (Samet et al., 2000). PM_2.5 contributes to mortality rates caused by health problems related to air pollution (Mukhtar et al., 2013a).

Arsenic exposure is associated with an increased risk of skin and lung cancer. Cadmium is associated with kidney and bone damage and has been identified as a potential carcinogen in humans, and is a potential cause of lung cancer, neurobehavioral effects in fetuses, infants, and children, and increased blood pressure in adults (Mukhtar et al., 2014). Chromium causes respiratory and digestive system disorders, while manganese at high doses causes nervous system disorders. Nickel, known as a carcinogen, also has non-cancerous effects, such as disorders in the endocrine system (WHO, 2007). The risk of heavy metal exposure through inhalation can affect both children and adults. In Delhi, India, high mortality risk is due to the Pb content in children's blood as well as cases of cancer due to Cd, Cr, and Ni (Khillare and Sarkar, 2012). The potential health risks of Pb have a serious impact on children, and the Pb found in PM_2.5 plays an important role in myocardial toxicity (Zhang et al., 2016).

The main sources of heavy metal pollution in rural areas are coal-burning, road dust, and soil, while the sources of pollution in urban areas are industrial emissions, road dust, soil, and vehicles (Liu et al., 2017). The Cu, Zn, Cd, Pb, and Hg contained in PM_2.5 at Tianjin, China, were identified from anthropogenic sources such as vehicles, waste, and coal-burning (Chen et al., 2015). The Pb concentrations found in fine particulates and coarse particulates were higher in the industrial area compared to the residential area in Serpong, Indonesia. In one study, the sources of pollutants in Serpong were found to be emissions from diesel vehicles (30%), oil and power plants (26%), road dust (17%), biomass combustion mixed with road dust (15%), and the lead industry mixed with road dust (12%) (Santoso et al., 2011). The ambient air in Surabaya has higher Zn and Pb compared to other cities. The sources of pollutants were emissions from biomass, vehicles, soil, the lead industry, the zinc industry, and the ferrous industry (Mukhtar et al., 2013b).

Surabaya City, Indonesia, especially its western area, is characterized by industrial areas, warehousing areas, final disposal sites (FDS), and port areas. Activities in those areas produce PM_2.5 and PM₁₀ emissions which contain metals. These particulates cause respiratory disorders and death. Therefore, research on the content of heavy metals in particulates in West Surabaya is needed.

3.1.2 Materials and methods

3.1.2.1 Sampling method

Sampling was conducted from October 15, 2019 to April 6, 2020, for 24 hours every six days, around industrial and warehousing areas in West Surabaya, Indonesia (shown in Fig. S1, located at 7°13′7.5′′S, 112°39′9.75′′E). A total of 32 samples were collected at the sampling site in Osowilangun Terminal.

A Gent Stacked Filter Unit (SFU Gent) was used to collect ambient PM_2.5 and PM_2.5-10 samples. It was placed on the roof of the building at a height of 5 m from ground level. The SFU Gent has an installed polycarbonate filter that can collect air particulates with a size of 2.5 µm to 10 µm and less than 2.5 µm. The unit is composed of two parts, the first being the filter container and the second the vacuum pump system with the timer and flow rate. The outermost part of the container is the impactor system inlet cut off, allowing dust less than 10 µm to deposit on the filter surface, with larger particle sizes not collected. The flow rate used in the SFU Gent was set to be 18 L min^–1 (Hopke, 2000). Inside the SFU Gent, two types of filters were used: coarse filters for collecting PM_2.5-10 and fine filters for collecting PM_2.5. All filters were conditioned for 24 hours in a cleanroom to control the temperature and humidity before use. A Kestrel 5500 meter was used to analyze the wind direction and wind speed. In addition to primary data, this study also collected secondary data such as industrial data for West Surabaya and Gresik Regency and rainfall data from the Meteorological Agency of Climatology and Geophysics (BMKG). The SFU Gent container, flow rate meter and pump, Kestrel meter, filters, and cassette filter are shown in Figs. S2 and S6.

3.1.2.2 Analysis of samples

The concentrations of fine and coarse particulates were analyzed using the gravimetric method. The samples were then analyzed using XRF (X-Ray Fluorescence, Model: Epsilon 5 by PANanalytical Inc.) to determine the elemental content and concentrations. XRF was used to identify the various elements in particles and to generate data sets of the concentrations of 25 to 30 elements (Santoso and Lestiani, 2014). To identify the contribution of a source based on the particulate composition, Positive Matrix Factorization (PMF) was utilized. PMF requires two input data sets, which here are species concentration data and uncertainty data, to estimate the contribution factor (G) and factor profile (F) (Reff et al., 2007; Rixson et al., 2016; Santoso et al., 2008; Wang et al., 2018). The selection of the number of factors can be made from the emission inventory data to determine sources of pollutants. The locations of the pollutant sources were estimated using the source contributions from the PMF combined with wind directions and wind speed data at the sampling location. The data were analyzed using the Conditional Probability Function (CPF).

3.1.3 Results and discussion

3.1.3.1 Concentrations of PM_2.5 and PM₁₀

The average daily concentrations of PM_2.5 and PM₁₀ were 11.47 µg m^–3 and 27.49 µg m^–3, or 11.45 µg Nm^–3 and 26.98 µg Nm^–3. Research by Ahmad and Santoso (2016) stated that the annual concentrations in Surabaya for PM_2.5 and PM₁₀ were between 8.53 µg m^–3 to 26.38 µg m^–3 and 18.35 µg m^–3 to 50.65 µg m^–3, respectively. Research conducted in Southeast Asia by Khan et al. (2016) also stated that PM_2.5 concentrations were between 6.64 µg m^–3 to 68.2 µg m^–3 during the southwest monsoon. Daily concentrations of PM_2.5 and PM₁₀ in West Surabaya meet the quality standards for daily PM concentration based on national regulations and the World Health Organization (WHO). The daily concentrations of PM_2.5 and PM₁₀, seen in Fig. 2, were between 3.95 µg m^–3 and 27.45 µg m^–3.

Fig. 2. (a) Daily concentration of PM_2.5; (b) Daily concentration of PM₁₀.

Increases and decreases in PM_2.5 concentrations coincided with increases and decreases in PM₁₀ concentration, as seen in Fig. S7. For example, on November 2, 2019, PM_2.5 concentrations increased to 14.22 µg m^–3 from 9.88 µg m^–3 the previous week. The increases and decreases in PM concentrations could have several causes, such as nearby anthropogenic activities and meteorological conditions.

Additionally, particulate concentrations are affected by the intensity or duration of sun exposure. Intense solar radiation causes the concentration of pollutants to increase because the hot and dry environment causes pollutants to be lifted into the air and float there (Cahyadi et al., 2016). The length of solar radiation on October 21, 2019, according to the BMKG, was recorded as 8.6 hours, while the average length of irradiation in October 2019 was 9 hours.

The concentrations of PM_2.5 and PM₁₀ were also influenced by activities around the site. In West Surabaya, there are many vehicles that transport goods from the warehouse area, as well as public transportation such as buses, and private vehicles.

High traffic activity can affect the concentration of PM_2.5 and PM₁₀. The concentration of PM₁₀ is highest when traffic is heavy compared to when traffic is moderate or low. In a study in Beijing, PM_2.5 concentrations around main roads with traffic and construction activities were found to be quite high, namely 62.16 ± 39.37 µg m^–3 (Elhadi et al., 2017; Gao et al., 2016). PM_2.5 and PM₁₀ concentrations are also affected by wind direction and wind speed. The dominant wind direction from October to December is relatively stable, from the northeast and west, while from January to February, the wind direction is from the west. In March, the wind comes from the northeast and northwest, while in April, the wind comes from the northwest and southwest. Upwind in these directions from the sampling site, there are various industrial activities, warehousing, road construction, and also quite dense traffic.

In several previous studies, PM_2.5 concentrations were found to be higher at industrial sites. In Dhaka, Bangladesh, for example, where it was observed that fossil fuels were the main contribution sources of PM (Merétei et al., 2017; Salam et al., 2008). The results showed that concentrations of PM_2.5 and PM₁₀ are highly affected by the presence of industrial and fuel-burning activities.

3.1.3.2 Multi-element Identification

Based on XRF analysis, particulate samples taken in West Surabaya from October 2019 to April 2020 were shown to contain 18 elements, including metal elements, as seen in Table 1. The elements contained in PM_2.5 and PM_2.5-10 were Na, Mg, Al, Si, S, K, Cl, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, and Pb. Measurement of the metal element composition contained in these particulates is an important factor for identifying possible sources of pollutants and can be used for air quality management. Airborne Mn in Surabaya met the WHO’s air quality standards, the Ontario Ministry of the Environment Ambient Air Quality Criteria (OAQC), and the Texas Commission on Environment Quality (TCEQ), but nevertheless needs close inspection because exposure to Mn can disrupt the nervous system, causing symptoms such as allergies, increased muscle tone, tremors, mental disorders, and death (WHO, 2007).

The average Ni concentrations in the PM_2.5 and PM_2.5-10 samples were 0.00053 µg m^–3 and 0.00048 µg m^–3, while the OAQC/TCEQ quality standard for Ni concentration with a 24-hour measurement is 2 µg m^–3. The concentration in PM_2.5 was comparable than that found in Lamongan which was on average 0.0003 µg m^–3 (Lestiani et al., 2023). The concentration of Ni found in West Surabaya at the time of the study thus met the quality standards of the OAQC/TCEQ. The presence of metallic elements such as Ni in these particulates needs to be considered even though these results show that the concentration is low. Ni exposure causes the disruption of the endocrine system, and Ni is known as a carcinogen. In Delhi, India, an increased risk of death from cancer was found to be due to exposure to Ni (Khillare and Sarkar, 2012; WHO, 2007).

Pb was found to have an average concentration of 0.017 µg m^–3 in the PM_2.5 sample and 0.011 µg m^–3 in the PM_2.5-10 sample. The Pb content (~436 mg kg^–1) in PM_2.5 was lower than in Lamongan, where it was 0.457 µg m^–3 (Lestiani et al., 2023), but higher than that observed in other cities such as Guilin, Beijing and Hutou Village in Yunnan Province, China, as reviewed by Chu et al. (2022). In Indonesia, Pb has been regulated in Government Regulation No. 22 of 2021 Appendix VII with 24-hour measurement quality standards of 2 µg m^–3. Elemental Pb quality standards have also been regulated by other organizations such as OAQC/TCEQ and the United States Environmental Protection Agency (EPA, NAAQS) with quality standards of 2 µg m^–3 for OAQC/TCEQ over 24 hours and 0.15 µg m^–3 for the United States EPA (NAAQS) over three months. The concentration of Pb in West Surabaya meets the requirements of the OAQC/TCEQ and the United States EPA (NAAQS) quality standards. Pb is a heavy metal element that needs to be monitored, especially in children, because of its carcinogenic properties. This element also plays an important role in myocardial toxicity (Khillare and Sarkar, 2012; Zhang et al., 2016).

Fig. 3 shows elemental concentrations, listed here in order from highest to lowest concentrations, of S, Na, Si, K, Fe, Ca, Zn, Al, Mg, Pb, Cl, Ti, Mn, Br, Cu, V, Ni, and Cr. In the PM_2.5 sample (Fig. 3(a)), sulfur (S) had the highest concentration, with an average of 685.5 ng m^–3. The high concentration of S might be caused by vehicles, and the Si may have come from road dust, which is present in the western part of the study site, while the Na is characteristic of sea salt (Ahmad and Santoso, 2016; Santoso et al., 2008). To the northeast and southeast of the study site, the ocean is a potential contributor to the particulate Na content.

Fig. 3. (a) PM_2.5 elemental concentrations; (b) PM_2.5–10 elemental concentrations.

In the PM_2.5-10 sample (Fig. 3(b)), the elements identified from high concentration to low concentration were Si, Ca, Fe, Cl, Al, Na, S, Mg, K, Zn, Ti, Pb, Mn, Br, Cu, V, Cr, and Ni. The three elements with the highest concentrations were Si, Ca, and Fe, with average concentrations of 719.4 ng m^–3, 705.9 ng m^–3, and 432.67 ng m^–3 respectively. High concentrations of these elements could be caused by construction/cement-manufacturing activities, or could result from soil, road dust, or traffic.

To the west of the research location, there are toll roads and toll road construction which could be a potential source of high concentrations of Si, Ca, and Fe at the study site. Elements that were identified in ambient air in several cities, such as Na, Mg, Al, Si, S, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Pb, were previously found in Surabaya, with Mg, Si, Ca, and Zn showing the highest concentrations (Mukhtar et al., 2013b).

Metal elements found in samples of PM_2.5 and PM_2.5-10 in West Surabaya were Ti, V, Cr, Mn, Ni, Cu, Br, and Pb. This is consistent with the study by Police et al. (2016), which found that PM₁₀ in industrial areas along the coast in Viskhapatnam, India, were characterized by chemical elements such as Al, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Pb, Cd, Cl^–, F^–, NO₃^–, SO₄^2–, Na⁺, K⁺, Mg²⁺, and Ca²⁺.

Results in West Surabaya show strong correlation between several elements, which can indicate a common source. In PM_2.5, Al concentrations had a very strong correlation with Si concentrations, with an R² value of 0.933. Al, a marker of ground dust (Jain et al., 2018), was also strongly correlated with Ca, Ti, and Fe, with R² values of 0.744, 0.735, and 0.772, respectively. PM_2.5-10 also contained S concentrations that were strongly correlated with Al, Si, K, Ca, Ti, Fe, and Mn concentrations. S is a marker of vehicular sources, while the strong correlation between S and other elements, such as Al, Si, and Ti, indicates a potential source from road dust. The Zn and Pb elements in PM_2.5 also have a very strong correlation, with an R² value of 0.855. According to Mukhtar et al. (2013b), Surabaya City has the highest concentration of Zn and Pb compared to other studied cities.

3.1.2.3 Pollutant source identification

PMF 5.0 was used for identification of the sources of pollutants in PM_2.5 and PM_2.5-10. The concentration data and uncertainty values were used as PMF inputs. The number of factors to assume was determined by looking at the surrounding conditions at the research location or the emission inventory data (Wang et al., 2018). Interpretable results were obtained using eight factors for PM_2.5 and seven factors for PM_2.5-10. The obtained goodness of fit was described with R² values of 0.7 and 0.9 for PM_2.5 and PM_2.5-10, respectively, as seen in Fig. S8(a) and Fig. S8(b). The two R² values obtained indicate that the concentration data from PM_2.5 and PM_2.5-10 were modeled correctly by PMF. The output of this PMF is a factor profile (F) and source contribution (G). The factor profiles are displayed in the bar charts in Fig. 4, and the factor contributions to PM_2.5 and PM_2.5-10 are shown in Fig. S9. The concentration of the species indicates the concentration of each species in a particular factor and the percent value for each species is used to determine the potential source of contaminants. For example, the identification of potential sources of Pb in PM_2.5 showed that 57% of the Pb was found in Factor 8. In identifying the source of PM_2.5-10 pollution in Factor 4, almost 70% of Zn was found to be contributed by this factor, while about 50% of Na and Cl were contributed by Factor 7.

Fig. 4. Factor profiles of pollutant sources. (a) PM_2.5; (b) PM_2.5-10.

In PM_2.5, eight factors representing potential sources of contaminants were identified (Fig. 4(a)). The first factor is a mixture of industrial activities using Cu and biomass combustion, characterized by the elements Cu, Mg, and K, while K also indicates biomass burning. Santoso et al. (2008) stated that biomass burning produces emissions containing K in the air around industrial areas. This factor contributed 24.1% to PM_2.5 (Fig. 4(a)).

The second factor, contributing 11.4% of PM_2.5, is identified as deriving from the metal industries that use Ni due to the high contribution of Ni in this factor. The third factor may come from the non-ferrous metal industry as well as from vehicular tire wire, due to the high Zn concentration. The contribution of this source to PM_2.5 amounted to 2.2%, and this finding is supported by Ahmad and Santoso (2016), who suggested that the Zn industry in Surabaya contributed 2% of the PM_2.5 in the area.

The fourth factor, with a contribution of 33% to PM_2.5, was characterized by high amounts of S and Na. Airborne S is produced by the conversion of SO₂ into sulfate through a homogeneous process, with SO₂ provided by diesel vehicle emissions (Begum et al., 2004; Chueinta et al., 2000). The fifth factor was characterized by the elements Mn, Fe, and Zn. Mn is commonly used in the steel industry, in the production of dry battery cells, and in the production of potassium permanganate. The sixth factor is likely to derive from construction activities because it has a high concentration of Ca, contributing 7.9% to PM_2.5

The seventh factor is characterized by high concentrations of Al, Si, and V, possibly derived from soil dust, burning petroleum, or emissions from ships. These elements act as markers for sources of soil dust; the concentrations of these elements tend to be high during the dry season compared to during the rainy season (Lestiani et al., 2013; Santoso et al., 2008). Existing emissions from ships in urban areas produce anthropogenic air pollution, indicated by the elements V, Ni, La, and Ce (Pandolfi et al., 2011). The eighth factor is possibly from the lead industry as well as from vehicles because it has a high Pb concentration.

In PM_2.5-10, the analysis identified seven factors (Fig. S9(b)). The first factor may come from construction activities because it has high concentrations of Ca and Mg: Ca is a marker of construction activities (Jain et al., 2018; Santoso et al., 2008). In research conducted in Southeast Asia by Khan et al. (2016), in research conducted in Southeast Asia, considered Mg and Ca to be marker elements of mineral dust, contributing 28.8% to that category.

The second factor, with a contribution of 32%, was characterized by the elements V, Al, and Si, possibly from soil dust and port activity. The third factor contributed 3.8%, as indicated by S content. Sulfur is a marker element for transportation, deriving from vehicle emissions and diesel fuel used both for land transportation and ship fuel. The fourth factor, characterized by Zn and Pb, may derive from the non-ferrous metal industry and contributed 3.7% of PM_2.5-10. The high concentration of Zn and Pb at the research site could be due to the steel and warehousing industries that store coal sand. This is supported by Dai et al. (2015) which found that the Pb and Zn in PM come from iron and steel industry activities. Aside from industry, Zn and Pb can also be contributed by coal-burning (Gao et al., 2016).

The fifth factor is possibly derived from the metal industry as well as other industries that use Cu, as indicated because it contains Ni and Cu, whereas the sixth factor, contributing 14%, may derive from road dust because it is characterized by Br: Br serves as a marker element of road activity (Alam et al., 2014; Banerjee et al., 2015; Du et al., 2019; Jain et al., 2018; Santoso et al., 2008, 2011). This road dust comes from a mixture of traffic activity and soil dust.

The seventh factor derives from sea salt because it is characterized by the elements Na and Cl, with a contribution of 10%. Na and Cl are marker elements of sea salt; as described in Mukhtar et al. (2014), the presence of Na indicates emissions from sea salt, and Santoso et al. (2008) further argued that the aerosolization of Na usually coincides with that of Cl.

3.1.4 Summary

The observed concentrations during the study period meet the quality standards of daily PM based on national regulations and the WHO. The XRF analysis of PM in West Surabaya identified 18 elements, consisting of Na, Mg, Al, Si, S, K, Cl, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, and Pb.

Based on the concentrations of the metal elements, eight and seven factors were obtained. For PM_2.5, potential sources of pollutants include a mixture of Cu industry and biomass combustion, Ni industry, non-ferrous metal industry, transportation, iron and steel industry, construction, soil, port dust, and Pb industry. For PM_2.5-10, potential sources of pollutants include construction, soil dust, transportation, the non-ferrous metal industry, the Ni industry, the port industry, the Br industry, salt, and the sea salt processing industry.

The estimated source locations for pollutants in PM_2.5 and PM_2.5-10 show that the potential sources of pollutants include port activity, soil dust, transportation, biomass burning, construction, sea salt, and industry located to the north and west.

3.2 Patterns of Size-fractionated PMs and PAHs in Urban and Rural Areas of Chiang Mai (Thailand)

3.2.1 Introduction

Size distribution analysis of ambient PMs is important because of the deposit capability of various particle sizes in different parts of the respiratory system (Kim et al., 2015). In Southeast Asia, including Thailand, only a few studies have investigated size-fractionated atmospheric PMs, especially nano and ultrafine levels, potentially more harmful to human health. This study aims to find the distribution patterns of atmospheric size-fractionated PMs and their PAHs from an urban and a rural area of Chiang Mai (Thailand).

3.2.2 Sampling of PMs and PAHs analysis

Ambient PMs (PM_9.0-0.43) were separated into seven fractions from coarse to fine particles (9.0 µm to 5.8 µm, 5.8 µm to 4.7 µm, 4.7 µm to 3.3 µm, 3.3 µm to 2.1 µm, 2.1 µm to 1.1 µm, 1.1 µm to 0.65 µm, 0.65 µm to 0.43 µm). The PM sampling was carried out during the intensive open burning (IOB) period in the dry season (March–April) and low open burning (LOB) period (May, June, and November) of 2019. The samples were collected in the urban area of Chiang Mai city (CM) and Chiang Dao (CD) rural area for 48 hours using eight-stage cascade impactors (Andersen series 20–800, TISCH Environmental, USA) with a flow rate of 28.3 L min^–1. Quartz filters (diameter 81 mm, TE-20-301-QZ, TISCH Environmental, USA) used for the sampling were weighed before and after the sampling using a five-digit microbalance (AB135-S/FACT, Mettler Toledo, Switzerland). The samples were collected every two days during IOP and once a month during LOP.

The samples were extracted using an optimized method (Yabueng et al., 2020) and analyzed for 16-PAHs (naphthalene (NAP), acenaphthylene (ACY), acenaphthene (ACE), fluorine (FLU), phenanthrene (PHE), anthracene (ANT), fluoranthene (FLA), pyrene (PYR), benz[a]anthracene (BaA), chrysene(CHR), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IND), dibenz[a,h]anthracene (DbA) and benzo[g,h.i]perylene (BPER)) by Agilent 7820A gas chromatograph coupled with a Hewlett Packard 5977E mass selective detector (GC-MS, Agilent Technologies Inc., USA.). The accuracy and precision of the analysis methods as well as the detection limits of the instrument for detecting these PAHs compounds were reported by Insian et al. (2022).

3.2.3 Results and discussion

The 48-hour PM mass concentrations (sum of 7 size fractions (9.0 µm–0.43 µm)) collected at both urban and rural areas of Chiang Mai Province were compared to PM₁₀ concentrations obtained from automatic active samplers (Beta Ray Attenuation measurement) installed at the Air Quality Monitoring (AQM) stations in Chiang Mai operated by the Pollution Control Department (PCD) of Thailand. The PM mass concentrations from the PCD station located in the suburban area (35t) were compared with the rural CD and those of the city PCD station (36t) were compared with the urban CM (Fig. 5). PM mass concentrations from both areas showed high correlations (r~0.84 to 0.93) with the PM₁₀ data of the PCD stations. PM_9.0-0.43 concentrations ranged from 64.7 µg m^–3 to 162.3 µg m^–3 (X̅ = 105.0 ± 29.8 µg m^–3) and 69 µg m^–to 209.4 µg m^–3 (X̅ = 128.4 ± 39.2 µg m^–3) in urban and rural areas, respectively. The highest concentrations were observed during 23–24 March (162.3 µg m^–3) in the urban and 29–30 March (209.42 µg m^–3) in the rural area. The average concentration of the rural CD was about 1.2 times higher than that of the urban CM because it was located near an open burning source.

Fig. 5. Concentrations of 48-hour size-fractionated PMs and PM₁₀ obtained from PCD stations.

Size-fractionated PMs were classified into two groups based on aerodynamic particle diameters (d_ae) as follows: 1) particle size range 2.1 µm ≤ d_ae < 9.0 µm as coarse PMs, and 2) particle size range 0.43 µm ≤ d_ae < 2.1 µm as fine PMs. The time series of 48-hour of size-fractionated PM mass concentrations together with percentage ratios of PM concentrations (pie charts) during an intensive open burning period at both areas are shown in Fig. 6. The results showed that fine PMs accounted for 61% (urban area) and 69% (rural area) of the total PMs. The finest particle size (0.65 µm–0.43 µm) contained the highest PM mass, which accounted for 26% (27.1 ± 10.5 µg m^–3) in urban and 32% (40.8 ± 16.5 µg m^–3) in rural areas. Higher PM concentrations (~1.0 to 1.5 folds) for each size range were found in the rural area because of open burning as a major pollutant source. However, the meteorological conditions (relative humidity, wind speed, and amount of rain) as well as the topography of the area were also important factors.

Fig. 6. Time series of 48-hour size-fractionated PM mass concentrations and the percentage ratios of PM concentrations during the IOB period.

The time series of 16-PAHs found in PM_9.0-0.43 samples during IOB and LOB periods in urban and rural areas are shown in Fig. 7. Concentrations of total PAHs (tPAHs) during the IOB period were higher than those in the LOB period as found in PM mass concentrations. During the IOB period, the average concentration of tPAHs in the rural area (6.55 ± 2.88 ng m^–3) was about 1.5 times higher than that of the urban area (4.27 ± 1.95 ng m^–3), while during the LOB period rural area values (0.58 ± 0.15 ng m^–3) were slightly lower than urban area (0.73 ± 0.20 ng m^–3) due to the suppression of open burning activity and greater impact of traffic emission in the urban area. The finest particle size (0.65 µm–0.43 µm) contained the highest PAHs concentration (5.10 ng m^–3), which accounted for 45% to 47% and 32% to 37% during IOB and LOB periods, respectively. During the IOB period, tPAHs concentrations in fine PMs were ~1.4 to 2.5 times higher in the rural area than urban area. Furthermore, fine PM of < 2.1 µm contained high tPAHs concentrations, accounting for 82% (urban) and 87% (rural) (Fig. 8). Strong correlations (r~0.8 to 0.9) were observed between concentrations of tPAHs and fine PMs, while low to moderate correlations (r~0.2 to 0.7) were found for coarse PMs. The proportion of tPAHs concentrations in fine PMs increased with the increased proportion of PM mass concentrations. The smaller particles have a larger specific surface area that can adsorb or absorb more PAHs (Li et al., 2019). The results indicated that PAHs concentration increased as particle size decreased. PAH content was higher in fine PM than in coarse PMs, especially the finest particle size (0.65 µm–0.43 µm). During the IOB period, PAH concentrations were high in the rural area because of high open burning activities in this area. Excluding haze periods, the main source of air pollution in the rural area was open burning, versus traffic emissions in the urban area. Therefore, during the LOB period, lower concentrations of PAHs in all size ranges were found in the rural area.

Fig. 7. Time series of concentrations of individual PAHs and PM samples during IOB and LOB periods 2019 at (a) urban and (b) rural areas.

Fig. 8. Concentrations of tPAHs in each PM size range and percentage ratios of tPAHs during (a) IOB and (b) LOB periods in urban and rural areas.

The percentage ratios of PAHs during IOB and LOB periods in PM samples from the urban and rural stations are shown in Fig. 9. During the IOB period, the concentrations of 16 PAH compounds in the rural area were higher than those in the urban area (except NAP, ACE, and FLU), while during the LOB period, most PAH concentrations were higher in the urban area than in the rural area. In conclusion, concentrations of both PMs and PAHs during the IOB period were much higher than those in the LOB period. Based on carcinogenic potential, 16-PAHs were separated into two groups as carcinogenic PAHs (cPAHs; BaA, CHR, BbF, BkF, BaP, IND, and DbA) and non-carcinogenic PAHs (ncPAHs; NAP, ACY, ACE, FLU, PHE, ANT, FLA, PYR, and BPER). The ratio values between cPAHs and ncPAHs are shown in Fig. 9(a). Due to their different volatilities, concentrations of cPAHs were higher than those of ncPAHs at both stations and in both periods. PAHs can also be classified based on molecular weights of species and the number of aromatic rings in their structures; i) low molecular weight (LMW-PAHs (2–3 rings)) including NAP, ACY, ACE, FLU, PHE, and ANT, and ii) high molecular weight (HMW-PAHs (4–6 rings)) including FLA, PYR, BaA, CHR, BbF, BkF, BaP, IND, DbA, and BPER). Concentrations of HMW-PAHs were generally higher than those of LMW-PAHs in both stations during both periods. Therefore, HMW-PAHs were the main compounds in PMs at both stations and both periods. Their contributions were 87% to 91% of tPAHs in urban and 84% to 92% in rural areas (Fig. 9(b)). The dominant compound of PAHs found during the IOB period was BbF (23%–24% of tPAHs), indicating that open burning was an important source. During the non-burning LOB period, high ratios of IND, BPER, and DbA were found, suggesting traffic emission as the main source in this period (Fig. 9(d)).

Fig. 9. Ratios of PAHs based on (a) carcinogenic potential, (b) molecular weight, (c) aromatic ring number and (d) 16-PAHs during IOB and LOB periods of 2019 at urban and rural stations.

Distribution of PAHs in different PM sizes is important because it is related to human health risks and also provides a route for identifying sources of pollution. Profiles of individual PAHs in each particle size range during IOB and LOB periods in urban and rural areas are shown in Fig. 10. MostPAH species showed high concentrations in fine PMs, especially in the size range 0.65 µm to 0.43 µm, at both stations and in both periods. However, during the LOB period, concentrations of LMW-PAHs were higher in coarse PMs than in fine PMs. During the IOB period, patterns of individual PAHs in coarse PMs were similar in both urban and rural areas, in which BbF (10%–12%), PYR (11%), CHR (10%–12%), and ANT (10%–11%) were the dominant PAHs. The patterns of PAHs found at both locations and periods were different due to changes in major source contribution in each period. HMW-PAHs in the rural area were observed only in the 3.3 µm to 2.1 µm particle size. In the case of fine PMs, BbF (22%–30%) was the main compound during the IOB period in both areas, while during the LOB period, IND (15%–20%), BPER (14%–19%) and BbF (14%–17%) were major compounds. A relatively high concentration of BbF was observed in all PM sizes during the SH period in both areas. This is because the ambient particles are mainly from open burning.

Fig. 10. Ratios of 16-PAHs in each PM size range during IOB and LOB periods.

3.2.4 Summary

Size-fractionated PMs and PAHs in urban and rural areas of Chiang Mai (Thailand) were analyzed. The highest PM mass and PAHs concentrations were found in the finest particle sizes (0.65 µm–0.43 µm) in all study periods (IOB and LOB) and in both urban and rural atmospheres. During the IOB period, higher PAHs concentrations were found in the rural area than in the urban area due to intensive open burning activity. Conversely, during the LOB period, their concentrations were lower than those in the urban area, where traffic emission is constantly dominant all year round. PAHs with high molecular weight (4 rings–6 rings) were dominant at both stations during both periods and accounted for 84% to 92% of total PAHs. BbF was the dominant PAH compound in all PM sizes during the IOB period. While during the LOB period, IND and BPER were found in both areas. This study indicated that patterns of size-fractionated PMs and PAHs varied between locations and seasons. We found open burning is a major contributor to air pollution in this area during the dry season. However, traffic emission is also a significant source in the urban area.

4 REDUCTION OF AIR POLLUTANTS FROM CHARCOAL KILNS IN THE VIETNAMESE MEKONG DELTA

4.1 Introduction

Charcoal is one of the most essential energy-producing biomass-based materials, utilized greatly in developing countries. Charcoal can be made from different types of woods such as palm oil shell, bamboo, mangrove, and melaleuca. Produced charcoals have different quality and productivity depending on the type of raw materials used. Charcoal can also be produced by different methods and instruments such as furnaces, drum kilns or traditional burning kilns (Zakaria et al., 2017). By these instruments, the initial raw materials (i.e., fuelwoods, solid wastes, sludges, etc.) undertake a complicated process and are finally converted into charcoal, generally termed the carbonization process. The process involves complex reactions, occurs at a wide temperature range (Ortiz et al., 2003) and is divided into various temperature stages depending on the method, for example, less than 200°C, from 200°C to 280°C, from 280°C to 500°C and over 500°C (de Oliveira Vilela et al., 2014). During the carbonization process, different substances are produced, including charcoal, tar, pyroligneous acid, and gases, and these emissions may affect human health and pollute the environment (Pennise et al., 2001). In Tanzania, more than one million tons of charcoal is produced every year, corresponding to 109,500 ha of forest loss (Msuya et al., 2011). Here, the local charcoal-making kilns have a conversion efficiency of around 11% to 19% for non-improved kilns and 27% to 30% for improved kilns. However, the majority of charcoal makers in Tanzania do not like to use improved kilns due to high investment costs. They prefer to use the non-improved conventional earth kilns because of their simplicity and low investment costs. When this type of kiln is used, unfortunately, a large volume of wood is converted into ash instead of charcoal. Charcoal production in Tanzania also contributes to climate change, reduces agricultural productivity, destroys the environment, and causes biodiversity loss.

In the Vietnamese Mekong Delta (VMD), charcoal production by traditional baked clay brick kilns is widespread in many provinces such as Soc Trang, Hau Giang, and Ben Tre. The charcoal production capacity of each kiln is estimated at 8 to 12 tons of charcoal per burn, and the burn time for carbonization is between 20 and 30 days depending on the input materials and operation. Raw materials are fuelwoods characterized as high carbon composition, easy to find and at a suitable price. However, the main charcoaling wood in the VMD is mangrove Rhizophora. Here, charcoal is continuously produced by traditional kilns and in the absence of modern techniques and controlling measures to reduce substances emitted during the carbonization process. A method to reduce pollutants emitted during the carbonization process, including carbon monoxide and particulate matter, is investigated here.

4.2 Method

4.2.1 Study site

The study was conducted at a charcoal-making village in Chau Thanh District, Hau Giang Province. Charcoal-making activity has gradually increased since 2013 due to rising charcoal demand, and production in 2016 was up to 70,593 tons year^–1. At the time of this investigation the total number of charcoal kilns at the study site was 525, all without air pollutant controlling systems. An investigation into the efficiency of air pollutant reduction was performed from October 2019 to April 2020.

4.2.2 Reduction method of air pollutant emission and structure of the treatment system

A treatment process for pyrolysis gases (fume) emitted from a charcoal kiln is illustrated in the flowchart (Fig. 11). Fume emitted from four fume-exhausting hollows of the kiln was collected by the fume hoods accordingly and then entered the treatment tower by passing through pipes (Fig. 12). The composite fume hoods of pyramid shape were located 0.2 m above the exhaust outlets and the space between was covered by a nylon sheet to limit the release of fume. The composite treatment tower utilized cyclonic separation, with a cyclone of 0.6 m × 1.5 m (diameter × height) and a funnel height of 0.6 m. Inside the treatment tower, a solution of water and sodium hydroxyl was sprayed in a top-down direction with fine drops by the nozzles. The total volume flow rate of the solution sprayed through the nozzles was 40 L h^–1 inside the tower. The concentration of sodium hydroxide solution was 50 mg L^–1 with a pH of 9.5 approximately. Pollutants in the exhaust contacted with and diluted into the solution. Hereafter, they settled to the base of the tower and were contained in a settling tank before being discharged into the environment. Treated air was emitted into the atmosphere by the stack.

Fig. 11. Flowchart of the air pollutant treatment system.

Fig. 12. Diagram and photo of the air pollutant treatment system.

Pollutants emitted from the kiln on the final days of carbonization were sampled at the inlet and outlet of the treatment tower. Air quality parameters of total dust and carbon monoxide were selected to assess the reduction efficiency of the treatment system.

4.3 Efficiency of Air Pollutant Treatment System

The treatment efficiency of the air pollution control system was estimated through three consecutive measuring days. The carbon monoxide treatment efficiency of the system was assessed based on its concentration measured at the inlet and outlet of the system. On day 15, the concentration of CO measured at the inlet was 3,869 ± 288.1 mg Nm^–3 and at the outlet was 2,247.3 ± 43 mg Nm^–3. Concentrations of CO rapidly increased on days 16 and 17. During these periods the average concentration measured at the inlet of the system were 5,679 ± 654.8 mg Nm^–3 and 13,004 ± 423.3 mg Nm^–3, and at the outlet were 3,298 ± 5.3 mg Nm^–3 and 7,549 ± 52.7 mg Nm^–3, respectively. The carbon monoxide treatment efficiency of the system was calculated to be 41.93% on average over the duration of the experiment. After treatment, carbon monoxide concentration still exceeded the allowable limit of the Vietnamese standard (1,200 mg Nm^–3). Results suggest that the sodium hydroxide solution had low efficiency in absorbing carbon monoxide from the air pollution stream emitted from the charcoal kiln. Theoretically, carbon monoxide is only efficiently absorbed by specific solutions such as liquid nitrogen and cuprous ammonium solution (Hainsworth and Titus, 1921). However, these solutions are not available at this study site due to their high investment costs.

Total dust concentrations on study days are shown in Table 2. The removal efficiency of the system was also rather low for total dust, about 47.5% on average. Efficiency of particulate matter removal depends on its properties (i.e., size, shape, and types of particulate matter), and the impact of particulate matter properties on treatment efficiency should be considered in further research. Removal efficiency was also affected by the drop size of the solution and the contact time between pollutants and the solution. In this study, the solution was sprayed into tiny drops (aerosol) inside the treatment tower.

Electrostatic precipitators (ESPs) have been attractive to heavy industry, especially coal-fired power plants and cement production works, and have been in commercial operation for decades. The trend in ESP development is now moving toward installation in multi-unit buildings or even consumer level because of heightened awareness of indoor air quality and progression toward energy-efficient and affordable air cleaners. Features such as low-pressure drop and high collection efficiency, mentioned in Sec. S4.2, suggest there can be an off-the-shelf market in ESPs, although there are still a lot of challenges waiting to be addressed in Vietnam.

5 CONCLUSIONS

The studies reported here on Aerosol and Air Pollution in Southeast Asian countries provide information on aerosol characterization, exposure and health effects, and the current technology aimed at removal of aerosol particles from sources. Observed PM concentrations during the study period in Surabaya, Indonesia meet the Indonesian ambient air quality standards as well as WHO standards. The XRF analysis of PM in West Surabaya identified 18 elements, consisting of Na, Mg, Al, Si, S, K, Cl, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, and Pb. In PM_2.5, S, Na, Si and K were the elements in highest concentrations, and in PM_2.5-10, Si, Ca, Cl, Na and Mg. The main sources of PM_2.5 were diesel vehicle emission, a mixture of Cu industry and biomass combustion, metal industries used Ni, and construction, with contributions of 33%, 24.1%, 11.4%, and 7.9% respectively, while the main sources of PM_2.5-10 were soil dust and port industry, construction, road dust, and sea salt, with contributions of 32%, 28.8%, 14%, and 10% respectively.

In Chiang Mai, the highest PM mass and PAH concentrations were found in the finest particle sizes (0.65 µm–0.43 µm) in the periods of IOB and LOB at both urban and rural sites, where the PAHs concentration (5.10 ng m^–3) in this fine fraction accounted for 45% to 47% and 32% to 37% during IOB and LOB periods, respectively. This study also indicated that patterns of size-fractionated PMs and PAH concentrations varied between locations and seasons. Open burning is a major source of PAHs in the rural area during dry season, while traffic emission is a significant source in the urban area.

The removal efficiency of the chemical treatment system using sodium hydroxyl applied to a charcoal kiln was considered low (about 47.5%). The efficiency of particulate matter removal from the system is dependent on properties of the particulate (i.e., size, shape and type) as well as the droplet size of the solution and the contact time. The properties of flue gas and the particulate matter should be considered in further research.

ESPs have been commonly used in heavy industry and coal-fired power plants to control particulate emissions with high collection efficiency and low-pressure drop, and have now been developed for office and residential applications.

ACKNOWLEDGMENTS

The authors would like to thank all the committee members of the 2022 Asian Aerosol Conference for the opportunity to gather the researchers/scientists from South Eastern Asia and collect the opinions regarding the latest aerosol and air pollution issues. This work was partly supported by the National Science and Technology Council, Taiwan (Grant Number NSTC 111-2221-E-041-003). The authors thank Mr. Wittawat Insian for generating the graphics presented in Section 3.2. The authors would also like to thank Prof. Jhy-Chern Liu (Chemical Engineering, Taiwan Tech) for valuable writing advice.