Some facts & tips about Chiang Mai burning season

Cassian Pirard PhD    -----    Draft Version    -----

Go to:  Health issues

              Air Pollution levels

                            Air Quality Determination

                            Air Pollution Variation

                           Air Pollution Composition

              Visibility

                           Fog vs. Haze

               Prevention

                           Filters & Masks

      

What, When and Why?

When: February to April (dry & hot season)

Cause: Mostly forest fires & weather conditions

Health: Respiratory issues, fatigue, headaches and numerous long term effects

        

            The smokey season is a phenomenon that occur every year in tropical Asia during the driest time of the year. The atmosphere becomes charged with small particules which can reach concentrations where it affects daily life, particularly causing breathing difficulties.

            The smokey season is particularly extreme in Northern Thailand and neighbouring regions and peaks during the months of February-March-April. The intensity of the season varies from year to year and can sometimes extend from late December to May.

            The main cause is a combination of factor (mostly forest fires and meteorological conditions) that allow a large amount of smoke to be accumulated in valleys. It is not an urban pollution as it also affects remote valleys and is a regional issue.

 

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Health issues

        There are a very large number of publications on the health effects of air pollution and several of them deal more particularly with particulate matter (PM) which is the type of pollution seen in Chiang Mai. However, most studies are focused on urban pollution (traffic & industrial sources) which are considerably different from biomass burning. It remains unclear what are the mechanism of harmful action in some of the PM component of biomass burning.

 

         The toxicity of particulate matter regarding short- and long-term exposure is quite established as increasing morbidity and mortality. PM is detrimental to the respiratory and cardiovascular systems and high levels are classically associated with an increase incidence of stroke and myocardial infarctions, increase in ER admission for asthma, chronic obstructive pulmonary disease (COPD), respiratory infection and increase incidence of lung cancer (Aungkulanon et al., 2016). There is also emerging evidence that exposure to PM increases risk for neurological and metabolic diseases such as dementia, cognitive impairment, diabetes.

 

        Current guidelines consider all PM fractions (10, 2.5, 0.1) to be equally toxic, regardless of their composition, shape, size and surface area. This is despite recent research showing that PM0.1 (ultrafine particles) are presented a very important factor in the toxicity of ‘inert’ haze as they induce the inflammation of lungs. In general, PM stimulate oxidative stress, inflammation and genotoxicity but it remains unclear if biomass burning haze is less, equal or more toxic than urban haze (Johnston et al., 2019).

 

Figure 1: Geographical distribution of SMR anomalies for various diseases in Thailand between 2001 and 2004 (Aungkulanon et al., 2016)

         The factual epidemiological evidence is that the Lanna region is a hotbed for respiratory diseases such as asthma, lung cancer and COPD and show a standard mortality ratio twice higher than the national average for Chiang Mai Province (Aungkulanon et al., 2016). Some districts are sometimes more affected than other such as Hang Dong, Sanpatong and Doi Lo where the risk of lung cancer is 50% higher than the provincial average (Rankantha et al., 2018). The strong correlation with PM2.5 levels from seasonal biomass burning haze indicates that this source of pollution is likely to have some impact. However, some alternative causes have been suggested such as different smoking and cooking practices than the rest of Thailand (Sittipunt, 2016) or higher radon level (Rankantha et al., 2018). This last suggestion is doubtful as radon level (Wattananikorn et al., 2008) in area of high lung cancer incidence are 2 to 6x below the (already quite low) threshold of recommended indoor level by WHO.

Air pollution levels

Origin: microparticules less than 1/10th of a hair thickness

Variation: from unhealthy to hazardous levels

           

          A few websites/apps/organization (i.e. AQICN, AirVisual,…) and personal devices provides pollution levels in your area. The main pollutant in Chiang Mai area are fine particulates grouped as PM2.5. The air pollution is combined with other minor pollutants but PM2.5 is the one considered to provide the Air Quality Index (AQI).

          The AQI is a scale to quickly estimate the air quality and the impact it can have on the health of people. It is color-coded to quickly assess the risk associated with a specific value along this index [See Figure X]. During the wet season (June to October) AQI is between 40 and 60 in Chiang Mai, but values of 20 are common outside the urbanized area. The cold season (November to January) and the end of the hot season (May-June), AQI numbers are in the range of 80-120. The burning season from February to April is on average 150 but it is common to have bad days where values above 300 can be encountered.

 

Figure 2: Variation of daily AQI in Chiang Mai City Centre between 2016 and 2019. The burning season is clearly visible with orange, red and purple colours (Public AQI stations Thailand)

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            Air Quality Index Determination

            The fine particulate (PM2.5) is one component of the AQI, along with coarse particulate (PM10), Ozone (O3), Carbon Monoxide (CO), Nitrogen Oxides (NOx) and Sulfur Dioxide (SO2). With the exception of PM10 which follows the behavior of PM2.5 and is a major component, other pollutants are present in minor amount in Chiang Mai air and generally do not pose an important threat to the air quality in itself.

            In Chiang Mai, the Air Quality Index is determined by the PM2.5 as it is the dominant aerosol. Keep in mind that the AQI is not a linear scale. It is proportional to the concentration of pollutants but the index is assessed on the health effect that each pollutant and different concentrations has on the human body. In consequence, for PM2.5 an AQI of 200 has 4x higher concentration than and AQI of 100; an AQI of 400 has 10x the concentration of an AQI of 100

 

Figure 3: Graphical representation of AQI calculations based on concentrations of particulate matter <2.5 microns, <10 microns and ozone

            Air Pollution variations

            Available sources of information regarding current air pollution levels often show variations between areas and during the day. There are several causes to these observations.

            Extreme variation (2 order of magnitude in PM2.5 concentrations; i.e. a clear day outside the dry season with one very polluted spot) is often the result of a local source that is particularly polluting the air near the monitor. Occasionally, it can be a defective monitor.

            For local variations (1 order of magnitude over a suburb), it can be a local source (i.e. burning, industry) or a specific feature (topography, winds, etc.)

 

Figure 4: Particularly high values at Samoeng Hospital during the 2019 burning season. AQI in this case is calculated on PM10 while other 'urban' values are PM2.5 - the cause is a local fire in Samoeng valley creating a lot of smoke where most larger particles have not settled

For smaller variations, natural variability is a factor but also the source of information. Data available on networks is most of the time non-reviewed and private contributors are likely to have uncalibrated detectors, with a possible time-drift and increasing imprecision for very high pollution concentration.

            Overall, the precision of measurement seems to be between 10 and 20% when all devices in similar conditions are considered. Considering the accuracy required, this imprecision has little impact on the interpretation of results and health decisions that would follow. When an AQI is stated as ‘hazardous’ (>300), it does not really matter if the true value is 380 or 420.

            An occasional source of misunderstanding is also found in the use of different scales and measurement characteristics. AirVisual relies on the US AQI scale, which is very similar to the Chinese scale of AQICN. Other scales (Europe, UK, Australia) are different, but health warning for similar pollution levels remains essentially the same. Keep in mind however that the type of pollution (and following health symptoms) can be very different from one area to another.

Another factor that sometimes bring confusion is the time integration of data. Although it becomes rare as ‘current’ public information, daily average are occasionally found. Most data are now provided on an hourly average. Instantaneous analysis can provide a quite different number from hourly or daily average depending on the type and source of pollution. Instantaneous analysis variability is only suitable for specific applications such as the quality and efficiency of an air filter installed in a small room.

            Air Pollution Composition

 

            The composition of the atmospheric pollution in Chiang Mai is surprisingly relatively constant despite variations of mean daily AQI from 40 to 200. The proportion of pollutant is roughly 55% of PM2.5, 25% of PM10, 10% of ozone, ~5% of nitrogen oxides and ~5% of sulfur dioxide. Carbon monoxide seems to be relatively low and is often not measured in Chiang Mai area.

 

Figure 5: Chiang Mai air pollution propportions based on AQI equivalenent normalised to 100 AQI. Particulate matter form >80% of the pollution in the city, probably more outside the urban area.

           The dynamics of these pollutants is complex as particulates can shift from ultrafine (PM0.1) to PM2.5 and to PM10 by various physical (coalescence of smaller particulates in larger ones) or chemical (gaseous components precipitating as particulates) processes. However, a few sources can be identified and traced such as SO2 and O3 which mostly find their origin in traffic, heating and industrial pollution [REF] and PM2.5 and PM10 which have a considerable component produced by biomass burning.

            A weak correlation between PM2.5 and some gaseous pollutants show that some part of the PM2.5 originates from traffic and other urban sources. This source is confirmed when examining the variation in the chemical composition of particulate matter itself as seen below, but remain a limited component. Finally, some of these gaseous pollutants such as nitrogen oxides and sulfur dioxide have in-cloud absorption that would produce acid rain. However, due to relatively low level of these components, pH from particulates is at 5.6±1.5 which is within the range of normal acidity for rain water [REF].

Figure 6:  Particulate matter distribution for fresh and detrained smoke as well as diesel exhaust and its secondary products. The vertical green line represents the effective limitation for standard NIOSH masks

There are many scientific studies on the type of particulate produced during wildfires. Depending on the type of fire, the intensity the burning stage (ignition, flaming, smoldering), the fuel condition (live, dead, wet), its distribution (dense/light, flat/sloped) and type (grass, foliage, branches, etc.), the humidity, the wind, etc., the particulate size distribution (and its composition) will change. The last two factors (wind and humidity) particularly play a role in the modification between fresh smoke and detrained smoke that has spent considerable time in the atmosphere. Vertical transport, in-cloud coagulation and secondary growth will increase particle size by 20% (Guyon et al., 2005).

         The pollution from forest fires in Chiang Mai occurs mostly as detrained smoke from surface forest fires (dried grass, shrub) with a mixed distribution and burning conditions. It results in a particle size distribution centered around 150nm with most particles (2σ) between 50nm and 2 microns (an exception can be seen in Figure X due to wildfire proximity). In terms of mass distribution, 2/3 of the mass is carried by PM2.5 and 1/3 by PM10. The amount of nanoparticles (PM0.1) produced during forest fires is relatively low compared to other sources such as fresh smoke from diesel engines [See Figure X]. Larger particles (10 to 50 microns) are likely to form less than a percent of the smoke as most elements this size would deposit relatively quickly (Samsonov et al., 2012).

Figure 7: Chiang Mai air pollution sources of particulate matter. The period from Novermber to June is dominated by biomass burning sources while the wet season is an equal share between biomass burning, urban sources and dust

Based on the composition of the particulates, a few solid interpretations can be extracted as well. Some elements are particularly informative as proxy for different sources of pollution. In short, zinc is a proxy for aerosols produced by tyres (Snider et al.,) and therefore a good indicator for ground vehicle traffic pollution; Potassium is strongly linked to plants and biomass burn and release a considerable amount of it in the atmosphere. Calcium can come from various source but resuspension from ground dust when wind, fire or sometimes rain remobilize it seems to dominate. Silicon and Aluminium are environmentally ubiquitous and I used them in this short interpretation as a normalizing factor.

           This elemental dataset shows that during the burning season, around 10% of PM are produced by urban sources. Another 15% is produced by ground dust that is mobilized due to dry weather through occasional wind gusts and fires. The remaining 75% are biomass burning through forest fires and slash-and-burn agriculture. According to Phairuang et al. (2017), the large majority of the biomass burning pollutant in Chiang Mai province are from forest fire with 12000 Mg of emission produced while rice fields would only account for 500 Mg.

          Other elements, particularly heavy metals such as lead, mercury, cadmium and arsenic are in trace amounts at all times and do not represent a major concern in Chiang Mai atmospheric pollution. A comparison with Bangkok air pollution (where the urban/industrial component is more important) shows that these elements are often more abundant. Arsenic, vanadium, nickel, aluminium and zinc are in concentration 10 to 100x higher than Chiang Mai; Chromium, iron, lead, copper, magnesium, manganese are in concentration 5 to 10x higher than Chiang Mai pollution per amount of dry particulate [REF].

           The modest impact of urban/industrial pollution in Chiang Mai atmosphere is also confirmed by the relatively low amounts of ammonium sulfates and nitrates in the particulate fraction of Chiang Mai, which would be in more dramatic concentrations in larger urban centers (Snider et al.,).

Among the carbon fraction of the PM pollutants is the concerning organics known as polycyclic aromatic hydrocarbons (PAH). Among these are a group of carcinogenic PAH that is a particular concern for epidemiological studies on the local population. Local activities are likely to be a considerable source such as markets (street food), open waste burning, nearby traffic and small factories. At a regional scale, rubbish burning and agrowaste are also potential sources (Chantaram, 2012). These sources tend to be more present in the eastern part of the city.

Figure 8:  Particulate matter mass distribution for detrained smoke. The vertical green line represents the effective limitation for standard NIOSH masks. The dotted blue line represent the potential for alveolar deposition in lungs at different size. It is very high for PM0.1 (~70%), high for PM2.5 (~25%) and drops quickly to insignificant levels at PM10.

           Finally, the PM0.1 is the smallest non-gaseous fraction of aerosol found in typical air pollution. Particles have a size between 1 and 100 nm and these are known to be particularly harmful to the body and not so easily filtered. These particles are often associated with diesel exhaust and are a common pollutant in urban area, while forest fires are not a particularly large producer of these nanoparticles. It can therefore be assumed that the burning season does not dramatically increase the PM0.1 content in the air pollution.

Visibility

Doi Suthep visibility can be used as an estimate of pollution levels

​It only works during the burning season (when humidity is low)

It is misleading when the humidity is high (error of 200 to >500%)

 

          Looking at Doi Suthep often gives a good idea of how bad the pollution is. Particles in the atmosphere will reduce how far you can see and there is strong connection between AQI and how clear we can see Doi Suthep.

 

During the dry season (February to April), the following numbers can be used

AQI <50, visibility is >50km

AQI 75, ~25km (Doi Suthep hard to see from the outskirts of Chiang Mai)

AQI 150, ~ 15km (Doi Suthep hard to see from Hang Dong)

AQI 175, 7km (Doi Suthep hard to see from the Old City)

AQI 200, 4km (Building hard to see in the distance)

AQI >300, smog visible on increasingly smaller distances within the city

 

Outside those 3 months, the humidity in the atmosphere plays a major role and visibility decreases considerably above 80% humidity.

         

 

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Fog Vs. Haze

            A good approximate indicator of the level of pollution for residents in the A. Muang Chiang Mai (and neighbouring districts) is how clear they can see Doi Suthep, the mountain right next to the city centre. Although imperfect, a good correlation can be made (R2>0.8) but several factors have to be considered before making an estimate.

            Aerosol concentration (particles & gases) reduces visibility through scattering, diffusion and absorption (Seinfeld & Pandis, 1998). PM2.5 and PM10 particles will behave slightly differently to each other (scattering is most intense between 1 and 0.1 microns), however, considering that the PM2.5/PM10 ratio is relatively constant in Chiang Mai (0.65 to 0.70 as AQI) and the behavior difference minimal, it does not bring large errors in the AQI estimation.

             There is a mild change depending on the altitude of observation as the air pollution is layered by the inverted temperature profile of the lower atmosphere. Changes between Chiang Mai ground (310m) and tall buildings and surrounding hills (~420m) can decrease the pollution by up to 30% and change the PM2.5/PM10 by 10% (Jeensorn et al., 2018). Visibility (through contrast) will also change a lot between scattering/diffusion caused by sunlight positioned behind the observer, above or behind the observed features (i.e. a sunset behind a mountain will considerably increase its general visibility).

 

Figure 9:  View of Doi Suthep at a distance of 7.5km from the ridge line. a. In good atmospheric conditions: Low AQI and medium humidity.

b. In mildly polluted conditions of AQI ~150 and medium humidity. c. In mildly polluted conditions of AQI~150 but in different viewing conditions (evening). d. In mildly polluted conditions of AQI ~150 and almost saturated humidity

           Other man-made aerosols are present in minor amounts and will not affect greatly the visibility. An essential factor however is the humidity in the atmosphere. In February, March and April, 70 to 80% of the time is spent in relatively dry conditions with less than 50% humidity and these mostly occur during the day. Relative humidity fluctuation in that range are not drastically changing the AQI estimation you could do by observing Doi Suthep or any other landmark.

            Outside these three months, the situation is different. Relative humidity can be higher, eventually reaching saturation (100%) and high humidity does not automatically occur at night and dawn. Therefore a grayish veil can be present and turn into a dense fog depending on meteorological conditions. The visibility can be reduced by 10x what it would be in the dry season for a similar AQI (i.e. An AQI of 100 in March (12pm) gives around 25 km of visibility, the same AQI outside the dry season when the humidity reaches 95% would limit your field of view to 4 km). A minor component in this drop in visibility in the interaction between nitrates and sulfates particulates and water, changing scattering coefficients.

Figure 10:  Visibility curves as a function of AQI levels. The different curves are for different relative humidity which becomes an important factor above 75% RH. As a reference, the distance to Doi Suthep ridge line from different places in Chiang Mai is given on the top part of the graph.

             The conclusion of this paragraph is that relying on atmospheric visibility to estimate air pollution outside the dry season might grossly overestimate the level of pollution. If you barely see Doi Suthep from the old city (~5km), it could mean that the AQI is 300-ish (and the haze is likely to have a yellow-orange tinge and firewood smell) or an AQI of 50 with a fog.

Prevention

 

Home HEPA filters are recommended to filter the air in an insulated room

N95 masks with valve are recommended for outside activities

Other masks (surgical, cloth,…) have insufficient filtering action to guarantee good breathing air quality

          There is not much that can be done at a personal level to prevent those regional burning events. Only a few options are left to decrease as much as you can the health impact of high AQI due to particulate matter. Leaving northern Thailand during those months is obviously not possible for most people so ensuring that the air quality that you breathe is down to safe level is the main mode of action.

         In your home, prevention consists mostly of airflow insulation and avoiding as much as possible to open windows and doors while installing filtering machine using HEPA filters. Additionally, you can also install HEPA filters on A/C units and homemade filtering unit using a standard fan.

         Outdoor, the best option is to use a mask with a good filtering rating. N95 with a valve is recommended because of its efficiency in blocking the bulk of PM2.5 particles, a compromise between the ability to breathe easily and filtering and avoiding accumulation of bacteria within the mask. Make sure the mask is properly fitted. It is not recommended to do exercise or strenuous work while wearing a mask.

 

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More on filters

 

Home filters in air cleaners or fitted on fans, A/C units and ventilation are designed to reduce the PM2.5 and PM10 concentration inside a room (and eventually other pollutants depending on the system). With a portable air quality monitor, you can check how clean the air is inside the room. Keep in mind that opening doors and windows, even for a few seconds, will mostly reset your pollution concentration inside your room.

 

HEPA Filters: Various ratings exists so it is important to make sure it is efficient. However, most filters available go from good to very good. Depending on the fan and design used, these filters can be efficient in processing large volumes. No filtering effect on gaseous pollutants.

E2F Filters: Very thorough in capturing particulates however, the overall efficiency is limited by the volume processed per time. These filters are good to maintain an already filtered air, very small rooms and breathing directly in the output stream. They are not efficient to clean a room flushed with polluted air.

NIOSH Masks: N95 rated mask is the basic requirement to breathe decent air, N99 and N100 will trap even more particles but they can be either more expensive, or make breathing difficult by limiting the air intake. The improvement of air purity between N95 and N100 is often negligible unless AQI levels are well in the hazardous zone. Keep in mind that not all masks labelling themselves as N95 have actually that rating so make sure you use reliable brands. P95, R95 and P- and R- higher ratings are not necessary in Chiang Mai, they are more expensive and would stop a class of pollutants that is not really present in high concentration in the Chiang Mai atmosphere.

 

Figure 11:  Filtration ability of PM2.5 by various masks. Values are in AQI of PM2.5 excluding all other pollutants.

            Outflow valve on masks are good for three reasons. The first one is that it doesn’t get soggy in your mask due to breath humidity and it also prevent the accumulation of CO2 in the mask; the third reason, for sensitive people, is that it helps avoiding bacterial infection by keeping your air intake relatively fresh and dry and prevent a buildup of bacteria in the mask. The only negative side is that the valve reduce very slightly the efficacy of the respirator.

 

           Respiratory protective masks are not just beneficial. As they filter air, they also reduce considerably the inflow of air in your lungs, decreasing the pulmonary ventilation for the oxygen intake required. When exercising, there is a threshold where the energy required to breathe properly exceed the amount of oxygen obtained from respiration through a mask. Without an outflow valve, this threshold is accentuated by the presence of high level of CO2 in the mask (Johnson, 2016). This effect has repercussion on muscular abilities, cardiovascular efficiency and mental skills.

 

         People with respiratory and cardio-vascular issues should consult a doctor before wearing a N95 or above mask. It is possible that such mask might cause more issues than breathing polluted air.

Children & people with beard should have a mask well-fitted. Leaks due to facial hair or improper size will not filter efficiently the polluted air while make breathing more difficult.

 

          Surgical masks: Surgical masks are designed to block infectious agents carried by aerosols, sputum, hair, etc. and should have a limited effect for blocking pollution. However, several studies (Langrish et al., 2009) show that there is some efficacy for these masks when properly worn. They filtrate between 60 and 90% of PM2.5 particles. Their equivalent in NIOSH mask would be around N75.

 

        Cloth masks: the inexpensive masks will stop the largest particles but around 40% of the smallest one, their NIOSH equivalent would be around N50.

Although these masks have an effect and are therefore not useless against Chiang Mai pollution, N95 mask from reputable brands remains the best protection when price, efficacy and usefulness are considered.

 

To give an idea, based on a bad day in Chiang Mai when the AQI would be around 250, the air you would breathe through a mask would have the following AQI: Cloth mask: ~175, Surgical Mask: ~130, N95: 40, N99: 8, N100: 1, R95 & P95 would be an AQI of 38. As a reminder, AQI values below 50 starts to be considered as good (best daily AQI in Chiang Mai is rarely below 40).

Figure 12:  Cumulated daily AQI over a year (the burning season is counted at the end of the graph for one calendar year). Without the burning season, particulate levels are higher (10-15%) in Bangkok than in Chiang Mai. In 2018, a mild burning season push the average yearly AQI for Chiang Mai to level similar to Bangkok. In 2019, the average yearly AQI was 15-20% higher than in Bangkok.

Notice the green dotted line is the yearly AQI if you breather filtered air for 50% of the time during the burning season.

REFERENCES

Arunrat, N., Pumijumnong, N. & Sereenonchai, S. (2018). Air-pollutant emissions from agricultural burning in Mae Chaem Basin, Chiang Mai Province, Thailand. Atmosphere, 9, 145. DOI: 10.3390/atmos9040145

Aungkulanon, S., Tangcharoensathien, V, Shibuya, K., Bundhamcharoen, K. & Chongsuvivatwong, V. (2016) Post universal health coverage trends and geographical inequalities of mortality in Thailand. International Journal for equity in health, 15, 190. DOI: 10.1186

Chantara, S. (2012). PM10 and its chemical composition: A case study in Chiang Mai, Thailand. Air Quality - Monitoring and Modelling. Dr Sunil Kumar (ed.), ISBN: 978-953-51-0161-1, InTech.

Chunram, N., Vinitkekumnuen, U., Deming, R.L. & Kamens, R.M. (2007). Distributions of fine particulate matter (PM2.5) in the ambient air of Chiang Mai-Lamphun Basin. Journal of Yala Rajabhat University, 2, 1, June 2007.

Chunram, N., Vinitkekumnuen, U., Deming, R.L. & Chantara, S. (2007). Indoor and Outdoor levels of PM2.5 from selected residential and workplace buildings in Chiang Mai. Chiang Mai Journal of Science, 34, 2, 219-226.

Guyon, P., Frank, G., Welling, M., Chand, D., Artaxo, P., Rizzo, L., Nishioka, G., Kolle, O., Fritsch, H., Silva Dias, M.A.F., Gatti, L.V., Cordova, M. & Andraea, M.O. (2005). Airborne measurements of trace gas and aerosol particle emissions from biomass burning in Amazonia. Atmospheric Chemistry and Physics Discussions 5, 2791-2831 - EGU - DOI:10.5194/acp-5-2989-2005

Jeensorn, T., Apichartwiwat, P. & Jinsart, W. (2018). PM10 and PM2.5 from haze smog and visibility effect in Chiang Mai Province, Thailand. Applied Environmental Research, 40, 3, 1-10.

Johnson, A. Y. (2016). Respirator masks protect health but impact performance: a review. Journal of Biological Engineering, 10:4. DOI:10.1186/s13036-016-0025-4

Jonhston, H.J., Mueller, W., Steinle, S., Vardoulakis, S., Tantrakarnapa, K., Loh, M. & Cherrie, J.W. (2019). How harmful is particulate matter emitted from biomass burning? A Thailand perspective. Current Pollution Reports, 5, 353-377. DOI:10.1007/s40726-019-001254

Junpen, A., Pansuk, J., Kamnoet, O., Cheewaphongphan, P, & Garivait, S. (2018). Emission of air pollutants from rice residue open burning in Thailand, 2018. Atmosphere, 9, 449, DOI:10:3390/atmos910449

Langrish, J.P., Mills, N.L., Chan, J.KK, Leseman, D.LAC., Aitken, R.J., Fokkens, P, HB, Cassee, F.R., Li, J.,  Donaldson, K., Newby, D.E. & Jiang, L. (2009). Beneficial cardiovascular effects of reducing exposure to particulate air pollution with a simple facemask. Particle & Fibre Toxicology, 6, 8. DOI:10.1186/1743-8977-6-8.

Snider, G., Weagle, C.L., Murdymootoo, K.K>, Ring, A., Ritchie, Y., Stone, E., Walsh, A., Akoshile, C., Xuan Anh, N., Balasubramanian, R., Brook, J., Qonitan, F.D., Dong, J., Griffith, D., He, K., Holben, B.N., Kahn, R., Lagrosas, N., Lestarf, P., Ma, Z., Misra, A., Norford, L.K., Quel, E.J., Salam, A., Schichtel, B., Segev, L., Tripathi, S., Wang, C., Yu, C., Zhang, Q., Zhang, Y., Brauer, M., Cohen, A., Gibson, M.D., Liu, Y., Martins, V.,  Rudich, Y. & Martin, R.V. (2016). Variation in global chemical composition of PM2.5: emerging results from SPARTAN. Atmospheric Chemistry and Physics, 16, 9629-9653. DOI:10.5194/acp-16-9629-2016.

Rankantha, A., Chitapanarux, I., Pongnikom, D., Prasitwattanaseree, S., Bunyatisai, W., Sripan, P. & Traisathit, P. (2018). Risk patterns of lung cancer mortality in northern Thailand. BMC Public Health DOI:10.1186/s12889-018-6025-1

Samsonov, Y.N., Ivanov, V.A., McRae, D.J. & Baker, S.P. (2012). Chemical and dispersal characteristics of particulate emissions from forest fires in Siberia. International Journal of Wildland Fire DOI 10.1017/WF11038 in CSIRO compilation

Wattananikorn, K., Emharuthai, S. & Wanaphongse, P. (2007). A feasibility study of geogenic indoor radon mapping from airborne radiometric survey in northern Thailand. Radiation Measurements, DOI:10.1016/j.radmeas.2007.04.011

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