Special Issue on 2022 Asian Aerosol Conference (AAC 2022) (V)

Karuna Jainontee1, Prapat Pongkiatkul2, Ying-Lin Wang This email address is being protected from spambots. You need JavaScript enabled to view it.3,4, Roy J.F. Weng5, Yi-Ting Lu5, Ting-Shiuan Wang6, Wang-Kun Chen7 

1 Faculty of Science and Agricultural Technology, Rajamangala University of Technology Lanna, Chiang Rai, Thailand
2 Department of Environmental Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
3 School of Public Health, Taipei Medical University, Taipei 11031, Taiwan
4 Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10617, Taiwan
5 Cameo Inc., Taipei 10066, Taiwan
6 Department of Resources Engineering, National Cheng Kung University, Tainan 701, Taiwan
7 Southeast Bangkok College, Krung Thep Mana Nakhon, Thailand

Received: January 12, 2023
Revised: March 19, 2023
Accepted: March 28, 2023

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

Download Citation: ||https://doi.org/10.4209/aaqr.220432  

Cite this article:

Jainontee, K., Pongkiatkul, P., Wang, Y.L., Weng, R.J.F., Lu, Y.T., Wang, T.S., Chen, W.K. (2023). Strategy Design of PM2.5 Controlling for Northern Thailand. Aerosol Air Qual. Res. 23, 220432. https://doi.org/10.4209/aaqr.220432


  • PM2.5 episodes were identified in dry season of Northern Thailand during 2019–2021.
  • Source of PM2.5 was mainly from nearby mountain areas with a shorter in distance.
  • Control strategy may potentially reduce open biomass burning in Northern Thailand.


The emission of fine particulate matter (PM2.5) in dry season from the open biomass burning has caused a long-term negative impact on residents’ health in Northern Thailand. This study takes Chiang Mai and Chiang Rai provinces in Northern Thailand as the study areas to identify pollution episodes, analyze PM2.5 source trajectories, and finally propose pollution control strategies accordingly. PM2.5 levels during 2019–2021 of three representative air pollution monitoring stations (i.e., Chaing Mai-35T, Chiang Rai-57T, and Mae Sai-73T) in these two provinces were collected and analyzed. The Air Quality Index (AQI) defined by PM2.5 level higher than 91 µg m–3 causing serious adverse health effects was adopted to define periods having pollution levels, and the days of the air pollution episodes were identified. Based on these episodes, we applied the backward trajectory model to identify the sources of pollutants. Results showed that PM2.5 levels were significantly higher between February to April compared with other months during 2019–2021 at all three monitoring stations, indicating the severity of PM2.5 episode during the dry season. The backward trajectory demonstrated that air mass transported through the Northern Thailand or nearby mountain areas (categorized as long- or short-transport-distance) contributed up to 21.6% and 75.9% of the total air mass, respectively. Although residents in these mountainous areas are accustomed to the biomass burning, we suggested that there should be urgent needs for the improvement of the long-term air quality in these two provinces. Therefore, this study proposes some control strategies including improvement of prevention knowledge, increase of the risk perception, cultivation of the protection behavior, and intensification of the social influence. In addition to reducing biomass burning pollution, this improvement plan also has a co-benefit of achieving resources recycling concomitantly. Providing effective management strategies may reduce the adverse health effects to Thai residents.

Keywords: Northern Thailand, Fine particulate matter, Pollution episode, HYSPLIT model, Control strategy


Thailand is a country founded on tourism. The annual tourism revenue accounts for more than 20% of its annual GDP (Statistica, 2022). Besides Phuket in the south and Pattaya in the middle, Northern Thailand is the most important tourist areas. The northern region includes places such as Chiang Rai, Chiang Mai, and Mae Sai. Because of the pleasant climate, highland-mountains geography, special Lanna cultures, and fewer industry coupled with the attractive mountain scenery in Northern Thailand, the unique agrotourism has long been the favorite for tourists who come here every year (Chaiphan and Patterson, 2016).

However, in March and April every year (dry season), the air pollution caused by the biomass burning from the northern mountains has become a lingering nightmare for the sightseeing of residents living in Northern Thailand at night due to the dust particles such as particulate matter (PM10) and fine particulate matter (PM2.5) (Junpen et al., 2018; Pasukphun, 2018; Punsompong and Chantara, 2018). PM10 denote particles with an aerodynamic diameter below 10 µm, whereas PM2.5 are finer particles with an aerodynamic diameter of less than 2.5 µm. These particles are the results of open burning, where farmers in Northern Thailand burn the indica after the crops being harvested in during the dry season. The agricultural wastes are turned into ashes as fertilizer. For years, this method of disposing the agricultural wastes has been the habit for farmers here (Visvanathan and Chiemchaisri, 2008; Silalertruksa and Gheewala, 2013), due to the convenience of solving agricultural residues on the spot without any cost. Although there have been many literatures discussing the pollution caused by biomass burning in this area (Li et al., 2022), these problems still have not been seriously dealt with.

Biomass burning is also the main source of air pollution in the Indo-China Peninsula (Pani et al., 2016, 2019), originating mainly from Myanmar, Northern Thailand, Laos, Northern Vietnam, and so on (ChooChuay et al., 2022). Many scholars have investigated the transnational pollution transport in Northern Thailand, Myanmar, Laos, Vietnam and other places. The ‘‘Seven Seas Project’’ chaired by Lin's co-organized scholars from many countries have used satellite observation to study the area to understand the phenomenon of transnational transmission of biomass burning (Sayer et al., 2016). Their research pointed out that the transport of biomass burning pollutants in this area is far from the mountainous areas of Bangladesh, and can span the mountainous areas of Northern Thailand, Southern China, Northern Vietnam, the South China Sea, and to Taiwan and the East China Sea (Lin et al., 2013, 2014).

PM2.5 is the most important air pollutant which can cause adverse health impact to human. It can enter the human body via the respiratory tract and lead to adverse pulmonary and cardiovascular effects, birth outcomes, and so on (Feng et al., 2016; Yang et al., 2022). In many countries, PM2.5 mainly comes from combustion sources such as industries and vehicle emissions (Khodeir et al., 2012; Pui et al., 2014; Ryou et al., 2018). However, the sources of high concentration of PM2.5 found in Northern Thailand every dry season of the year were different from those in other countries. A number of studies revealed that most of the PM2.5 in Northern Thailand are the results of open burning including forest fires and burning of agro-residues (Chantara et al., 2012; Phairuang et al., 2019; Xing et al., 2020). Phairuang et al. (2017) also investigated the influence of agriculture activity, forest fire, and agro-industries on air quality for the provinces of the upper northern, lower northern, and northeast in Thailand. The backward trajectory analysis of the air mass arriving at the Pollution Control Department (PCD) station was calculated to understand this influence. Results showed that garbage burning in the rural area, crop residue burning, and forest fires were the major sources of biomass burning emission in Northern Thailand (Phairuang et al., 2017). Langmann et al. (2009) and Yin et al. (2019) also reported that particles emitted from vegetation fires would cause profound impacts on air quality than those from other regional emission sources. However, Yadav et al. (2017) pointed out that transport of pollutants from other countries could be another major factor causing the pollution in Northern Thailand.

Surrounded by high mountains and coincidentally characterized by specific meteorological conditions (e.g., calming winds and temperature inversion) the Northern Thailand is prone to the stagnation of the air mass and the accumulation of air pollutants, which make it the most impacted area in Thailand during the dry season (Pani et al., 2018). Besides, the transboundary of PM2.5 from different borders, Myanmar, Laos, and India, are nonnegligible (Khamkaew et al., 2016; Amnuaylojaroen et al., 2020). The government of Thailand implemented a “zero-burning” plan since 2013, aiming to control the open burning in the nine provinces of Northern Thailand during dry season. However, although the open burning activities and the PM2.5 could be reduced from March to April, this policy prolonged the smoke haze situation from two months to three months (from March to May) (Wen et al., 2020; Yabueng et al., 2020). Additionally, farmers still need to clear out the land field by open burning after the zero-burning time range limit, so the zero-burning can improve situation of high smog but not a sustainable way (Adeleke et al., 2017). Therefore, the episode periods of hazardous PM2.5 and control methods for PM2.5 in Northern Thailand are still worthy of our attention in this study.

Chiang Rai, Chiang Mai, and Mae Sai in Northern Thailand were the target areas of this study. Monthly civil registration demographics from Office of Registration Administration (ORA) in the Department of Provincial Administration (DPA) reported that the population density of Muang Chiang Mai, Muang Chiang Rai, and Mae Sai District in 2021 was 93, 90, and 250 people km2, respectively (DPA, 2021; MOI, 2020a, 2020b). The air pollution monitoring stations in Mae Sai and dominant districts of Muang Chiang Rai and Muang Chiang Mai represent the border community and urban areas, individually. Chiang Rai, which shares the border with Laos and Myanmar, is mainly located in the northernmost province of Northern Thailand. In general, Chiang Rai is known for having a pristine atmosphere for around 9 to 10 months each year, and yet its air quality drastically deteriorates during the period of biomass burning (Pongpiachan et al., 2013). Mae Sai district mainly locates in the northernmost district of Chiang Rai province in Northern Thailand, highest level of smoke haze has been observed in March, 2016 (Pasukphun, 2018). Meanwhile, Mae Sai is an area where border economy is shared between Thailand and Myanmar, which could also suffer from the air pollutants generated from transportation and waste. Lastly, Chiang Mai is the second largest province in terms of population and the main tourist attraction city in Northern Thailand, attracting over seven million visitors every year (Kitirianglarp, 2015; Pani et al., 2018). However, because of its mountainous geographical features, the problem of haze from wildfire emission in March and April has caused severe environmental and health impacts, reduced the visibility, and hindered the development of the tourism industry (ChooChuay et al., 2022; Viswanathan et al., 2006; Xiao et al., 2014).

Therefore, this article aims at exploring the main cause of air pollution in the region and provide pollution control strategies for improving air quality in Northern Thailand. Based on the above the motivations, several research questions raised as follows. First, what are the main pollution sites and main pollutants in Northern Thailand? Second, where do these pollutants come from and can the sources of pollution at that time be traced based on the time when the maximum concentration of pollution occurred? Third, are there any effective methods that can assist in solving or controlling the sources or formation of these air pollutants? Briefly, the objectives of this study are shown below: (1) to find out the PM2.5 pollution episode in Northern Thailand; (2) to track the source of the PM2.5 pollution using HYSPLIT model; and (3) to provide the possible pollution control strategy in Northern Thailand.


2.1 Episode Time Analysis

The air pollution index (AQI) defined by Pollution Control Department (PCD), Ministry of Natural Resources and Environment (MNRE), Thailand was based on the AQI system of U.S. Environmental Protection Agency (U.S. EPA), which was calculated by converting measured air pollutants concentrations into a uniform index (Kanchan et al., 2015). The air pollutants include PM2.5, PM10, ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), which is shown in Table 1. The AQI is also classified into five levels according to the degree of health implications of each pollutant (Li et al., 2022). The baseline for the identification of pollution episode was set as the level of pollutants started to become into toxic condition and the time range of toxic condition higher than baseline was selected and defined as episode (Luo et al., 2022).

Table 1. Concentration of air pollutants equivalent to the AQI value in Thailand.

We found in the data base of PCD that PM2.5 of all the monitoring stations, which was obtained using the gravimetric method based on Federal Reference Method (FRM) proposed by the U.S. EPA (U.S. EPA, 2011), was more available and missed the fewest data points comparing to other criteria air pollutants (i.e., PM10, O3, CO, NO2, and SO2). Meanwhile, the AQI calculated by considering the health effects of PM2.5, PM10, O3, CO, NO2, and SO2 was not covered at all stations of PCD database, while the most complete index for evaluation of the air quality is PM2.5 in Thailand. The procedure of PM2.5 gravimetric analysis was conducted based on the method provided by U.S. EPA (2008). Firstly, the flow rate of an air sampling instrument should be calibrated using the primary air flow meter. Filters were pre-conditioned before the sampling, while environmental temperature and pressure of air sampling instrument should also be calibrated before sampling of PM2.5. The microbalance should be routinely checked by using certified mass standards before the filters were weighed. Finally, field, laboratory, trip, and lot blanks were also weighed for quality assurance purposes. The Quality Assurance and Quality Control (QA/QC) for data used in this study was performed via preliminary data exploration by filtering and selecting only similar range of monitoring time during 2019–2021, where data from those three stations are the most complete to explore PM2.5 episodes. The missing values (NA) were handled using na.approx in R statistics programing to replace NA by interpolation. NA approximation was performed to manage NULL values (Faybishenko et al., 2021).

PCD database defined the value of PM2.5 higher than 91 µg m–3 as very unhealthy to human (Table 1), which was also the threshold value of PM2.5 to classify the episode of poor air quality in this study. The daily PM2.5 in 2019–2021 of the three representative stations (i.e., Chaing Mai-35T, Chiang Rai-57T, and Mae Sai-73T) of Northern Thailand was chosen and the AQI in each year of the stations were clustered by level of PM2.5 concentration using conditional formatting in Microsoft Excel. The time zone of poor air quality (i.e., the concentration of PM2.5 higher than 91 µg m–3) was selected to visualize the PM2.5 episodes (Plaia and Ruggieri, 2011; Liu et al., 2022; Zaib et al., 2022).

2.2 Back Trajectory Analysis

2.2.1 HYSPLIT model and cluster analysis

In order to identify the air pollution trajectory, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration (NOAA), USA was applied. The HYSPLIT model uses either puff or particle approaches to obtain the air pollution trajectories, dispersion, and deposition. This model associates the mass of a particular specie (pollutant) with the release of either puffs, particles, or a combination of both to calculate air concentrations. Air concentrations are calculated for each grid cell of advection and diffusion, and the advection and diffusion of particles are computed from their initial location. In this study, the raw-data-based method was used for the air pollutant trajectory clustering. The K-means cluster analysis based on the Euclidean distance to divide the air pollutant trajectories into categories (Šauliene and Veriankaite, 2006; Zhang et al., 2019; Wang et al., 2022).

The 5-day backward trajectory analysis using a HYSPLIT model was performed based on the NCEP/NCAR Global Reanalysis Archive meteorological data, available online at http://www.arl.​noaa.gov/ready/hysplit4.html. The data consisted of daily and monthly atmospheric model output from 1948 to near present. It included 17 pressure levels and 28 sigma levels with spatial coverage of 2.5 degree × 2.5 degree global grid. The surface and near-surface data consisted of wind speed, wind direction, temperature, and precipitation, where were available for every 6 hours (Aerospace Corporation, 2023).


3.1 Frequency of the Episodes in Northern Thailand

3.1.1 Levels of PM2.5

The air quality monitoring data were obtained from the PCD of Thailand, which have established at least three monitoring stations in each province of the country. PCD data source of daily PM2.5 in 2019–2021 of the three studied air quality monitoring stations in Northern Thailand was selected to characterize the PM2.5 in those areas and identified the periods where pollution episodes occurred. Station number 35T (18°50′14.2′′N, 98°58′15.2′′E) was selected based on the representative of urban and main city in the northern part of Thailand. Station number 57T (19°54′35.6′′N, 99°49′24.4′′E) was selected to be the presenter of secondary city and rural communities. Besides, Station 73T (20°30′33.6′′N, 99°54′27.4′′E) was selected to be the presenter of border communities.

The data collection and sorting of the daily PM2.5 for the three stations in 2019–2021 was performed using conditional formatting in Microsoft Excel (data is not shown). The period that people in Northern Thailand suffering from the poor air quality was obtained in a long period of the very unhealthy zone of the three stations that selected as the range of the beginning of unhealthy AQI starting until the last day in the considered year. Distinct colored label was given for each AQI criteria to show characteristic of air quality in each year. For example, the AQI Level 4 was marked in red, which is characterized by a “very unhealthy” period during the whole sampling time (Table 2).

Table 2. Frequency of different PM2.5 AQI Level of the selected stations in 2019–2021.

The data for the 35T-Chaing-Mai station in 2019–2021 were obtain during March-11 to April-13, February-28 to April-12, and March-3 to April-3, summing up to a total day range of 34, 45, and 32, for year 2019, 2020, and 2021, respectively. Notably in 2020, the 35T-Chaing-Mai station witnessed a high concentration of PM2.5 which lasted 1–2 weeks longer than those for year 2019 and 2021. Overall, the “very unhealthy” period occupied around 76%, 51%, and 31% of the total observation days during year 2019, 2020, and 2021, individually. Previous studies pointed out haze period was observed from March to April in upper Southeast Asia (SEA), resulting in high PM2.5 concentrations in the dry season (Sresawasd et al., 2021; Pani et al., 2019; Thepnuan et al., 2019). Furthermore, the MODIS satellite detected a total of 10,343 fire hotspot over nine provinces of Northern Thailand in 2019, while 9,859 of them occurred from January to May (Chinsorn and Papong, 2021). Although the high PM2.5 level was observed during the haze period, there was at times some haze periods with the weather turning into better conditions, which was similar to that shown by Sresawasd et al. (2021). In their study, Sresawasd et al. (2021) pointed that PM concentration can be reduced sometimes because of high wind speeds and precipitation favored. Chantara et al. (2009) showed the pattern of PM10 in Chiang Mai and Lamphun Provinces in 2005–2006 increased at the beginning of dry season (December) and reached its peak in March before decreasing by the end of April. The situation of high episodes of PM2.5 in 2019-2021 in this study had similar trend of PM10 at Chiang Mai station. Moreover, Punsompong and Chantara (2018) utilized the potential source contribution function (PSCF) to study emission sources of PM10 at Chaing Mai station in the dry season (February–April) between year 2010 to 2015. The results indicated that 26.8% and 73.2% contribution of emission were found from local of Chaing Mai and transboundary from Myanmar, respectively. During the years 2010–2015, the two major burning sources in March and April which were related to PM2.5 episode at Chaing Mai station were mainly found in agricultural areas and forested areas of Myanmar, while agricultural areas of Thailand contributed to the PM10 concentration in Northern Thailand was in February. Kraisitnitikul et al. (2024) also pointed out the average PM2.5 concentration of smoke haze episode in warm El Niño year (2019) was much higher than that in cold La Niña year (2017) in Chiang Mai, Thailand, which could be resulted from the differences in climatic conditions and other related meteorological factors.

The trend of the distribution of AQI from PM2.5 of station 57T-Chiang-Rai in 2019–2021 was similar to that at station 35T-Chaing-Mai. In year 2021, the periods with AQI value classified as “very unhealthy” lasted around two weeks shorter than those in years 2019 and 2020. The data for the 57T-Chiang-Rai station in 2019–2021 were obtained during March-14 to May-3, February-17 to April-9, and March-8 to April-4, summing up to a total day range of 51, 53, and 28, for years 2019, 2020, and 2021, respectively. Overall, the “very unhealthy” period occupied around 45, 53, and 29% of the total observation days during years 2019, 2020, and 2021, respectively. For both station 35T-Chaing-Mai and 57T-Chiang-Rai, it was shown that the occurrence frequencies of the worst pollution level had been decreased since 2021. Luong et al. (2022) assessed the influence of biomass burning sources on PM2.5 level from Hanoi, Vietnam, and Chaing Rai, Thailand during January to April in 2021 using the integrated approach of PM2.5 in-situ measurement, receptor and trajectory modelling techniques, and satellite remote sensing. This work also presented that PM2.5 level measured in the sampling site in Chaing Rai was raised from March to April, which was related to the intensive biomass burning activities in the SEA area.

The characteristics of the pollution level for the 73T-Mai Sai station in 2019–2021 were not significantly different in each year. The data for the 73T-Mai Sai station in 2019–2021 were obtained during March-11 to May-3, February-16 to April-10, and March-1 to April-24, summing up to a total day range of 54, 55, and 55, for years 2019, 2020, and 2021, respectively. Overall, the “very unhealthy” period occupied around 78, 71, and 64% of the total observation days during years 2019, 2020, and 2021, respectively. From February to April in 2008–2010, Sukitpaneenit and Kim Oanh (2014) reported that daily CO and PM10 were correlated with the forest fire hotspot counts, especially in the rural areas (i.e., Chiang Rai, Maehongson, Lampang, and Nan provinces). Janta et al. (2020) evaluated the air quality in 8 provinces of Northern Thailand based on the hourly data of PM10 mass concentration at 13 monitoring stations derived from PCD database and hotspot data from NASA’s Earth Observatory website during 2006 to 2016. It appeared the same pattern every year, besides, the high levels of hotspot and PM10 were also found in biomass season with the highest one in March. Hongthong et al. (2022) investigated the emission inventory of daily, monthly, and annual PM10 and PM2.5 from biomass burning in 9 provinces of Northern Thailand adjacent to the border of Myanmar and Laos during 2012–2016, appearing a clearly higher PM emission during February-April compared with other months in the investigate years.

We also performed the Pearson correlation on PM2.5 level among eight air pollution monitoring stations (Table 3). High correlation (r = 0.65–0.98, p < 0.001) among the PM2.5 levels were found in the Northern Thailand. It means that the emission sources of PM2.5 were not located in one area but covered the whole area of Northern Thailand, showing the importance of the transboundary pollution transport here which would be discussed in Sect. 3.2.

3.1.2 Episode

The visualization of the day range is shown below with the PM2.5 AQI Level of “very unhealthy” marked by the dash box. These time periods were also denoted as the time where episode occurred. In this research, the data of PM2.5 at the selected stations was obtained during year 2019–2021, and the episode selections were performed by directly plotting on a year-by-year basis, which are shown Fig. 1 through Fig. 3.

Fig. 1. The episode selection for the station 35T-Chiang-Mai in 2019–2021. The unit of PM2.5 in y-axis is µg m–3.Fig. 1. The episode selection for the station 35T-Chiang-Mai in 2019–2021. The unit of PM2.5 in y-axis is µg m3.

Fig. 2. The episode selection for the station 57T-Chiang-Rai in 2019–2021. The unit of PM2.5 in y-axis is µg m–3. Fig. 2. The episode selection for the station 57T-Chiang-Rai in 2019–2021. The unit of PM2.5 in y-axis is µg m3.

From Fig. 1, the result of the PM2.5 episode selection for 35T-Chiang Mai station during 2019–2021 consisted of three periods in 2019 (March 9–March 17, March 21–March 26, and March 28–April 8); three periods in 2020 (March13-March15, March18–23 and April 3–April 7); and two periods in 2021 (March 5–March 12 and April 1–April 5). Amnuaylojaroen (2022) reported the seasonal variation of PM2.5 in Chiang Mai, Lampang, and Nan provinces of Northern Thailand by separating PM2.5 monitoring data into two seasons (wet season: May–October; dry season: November–April). The PM2.5 concentrations in dry seasons was shown higher than those found in wet seasons.

From Fig. 2, the result of the PM2.5 episode selection for the 57T-Chiang-Rai station during 2019–2021 consisted of five periods in 2019 (March 13–March 18, March 20–March 26, March 29–April 4, April 11–April 18, and April 29–May 4); three periods in 2020 (March 9–March 22, March 22–April 1, and April 3–April 10); and two periods in 2021 (March 7–March 11 and March 29–April 6).

From Fig. 3, the result of PM2.5 episode selection for the 73T-Mae-Sai station during 2019–2021 consisted of five periods in 2019 (March 10–March 18, March 19–March 27, March 29–April 8, April 8–April 18, and April 28–May 4); four periods in 2020 (March 6–March 11, March 11–March 17, March 17–March 22, and March 22–April 11); and four periods in 2021 (March 4–March 15, March 16–March 23, March 27–4 April, and April 19–April 26).

Fig. 3. The episode selection for the station 73T-Mae Sai in 2019–2021. The unit of PM2.5 in y-axis is µg m–3.Fig. 3. The episode selection for the station 73T-Mae Sai in 2019–2021. The unit of PM2.5 in y-axis is µg m3.

The episodes of air pollution among different months can be analyzed using the analysis of variance (ANOVA). The analysis showed the same trend of visualization of episode selection. The results of the average PM2.5 data from Station 57T and 73 T in Chiang Rai province were shown Tables S1 and S2. The ANOVA analysis showed independently differences in the mean concentrations of PM2.5 for February, March and April, as compared to the means of other remaining months. March was the month with the highest PM2.5 concentrations. This can confirm the significance of PM2.5 episode during the dry season in Chiang Rai province. On the other hand, Table S3 shows the results at Station 35T in Chiang-Mai, appearing that PM2.5 episodes also mainly occurred in dry season.

3.2 Pollution Source and Trajectory of Air Pollution in the Episode Time

A total of 245 airmass backward trajectories were generated at 1,000-meter height from the ground level on the episode days to assess the potential sources of long-range transport PM2.5 from a period of 2019–2021. This start level (1,000-meter height) is the average daytime mixing height for Thailand. However, a preliminary analysis comparing different starting heights (0, 100, 300, 500, 1000 meters) showed that the trajectories obtained from 500- and 1000-meter start points were almost agreed together (75% matched with a test data on January 2019). On the other hand, air mass trajectories having the start height lower than 500-meter height may be short and interfered by more local pollution sources. The results during the high PM2.5 episodes found that the airmass transport in a short distance due to the influence of high pressure from the North of Thailand, as mentioned in the previous section. The K-means cluster analysis was also conducted to group the type of air mass resolved by the model. Locations of the airmass from 245 trajectories (in latitude and longitude) for every 6 hours were input into the SPSS statistical program for the analysis.

The results of K-means cluster analysis found that the air mass backward trajectory can be divided into 2 separated groups based on their movement during the Chiang Rai’s episode, which is shown in Fig. 4. The first group was the air mass that commonly transport from a long distance passing through the Northern Thailand (account for around 21.6% of the total air mass trajectories during the episode), while the air mass in second group transported from other nearby mountain areas with a shorter distance (account around 75.9% of the total air mass trajectories during the episode). Average PM2.5 concentration estimated from the days with long distance trajectories in the first group (156.8 µg m–3) was slightly higher than the second one (141.7 µg m–3), similarly with their standard deviations. Details air mass trajectories from each group are presented in Fig. S1 to Fig. S3 of the supplementary material. The results suggested that transboundary might be an issue in the target area.

Fig. 4. Comparison between PM2.5 concentrations estimated from the groups of air mass trajectories generated by the K-mean cluster analysis.Fig. 4. Comparison between PM2.5 concentrations estimated from the groups of air mass trajectories generated by the K-mean cluster analysis.

Sirithian and Thanatrakolsri (2022) elaborated that the main contributor of hotspot locations was found from upwind neighboring provinces, i.e., Chiang Rai, Chiang Mai, Mae Hong Son, and Nan provinces, which accounted for 65% of hotspots. The minor contributor was from neighboring countries (i.e., Myanmai). The distribution of the two contributors was comparable to the result of this study. Amnuaylojaroen et al. (2020) presented the backward trajectory map of PM2.5 in Northern Thailand during high biomass burning episodes, showing one channel was originated from the Northeast Thailand, and the other one was from other neighboring countries, including some parts of India, Eastern China, Northern Vietnam, Laos, and Myanmar. Among these countries, Myanmar and Laos respectively contributed 37% and 28% of hotspot locations, which were the two highest of all countries in Southest Asia. The evaluation of pollution sources in Northern Thailand from this study and the previous ones urge the development of strategical air quality management plan for this region, which is still lacking till this day. In Sect. 3.3, we attemp to propose some management strategies to improve the air quality in Northern Thailand.

3.3 Air Quality Management Strategy

The improvement of air quality requires collaboration between experts from different fields. Shi et al. (2014) pointed out the strategy can be divided into the following eight categories, including (a) monitoring, inventory and assessment; (b) scientific research; (c) policy formulation, implementation and evaluation; (d) regulatory instrument; (e) economic incentives; (f) information, education, and societal empowerment; (g) technology development and deployment; and (h) social norms. Based on the results of previous studies, we propose a pollution prevention and control strategy in Northern Thailand, citing the theory of planning behavior, as shown in Fig. 5 (Ajzen, 1988, 1991; Armitage et al., 2002; Godin and Kok, 1996). The whole control strategy of this theory is composed of four aspects, namely prevention knowledge, risk perception, protection behavior, and social influence. It is hereby explained as follows.

Fig. 5. Strategy map of Northern Thailand biomass burning pollution prevention.Fig. 5. Strategy map of Northern Thailand biomass burning pollution prevention.

3.3.1 Control Strategy A: Improve the prevention knowledge

Having sufficient prevention knowledge is the basis for successful pollution prevention and control. The main prevention knowledge includes three parts, including source abatement, biomass reutilization, and waste reduction. These three parts all involve quite advanced technology, so it depends on academic institutions and technical consulting institutions to continue to handle relevant training courses to make the technology of pollution prevention and control truly implement.

(1) Strategy A.1: Source abatement

One of the control strategies is source abatement, which is to effectively reduce the pollution emissions of biomass burning. Intra et al. (2010) and Moran et al. (2019) found that the use of different combustion and agriculture operation methods can effectively reduce the pollution emissions of reproductive combustion. Therefore, by teaching local farmers effective low-pollution combustion methods, the pollution emissions of biomass combustion in Northern Thailand can be reduced to a small extent.

The intensification of agricultural operations to meet these growing demands is associated with a number of environmental and human health risks. Udeigwe et al. (2015) therefore broadly identified agricultural practices with potentially negative impacts on the environment and human health as: (a) use of biosolids and animal manure, (b) use of agrochemicals, (c) management of post-harvest residues, (d) irrigation, and (e) tillage operations. In addition, soil, water and air pollution from nutrients, heavy metals, pathogens, and pesticides, as well as air pollution from PM10, toxic gases, and pathogens are the main environmental impacts. Therefore, in order to reduce the harm of biomass burning, it is necessary to continue to teach them these concepts.

(2) Strategy A.2: Biomass reutilization

The second control strategy is biomass reutilization. Farmers have no choice but to dispose of agricultural waste biomass in the most convenient way because they do not know that biomass is actually a good resource for reuse. Besides, biomass open burning in Northern Thailand would also cause severe air pollution. Therefore, if the biomass could be converted into biofuel, it will provide the high economic value. Sun et al. (2016) proposed some recommendation. For example, straw returning can improve soil organic matter content and texture as well as avoid air pollution from biomass burning. Additionally, it can also improve straw utilization efficiency such as straw gasification. Cations, biomethanation, and crop straw power generation are also benefits brought by this scheme at the same time. The commercial application of biomass energy is also a strategy to be considered. However, in order to implement these policies, decision makers should consider various conditions, including the local economic level, energy utilization, living conditions of residents, and other environmental factors.

(3) Strategy A.3: Waste reduction

The third control strategy is waste reduction. The source of biomass burning is from agricultural wastes. If the agricultural waste was reduced, the relative amount of air pollution will naturally decrease. Since there were fewer farming areas in Northern Thailand, the agricultural cost was also less. However, due to a large number of farmers cutting down forests to plant cash crops recently, there is a problem of burning agricultural wastes after harvesting. Therefore, not cutting down forests indiscriminately and replanting cash crops arbitrarily is the solution to the root cause. In this way, due to the reduction of agricultural wastes, the amount of air pollution caused by burning agricultural waste will naturally also be reduced.

Introducing Conservation Agriculture Technologies can reduce the air pollutants emission. Conservation agriculture (CA) is an agricultural system that restores degraded land, prevents loss of cultivated land, promotes the maintenance of permanent soil cover, uses minimal soil disturbance, and preserves diverse plant species. CA helps to increase water and nutrient use efficiency and can improve and maintain crop production (Fouzai et al., 2018; Lahmar et al., 2012). CA principles are applied to all agricultural landscapes and land uses, and employ locally adapted practices. Soil interventions (such as soil mechanical disturbance) are reduced to an absolute minimum or avoided, and external inputs (such as agrochemicals and plant nutrients of mineral or organic origin) are optimally applied in such a way and in amounts that they do not interfere with or disrupt biological processes. In addition, CA reduces the demand of wastes burning. It promotes good agronomy, such as timely handling, and improves overall land management for rainfed and irrigated production. This is complemented by other known good practices, including the use of high-quality seeds and integrated management of pests, diseases, nutrients, weeds, and water. Undoubtedly, the use of CA technology also reduces the emission of air pollutants.

3.3.2 Control Strategy B: Increase the risk perception

Air pollution is mainly caused by human's polluting behaviors (Lin et al., 2017) because they do not know the influence of air pollution caused by such behaviors, which was called air pollution risk perception. With sufficient risk perception, people will naturally cease the pollution-related behaviors. Information disclosure is viewed as an effective way to improve people’s risk perception, which can be performed mainly through three methods. The first is to make the public understand the danger of pollution, the second to improve the civil attitude of the entire community, and the third to form the group opinion of the public in the community.

(1) Strategy B.1: Pollution awareness

One of the strategies to improve the risk perception is to make the public understand the danger of air pollution. It depends on not only the propaganda of school teachers at all levels, but the information disclosure of air quality monitoring information. After the actual interviews of the research group, it was found that the residents are aware of the phenomenon of biomass burning here, and they all feel that the biomass burning causes inconvenience to their lives and physical discomfort. However, they have not yet related this pollution phenomenon to their health. As a link, if the air quality monitoring data here could be regularly released to the local people, and when the people know that they are living in an air environment that is extremely harmful to their health, their risk perception will naturally be greatly improved. Satellite image monitoring is a good technology to monitor the air quality (Engel-Cox et al., 2004; Sukitpaneenit and Kim Oanh, 2014), therefore, if it can be used in the future, it will help to improve residents' awareness of environmental protection. In addition, if low-cost monitors could be widely used and the monitoring data can be transmitted to the social media in real time, it will definitely be of great help to the growth of residents' awareness of environmental protection.

(2) Strategy B.2: Civil attitude

The second strategy to improve risk perception is to promote the local civil attitude and let everyone know that biomass burning is a behavior that endangers the health of nearby residents, so everyone should not engage in this behavior. If the civil attitude of the residents in the community increased, it can be expected that any of their open biomass burning behaviors will be greatly reduced, and thus the amount of air pollution will also be decreased. It is very important for the public to understand the sources of air pollutants and various methods to control them. Only when citizens know where pollution comes from and how to control it, they will further want good regulations to regulate it. Therefore, this is also an important process to form civic consciousness.

(3) Strategy B.3: Group opinion

The third strategy to improve risk perception is to promote having an overall clean air by reducing unregulated biomass burning a group consensus of local residents. Although the residents might understand that biomass burning will cause air pollution, a group consensus of improving air quality must be formed so that important policy from the government and various control schemes can be carried out. Therefore, we suggested making ‘‘regulating biomass combustion to improve air quality’’ a group consensus of local community residents.

3.3.3 Control Strategy C: Cultivate the protection behavior

The protection behavior of air pollution is an important key to evaluate whether residents can protect themselves. If the public had a good protection behavior, it can be facilitated through two methods such as advocacy and incentives. It mainly includes three aspects, namely, behavior change, protection skill, and prevention habit.

(1) Strategy C.1: Behavioral change

The first method to form public protection behavior is behavior change. The residents are used to directly burning biomass in the open air after the harvest. For them, this is a very convenient way to deal with it, and it does not cost any money and manpower. However, such consequences are at the expense of the deterioration of the air quality of the environment. Therefore, to let the residents change their behavioral habits such as adopting a less polluting way of disposal instead of directly burn in the open air would be a better way to manage the air quality (Semenza et al., 2008). Moreover, environmental management goals can be achieved through behavioral change, and there have been some successful examples in the past. For example, Taiwan has successfully implemented a trash bag charging policy (Lai and Lee, 2022; Tsai, 2022) to achieve litter reduction. The government stipulates that garbage must be disposed in designated garbage bags. Because garbage bags need to be bought by money, people will naturally consider the cost of garbage bags when throwing out garbage, and automatically reduce the amount of garbage discarded. In the same way, we can use the theory of behavior change in social psychology to change the burning behavior of farmers in this area.

(2) Strategy C.2: Protection skills

The second way to form people's protection behavior is to improve their protection skills. This protection skill refers to behaviors that effectively protect themselves, such as wearing masks, not opening windows when the air is polluted, not doing strenuous exercise outdoors when the air quality deteriorates, and so on.

(3) Strategy C.3: Prevention habits

To build long-term effects of residents’ protection behavior, it is necessary to enable the public to have good prevention habits. In order to develop good habits for general public, social education must be used, which could be conducted through social media, TV broadcasts, newspapers, community advocacy, and so on. However, behavior change policies should be accompanied by incentives. Garbage bag charging policy provides the incentive to households for wastes reduction via less demand of waste-intensive packaging, more recycling, and other sources reduction (Yang and Innes, 2007). Similarly, if we would like to design air pollution control strategies for biomass burning with economic incentives, this strategy must match the economic incentives behind the behavior.

The open biomass burning causes serious air pollution in Northern Thailand. Nonetheless, instead of burning biomass, biomass can be converted into energy which would also reduce the pollution. The current practice of biomass is direct combustion without any treatment, causing negative impacts on air quality. Apart from a small amount of nutrients that may be recycled by the farmland, this practice has actually produced few positive effect. Therefore, the current open biomass burning, like the previous case of littering, is an environmentally harmful behavior that is not beneficial to society as a whole.

Fig. 6 highlights the significance of shifting from uncontrolled biomass burning to planned biomass utilization. The main problem of biomass burning is the air pollutants it generated, including PM10, PM2.5, gaseous pollutants, and other hazardous air pollutants (HAPs). While providing some nutrients to the soil, uncontrolled biomass burning typically resulted in negative consequences for the atmosphere (Chen et al., 2023; Peng et al., 2023). This pollution poses serious risks to human health and the environment, contributing to respiratory problems, existing health issues, and diseases such as lung cancer (Dockery, 2009; Ryan et al., 2021). Additionally, uncontrolled biomass burning can reduce visibility, posing threats to drivers, pilots, and other professionals that rely on clear visibility for safe job performance (Keywood et al., 2015; Zhang et al., 2017). Furthermore, biomass burning can leave stains and damage on any surfaces, such as walls, roofs, and even historical heritages, affecting the appearance of buildings and infrastructure (Striegel et al., 2003; Wu et al., 1992).

Fig. 6. Comparison between unregulated biomass burning and planned biomass use.Fig. 6. Comparison between unregulated biomass burning and planned biomass use.

To mitigate the negative impacts of biomass burning, it is crucial to control and manage these burns. Planned utilization of biomass materials can offer potential benefits, such as the generation of biomass without incidental pollution (Bajwa et al., 2018; Mohanakrishna and Modestra, 2023). Technological advancement can minimize or eliminate negative impacts on the environment and human health as biomass could be converted into energy or other useful products (Von Blottnitz and Curran, 2007). For instance, modern biomass energy systems can significantly reduce emissions of harmful pollutants, such as PM10, NO2, and SO2 (Nyashina et al., 2022; Saidur et al., 2011). Furthermore, planned biomass utilization can produce electricity through various technologies, including combustion, gasification, and anaerobic digestion. Electricity generated from biomass can reduce our reliabilities on fossil fuels, lowering greenhouse gas emissions and leading to positive outcomes for the environment and public health (Babu et al., 2022; Usmani et al., 2021).

In conclusion, the benefits of planned biomass utilization, such as the generation of biomass without incidental pollution and the production of electricity, support the idea of properly planning and implementing the utilization of biomass as a sustainable and environmentally friendly source of energy.

3.3.4 Control Strategy D: Intensify the social influence

In the theory of planned behavior, prevention knowledge, risk perception, and protection behavior are independent variables, while control performance is a dependent variable. In addition, social influence is an adjustment variable for pollution prevention and control strategies. Although the first three independent variables directly affect the effectiveness of the control strategy, their actual effectiveness is regulated by the influence of social influence. Social influence is mainly carried out by outlaw and suppress. It includes three parts, namely, encouragement and reward, domestic regulation, and international convention.

(1) Strategy D.1: Encourage and rewards

Encourage and reward is to give the relative compensation to the community people. Because people may have some financial losses when they change the original treatment method, giving them financial subsidies will increase their motivation to perform low pollution prevention and control behaviors. Agricultural wastes can produce the feed, fertilizer, and supplies for industry. Therefore, through policies with incentives, farmers can change their past behaviors and adopt farming methods that do not harm the environment. Economic growth is closely related to improved living standards, so farming activities should not harm the environment as long as they are moderately regulated.

(2) Strategy D.2: Domestic regulation

Secondly, the domestic regulation must be formulated and completed, so that those who cause pollution will be punished appropriately, thereby producing a deterrent effect. Moran et al. (2019) summarized the possible regulation that has been recommended by the previous researchers. Pongpiachan et al. (2017) recommended raising the vehicle tax, developing bicycle lanes, and increasing the use of public transportation. Although doing these things is not so close with the biomass burning, but it helps to reduce the air pollution in Northern Thailand. Pongpiachan and Paowa (2015) also proposed that anti-burning and smog prevention campaigns were effective methods in decreasing the level of air pollution. Sirimongkonlertkun (2012a) suggested Thai government should establish a specific policy to declare a repeatable burning area as an emergency area. Tiyapairat (2012) suggested Thai government should decentralize more and run serious prevention campaigns to strengthen residents’ understanding and participation and local communities’ networking. Tiyapairat and Sajor (2012) suggested Thailand should move away from top-down system of control. An understanding of localities would provide better results than a one-size-fit-all sent down to every situation. Janta and Chantara (2017) introduced a road-traffic management system to provide more air ventilation and reduce air pollutant accumulation of high traffic density location. Kanabkaew and Kim Oanh (2011) updated Thailand emission database with the data from crop and other biomass burning sources. The updated information would raise the awareness in minimization of behaviors deteriorating the air quality. Kim Oanh and Leelasakultum (2011) introduced a haze warning forecast to alert the public when the meteorological conditions favor the formation of haze. Pengchai et al. (2009) updated Thailand’s air quality criterion to account for the carcinogenicity of polycyclic aromatic hydrocarbons (PAHs).

(3) Strategy D.3: International convention

International convention is the third part of social influence. The air pollution in Northern Thailand is difficult to control because the air pollution here comes from different countries. As shown in Fig. S1 to Fig. S3, according to the calculated results of the backward trajectory model, the pollution sources came from Bengal, Myanmar, Thailand, and China. Since the laws and regulations of each country are different, it must be handled through the international convention, which was also proposed by Sirimongkonlertkun (2012b).

3.3.5 Key stakeholders involved with the strategies

When promoting air pollution prevention and control strategies, many stakeholders will participate and they also play important roles that determine the success of the strategy implemented. The stakeholders of each control strategy are listed in Table 4. Stakeholders in environmental protection can include government agencies, environmental advocacy groups, industries and businesses, individuals and households, communities and citizens, scientific and academic communities, regulatory agencies, engineering and technical experts, agricultural producers, waste management and recycling companies, public health advocacy groups, community organizations, international organizations, healthcare providers, educators, health and safety professionals, and researchers. These stakeholders can work together to promote and implement policies, regulations, and practices aimed at reducing air pollution and protecting the environment. Regulatory agencies are responsible for setting and enforcing standards for air quality, and may work to promote the use of source abatement techniques, biomass reutilization, and waste reduction practices. Environmental and public health advocacy groups may be more interested in raising public awareness of air pollution risks and promoting measures to reduce pollution. Community organizations and educators may play a role in promoting awareness and understanding of air pollution risks and the importance of individual behavior change in supporting effective air pollution control measures.

Table 4. Key Stakeholders of different control strategy for preventing the pollution of biomass burning in Northern Thailand.


PM2.5 data was collected at three air pollution monitoring stations in Northern Thailand during 2019–2021, and it was found that February to April (dry season) was the most common polluted season which had more pollution episodes than the other months. The pollution sources of PM2.5 during periods of severe pollution was analyzed by the HYSPLIT model, showing air mass transport from nearby mountain areas with shorter transport distance was the main contributor, followed by that from a long distance passing through the Northern Thailand. Moreover, the investigated areas are mainly located in a mountainous area, with the main source of pollution being the open burning of agricultural wastes produced by the residents. These pollution sources are hard to control since the local residents have been accustomed to them, while the sources are also highly scattered. Therefore, we proposed the control strategies such as the improvement of prevention knowledge, increase of the risk perception, cultivation of the protection behavior, and intensification of the social influence to not only reduce air pollution, but promote the environmental protection and sustainable development in this area. Although the air pollution caused by open biomass burning has become an environmental issue in Northern Thailand, this study suggested some effective management strategies to improve the air quality and thus ameliorate Thai residents’ health and quality of life.


We thank the daily PM2.5 data from the Pollution Control Department, Ministry of Natural Resources and Environment, Thailand.


  1. Adeleke, A., Apidechkul, T., Kanthawee, P., Suma, Y., Wongnuch, P. (2017). Contributing factors and impacts of open burning in Thailand: perspectives from farmers in Chiang Rai Province, Thailand. J. Health Res. 31, 159–167. https://doi.org/10.14456/jhr.2017.20

  2. Aerospace Corporation (2023). Physical Sciences Laboratories. (accessed in 15 March 2023).

  3. Ajzen, I. (1988). Attitudes, personality and behaviour. Milton Keynes, Open University Press, UK. pp. 112–145.

  4. Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

  5. Amnuaylojaroen, T., Inkom, J., Janta, R., Surapipith, V. (2020). Long range transport of Southeast Asian PM2.5 pollution to Northern Thailand during high biomass burning episodes. Sustainability 12, 10049. https://doi.org/10.3390/su122310049

  6. Amnuaylojaroen, T. (2022). Prediction of PM2.5 in an urban area of northern Thailand using multivariate linear regression model. Adv. Meteorol. 2022, 1–9. https://doi.org/10.1155/​2022/3190484

  7. Armitage, C.J., Norman, P., Conner, M. (2002). Can the theory of planned behaviour mediate the effects of age, gender and multidimensional health locus of control? Br. J. Health Psychol. 7, 299–316. https://doi.org/10.1348/135910702760213698

  8. Babu, S., Singh Rathore, S., Singh, R., Kumar, S., Singh, V.K., Yadav, S.K., Yadav, V., Raj, R., Yadav, D., Shekhawat, K., Ali Wani, O. (2022). Exploring agricultural waste biomass for energy, food and feed production and pollution mitigation: A review. Bioresour. Technol. 360, 127566. https://doi.org/10.1016/j.biortech.2022.127566

  9. Bajwa, D.S., Peterson, T., Sharma, N., Shojaeiarani, J., Bajwa, S.G. (2018). A review of densified solid biomass for energy production. Renewable Sustainable Energy Rev. 96, 296–305. https://doi.org/10.1016/j.rser.2018.07.040

  10. Chaiphan, N., Patterson, P. G. (2016). Study of agrotourism in Northern Thailand: key success factors and the future of the industry. Master of Science Program in Marketing, Faculty of Commerce and Accountancy, Thammasat University.

  11. Chantara, S., Wangkarn, S., Tengcharoenkul, U., Sangchan, W., Rayanakorn, M. (2009). Chemical analysis of airborne particulates for air pollutants in Chiang Mai and Lamphun Provinces, Thailand. Chiang Mai J. Sci. 36, 123–135.

  12. Chantara, S., Sillapapiromsuk, S., Wiriya, W. (2012). Atmospheric pollutants in Chiang Mai (Thailand) over a five-year period (2005–2009), their possible sources and relation to air mass movement. Atmos. Environ. 60, 88–98. https://doi.org/10.1016/j.atmosenv.2012.06.044

  13. Chen, L., Gao, Y., Ma, M., Wang, L., Wang, Q., Guan, S.,Yao, X., Gao, H. (2023). Striking impacts of biomass burning on PM2.5 concentrations in Northeast China through the emission inventory improvement. Environ. Pollut. 318, 120835. https://doi.org/10.1016/j.envpol.2022.120835

  14. Chinsorn, A., Papong, S. (2021). The estimation of PM2.5 pollution using statistical analysis and MERRA-2 aerosol reanalysis for health risk assessment in Northern Thailand. Thai Environ. Eng. J. 35, 31–40.

  15. ChooChuay, C., Pongpiachan, S., Tipmanee, D., Deelaman, W., Iadtem, N., Suttinun, O., Wang, Q., Xing, L., Li, G., Han, Y. (2022). Effects of agricultural waste burning on PM2.5-bound polycyclic aromatic hydrocarbons, carbonaceous compositions, and water-soluble ionic species in the ambient air of Chiang-Mai, Thailand. Polycyclic Aromat. Compd. 42, 749–770. https://doi.org/​10.1080/10406638.2020.1750436

  16. Department of Provincial Administration (DPA) (2021). Report on Population Size for Thailand in 2021. (accessed 13 March 2023). (in Thai)

  17. Dockery, D.W. (2009). Health effects of particulate air pollution. Ann. Epidemiol. 19, 257–263. https://doi.org/10.1016/j.annepidem.2009.01.018

  18. Engel-Cox, J.A., Hoff, R.M., Haymet, A.D.J. (2004). Recommendations on the use of satellite remote-sensing data for urban air quality. J. Air Waste Manage. Assoc. 54, 1360–1371. https://doi.org/10.1080/10473289.2004.10471005

  19. Faybishenko, B., Versteeg, R., Pastorello, G., Dwivedi, D., Varadharajan, C., Agarwal, D. (2021). Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data. Stochastic Environ. Res. Risk Assess. 36, 1049–1062. https://doi.org/10.1007/s00477-021-02106-w

  20. Feng, S., Gao, D., Liao, F., Zhou, F., Wang, X. (2016). The health effects of ambient PM2.5 and Potential Mechanisms. Ecotoxicol. Environ. Saf. 128, 67–74. https://doi.org/10.1016/j.ecoenv.​2016.01.030

  21. Fouzai, A., Smaoui, M., Frija, A., Dhehibi, B. (2018). Adoption of Conservation Agriculture Technologies by Smallholder Farmers in the semiarid region of Tunisia: Resource constraints and partial adoption. J. New Sci. 6, 105–114.

  22. Godin, G., Kok, G. (1996). The theory of planned behavior: a review of its applications to health-related behaviors. Am. J. Health Promot. 11, 87–98. https://doi.org/10.4278/0890-1171-11.2.87

  23. Hongthong, A., Nanthapong, K., Kanabkaew, T. (2022). Biomass burning emission inventory of multi-year PM10 and PM2.5 with high temporal and spatial resolution for Northern Thailand. Sci. Asia 48, 302–309. https://doi.org/10.2306/scienceasia1513-1874.2022.040

  24. Intra, P., Limueadphai, P., Tippayawong, N. (2010). Particulate emission reduction from biomass burning in small combustion systems with a multiple tubular electrostatic precipitator. Part. Sci. Technol. 28, 547–565. https://doi.org/10.1080/02726351003758444

  25. Janta, R., Chantara, S. (2017). Tree bark as bioindicator of metal accumulation from road traffic and air quality map: A case study of Chiang Mai, Thailand. Atmos. Pollut. Res. 8, 956–967. https://doi.org/10.1016/j.apr.2017.03.010

  26. Janta, R., Sekiguchi, K., Yamaguchi, R., Sopajaree, K., Plubin, B., Chetiyanukornkul, T. (2020). Spatial and temporal variations of atmospheric PM10 and air pollutants concentration in upper Northern Thailand during 2006–2016. Appl. Sci. Eng. Prog. 13, 256–267. http://doi.org/​10.14416/j.asep.2020.03.007

  27. 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. https://doi.org/10.3390/atmos9110449

  28. Kanabkaew, T., Kim Oanh, N.T. (2011). Development of spatial and temporal emission inventory for crop residue field burning. Environ. Model. Assess. 16, 453–464. https://doi.org/​10.1007/s10666-010-9244-0

  29. Kanchan, K., Gorai, A.K., Goyal, P. (2015). A review on air quality indexing system. Asian J. Atmos. Environ. 9, 101–113. https://doi.org/10.5572/ajae.2015.9.2.101

  30. Keywood, M., Cope, M., Meyer, C. M., Iinuma, Y., Emmerson, K. (2015). When smoke comes to town: The impact of biomass burning smoke on air quality. Atmos. Environ. 121, 13–21. https://doi.org/10.1016/j.atmosenv.2015.03.050

  31. Khamkaew, C., Chantara, S., Wiriya, W. (2016). Atmospheric PM2.5 and its elemental composition from near source and receptor sites during open burning season in Chiang Mai, Thailand. Int. J. Environ. Sci. Dev. 7, 436. https://doi.org/10.7763/IJESD.2016.V7.815

  32. Khodeir, M., Shamy, M., Alghamdi, M., Zhong, M., Sun, H., Costa, M., Chen, L.C., Maciejczyk, P. (2012). Source apportionment and elemental composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia. Atmos. Pollut. Res. 3, 331–340. https://doi.org/10.5094/APR.2012.037

  33. Kim Oanh, N.T., Leelasakultum, K. (2011). Analysis of meteorology and emission in haze episode prevalence over mountain-bounded region for early warning. Sci. Total Environ. 409, 2261–2271. https://doi.org/10.1016/j.scitotenv.2011.02.022

  34. Kitirianglarp, K. (2015). Chiangmai: The future of the creative economy in Thailand. Kyoto Review of Southeast Asia (Issue16).  (accessed on 23 February 2023).

  35. Kraisitnitikul, P., Thepnuan, D., Chansuebsri, S., Yabueng, N., Wiriya, W., Saksakulkrai, S., Shi, Z., Chantara, S. (2022). Contrasting compositions of PM2.5 in Northern Thailand during La Niña (2017) and El Niño (2019) years. J. Environ. Sci. 135, 585–599. https://doi.org/10.1016/j.jes.​2022.09.026

  36. Lahmar, R., Bationo, B.A., Lamso, N.D., Guéro, Y., Tittonell, P. (2012). Tailoring conservation agriculture technologies to West Africa semi-arid zones: building on traditional local practices for soil restoration. Field Crops Res. 132, 158–167. https://doi.org/10.1016/j.fcr.2011.09.013

  37. Lai, Y.Y., Lee, Y.M. (2022). Management strategy of plastic wastes in Taiwan. Sustainable Environ. Res. 32, 11. https://doi.org/10.1186/s42834-022-00123-0

  38. Langmann, B., Duncan, B., Textor, C., Trentmann, J., Van Der Werf, G.R. (2009). Vegetation fire emissions and their impact on air pollution and climate. Atmos. Environ. 43, 107–116. https://doi.org/10.1016/j.atmosenv.2008.09.047

  39. Li, J., Han, Z., Surapipith, V., Fan, W., Thingboonchoo, N., Wu, J., Li, J., Tao, J., Wu, Y., Macatangay, R. (2022). Direct and indirect effects and feedbacks of biomass burning aerosols over Mainland Southeast Asia and South China in springtime. Sci. Total Environ. 842, 156949. https://doi.org/​10.1016/j.scitotenv.2022.156949

  40. Lin, B., Huangfu, Y., Lima, N., Jobson, B., Kirk, M., O’Keeffe, P., Pressley, S.N., Walden, V., Lamb, B., Cook, D.J. (2017). Analyzing the relationship between human behavior and indoor air quality. J. Sens. Actuator Netw. 6, 13. https://doi.org/10.3390/jsan6030013

  41. Lin, N.H., Tsay, S.C., Maring, H.B., Yen, M.C., Sheu, G.R., Wang, S.H., Chi, K.H., Chuang, M.T., Ou-Yang, C.F., Fu, J.S., Reid, J.S., Lee, C.T., Wang, L.C., Wang, J.L., Hsu, C.N., Sayer, A.M., Holben, B.N., Chu, Y.C., Nguyen, X.A., Sopajaree, K., et al. (2013). An overview of regional experiments on biomass burning aerosols and related pollutants in Southeast Asia: From BASE-ASIA and the Dongsha Experiment to 7-SEAS. Atmos. Environ. 78, 1–19. https://doi.org/10.1016/j.atmosenv.​2013.04.066

  42. Lin, N.H., Sayer, A.M., Wang, S.H., Loftus, A.M., Hsiao, T.C., Sheu, G.R., Hsu, N.C., Tsay, S.C., Chantara, S. (2014). Interactions between biomass-burning aerosols and clouds over Southeast Asia: Current status, challenges, and perspectives. Environ. Pollut. 195, 292–307. https://doi.org/​10.1016/j.envpol.2014.06.036

  43. Liu, X., Cui, K., Hsieh, Y.K., Wang, Y.F., Wang, R. (2022). Study on air quality index, atmospheric pollutants and dry deposition of PCDD/Fs in the ambient air near Southwest China. Aerosol Air Qual. Res. 22, 220160. https://doi.org/10.4209/aaqr.220160

  44. Luo, S., Zhu, Y., Chen, S.X. (2022). Episode based air quality assessment. Atmos. Environ. 285, 119242. https://doi.org/10.1016/j.atmosenv.2022.119242

  45. Luong, N.D., Chuersuwan, N., Viet, H.T., Trung, B.Q. (2022). Impact of biomass burning sources during the high season on PM2.5 pollution observed at sampling sites in Hanoi, Vietnam and Chiang Rai, Thailand. APN Sci. Bull. 12, 56–65. https://doi.org/10.30852/sb.2022.1849

  46. Ministry of Interior (MOI) (2020a). Area of each district in Chiang Mai. (accessed on 13 March 2023).

  47. Ministry of Interior (MOI) (2020b). Area of each district in Chiang Rai. (accessed on 13 March 2023).

  48. Mohanakrishna, G., Modestra, J.A. (2023). Value addition through biohydrogen production and integrated processes from hydrothermal pretreatment of lignocellulosic biomass. Bioresour. Technol. 369, 128386. https://doi.org/10.1016/j.biortech.2022.128386

  49. Moran, J., NaSuwan, C., Poocharoen, O.O. (2019). The haze problem in Northern Thailand and policies to combat it: A review. Environ. Sci. Policy 97, 1–15. https://doi.org/10.1016/j.envsci.​2019.03.016

  50. Nyashina, G., Dorokhov, V., Romanov, D., Strizhak, P. (2022). Gas composition during thermochemical conversion of dry solid fuels and waste-derived slurries. Environ. Sci. Pollut. Res. 30, 24192–24211. https://doi.org/10.1007/s11356-022-23824-w

  51. Pani, S.K., Wang, S.H., Lin, N.H., Lee, C.T., Tsay, S.C., Holben, B., Janjai, S., Hsiao, T.C., Chuang, M.T., Chantara, S. (2016). Impact of springtime biomass-burning aerosols on radiative forcing over northern Thailand during the 7SEAS campaign. EGU General Assembly Conference, Vienna Austria, id. EPSC2016-11795.

  52. Pani, S.K., Lin, N.H., Chantara, S., Wang, S.H., Khamkaew, C., Prapamontol, T., Janjai, S. (2018). Radiative response of biomass-burning aerosols over an urban atmosphere in northern peninsular Southeast Asia Sci. Total Environ. 633, 892–911. https://doi.org/10.1016/j.scitotenv.​2018.03.204

  53. Pani, S.K., Chantara, S., Khamkaew, C., Lee, C.T., Lin, N.H. (2019) Biomass burning in the northern peninsular Southeast Asia: Aerosol chemical profile and potential exposure. Atmos. Res. 224, 180–195. https://doi.org/10.1016/j.atmosres.2019.03.031

  54. Pasukphun, N. (2018). Environmental health burden of open burning in Northern Thailand: a review. PSRU J. Sci. Tech. 3, 11–28. https://ph01.tci-thaijo.org/index.php/Scipsru/article/​view/143514

  55. Peng, Y., Cai, J., Feng, Y., Jiang, H., Chen, Y. (2023). Emission characteristic of OVOCs, I/SVOCs, OC and EC from wood combustion at different moisture contents. Atmos. Environ. 298, 119620. https://doi.org/10.1016/j.atmosenv.2023.119620

  56. Pengchai, P., Chantara, S., Sopajaree, K., Wangkarn, S., Tengcharoenkul, U., Rayanakorn, M. (2009). Seasonal variation, risk assessment and source estimation of PM10 and PM10-bound PAHs in the ambient air of Chiang Mai and Lamphun, Thailand. Environ. Monit. Assess. 154, 197–218. https://doi.org/10.1007/s10661-008-0389-0

  57. Phairuang, W., Hata, M., Furuuchi, M. (2017). Influence of agricultural activities, forest fires and agro-industries on air quality in Thailand J. Environ. Sci. 52, 85–97. https://doi.org/10.1016/​j.jes.2016.02.007

  58. Phairuang, W., Suwattiga, P., Chetiyanukornkul, T., Hongtieab, S., Limpaseni, W., Ikemori, F., Hata, M., Furuuchi, M. (2019). The influence of the open burning of agricultural biomass and forest fires in Thailand on the carbonaceous components in size-fractionated particles. Environ. Pollut. 247, 238–247. https://doi.org/10.1016/j.envpol.2019.01.001

  59. Plaia, A., Ruggieri, M. (2011). Air quality indices: a review Rev. Environ. Sci. Biotechnol. 10, 165–179. https://doi.org/10.1007/s11157-010-9227-2

  60. Pongpiachan, S., Choochuay, C., Chonchalar, J., Kanchai, P., Phonpiboon, T., Wongsuesat, S., Chomkhae, K., Kittikoon, I., Hiranyatrakul, P., Cao, J., Thamrongthanyawong, S. (2013). Chemical characterisation of organic functional group compositions in PM2.5 collected at nine administrative provinces in Northern Thailand during the haze episode in 2013. Asian Pac. J. Cancer Prev. 14, 3653–3661. https://doi.org/10.7314/APJCP.2013.14.6.3653

  61. Pongpiachan, S., Paowa, T. (2015). Hospital out-and-in-patients as functions of trace gaseous species and other meteorological parameters in Chiang-Mai, Thailand. Aerosol Air Qual. Res. 15, 479–493. https://doi.org/10.4209/aaqr.2013.09.0293

  62. Pongpiachan, S., Hattayanone, M., Cao, J. (2017). Effect of agricultural waste burning season on PM2.5-bound polycyclic aromatic hydrocarbon (PAH) levels in Northern Thailand. Atmos. Pollut. Res. 8, 1069–1080. https://doi.org/10.1016/j.apr.2017.04.009

  63. Pui, D.Y., Chen, S.C., Zuo, Z. (2014). PM2.5 in China: Measurements, sources, visibility and health effects, and mitigation. Particuology 13, 1–26. https://doi.org/10.1016/j.partic.2013.11.001

  64. Punsompong, P., Chantara, S. (2018). Identification of potential sources of PM10 pollution from biomass burning in northern Thailand using statistical analysis of trajectories. Atmos. Pollut. Res. 9, 1038–1051. https://doi.org/10.1016/j.apr.2018.04.003

  65. Ryan, S.M., Marker, H.C., Van Wickle, K., Winch, P.J. (2021). A scoping review of interventions targeting small-scale, individual-initiated burning practices. Environ. Res. 195, 110794. https://doi.org/10.1016/j.envres.2021.110794

  66. Ryou, H.G., Heo, J., Kim, S.Y. (2018). Source apportionment of PM10 and PM2.5 air pollution, and possible impacts of study characteristics in South Korea. Environ. Pollut. 240, 963–972. https://doi.org/10.1016/j.envpol.2018.03.066

  67. Saidur, R., Abdelaziz, E.A., Demirbas, A., Hossain, M.S., Mekhilef, S. (2011). A review on biomass as a fuel for boilers. Renewable Sustainable Energy Rev. 15, 2262–2289. https://doi.org/​10.1016/j.rser.2011.02.015

  68. Šauliene, I., Veriankaite, L. (2006). Application of backward air mass trajectory analysis in evaluating airborne pollen dispersion. J. Environ. Eng. Landscape Manage. 14, 113–120. https://doi.org/10.1080/16486897.2006.9636887

  69. Sayer, A.M., Hsu, N.C., Hsiao, T.C., Pantina, P., Kuo, F., Ou-Yang, C.F., Holben, B.N., Janjai, S., Chantara, S., Wang, S.H., Loftus, A.M., Lin, N.H., Tsay, S.C. (2016). In-situ and remotely-sensed observations of biomass burning aerosols at Doi Ang Khang, Thailand during 7-SEAS/BASELInE 2015. Aerosol Air Qual. Res. 16, 2786–2801. https://doi.org/10.4209/aaqr.2015.08.0500

  70. Semenza, J.C., Wilson, D.J., Parra, J., Bontempo, B.D., Hart, M., Sailor, D.J., George, L.A. (2008). Public perception and behavior change in relationship to hot weather and air pollution. Environ. Res. 107, 401–411. https://doi.org/10.1016/j.envres.2008.03.005

  71. Shi, T., Liu, Y., Zhang, L., Hao, L., Gao, Z. (2014). Burning in agricultural landscapes: an emerging natural and human issue in China. Landsc. Ecol. 29, 1785–1798. https://doi.org/10.1007/​s10980-014-0060-9

  72. Silalertruksa, T., Gheewala, S.H. (2013). A comparative LCA of rice straw utilization for fuels and fertilizer in Thailand. Bioresour. Technol. 150, 412–419. https://doi.org/10.1016/j.biortech.​2013.09.015

  73. Sirimongkonlertkun, N. (2012a). Clasification for burned area using Landsat 5TM and identification of backward trajectory air transport to Chiang Rai, Thailand. 33rd Asian Conference on Remote Sensing, Pattaya, Thailand, pp. 112–117.

  74. Sirimongkonlertkun, N. (2012b). Effect from open burning at Greater Mekong Sub-Region Nations to the PM10 concentration in Northern Thailand: a case study of backword trajectories in March 2012 at Chiang Rai Province. 1st Mae Fah Luang University International Conference, Chiang Rai, Thailand, pp. 29–30. 

  75. Sirithian, D., Thanatrakolsri, P. (2022). Relationships between meteorological and particulate matter concentrations (PM2.5 and PM10) during the haze period in urban and rural areas, Northern Thailand. Air Soil Water Res. 15, 1–15. https://doi.org/10.1177/11786221221117264

  76. Sresawasd, C., Chetiyanukornkul, T., Suriyawong, P., Tekasakul, S., Furuuchi, M., Hata, M., Malinee, R., Tekasakul, P., Dejchanchaiwong, R. (2021). Influence of meteorological conditions and fire hotspots on PM0.1 in Northern Thailand during strong haze episodes and carbonaceous aerosol characterization. Aerosol Air Qual. Res. 21, 210069. https://doi.org/10.4209/aaqr.​210069

  77. Statistica (2022). Countries with the highest total contribution of travel and tourism to employment worldwide from 2019 to 2021. (accessed on 23 August 2022).

  78. Striegel, M.F., Guin, E.B., Hallett, K., Sandoval, D., Swingle, R., Knox, K., Best, F., Fornea, S. (2003). Air pollution, coatings, and cultural resources. Prog. Org. Coat. 48, 281–288. https://doi.org/​10.1016/j.porgcoat.2003.05.001

  79. Sukitpaneenit, M., Kim Oanh, N.T. (2014). Satellite monitoring for carbon monoxide and particulate matter during forest fire episodes in Northern Thailand. Environ. Monit. Assess. 186, 2495–2504. https://doi.org/10.1007/s10661-013-3556-x

  80. Sun, J., Peng, H., Chen, J., Wang, X., Wei, M., Li, W., Yang, L., Zhang, Q., Wang, W., Mellouki, A. (2016). An estimation of CO2 emission via agricultural crop residue open field burning in China from 1996 to 2013. J. Cleaner Prod. 112, 2625–2631. https://doi.org/10.1016/j.jclepro.2015.​09.112

  81. Thepnuan, D., Chantara, S., Lee, C.T., Lin, N.H., Tsai, Y.I. (2019). Molecular markers for biomass burning associated with the characterization of PM2.5 and component sources during dry season haze episodes in Upper South East Asia. Sci. Total Environ. 658, 708–722. https://doi.org/10.1016/j.scitotenv.2018.12.201

  82. Tiyapairat, Y. (2012). Public sector responses to sustainable haze management in Upper Northern Thailand. Environmentasia 5, 1–10

  83. Tiyapairat, Y., Sajor, E.E. (2012). State simplification, heterogeneous causes of vegetation fires and implications on local haze management: case study in Thailand. Environ. Dev. Sustain. 14, 1047–1064. https://doi.org/10.1007/s10668-012-9358-4

  84. Tsai, W.T. (2022). Environmental policy for the restriction on the use of plastic products in Taiwan: regulatory measures, implementation status and Covid-19’s impacts on plastic products recycling. Environments 9, 7. https://doi.org/10.3390/environments9010007

  85. U.S. Environmental Protection Agency (U.S. EPA) (2008). Standard Operating Procedure for Particulate Matter (PM) Gravimetric Analysis. (accessed in 16 March 2023).

  86. U.S. Environmental Protection Agency (U.S. EPA) (2011). Guidelines for FRM and FEM Applicants. (accessed in 13 March 2023).

  87. Udeigwe, T.K., Teboh, J.M., Eze, P.N., Stietiya, M.H., Kumar, V., Hendrix, J., Mascagni, H.J., Ying, T., Kandakji, T. (2015). Implications of leading crop production practices on environmental quality and human health. J. Environ. Manage. 151, 267–279. https://doi.org/10.1016/j.​jenvman.2014.11.024

  88. Usmani, Z., Sharma, M., Awasthi, A.K., Sivakumar, N., Lukk, T., Pecoraro, L., Thakur, V.K., Roberts, D., Newbold, J., Gupta, V.K. (2021). Bioprocessing of waste biomass for sustainable product development and minimizing environmental impact. Bioresour. Technol. 322, 124548. https://doi.org/10.1016/j.biortech.2020.124548

  89. Visvanathan, C., Chiemchaisri, C. (2008). Management of agricultural wastes and residues in Thailand: wastes to energy approach. Asian Institute of Technology, Thailand.

  90. Viswanathan, S., Eria, L., Diunugala, N., Johnson, J., McClean, C. (2006). An analysis of effects of San Diego wildfire on ambient air quality. J. Air Waste Manage. Assoc. 56, 56–67. https://doi.org/10.1080/10473289.2006.10464439

  91. Von Blottnitz, H., Curran, M.A. (2007). A review of assessments conducted on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and environmental life cycle perspective. J. Cleaner Prod. 15, 607–619. https://doi.org/10.1016/j.jclepro.2006.03.002

  92. Wang, Q., Zhao, T., Wang, R., Zhang, L. (2022). Backward trajectory and multifractal analysis of air pollution in Zhengzhou Region of China. Math. Probl. Eng. 2022. https://doi.org/​10.1155/2022/2226565

  93. Wen, X., Chen, W., Chen, B., Yang, C., Tu, G., Cheng, T. (2020). Does the prohibition on open burning of straw mitigate air pollution? An empirical study in Jilin Province of China in the post-harvest season. J. Environ. Manage. 264, 110451. https://doi.org/10.1016/j.jenvman.2020.​110451

  94. Wu, Y.L., Davidson, C.I., Dolske, D.A., Sherwood, S.I. (1992). Dry deposition of atmospheric contaminants: the relative importance of aerodynamic, boundary layer, and surface resistances. Aerosol Sci. Technol. 16, 65–81. https://doi.org/10.1080/02786829208959538

  95. Xiao, S., Wang, Q.Y., Cao, J.J., Huang, R.J., Chen, W.D., Han, Y.M., Xu, H.M., Liu, S.X., Zhou, Y.Q., Wang, P., Zhang, J.Q., Zhan, C.L. (2014). Long-term trends in visibility and impacts of aerosol composition on visibility impairment in Baoji, China. Atmos. Res. 149, 88–95. https://doi.org/​10.1016/j.atmosres.2014.06.006

  96. Xing, L., Li, G., Pongpiachan, S., Wang, Q., Han, Y., Cao, J., Tipmanee, D., Palakun, J., Aukkaravittayapun, S., Surapipith, V., Poshyachinda, S. (2020). Quantifying the contributions of local emissions and regional transport to elemental carbon in Thailand. Environ. Pollut. 262, 114272. https://doi.org/10.1016/j.envpol.2020.114272

  97. Yabueng, N., Wiriya, W., Chantara, S. (2020). Influence of zero-burning policy and climate phenomena on ambient PM2.5 patterns and PAHs inhalation cancer risk during episodes of smoke haze in Northern Thailand. Atmos. Environ. 232, 117485. https://doi.org/10.1016/j.​atmosenv.2020.117485

  98. Yadav, I.C., Devi, N.L., Li, J., Syed, J.H., Zhang, G., Watanabe, H. (2017). Biomass burning in Indo-China peninsula and its impacts on regional air quality and global climate change-a review. Environ. Pollut. 227, 414–427. https://doi.org/10.1016/j.envpol.2017.04.085

  99. Yang, H.L., Innes, R. (2007). Economic incentives and residential waste management in Taiwan: an empirical investigation. Environ. Resource Econ. 37, 489–519. https://doi.org/10.1007/​s10640-006-9040-0

  100. Yang, L., Zhang, Y., Qi, W., Zhao, T., Zhang, L., Zhou, L., Ye, L. (2022). Adverse effects of PM2.5 on cardiovascular diseases. Rev. Environ. Health 37, 71–80. https://doi.org/10.1515/reveh-2020-0155

  101. Yin, S., Wang, X., Zhang, X., Guo, M., Miura, M., Xiao, Y. (2019). Influence of biomass burning on local air pollution in mainland Southeast Asia from 2001 to 2016. Environ. Pollut. 254, 112949. https://doi.org/10.1016/j.envpol.2019.07.117

  102. Zaib, S., Lu, J., Bilal, M. (2022). Spatio-temporal characteristics of air quality index (AQI) over Northwest China. Atmosphere 13, 375. https://doi.org/10.3390/atmos13030375

  103. Zhang, H., Hu, J., Qi, Y., Li, C., Chen, J., Wang, X., He, J., Wang, S., Hao, J., Zhang, L., Zhang, L., Zhang, Y., Li, R., Wang, S., Chai, F. (2017). Emission characterization, environmental impact, and control measure of PM2.5 emitted from agricultural crop residue burning in China. J. Cleaner Prod. 149, 629–635. https://doi.org/10.1016/j.jclepro.2017.02.092

  104. Zhang, J., Wu, X., Liu, S., Bai, Z., Xia, X., Chen, B., Zong, X., Bian, J. (2019). In situ measurements and backward-trajectory analysis of high-concentration, fine-mode aerosols in the UTLS over the Tibetan Plateau. Environ. Res. Lett. 14, 124068. https://doi.org/10.1088/1748-9326/ab5a9f

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