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CHIANG MAI BURNING SEASON
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This is a short summary of the scientific research on the burning season in Chiang Mai and Northern Thailand. It is covering most aspects related to this seasonal event (health, public data, chemistry, physics, meteorology, climate, prevention, policies & actions, etc.)
You can find some short answers in the Frequently Asked Questions page.
A more complete (but still accessible for non-experts) version will be available at a later time, going a bit deeper in each aspect of this summary. The link will be HERE.
The main purpose of this page is to circumvent all the misinformation and misunderstanding regarding this annual meteorological phenomenon by giving direct access (through the reference list at the bottom of this page) to scientific sources of information.
Go directly to:
Forecast, Future trends & Actions
The burning season (smokey season, haze episode, etc.) is a weather phenomenon that occurs every year in tropical Asia during the driest time, causing unhealthy air pollution.
The atmosphere becomes charged with small dust particules that can reach concentrations where it affects daily life, particularly causing breathing difficulties for sensitive individuals.
The burning season is particularly extreme in Northern Thailand and neighbouring regions (Northern Laos, Central Myanmar)
It occurs every year at the same time, between mid-January and mid-April. The intensity of the season varies from year to year and can sometimes prolong to the month of May.
The main cause is a combination of two factors: forest fires and meteorological conditions, allowing a large amount of smoke to be produced and accumulated in valleys and river basins of Northern Thailand. It is not an urban pollution as its impact is regional and also affects remote valleys far from main cities.
Figure 1: Record of air pollution in Chiang Mai between 2016 and 2019. Green and Yellow are days with relatively low air pollution. Orange, Red and Purple are days with high air pollution with unhealthy to hazardous levels. These days are mostly present in February, March and April (Public AQI record stations).
Air pollution creates stress on the organism, which commonly result in allergy-like reactions in healthy individuals.
Acute health effects are particularly affecting children, the elderly and high-risk groups, mostly through respiratory diseases.
Hospital admission for asthma, COPD, cardiovascular and respiratory diseases increases considerably when air pollution is high.
Diseases related to long term exposure are not clearly identified. There is no statistical relationship between lung cancer and exposure to high level of PM2.5 in Chiang Mai.
Air pollution and its effects on health have been studied for a long time. A large number of scientific publications deal specifically with particulate matter (PM) which is the type of pollution seen in Chiang Mai. However, most studies are focused on urban pollution (traffic, industrial, human activities, etc.) which are considerably different from biomass burning, the main pollution source in Northern Thailand. Differences exists in heavy metal contents, organic carcinogenic chemicals and PM size itself. At this stage, it remains unclear if very high concentration of particulate matter in Chiang Mai is more toxic than an average urban pollution in large Asian cities (Johnston et al., 2019; Liu et al., 2019; Mueller et al., 2020).
The particulate matter in Chiang Mai air pollution has a general mechanism of harmful action through stimulation of oxidative stress and causing inflammation and genotoxicity (Johnson et al., 2019). The particulates themselves produce a series of reaction in respiratory airways, inducing defense mechanisms from the human body.
Figure 1: Schematic representation of particulate matter penetration inside the respiratory system according to their size (Almeida-Silva et al., 2022; Pongpiachan et al., 2013 ; Othman et al., 2022; Phairuang et al., 2022a; Kwon et al., 2020; Schraufnagel et al., 2020; Sonwani et al., 2021). Coarse particles (>5 microns) are eliminated through sneezing, cough and digestive system; 2-5 microns are the highest deposition level, in main bronchii; particles smaller than 2 microns can have toxins transferred to the blood stream]
Despite differences in composition, surface area, shape, size, source, etc., current recommendations consider all PM fraction, from 10 microns to 0.1 to be equally toxic, reflecting the difficulty to set clear guidelines since sea salt microparticulates and tobacco smoke would have similar toxicity, while having in reality very different toxicity potential.
In theory, coarse and fine particulate are relatively inert and mostly lead to allergice reaction while fine and ultrafine particulates have potential to carry large amount of toxic chemical and release it in capillaries (Schraufnagel, 2020; Phairuang et al., 2022b; Niampradit et al., 2022)
Heavy metals are often a concern in particulate matter as they can enter the body and are potentially carcinogen. However, biomass burning does not produce large amounts of such metals compared to urban sources (traffic, industry) and Bangkok has systematically higher level in heavy metals than the thickest Chiang Mai smoke
et al., 2022b; Niampradit et al., 2022).
Polycyclic Aromatic Hydrocarbons (PAH) are a class of chemicals similar to benzene and known carcinogens. A significant content of these chemicals is present in the particulate matter of biomass burning. However, the concentration in Chiang Mai air pollution is 2 to 9x lower than Bangkok and PAH concentration in Chiang Mai is in fact not so different to many European and Japanese cities (Pengchai et al., 2018; Kawichai et al., 2020; Choomchuay et al., 2022; Chantara et al., 2009; Thepnuan & Chantara, 2020).
For healthy individuals, the effect is often limited to allergic reactions with mild to very mild symptoms, including allergic rhinitis, congestion, sore throat, itchy eyes, headaches, more rarely skin reactions and when PM levels are particularly high, shortness of breath (Wu et al., 2018). It also indirectly leads to aeroallergen sensitization, making individuals more prone to develop a histamine intolerance from other allerge (Sompornrattanaphan et al., 2020).
Another effect of high PM levels is the absorption of sunlight and as a consequence, decreased levels of vitamin D production (Wang et al., 2020). Lower solar UV intensity also reduces the risk of sunburn.
Short term diseases
The toxicity of particulate matter regarding short-term exposure is quite established and known to increase morbidity and non-accidental mortality risk (Othman et al., 2022; Pothirat et al., 2019) with significant effects for respiratory diseases morbidity, cardiovascular morbidity, and adult mortality in high-risk groups (Johnston et al., 2019; Uttajug et al., 2020, 2022; Vajanapoom et al., 2020). Health issues affect particularly children and elderly (Pope et al., 2002; Samet et al., 2000). High levels of PM are typically associated with increased hospitalization for asthma, COPD, cerebrovascular diseases, myocardial infection, coronary heart diseases and ischemic heart diseases.
Figure 2: Positive and negative anomalies of several diseases in Thailand as provincial standardised mortality ratio over the national average. Important anomalies exist in the North for respiratory diseases such as COPD, Asthma, Lung Cancer, but not Pneumonia (Aungkulanon et al., 2016).
Long term diseases
Evidence that exposure to PM increases the risk of neurological and cognitive (dementia, cognitive impairment, cognitive development) and metabolic diseases (diabetes) is unconclusive as all studies are done typically on urban pollution, which is considerably different from biomass burning. Low birth weight and pre-term birth are however health features characteristic of poorest populations in SEA living in areas where air pollution is particularly high (i.e. Northern Laos, Western Myanmar) (Karanasiou et al., 2021; Reddington et al., 2021).
Lung cancer mortality show a higher rate increase in Northern Thailand compared to the whole country (Aungkulanon et al., 2016; Rankantha et al., 2018; Pongnikorn et al., 2018). At first glance, a correlation with air pollution could seemingly be made, but several statistical features makes air pollution almost insignificant. The highest risk is found in Hang Dong, Doi Lo and San Pa Tong districts, with Chiang Mai province as a whole carrying a very significant anomaly compared to neighbouring provinces where air pollution is mostly identical. The causative link with the prevalence of lung cancer is therefore weak and other causes have been suggested such as the presence of indoor radon. Although possible, radon levels estimated in Chiang Mai province are 2 to 6x lower than the already low WHO treshold (Wattananikorn et al., 2008). A recent study (Somsunun et al., 2022) has found a higher variability in the Northern region, but correlation between ground radon and lung cancer remains weak. At this stage, the reasons for locally high lung cancer cases is still unknown.
Figure 3: Visual comparative for 6 provinces of Northern Thailand for (left) risk pattern of lung cancer mortality (modified from Rankantha tet al., 2018) and (right) modelised distribution of indoor radon concentration (modified from Somsunun et al., 2022). Air pollution is unable to explain the heterogeneity of risk in Northern Thailand, and inddor radon is equally inadequate.
AIR QUALITY INDEX
The Air Quality Index (AQI) is a tool to estimate the health risk level of air pollution through a numerical scale and color code. It ranges from <50 (Good) to >300 (Hazardous).
The main pollutant in Chiang Mai is PM2.5 and the AQI is based on that value
The AQI during the rainy season (June-October) is 40-60 with values frequently below 20 outside urbanised areas.
The AQI in transitional months (November-January & May-June) is in the range 80-120.
The AQI during the burning season is 150 (Unhealthy) on average but values above 300 are frequent.
The Air Quality Index (AQI) is a quick estimation tool to assess the level of air pollution and its effects on human health. The scale is proportional to the concentration of pollutants but the index is assessed on the health effect of each pollutant on the human body with concentration ranges. As a result, the correlation between AQI and pollutant is not continuous and change when tresholds are passed. For particulate matter, it takes an average of PM-related health effects established by governement agencies, reassessed every few years based on recent research and changes in air pollution in the region where the AQI is applied.
Components that enter into the AQI determination are fine particulate (PM2.5), coarse particulate (PM10), ozone (O3), Carbon Monoxide (CO), Nitrogen Oxides (NOx) and Sulfur Dioxide (SO2). Ammonium (NH3) and Lead (Pb) are also measured locally in some scales (India, Australia).
In Chiang Mai, The AQI is determined by the PM2.5 levels as it is the dominant aerosol in terms of health effects. Rarely, PM10 takes over, but it is temporary and the result of nearby forest fires.
Figure 4: Relationship between AQI(US) and the concentration of fine particulate (PM2.5), coarse particulate (PM10) and Ozone (O3), the three main pollutant in Chiang Mai air pollution.
SOURCES OF AIR POLLUTION
Air pollution in Chiang Mai is more than 80% particulate matter (PM) and minor amounts of gases, mostly of urban origin.
Particulate matter is mostly produced by the burning of forest (90%), grassland (5%) and agricultural lands (5%)
The reasons for forest fires is 75% to help collecting forest products (mushrooms, plants, hunting); 6% for agricultural reasons and 20% for various causes.
Rice field burning has a low incidence on the burning season as most burning occur outside the burning season.
Corn fields have no significant contribution to the smoke in the burning season
Traffic & Urban sources contribute to 2-3% of pollution during the burning season
Transboundary pollution originates from vegetation burning and represents up to 75% of the pollution in Chiang Mai
Atmospheric pollution in Chiang Mai is mostly due to particulate matter (PM around 80%) with minor amounts of ozone (O3 <10%), nitrogen oxides (NOx <5%) and sulfur dioxide (SO2 <5%). Carbon monoxide (CO) is not a significant pollutant in Northern Thailand.
Gaseous components (O3, NOx, SO2, CO) are mostly of urban origin although some variations occur with the intensity of biomass fires.
The origin of particulate matter pollution in Chiang Mai, Northern Thailand, and northern South-East Asia as a whole is clearly identified as biomass burning. This term includes open-burning of existing or dead vegetation and covers agricultural residue, forest & grass fires (Phairuang et al., 2016b) and excludes ground dust, traffic, industrial and agro-industrial aerosol sources.
Chiang Mai province is heavily forested with around 80% of the province covered; 5.5% of land allocated to rice paddies and another 4.5% to other crops (Khankaew et al., 2016). Of these 80%, 70% are considered by the Forest Fire Control Division as subject to fire, with deciduous forests carrying the highest risk ans also representing the largest surface (86%) (Janta et al., 2020; Thammanu et al., 2021).
Figure X: Land use map of Thailand in 2013. Northern provinces are dominated by forest, with minor crops (rice, cassava) and orchards (Phairuang et al., 2021a)
The contribution of forest fires to PM druing the haze epsiode is estimated between 80 and 92% (Hongthong et al., 2022; Khankaew et al. 2016; Kim Oanh & Leelasakultum, 0211; Sillapapiromsuk et al., 2013). According to the Forest Fire Control Division, for the period between 2016-2018, the reasons of forest fires are 63% for the collection of products (mushrooms, bamboos, herbs); 10.2% for hunting; 4.45% for land clearing and cultivation; 1.15% for animal farming; 0.33% of incidents; 0.18% for illegal logging and 20% due to other causes.
Their contribution represents around 10% of production of PM with 1 to 5.4% of rice field burning; 1 to 8% of burning of other crops fields and 7 to 12% of grassland (often included in forest burning).
Agricultural burning is applied to rice, sugarcane, cassava, corn, soybean and potato before or after harvesting (Phairuang et al., 2016). However, in Northern Thailand, ricefield burning is the main source, affecting 7% of rice paddies (Junpen et al., 2018) with 60% of burning occuring in December-January, only 3% in February-March and the rest from April to November (Phairuang et al., 2016b).
The reasons behind agricultural burning are mostly traditional and similar over all South-East Asia, sourthern China and India. Farmers believe that it makes tilling easier, control insects, diseases and weeds, release nutrient and increase yield. However, there are scientific evidence that the last two reasons are wrong, burning leading to loss of organic soil, a lower soil fertility and ultimately a lower crop production (Chiaranaikun, 2017; Arunrat et al. 2018; Narita et al. 2019; Pasukphun, 2018).
Figure X: Monthly percentage of burning for forest, maize & corn and rice residues (modified from Junpen et al., 2018; Arunrat et al., 2018; Janta et al., 2020). The percentage is not representative of the emitted pollution as it depends on a long list of factors (mass, source, burning parameters, envrionment parameters,...)
Chiang Mai has no very polluting industry and the contribution of industrial pollution to PM2.5 in Chiang Mai is estimated at 0.08% (Kim Oanh & Leelasakultum, 2011)
Around 2.6% of particulate matter is attributed to traffic and urban sources during the burning season (Hongthong et al., 2022; Khankaew et al. 2016; Kim Oanh & Leelasakultum, 2011; Sillapapiromsuk et al., 2013). The contribution percentage during the rainy season can be higher but the production remains mostly the same.
By far the most important source, it represents 40 to 75% of the air pollution during the burning season, depending on weather conditions in source areas and wind patterns. Local smoke production represents 11 to 39% of air pollution and the local contribution increases as AQI gets into unhealthy-hazardous range (Punsompong et al., 2021).
Atmospheric modelling show that biomass burning in Southern and coastal Myanmar travel at an average rate to 300km/day to an altitude up to 3000m (Choommanivong et al., 2019) and eventually accumulate in Chiang Mai area. The situation varies througout the year and spatially. For example, in Chiang Rai, a signicant amount of smoke can also come from Northern Laos.
Figure X: Probabilities of particulate matter origin 3 days before its arrival in Chiang Mai. Left: Models for different altitudes (surface, 1000 and 1500m above ground). The source remains in the same direction but travelled distance increase considerably at high altitude. Right: Models for the 3 months of burning season showing a clear origin from coastal Myanmar in March & April and various sources in February (modifiied from Punsompong & Chantara, 2018).
Air pollution in Chiang Mai is essentially particulate matter produced by biomass burning
Particulate matter is made of 60% carbon under various molecular forms.
Particulate matter contains 25% of sulfates, nitrates and ammonium compounds
Particulate matter contains 10% of metal cations including 2% of potassium from fertilizers and biomass and 2% of aluminosilicates, calcium and sodium from soil dust
Heavy metals levels in particulate matter are low and not a health concern
Levels of carcinogenic hydrocarbons are at medium levels, not different from any big city.
Carbon is the main component in particulate matter. It forms 50-60 wt.% (Suriyawong et al., 2022) and can be divided into two fractions called brown and black carbon. This chemical distinction is used when assessing the source and maturation of aerosols.
Other major components are nitrogen (5-10%, Kawichai et al., 2022), oxygen and hydrogen, all combined into complex organic molecules.
Figure 9: Average chemical composition of particulate matter in Northern Thailand. 'Others' mostly include ions not present as OC-EC, nitrogen, oxygen, hydrogen, sulfur, phosphorous, not included in compounds already represented.
Among these 'brown carbon' molecules are PAH (Polycylic Aromatic Hydrocarbons), which are of particular concern due to their carcinogenic properties. Their concentrations in Chiang Mai PM pollution is <1 to 26 ng/m3 (Kawichai et al., 2020; Choomchuay et al., 2022; Chantara et al., 2009; Thepnuan & Chantara, 2020), which is concerning, but has to be put in perspective. Bangkok annual average is 36 to 55 ng/m3 and Beijing, 30 to 279 ng/m3 (Thepnuan & Chantara, 2020). PAH are mostly produced by vehicle exhaust (55%) but during strong haze episodes, the contribution from biomass burning is significant (15% from agriculture and 30% from forests) (Pongpiachan et al., 2017; Inisian et al., 2022).
Another interesting organic molecule, which is not a health concern, is levoglucosan. It is the product of cellulose combustion, an anhydrous sugar somewhat similar to caramel. Its interest lies in some chemical ratio such as potassium/levoglucosan, which allow to estimate the specific contribution of forest and agricultural residue burning in air pollution without relying on indirect means such as remote sensing and surveys (Song et al., 2022; Thepnuan et al., 2019).
Other major components in particulate matter are 13 to 15% sulfate, 4-5% ammonium, 4% nitrate and 2% potassium. Calcium makes around 1%, sodium, chlorine and magnesium around 0.5% (Saejiw et al., 2020; Thepnuan et al., 2019; Chantara et al., 2009). Correlations between potassium, chlorine, nitrate and ammonium are partly the result of fertilizers which are mostly KCl and NH4Cl (Khamkaew et al., 2016), giving some additional constraints on the contribution of agriculture to biomass burning. KNO3 and NH4SO4 provide a proxy for fresh vs. detrained smoke as it is the result of aging of aerosols through oxidation and photochemical reactions (Li et al., 2015). Sodium is a crustal component but occasionaly associated with chlorine indicating a marine source. NH4SO4 is a urban pollutant forming directly from gaseous precursors through fossil fuel combustion and is preponderant outside the burning season (Changseusubsri et al., 2021b; Janta et al., 2020; Sopajaree et al., 2011; Khamkaew et al., 2017). Due to the accumulation of SO2 and NOx in the atmosphere during the dry seasonm the pH of PM is between 4 and 7 (Chantara et al., 2009) and the first rains in April tend to be mildly acidic at 5.6±1.5 (Wiriya & Chantara, 2008).
Trace elements include heavy metals and have been studied since they can have an important health impact. Overall, all heavy elements in Chiang Mai air pollution are below WHO limits and not a significant concern for public health (Niampradit et al., 2022; Chantara et al., 2009). These are systematically below Bangkok air pollution, which is loaded with particle emission from intense traffic, factories and various urban activities. The only exception in Northern Thailand are calcium, aluminium, silicon, iron and strontium, which are typically associated with ground dust (Kayee et al., 2020). Zinc, cobalt, nickel, vanadium, cadmium and lead are linked with traffic through tyre wear, brake linings, diesel burning (Elhadi et al., 2017; Kayee et al., 2020). Tin is possibly associated with garbage burning (Zhang et al., 2013; Christian et al., 2010), etc.
Particulate matter size ranges from coarse (0.01-0.02mm) to nanoparticulates (<0.00003 mm)
Fraction around 0.004mm is the most abundant but PM2.5 (<0.0025mm) is reported as it is the main health concern
Increase in PM is marked in mid-January and stops in mid-April, defining the burning season
Daily variations show PM values up to twice higher between 5 and 10 am compared to the rest of the day and increase again in the evening
Weekly variations show peak PM concentration in March but is highly dependent on weather conditions and human activities.
Annual variations are linked to climate patterns. La Nina atmospheric setup lead to more rain and weak winds producing low intensity burning season.
Spatial variations at a regional scale are due to topography, meteorology and fire sources
Spatial variations at a local scale are due to a large number of factors.
The particulate matter that forms most of the air pollution in Chiang Mai is separated in several fractions for analytical, monitoring and public health purposes.
Very coarse particulates: Largest fraction, bigger than 10 microns and are often ignored as they represent a relatively small fraction of the pollution with no significant health concern.
Coarse particulates: PM10 fraction includes all particulates that have a diameter of 10 microns or less, which is on average 1/10th of the thickness of a hair. There are historically the first fraction monitored since detection was easier. These are a concern since particulates smaller than 0.005 mm can penetrate into lungs.
Fine particulates: PM2.5 is the most common fraction that is reported in public announcement and monitoring devices as it represents the most significant part of air pollution. These are particulates smaller than 0.0025 mm.
Very fine particulates: PM1.0 is sometimes used to do a finer characterization of PM size distribution.
Ultrafine particulates: PM0.3 is used for technical purposes due to a transition physical behaviour. There are important in filtering quality assessment.
Nanoparticulates: PM0.1 is the smallest non-gaseous fraction of air pollution. There are particulates smaller than 0.0001 mm. These are difficult to collect and analyse and data on that fraction is not as abundant as larger particulates.
Gaseous components are gases which include ozone (O3), sulfure dioxide (SO2), nitrogen oxides (NOx), CO2 and carbon monoxide and cannot be mechanically filtered.
There are many scientific studies on the type of particulate matter produced during wildfire. Depending on the type of fire, the intensity of the burning stage (ignition, flaming, smoldering), the fuel conditions (live, dead, wet), its distribution (dense/light; flat/sloped) and type (grass, leaves, branches), humidity, wind, O2, CO2 and CO levels, etc., the particulate size distribution and composition will change.
Wind & humidity in particular, also play a role in transforming fresh smoke (from local sources) into detrained smoke, which is fully oxidised and as seen considerable vertical and horizontal transport and a secondary growth through in-cloud coagulation, increasing particles size by 20% (Guyon et al., 2005). Since the particulate matter in Chiang Mai is mostly transboundary smoke it fit into that detrained smoke category. The fresh smoke is produced by low intensity surface fires (Chernkhunthod & Hioki, 2020) of dried grass, dried leaves and some shrubs. In 2020, the Doi Suthep/Pui fire was fueled by 60 to 70% of litter (dried leaves).
Figure X: Distribution of particle size in fires and air pollution. Detrained smoke sees atmospheric coagulation of smaller particulates and deposition of the largest particulates.
The distribution of PM fractions is 40% PM2.5 - 60% PM10 outside the burning seaso and climb to 48% PM2.5 - 52% PM10 in haze days (Othmann et al., 2022). Since the AQI is based on the most harmful pollutant as a function of its concentration, despite having higher content in PM10, PM2.5 is the variable that dictate the AQIvalue most of time.
Within the burning season, the correlation between PM10 and PM2.5 is very strong, and also with gaseous pollutants except carbon monoxide (Mueller et al., 2020). Long distance transport homogenise the air pollution and variation in ratio are minimal. An exception occur when forest fires are in close proximity (Samoen in 2019; Doi Suthep/Pui in 2020). Coarser particles do not have the time to settle and PM10 increase at a higher rate than PM2.5.
Ultrafine particulates (PM0.1) also increase during the burning season, but at a rate of 33% of PM2.5, indicating among other things that PM0.1 is essentially due to urban sources and minorly related to biomass burning (Phairuang et al., 2022c). It also means that two-thirds of the source of PM0.1 remains in the rainy season but it does not accumulate in the lower atmosphere.
Figure X: Screenshot of an AQI calculation in Samoeng in 2019. PM10 is the dominant pollutant and give an AQI of 989 (PM2.5 only gives 796). Both values are outside the range (>500) of American, Chinese and Thai index scale.
Spatial & Temporal characterization
The air pollution in Chiang Mai is characterised seasonally by a dramatic increase in mid-January, marking the beginning of the burning season, and its end with the regular rains in mid-April (Kayee et al., 2020; Phairuang et al., 2017; Thepnuan et al., 2019). Variations occurs on the starting and end date by a few weeks.
Within the 3-4 months of burning season, the level of particulate matter has trends and variations over several hours or days, as sources & meteorological conditions changes. It also varies between different locations for a series of reasons.
Daily variations are systematic changes in PM concentrations throughout the day due to temperature and atmospheric characteristics. The highest PM values occur in the early morning from 5 to 10am, drop during the day to a low at 3pm, and rise again between 7 and 10pm (Othmann et al., 2022; Anusasananan et al., 2021; Janta et al., 2020; Punsompong & Chantara, 2018).
Figure X: Hourly variation of temperature (green) and PM2.5 (red). Air pollution peaks between 7 and 10am when near-ground air is colder than higher air heated up by the sun (yellow represents sunrise/sunset) and reaches its lowest at 3pm, coinciding with the highest ground temperature and the weakest temperature inversion. Modified from Anusasananan et al., 2021; Janta et al., 2020). The curve can vary on location (mountain shadow and slope winds) and between january and April as sunrise will occur 1h earlier and atmospheric propreties will change.
Weekly variations are associated with fire sources and meteorology. Peak particulate matter are controlled by moderate temperatures, low humidity, lack of wind and strong thermal inversion (Chunram et al., 2007).
Monthly variations are linked to climatic trends. Increasing temperatures and prolonged lack of rainfall increase the fire risk, increasing the number of fire hotspots from January to the end of March. In addition, increasing insolation of the atmosphere in the early morning compared to the ground creates a stronger thermal inversion layer over that period to almost disappear in April. In February, the Asian Winter Monsoon blow additional winds from the south-west, charging the air with particulate matter from Myanmar and provinces in between.
Significant annual variations are mostly due to the El Nino Southern Oscillation. La Nina (2011, 2022) has a strong effect on precipitation and airflow in East Asia, a direct effect on air quality (Kraisitnikul et al., 2022) and an indirect effect by limiting the occurence of forest fires and reducing the duration of the haze episode (Gao & Li, 2015). Dominany winds during La Nina are scattered while El Nino and Neutral ENSO establish a NW-SE dominant wind at all altitudes bringing transborder pollution.
Outside the dry season, local variations can be extreme and are often the result of particularly polluting local source near the monitor or occasionally a defective detector.
Less extreme variations (<10x) are better explained by a local source (burning, house fire, industry) or a specific feature (topography, winds, etc.) than can cause a short-lasting anomaly. Particulate matter size distribution and chemical composition can also change with this type of fluctuation.
Minor variations are the result of natural variability in air pollution, not so different from clouds in the sky, but at ground level. Detectors can also be the cause of some inconsistencies as most data available from detector networks are non-reviewed, uncalibrated detectors from private contributors and are subject to time drift and increasing imprecision for very high pollution concentration. Overall however, the precision of a measurement seems to be around 10-20% and considering the accuracy required, this imprecision has no impact on the everyday interpretation of results and health decisions that would follow (i.e. when an AQI is stated as 'hazardous' (>300), it does not really matter if the true AQI value is 380 or 420.
A definite source of misunderstnading lies in the use of different scales and measurement characteristics. The scientific approach of air pollution exclusively use mass concentration per volume, per time but AQI is a non-linear index based on health factors with small differences between scale. Many applications rely on the US AQI scale, which is, except for the details, identical to the Chinese AQICN scale and many other national scales. Other scales used in Europe, UK, Australia, India, etc. can have quite different indices but health warnings for similar pollution leves remain essentially the same. The Thai scale (CMUCCDC) is in appearance similar to the US & Chinese scale, but at low level, is up to 400% different and still 15-25% different at high pollution level, it is poorly defined and unclear how this scale is used. In 2023, Thailand has moved its warning level from 50 to 37.5 mcg/m3 (AQI 135 to AQI100 on US scale; AQI100 to AQI50 on Thai scale), regardless of the scale used.
Some confusion also lies in the time integraton of some available data. Most public and private detectors now provide instantaneous or hourly average but it is not unusual in older dataset or misinterpreted data records to use unspecified daily averages.
Atmospheric factors play a major role in trapping pollutants close to the ground
The layer of the atmosphere where vertical movement is possible (Mixing Layer) considerably shrinks during the dry season.
Seasonal conditions keep the air near the ground considerably colder than the atmosphere one kilometer above creating a Thermal Inversion Layer
The reduction of the turbulent zone and thermal inversion creates a dense static air at ground level, preventing pollutant to escape.
Horizontal movements are weak, but sufficient to bring pollutants from further away but not escaping valleys and basins where they accumulate.
Distant forest fires alon would not be able to create the high level of air pollution seen in Chiang Mai if it wasn't for a series of other factors to be considered to have a full picture of the conditions necessary for the haze episode to occur.
Particulate matter levels are strongly correlated with the number of fires (thermselves correlated with temperature and humidity) (Phairuang et al., 2017). However, it appears that medium ground temperature, low humidity, low wind speed, stable atmosphere, temperature inversion and topography are all interconnected to produced meteorological conditions adequate for the trapping of aerosols (Sirithian & Thanatrakolsri, 2022 ; Nakapana & Choopuna, 2018; Punsompong & Chantara, 2018; Ruttanawongchai et al., 2018; Vongruang & Pimonsree, 2020).
Figure X: Temperature profile with altitude in February, March and April. Horizontal lines are the upper limit of Mixing Layer. The strongest thermal inversion occur in March, with the lowest mixing layer. In April, the mixing layer rise considerably and the thermal inversion almost disappear. Purple line is a relative mass distribution of particulate matter in the atmopshere (Solanki et al., 2019; Pani et al., 2016; Kim Oanh & Leelasukultum, 2011).
During January, the Asian Winter Monsoon develops into two main aeolian channels, one a t ~500m high, originating from the North-East, continental, diurnal and passing through northern Laos, and another one at 3000m, North-West, passing through Myanmar (Amnuaylojaroen et al., 2020). These seasonal winds are responsible for bringing aerosols from SE Myanmar in the western part of Northern Thailand and from Northern Laos into the eastern part of Northern Thailand. As the burning season progresses, South-East winds become a significant carrier of pollution at all altitudes.
Ground winds are relatively weak and barely perceptible with a maximum speed of 2 m/s (Sirithian & Thanatrakolsri, 2022; Janta et al., 2020; Punsompong & Chantara, 2018). The layer where turbulent atmospheric flow occur is called the mixing layer. For most of the year, its limit lies at 3.5km high, but it starts to shrink in December to reach 1.5km high in March (Khamkaew et al., 2016). With a reduced mixing layer, so is the wind activity in that zone creating a homogenous high pressure static layer.
As pollution is blocking some part of the Sun's heat and with the weak winter sun, ground heating is inefficient in the morning and ground temperature can be as 10ºC lower than air temperature at 1000m high (Choommanivong et al., 2019). Lower temperature means higher air density and anything in that zone will tend to stay close to the ground with no possibility to go up.
To these factors, the local topography has to be considered, with valleys and basins completely into that mixing layer, limiting winds and helping thermal inversion, aerosols cannot be dispersed either vertically or horizontally and keep being accumulated by weak surface winds slowly blowing smoke from further away (Amnuaylojaroen, 2009). This whole atmospheric dynamic explains why particulate matter gets accumulated in basins from January to April, and it also explains daily variations and some spatial heterogeneities of PM levels.
Figure X: Atmospheric profile across Chiang Mai province in the dry season show a low mixing layer down to 1.5km in basins, and a strong thermal inversion a few 100s meters above the ground, trapping pollutant near the surface. In the rainy season, the mixing layer is at 3.5km with no thermal inversion and pollutant are diluted and blown over the whole lower atmosphere thickness.
Visibility is lowered with particulate matter air pollution and goes from 30km (AQI75) to 10km (AQI200) .
For high AQI(>200), effects are visible within the city
Visibility is lowered with high humidity and goes from 30 km (~50% RH) to 10 km (90% RH)
Visibility also very dependent on the time of the day and a few other factors
Doi Suthep can be used for a quick estimation of pollution levels but only valid during the dry season
Since Doi Suthep is visible from most of part of the city when the weather allows it, it often gives an idea how bad the pollution is. Particulate matter absorb and scatter light, giving a strong correlation between AQI and Doi Suthep visibility.
Atmospheric visibility is dependent on a few parameters, the most important are particulate matter content and humidity.
Figure X: View of Doi Suthep from Dr Artima Medical Clinic in Mae Hia (7.5km from ridge line). a. in good atmospheric conditions (Low AQI, medium humidity) b. in mildly polluted conditions (AQI ~150) and medium humidity. c. In mildly polluted conditons (AQI ~140) but back lit from the sun. d. In midly polluted conditions (AQI ~150) with very high humidity (>95%)
Particulate matter absorb, diffuse and scatter sunlight (Seinfeld & Pandis, 1998). The strongest effect is between 0.1 and 1 micron but since PM size distribution does not vary much, the correlation between visibility and PM content is constant. When the AQI<50, the visibility is mostly unaffected except for long distances. Between AQI75 and AQI200, the visibility decrease from 30 to 10 km, progressively hiding topographical features on the horizon. For AQI above 200, the visibility steadily decrease and is eventually observable within the city where light poles, bridges, buildings start to disappear in the distance when driving on straight highways.
Relative atmospheric humidity starts to have a very strong effect above 80%. Most of the burning season has values between 40 and 60% so this factor is only minor between January and April, but has to be considered outside the dry season.
Outside the burning season, relative humidity can frequently stay above 80% and has a very strong effect on how far you can see. A grayish (not brownish) veil can appear and turn into a dense fog when meteorological conditions are right. In terms of Doi Suthep visibility, 95% relative humidity gives the same effect than an AQI of 300 or more. So, outside the burning season, it's not because you don't see Doi Suthep that pollution levels are high.
Contrast is also very important. Doi Suthep is always visible at dusk. That is because the sun is right behind it creating a very strong contrast, and also because air pollution is a bit lower than in the morning. In the morning, looking at Doi Suthep, the sunlight is scattering and reflecting on dust particulates preventing any contrast between the ridgeline and the sky. The effect is similar to car headlights in a heavy fog. It is hard to see an object in front of you but if it's a car coming at you with headlights, you will see that object more easily.
Altitude of observation also has a minor effect as air pollution can be less dense in some places.
Other pollutants have an effect but their concentrations are mild so it is mostly insignificant, except with high humidity where interaction with nitrates and sulfates change scattering coefficients.
Figure X: Approximate visibility tresholds of Doi Suthep ridge lines (grey) as a function of AQI (PM2.5). This map is only suitable between 10am and 3pm for relative humidity below 60%.
Air filtering is the most effective preventive measure against health effects of particulate matter pollution.
For indoor air purifiers, HEPA filters are the most adapted device to handle Chiang Mai air pollution
For masks, it's a balance between optimal filtering and negative effects from masks & respirators. N95 or equivalent masks is the recommended choice.
Higher efficiency masks are often not worth the negative effects or unsuitable for Chiang Mai air pollution.
Many masks (non N95) are fine but can lack a good fit and proper seal.
Many masks (non N95) are fine but the lack of manufacturing standard means that some brands and designs are very inefficient at filtering particulate matter.
Alternative filtering/purifying devices have little to no use in Chiang Mai
Filters, as home devices or masks, are system designed to reduce the particulate matter (and eventually other gaseous pollutants) from the air. The efficacy of these filteres is often very high for a wide range of particle range. Air filters are not just simple sieves, as it was often implied during the COVID pandemic. There are several physical processes at work in an air filter and the sieving effect only applies to the largest particulates.
Based on these physical processes, the filtering capability of a membrane is measured at its lowest efficiency, which is at 300nm (that's 0.0003 mm). For larger particle size, many filters are close to 100% efficiency, and the same applies to some extent for smaller particulates.
Five mechanisms exist in the filtering process:
- Straining: Coarse particulates (often 2-10 microns) are too large to pass through the mesh size of the filter.
- Impaction: Fine particulates (0.5-2 microns) hit the material of the filter and bounce or stop in the process.
- Interception: Ultrafine particulates (0.1-0.5 microns) flow through a random mesh, deviate and eventually slow down and never pass through the thickness of the filter.
-Diffusion: Nanoparticulates (0.05-0.3 microns) are subject to a physical property alled Brownian motion that will eventually block them.
Adsorption: Half way between a physical and chemical process, molecules and nanoparticulates are stuck in a porous material and eventually stick to the surface of that material.
Figure X: Distribution of different pollutions based on the particulate matter size. Semi-quantitative distribution of Chiang Mai air pollution and efficacy range of different filter types
HEPA filters: it stands for High-Efficiency Particulate Air filter, made of a random mesh of polypropylene or borosilicate glass fibers. These are very effective filters with different ratings but for Chiang Mai pollution, most filters are good to very good with a filtering efficiency at 0.3 microns of 99.97%. Its capacity in terms of liters/m3 is dependent on the filter size and fan power but for most application, there are very efficient.
ULPA filters: it stands for Ultra-Low Particulate Air and is the level above HEPA. They have a similar design than HEPA filters but with increased porosity. They are designed to filter 99.999% of particles at 0.12 microns and are particularly effective in that low efficiency trap that other filters have. The reason we do not see them often is that with increased filtering efficieny comes some disadvantages. The airflow is lower so large volume cannot be cleaned as quickly, they cost a bit more and need replacement more often. Their use is mostly in specialised applications for clean rooms in medical and chemical laboratories, microelectronics, etc.
Carbon filters: It's a different type of filter made of activated carbon. The very high surface area of this porous form of carbon can capture ultrafines and gaseous contaminants by adsorption on the surface of this carbon filter. Silica gel and zeolites are sometimes used for similar purposes. Two types exist (granular activated carbon & activated carbon fibers) for different purposes. They are not suitable for fine and coarse particulate matter as they will saturate quickly and do not work when humidity is too high.
Air ionizers: A device that creates a high voltage to ionise molecules in the air. Through electrostatic attraction, ionised molecules can be removed from the air. The issue with ionised air purifiers is that there is no standard applicable to these devices. Some might be very efficient and safe to use; other will not be so efficient and can eventually be harmful as high electric current can produce compounds such as ozone, nitrogen oxides, formaldehydes, etc. whic are all toxic gases. Regarding particulate matter, there are not well-suited for anything but the removal of the finest particulates (Cheng Qian, 2021).
Ultraviolet purifiers: It essentially work by emitting a strong UV-C source in the pumped air and disinfect the air from bacteria and viruses by damaging their DNA. It is a true process in theory but in practice, the UV exposure in pumped air is 10 to 100x too short to significantly kill microbes and it does nothing to particulate matter pollution. Again, due to the lack of standard for these type of purifiers, UV-C are ionising radiation that can produce large amount of ozone (FDA, 2020).
A good quality mask with high filtering efficiency is strongly recommended when breathing a dangerously high PM-based AQI. Amon standardised masks, a whole range of good to extremely good filtering is available. Unless you have specific requirements, they are no medical recommendation (in fact, more drawbacks) to have a mask above a N95 or equivalent rating.
Outflow valve on masks are good for three reasons. It does not get soggy due to breath humidity, it prevents the accumulation of CO2 in the mask and for sensitive indivduals, it reduces the risk of rash and bacterial infection. It also minimally improves the efficacy of the respirator by maintaining negative or neutral pressure.
Masks are not completely beneficial. Some individuals are psychologically affected wearing maks with feelings of discomfort, anxiety and claustrophobia and the increase in humidity, temperature and lower oxygen intake affect all mask-wearers (Johnson et al., 2016). These effects have minor repercussions on blood glucose level and muscular abilities, cardiovascular efficiency and mental skills.
People with respiratory and/or cardio-vascular issues should consult a doctor before wearing a N95 or above respiratory mask. It is possible that such mask might cause more issues than breathing polluted air.
Children and people with facial hair should wear a well-fitted mask. Leaks due to bear or improper size will make the mask not filtering efficiently the polluted air while making breathing more difficult.
Commercial masks (all materials)
A lot of masks are available for sale and do not follow any filtration standard. There are made of all kinds of materials and have different filtering abilities. While a basic tightly woven cloth already reduce particulate matter by 30-40%, commercially available non-standard masks range from very bad (less than 20% for anything below 1 micron) to very good (almost equivalent to a N99 in some cases). The main issue with these masks is reliability. Since there is no standard enforce, you can only obtain information from surveys and trust reliable brands. Keep in mind that even standard rated mask from some companies can also fail the filtering efficiency they are supposed to achieve.
Surgical masks (EN14683, ASTMF2100, YY0469)
These masks are traditionally used in the medical environement. They are made of a random mesh of polypropylene fibers like N95 respirators. Surprisingly, and contrary to the popular opinion, the material of surgical mask is almost as effective as N95 in filtering particulate matter. However, they are not negative pressure masks and allow airflow on the side, limiting the effective efficiency.
A common medical practice is to tape the side of surgical mask to seal off opening during infectious situation. In such cases, the filtering efficiency of a surgical mask is relatively high, and when following standards, the filtering power is above 95 to 98% for 3 microns and 30 to 98% for 0.1 micron. Single-use face masks are often, but not always, equivalent to surgical maks as their standards are not as restrictive and their filtering efficiency might be lower.
Figure X: Electron microscope imaging of various mask at different magnification. (a) Cotton flannel mask with disorganised layer (b) Woven polyester mesh with very organised layer. Studies show that randomness helps filtering particles. (c) is a cross section through the filtering layer of a N95 mask made of melt blown polypropylene fibers (NIST, 2021)
There is a whole range of respirators for different purposes. It goes from the most basic air-purifying mask to air-supplied systems. This latter group is not covered here as it is used for very specific application in extremely harmful environments.
N95 (also FFP2, KN95, KF94, P2, DS2, PFF2 with almost similar requirements) is a respirator that filter 95% of particulates at 0.3 micron. It is made of a random mesh of polypropylene fibers. For the purpose of filtering air in Chiang Mai, N95 respirators can be reused until damaged, soiled or causing increased breathing resistance. It is only in medium biosafety settings that N95 should be systematically discarded after use (NIOSH, 2018).
N99 (also FFP3) is a respirator that filter 99% of particulates at 0.3 micron.
N100 is a respirator that filter 99.97% of particulates at 0.3 micron.
R-rating: Similar to N-rated mask but are oil-resistant. They have a short-life and have to be disposed after use. These are for technical work in oil-ladden atmosphere and have no use in Chiang Mai pollution.
P-rating: Similar to R-rated respirators but oil-proof, giving them a long lifetime. Again, no use in Chiang Mai pollution.
HE: High Efficiency respirators are similar to P100 but require a powered source. It is only for specific applications and persons medically disqualified from negative-pressure respirators.
Non-mechanical respirators: These respirators use chemical cartridges such as activated carbon and resins to filter toxic chemical from the air. They are not suitable to filter particulate matter but effective to remove gaseous pollution and nanoparticulates. They are o no particular use in Chiang Mai pollution.
Figure X: Effective ideal filtration (or measured range) of various masks for Chiang Mai air pollution PM distribution. Values are given in PM2.5 AQI for input air (vertical axis) and filtered air (horizontal axis. The true filtering potential is higher since calculation are made for 0.3 microns particulates (Langrish et al., 2009)
This section covers techniques sought to reduce air pollution in outdoor situation without direct action on PM fire sources. It includes water canon, giant air filters, foggers, cloud seeding, etc. These techniques have some success in some specific conditions but their application in most situation is useless or can make it worse.
Every year, the local government promote the use of large water canons spraying droplets in the air in various suburbs and around Thapae Gate. This technique is commonly used and effective in mine sites, dust-producing factories, etc. where the dust source is localized but studies of open water-spraying in urban environment show no effect (Asif et al., 2022).
Giant air filters
It comes up regularly in advertised techniques by the local government in the last few years. In theory, pushing air through an HEPA filter will work but the scale of the task makes it completely impractical. As a comparision, a A380 jet engine working at full power would take two weeks to filter one square kilometer assuming no polluted air is coming in. It would take thousands of these huge jet engine to have an effect over the Chiang Mai metropolitan area, excluding the pollution produced by running such devices.
Sometimes presented as protection against PM pollution in addition to the cooling effect these system have on outdoor areas, it sprays a mist of small water droplets and would create a wet barrier against particulate matter. Results show that this technique might actually make things worse by dissolving and then reprecipitating soluble particulate matter (Knight et al., 2021)
Yearly suggestion in Thailand that has been part of the Royal Rainmaking project since the late 50s. The technique use dry ice crystals (or other chemicals) spread in clouds, initiating a precipitation that lead to rainfall. It has been used in Thailand to help farmers in their struggle against drought.
Cloud seeding requires very specific meteorological conditions, most importantly a high relative humidity over the whole lower atmosphere. Even then, there is little scientific evidence that the techniques and all its derivatives actually work.
Elsewhere, other attempts have been made such as giant purification towers or activated urban surfaces to trap gas. All these techniques have unclear results.
FORECAST, FUTURE TRENDS & POLICIES
Air pollution weather forecast is unreliable. In additional to meteorological factors, there is a large number of human factors inside and outside Thailand to be considered. Advances in complex numerical modelling provide a reliable forecast 24h in advance and could give some preventive warning(Kim Oanh & Leelasakultum, 2011; Intarapak & Supapakorn, 2021; Kheatkanya; Yan & Wu, 2016; Han et al., 2022; Mitmark & Jinsart, 2017; Witwatwattana, 2021; Thongrod et al., 2022; Punsompong & Chantara, 2018). Climate statistics over the past few decades correlated with air pollution also have resulted in ENSO predictions with low-intensity burning season when La Nina is active.
The seasonal pollution in Northern Thailand has been there for a long time and been mentioned in reports and diaries since the beginning of the last century (Eisenhofer, 1909; Kerr, 1910; Weiler, 1913), with systematic recording since 1972 (Suwanprasit et al., 2018). It has become progressively worse in the late 20th century and the increasing urbanization of Chiang Mai, with a large amount of expansion, construction, traffic and urban activities could have made the air pollution worse (Kasem & Thapa, 2012; Oanh et al., 2018). This worsening trend has eventually piqued academic research interest and proper scientific investigation of the phenomenon has been ongoing for the past 15 years. For the past two decades, the tendency is towards lower monthly averages although peaks of very high level of pollution still occur.
Trends over the past decades in other regions of Thailand suggest that the growing demand for sugarcane to supply the production of renewable energy such as bioethanol (Phairuang, 2021) will eventually have an effect on the northern provinces. Changes in future agricultural policies could also have an effect. In 2012, the rice pledging scheme has produced a 64% increase in rice field burning within a long term trend of 8.7% yearly reduction (PCD, 2012; Junpen et al., 2018).
Global and regional climate also have a role to play in possible future trends. Particulate matter has a direct role on climate change as it changes the ability of the atmosphere to absorb or reflect sun rays. Models show that in the worse climate change scenario, the dry season PM concentration would increase by 1 to 10 mcg/m3 and make 400 mcg/m3 peaks more likely. However, it would lower the pollution during the rainy season by 10 to 20 mcg/m3 (Amnuaylojaroen et al., 2022a, 2022b)
Actions & Policies
Actions on air pollution goes back to 1977 in Thailand. It started to be discussed internationally in ASEAN meetings in 1990 and 1992 and a cooperation plan for transboundary haze was impletement in 1997 (Tyiapairat, 2012).
In March 2007, the air quality in Chiang Mai had deteriorated enough to put pressure on various level of the governement to force more direct actions such as rewards to report arsonists, fire bans, etc. An upgrade in 2010 for urgent solving of haze in Northern Thailand installed long term strategies domestically and internationally. However, all these actions were highly regulatory policies of command-and-control where forest fires were seen as an administrative task of control and penalization of perpetrators. These centralized policies had no effect at a local scale since low level public offices were left to guess the implementation of these regulations (Pasukphun, 2018)
The ground reality is that all policies prior to 2012 did not take into account the heterogeneity and complexity of local conditions and the drivers of burning practices. The top down approach of central governement policies, cascading down to the province, district, sub-district and villages just never happened (Tiyapairat & Sajor, 2012).
Eventually, it was understood that policying the burning of biomass will not happen without grassroot participation at village level. Since lower administrative levels have to deal with poverty, drought and flooding, which have direct and immediate impact on their constituency and haze was clearly not a priority and funds were allocated at higer level specifically for this task (Tiyapairat, 2012).
Agricultural burning, while a minor issue in Northern Thailand, is the main local PM source in other regions of Thailand (Isaan, Central). Attempts to change behaviours through discouragement, legal consequences, and more importantly, providing alternatives with potential financial gains have met some success (Junpen et al., 2018). Plowing fields, producing & using organic fertilizers, alternative energy, baling for animal feed are all techniques used to limit agricultural burning (Suriyawong et al., 2022).
Forest burning is more difficult to tackle as it requires sensibilization and education of rural populations (Tiyapairat, 2012; Junpen et al., 2018) with financial loss. In recent years, more and more low administrative level actions are seen such as fire watch systems, patrols, checkpoints, firebreaks, etc. However, it has become clear that forest burning cannot be banned without an appropriate management system. Lead litter is abundant in Northern Thailand and the replacement time is only a few years (Chernkhunthod & Hioki, 2020) and the same applies to grassland. Therefore, any semi-permanent burning ban will have to be accompanied with prescribed burning with proper incineration management at the right time and place.
Ultimately however, since more than half of particulate matter originate from Northern Laos and South-Eastern Myanmar, a more regional solution will have to found within ASEAN. Despite an agreement since 2002 to reduce haze episode by imposing total bans on open-burning (Pasukphun, 2018), ASEAN accords have no liability or compensation to hold responsibility between states as it is seen as a part of the non-intervention aspect of the ASEAN charter.
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