Sarawut Sukkhum1,2,4, Apiradee Lim This email address is being protected from spambots. You need JavaScript enabled to view it.1,4, Thammasin Ingviya3,4, Rattikan Saelim1,2 

1 Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani 94000, Thailand
2 Centre of Excellence in Mathematics, Commission on Higher Education, Ratchathewi, Bangkok 10400, Thailand
3 Department of Family and Preventive Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai Campus, Songkhla 90110, Thailand
4 Air Pollution and Health Effect Research Center, Faculty of Engineering, Prince of Songkla University, Hat Yai Campus, Songkhla 90110, Thailand


Received: November 17, 2021
Revised: March 22, 2022
Accepted: March 24, 2022

 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.210318  


Cite this article:

Sukkhum, S., Lim, A., Ingviya, T., Saelim, R. (2022). Seasonal Patterns and Trends of Air Pollution in the Upper Northern Thailand from 2004 to 2018. Aerosol Air Qual. Res. 22, 210318. https://doi.org/10.4209/aaqr.210318


HIGHLIGHTS

  • Air pollutants in the upper northern of Thailand have clear seasonal patterns.
  • Significant increasing trends in CO, O3, and SO2 during the past 15 years were found.
  • Increases in CO, O3, and SO2 are small compared to health-based air quality criteria.
  • NO2, NOx and PM10 had significant decreasing trends.
 

ABSTRACT


The seasonal patterns and trends of air pollutants can be used in assessments of air quality, facilitating authorities’ decisions on policies for monitoring and managing. The objective of this study was to investigate the seasonal patterns and trends of air pollutants in the upper northern Thailand (UNT) from 2004 to 2018. The hourly air pollutant concentration data including carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NOx), sulfur dioxide (SO2), ozone (O3), and particulate matter ≤ 10  µm (PM10) recorded from 6 monitoring stations in the UNT were obtained from the Pollution Control Department, Ministry of Natural Resources and Environment of Thailand. Cubic splines were used to assess seasonal patterns and trends of the air pollutants. Linear regression was used to estimate the average increase in concentrations of air pollutants at each monitoring station. The results exhibited seasonal patterns for CO, NOx, NO2, O3 and PM10, in all stations while SO2 exhibited seasonal patterns only in one station in Lampang and all stations in Chiangmai. The concentrations of these pollutants rose during August and September and reached peak levels in March. In the past 15 years, the levels of overall CO, O3 and SO2 in the UNT had significantly increased, on average by 0.015 ppm, 0.012 ppb and 0.015 ppb, respectively. In contrast, NO2, NOx and PM10 had significantly decreased on average of –0.010 ppb, –0.008 ppb and –0.011 µg m–3, respectively. The results provide information to the authorities for setting up proper measures and policies to control and mitigate air quality impacts especially in the dry season.


Keywords: Air pollutants, Air quality, Particulate matter, Northern Thailand


1 INTRODUCTION


The ambient air pollution level has received considerable attention in many countries, since air quality is a critical factor in climate change and also causes environmental and public health problems (Orru et al., 2017). Air pollution is caused by various events or activities, including human activities, volcanic eruptions, forest fires, crop burning, fossil fuel combustion for transportation, and various factories and power plants (Li et al., 2016). The main air pollutants contaminating in the atmosphere and affecting human health and environment consist of carbon monoxide (CO), ground-level ozone (O3), nitrogen oxides (NOx), sulfur dioxide (SO2) and particulate matter (PM) (Mustafić et al., 2012).

Investigating patterns and trends of air pollutant concentration is essential for authorities to set up proper air quality control policies and impact mitigation plans. The repeating patterns of air pollutants generally can exhibit variations by season, whereas a trend identifies tendency to increase, decrease or remain constant over time. Most air pollutants vary by season in the tropical countries such as Malaysia and Thailand, where CO, O3, NO2, SO2 and PM10, are comparatively high from January to March (Mohtar et al., 2018). All of these air pollutants are commonly higher in the dry season and lower in the rainy season (Matsuda et al., 2006; Kliengchuay et al., 2018, Lalitaporn, 2018; Outapa and Ivanovitch, 2019; Lalitaporn and Boonmee, 2019). Air pollution trends differ by pollutant and by country. In Japan and Thailand, decreasing trends of CO, NOx, SO2 and PM were found, whereas O3 was increasing (Aziz et al., 2016; Ito, et al., 2021). In Malaysia, CO and SO2 had decreasing trends, and O3 an increasing trend (Mohtar et al., 2018).

The northern region of Thailand faces major smog problems yearly due to crop residue burning during the dry season. The burning causes various pollutants, including particulate matter, gases and biological molecules, whose levels tend to exceed the air quality standards (Wiwatanadate and Liwsrisakun, 2011). The upper northern Thailand (UNT) has vast mountains reaching high altitudes, causing variable seasonal temperatures. The weather in the North is hot and humid, alternating with the dry season. There are three predominant seasons: the rainy season (May–October), the cold season (October–February), and the hot season (February–May) (Chantara et al., 2012). Every year between December and April, drought and forest fires occur together with crop residue burning by farmers to prepare for the next planting season at the onset of the rainy season. These activities cause haze over the whole UNT region (Chuang et al., 2016). Additionally, the government has formed policies and measures to prevent and tackle northern haze and forest fires. The reduction in burning in agricultural areas depends on provincial policy measures, which operate as guidelines under the existing legal mechanisms such as the Public Health Act 1992 and the National Disaster Prevention and Mitigation Act 2007, to control areas or the sources that cause PM10 (Department of Disaster Prevention and Mitigation, 2015).

The Thai Pollution Control Department, Ministry of Natural Resources and Environment, has continuously collected hourly air quality data in UNT from six air monitoring stations since 1996 until present (Pollution Control Department, 2020). However, the utilization of these longitudinal air pollution data to assess the air pollution situation in this area has not been vigorous. Moreover, there are limited studies on air pollutants situation in this area using rigorous statistical methods. Therefore, this study aimed to investigate seasonal patterns and trends of air pollutants in the UNT.

 
2 METHODS


 
2.1 Data Source

The air quality data collected from 2004 to 2018 were obtained from the Pollution Control Department, Ministry of Natural Resources and Environment of Thailand. The hourly air pollutant concentration data of CO, O3, NO2, NOx, SO2 and PM10 were assessed from 6 air quality monitoring stations in UNT (3 stations in Lampang, two stations in Chiang Mai, and one station in Nan provinces) as shown in Table 1. The locations of the monitoring stations are shown in Fig. 1.

Table 1. The characteristic of six air quality monitoring stations in the UNT.

Fig. 1. Map of the study area in the UNT.
Fig. 1. Map of the study area in the UNT.

The national ambient air quality standards in Thailand and the ambient air concentration measurement methods for each pollutant are listed in Table 2. The average values of the measurements were transmitted hourly to a central computer from each station by a data logger and a modem.

Table 2. National ambient air quality standards in Thailand: methods and techniques used in each air quality monitoring station for measuring ambient air pollutant concentrations.

The outcomes of this study are assessment of air pollutant concentrations of CO, O3, NO2, NOx, SO2 and PM10. The hourly observations of these pollutants were aggregated into weekly averages, thus obtaining 52 observations per year. The determinant is time (52 weeks from 15 years each). There are 780 records for each station. Data transformation was performed by taking the natural logarithm, after adding one and dividing or multiplying constant value to all air quality pollutants.

 
2.2 Statistical Analysis

Descriptive analysis was carried out to summarize the data using measures of central tendency and dispersion. Boxplots and quantile-quantile (Q-Q) plots were used to assess the distribution of each pollutant. The trends and seasonal patterns of air pollutants were assessed from weekly average data. The seasonal patterns were presented by plotting the average weekly air pollutant concentration against the weeks of the year by station. The trends of air pollutants were shown by plotting the average weekly air pollutant concentrations against 15 years by station. The spline function was used to display the trends and seasonal patterns of each air pollutant at each station. Time series plots were created for each air pollutant and station with an adequate number of knots, and their positions were fixed to smoothen the spline curve. The formula for a cubic spline function is as follows

 

where s(t) is the spline function, a, b, and ck are parameters in the model, k indexes the location of each knot, i represents the observation from 1, 2, …, n, ti denotes time in weeks, that is specified from 15 years, t1 < t2 < … < tp are specified knots and (ti  tk)+ means the positive part of (ti  tk) (i.e., set to zero if negative). Six knots were assigned to the data. Then, linear model was used to estimate the seasonality for the time series (Wongsai et al., 2017). The model takes the following form

 

where a is constant, b0, b2, …, bk are the coefficients from the model, c1, c2, …, ck are the coefficients derived from cubic spline function with k = 3 as we assigned six knots to the data, more details are explained elsewhere (Abdulmana et al., 2021). Then, the air pollutant concentration data were seasonally adjusted by subtracting the fitted values from observed air pollutants in each time series observation and adding the mean of the seasonal component. The formula takes the form,

 

where, zi is the seasonally adjusted air pollutant concentration at time i, yi is the observed air pollution concentration, ŷi is the fitted value from the linear model and y̅ is the overall mean of observed air pollutant.

Time series models are based on stationarity with observed values having constant mean and variance over time. In this study, the autoregressive integrated moving average (ARIMA) model was used to analyze annual trends of each pollutant at each station. The parameters in this model are p, d, and q, where p is the number of autoregressive terms, d is the number of non-seasonal differences, and q is the number of lagged forecast errors in the prediction equation (Brockwell, 2010). The performance evaluations of the adopted models are carried out on the basis of correlation coefficient (r2). In these studies, we used ARIMA (1, 0, 0), as the air pollutant concentration data were already seasonally adjusted. Therefore, differencing parameter (d = 0) was equal to zero and the parameter of moving average was also equal to zero. Therefore, an autoregressive (AR) model using cubic spline function (Eq. (4)) was used for fitting the annual trend of each pollutant at each station. The formula takes the form,

 

where α is a constant, ϕ is autoregressive parameters, and ei or error is white noise as the error value at time i. The filtered autocorrelation in seasonally adjusted air pollutant concentration (vi) can be calculated using the following equation.

 

where, e is the fitted residual from AR model, s is the standard deviation of each air pollutant and µi is constant term, which is the mean of the seasonally adjusted air pollutants.

A multivariate regression model incorporating the filtered autocorrelation in seasonally adjusted weekly average air pollutant concentrations was used to examine the average changes of air pollutants concentration over 15 years at six stations as follows.

 

Here, fij is the filtered autocorrelation in seasonally adjusted air pollutant concentration at observation i in station j, αi is the intercept, β is the regression coefficient, and εij is the error term. After fitting the multivariate regression model, the normality assumption of residuals was evaluated for determining whether this model is appropriate. Confidence interval plots were created to illustrate the overall increase of air pollutant concentration at each station and the whole UNT area. All statistical analyses and graphical plots were performed using R statistical program version 3.6.3 (R Core Team, 2020).

 
3 RESULTS


The percent of missing data by each air pollutant, station and year ranged from zero to 76.9% as shown in Table 3. The means and standard deviations by pollutants, stations and years are shown in Supplementary Material. The distributions of the weekly average concentration of the transformed pollutants are shown in Fig. 2. All pollutants except SO2 followed the normal distribution as most of data points are along the diagonal line. The percentages of missing values are also shown in the plot with the range from 10.1% for SO2 to 18.4% for CO.

Table 3. Percent of missing values for each pollutant, by station and year.

Fig. 2. Quantile-quantile (Q-Q) plots of weekly average air pollutant concentrations in UNT from 2004 to 2018.Fig. 2. Quantile-quantile (Q-Q) plots of weekly average air pollutant concentrations in UNT from 2004 to 2018.

The distribution of the weekly average concentration of each air pollutant by station in the UNT is shown in the boxplots of Fig. 3. The percent of missing data for each pollutant by station is also depicted in the figure. The highest median CO, NO2, NOx and PM10 concentrations were found at CM2 station (0.77 ppm, 14.18 ppb, 22.77 ppb and 38.45 µg m3, respectively) while the highest median O3 and SO2 were found at CM1 station in Chiang Mai and at LP3 station in Lampang province (20.5 ppb and 1.11 ppb, respectively). The LP3 station in Lampang province had the lowest medians for CO and NO2 (0.28 ppm and 2.68 ppb, respectively). On the other hand, the CM2 station in Chang Mai had the lowest median O3 at 15.79 ppb. PM10 in NN station in Nan province had the lowest median at 29.87 µg m3.

Fig. 3. Distribution of weekly average air pollutant concentrations at 6 stations in the UNT between 2004 and 2018.Fig. 3. Distribution of weekly average air pollutant concentrations at 6 stations in the UNT between 2004 and 2018.

 
3.1 Seasonal Patterns of Air Pollutants

Figs. 4 and 5 show seasonal patterns of weekly average air pollutant concentrations over 15 years at each station. The cubic spline was fitted to the seasonal pattern of an air pollutant and plotted against the 52 weeks of year for each station. Medians were fitted to assess the seasonal patterns of each air pollutant concentration and these are shown as blue lines. Besides, the six knots placed at week 1, 9, 19, 34, 44, 52 were selected to get smooth spline curves. The positions of the knots are shown with plus (+) symbols. The results showed that, overall, the air pollutant concentrations had seasonal patterns. The concentration gradually increased during the first week in December (cold) and peaked between March and April (summer season). Then it gradually decreased from May to September (rainy season), except for SO2 which had no seasonal patterns in LP2, LP3 and NN stations.

Fig. 4. Seasonal patterns of weekly average air pollutant concentrations at LP1, LP2 and LP3 stations.Fig. 4. Seasonal patterns of weekly average air pollutant concentrations at LP1, LP2 and LP3 stations.

 Fig. 5. Seasonal patterns of weekly average air pollutant concentrations at CM1, CM2 and NN1 stations.Fig. 5. Seasonal patterns of weekly average air pollutant concentrations at CM1, CM2 and NN1 stations.

 
3.2 Trends of Air Pollutants

Figs. 6 and 7 show plots of annual trends, or show the season-adjusted time series of weekly average air pollutant concentrations for 15 years at each station, with time (years) on x-axis and pollutant concentration on y-axis. The coefficients of autocorrelation and r-squared are shown on top of each plot. Four knots placed in 2004, 2009, 2014 and 2019 were selected for smooth spline curves, which divided the data equally into 5-year intervals. The positions of the knots are shown with plus (+) symbols. Annual trends of weekly average air pollutant concentrations for 15 years at 6 stations in the UNT revealed that overall, the air pollutant concentrations of NO2, NOx and PM10 in UNT had decreasing trends. In contrast, most CO, O3, and SO2 concentrations had increasing trends. Also, the trends at some stations had a fluctuating pattern, with some fluctuations increasing or decreasing with the average level of the time series. As an example, the CO concentration levels at LP1 and LP2 stations had decreasing trends from 2004 to 2008 and then the gradually increasing trends occurred from 2009 to 2013, with decreasing again from 2014 to 2018.

Fig. 6. Annual trends of weekly average air pollutant concentrations at LP1, LP2 and LP3 stations.Fig. 6. Annual trends of weekly average air pollutant concentrations at LP1, LP2 and LP3 stations.

Fig. 7. Annual trends of weekly average air pollutant concentrations at CM1, CM2 and NN1 stations.Fig. 7. Annual trends of weekly average air pollutant concentrations at CM1, CM2 and NN1 stations.

Fig. 8 shows the increases in weekly average air pollutant concentrations with 95% confidence intervals for each station in UNT, from 2004 to 2018, using the linear model. The results show that overall trends of weekly average CO concentration levels at LP1, LP2, CM2 and NN stations had significantly increased means by 0.032 ppm (95% CI, 0.027 to 0.038 ppm), 0.073 ppm (95% CI, 0.068 to 0.078 ppm), 0.005 (95% CI, 0.001 to 0.009 ppm) and 0.026 ppm (95% CI, 0.017 to 0.036 ppm), respectively. In contrast, the stations LP3 had significantly decreased mean by –0.022 ppm (95% CI, –0.035 to –0.010 ppm) and CM1 had borderline significantly decreased mean by –0.006 ppm (95% CI, –0.012 to 0.0001 ppm). Besides, the weekly average O3 concentrations at LP1, LP2, LP3, CM1 and CM2 stations had significantly increased means by 0.016 ppb (95% CI, 0.014 to 0.018 ppb), 0.010 ppb (95% CI, 0.008 to 0.012 ppb), 0.022 ppb (95% CI, 0.020 to 0.025 ppb), 0.004 ppb (95% CI, 0.002 to 0.006 ppb) and 0.008 ppb (95% CI, 0.005 to 0.011 ppb). The overall trends of weekly average NO2 concentrations at LP1 and LP2 stations had a significantly decreased means by –0.045 ppb (95% CI, –0.050 to –0.039 ppb) and –0.004 ppb (95% CI, –0.007 to –0.0004 ppb). In contrast, the stations LP3 had significantly increased mean by 0.008 ppb (95% CI, 0.004 to 0.012 ppb). Additionally, the weekly average of the NOx concentration at LP1 and LP3 stations had significantly decreased means by –0.048 ppb (95% CI, –0.055 to –0.042 ppb) and –0.009 (95% CI, –0.014 to –0.003 ppb). However, LP2 and CM2 had significantly increased means by 0.021 ppb (95% CI, 0.015 to 0.026 ppb) and 0.007 ppb (95% CI, 0.003 to 0.011 ppb). The weekly average of SO2 concentration at LP1, LP2, LP3 stations and CM1 station had significantly increased means by 0.041 ppb (95% CI, 0.035 to 0.046 ppb), 0.040 ppb (95% CI, 0.035 to 0.045 ppb), 0.007 ppb (95% CI, 0.001 to 0.013 ppb) and 0.008 ppb (95% CI, 0.002 to 0.013 ppb), respectively. The average weekly PM10 concentration level at LP1, LP2, CM1 and CM2 stations had significantly decreased means by –0.021 µg m3 (95% CI, –0.026 to –0.015 µg m3), –0.017 µg m3 (95% CI, –0.023 to –0.011 µg m3), –0.013 µg m3 (95% CI, –0.020 to –0.006 µg m3) and –0.018 µg m3 (95% CI, –0.023 to –0.012 µg m3), respectively. For the whole of UNT region, CO, O3 and SO2 had significantly increased, on average by 0.015 ppm (95% CI, 0.012 to 0.017 ppm), 0.012 ppb (95% CI, 0.011 to0.014 ppb) and 0.015 ppb (95% CI, 0.013 to 0.018 ppb), respectively. On the other hand, NO2, NOx and PM10 had significantly decreased on average by –0.010 ppb (95% CI, –0.012 to –0.008 ppb), –0.008 ppb (95% CI, –0.010 to 0.005 ppb) and –0.011 µg m3 (95% CI, –0.014 to –0.007 µg m3), respectively.

Fig. 8. Average weekly air pollutant concentrations increase from 2004 to 2018 with confidence intervals of each station in the UNT.Fig. 8. Average weekly air pollutant concentrations increase from 2004 to 2018 with confidence intervals of each station in the UNT.

 
4 DISCUSSION


This study applied robust statistical techniques to investigate seasonal patterns and trends of air pollutant concentrations in the UNT during the years 2004 to 2018. Seasonal patterns of CO, O3, NO2, NOx, SO2 and PM10, were found, with peak concentrations observed in March. CO, O3 and SO2 had significantly increasing trends whereas NO2, NOx and PM10 had significantly decreasing trends over time since 2004.

In our study, all air pollutants peaked in March. This finding is consistent with studies conducted by Lalitaporn (2018) and Lalitaporn and Boonmee (2019), which reported that seasonal emissions in UNT caused high levels of CO and NO2 during the dry season, from anthropogenic and biomass burning activities, while the levels were lower during the rainy season. Matsuda et al. (2006) also reported that SO2 and O3 concentrations increased in the dry (January–April) season and decreased in the wet (May–August) season. Such trends are caused by the dry deposition above a forest in a tropical savanna climate in Mae Moh, Lampang Province. In addition, the O3 formation caused by secondary reactions between Volatile Organic Compounds (VOCs) and NOx, catalyzed by ultraviolet radiation, were also higher in the summer than in the winter. NO2 and PM10 in northern Thailand were higher during the dry season between January and April (Wiriya et al., 2013; Kliengchuay et al., 2018; Outapa and Ivanovitch, 2019; Lalitaporn and Boonmee, 2019). An increase in PM10 is common in summers with highest level in March due to low precipitation and open burning in agricultural areas during harvesting season (Janta, 2020).

The overall CO concentration was found to have significantly increased. This finding supports a study conducted by Lalitaporn (2018), who found a significant increase in CO concentrations in the UNT from 2001–2015. This might be due to the intensive biomass burning and gasoline-powered vehicles.

A significant increase in the overall concentration of O3 was also found in our study. This result is consistent with findings of Sonkaew and Macatangay (2015), which showed that the O3 in the troposphere of northern regions in Thailand had a slightly increasing 8-year trend from 2005 to 2012, especially during the forest-clearing and agricultural burning season that usually occurs during February to April. NO2 and NOx in our study had been significantly declining in some station over time since 2004. Our finding in contrast to a study conducted by Lalitaporn and Boonmee (2019), which found a slight increase over 6 years in NO2 concentration levels from satellite and ground measurements over northern Thailand for 2010 to 2016. NOx and NO2 concentrations both have the same sources from biomass combustion and fossil fuels (Dell’Antonia et al., 2012). In addition, the O3 formation is caused by secondary reactions between Volatile Organic Compounds (VOCs) and NOx, catalyzed by ultraviolet radiation (Laothawornkitkul et al., 2009; Fakkaew et al., 2019). Han et al. (2011) reported an inverse relationship between O3 and NOx/NO2 whereas NOx and NO2 have a positive linear relationship.

Significant increase overall in SO2 concentration was observed in our study, especially at LP1 and LP2 stations, consistent with a study conducted by Thepanondh et al. (2002). This might be due to the emissions of SO2 and sulphate from coal-fired power plants operated in Lampang province, Thailand (Thepanondh et al., 2002).

Moreover, this study found that PM10 had significantly decreased over 15 years. The result is in line with the finding by Aziz et al. (2016) of decreasing trend for PM10 in Bangkok. The decrease of PM10 in UNT might due to the implementation of the zero-burning policy on open burning, for about a 3-month-long period (mid-February to mid-May) in the dry season of northern Thailand during 2017 to 2018 (Yabueng et al., 2020). This might have reduced air pollutants, compared to 15 years prior.

The statistically significant increases in CO, O3, and SO2 in the past 15 years by 0.015 ppm, 0.012 ppb, and 0.015 ppb in this study are relatively small when compared to health-based air quality standards in Thailand (Pollution Control Department, 2021). On the other hand, the significant decreases in NO2, NOx, and PM10 by –0.010 ppb, –0.008 ppb, and –0.011 mg m–3 are also relatively small. These small changes could be due to very small changes in emissions or as a consequence of weather conditions that were not taken into account in this study. A limitation of this study is that climatological factors such as wind speed, wind direction, mixing height, rainfall, humidity, and temperature were not considered.

 
5 CONCLUSIONS


This study concludes that the seasonal patterns of most pollutant concentrations in the UNT were similar with the highest levels during the summer. The overall trends in most stations of CO, O3 and SO2 concentrations were steadily increasing, whereas PM10 concentration had been declining over the 15 years assessed in this study. Furthermore, NO2 and NOx concentrations had very slightly decreased. Despite regulations to control open crop residue burning, the air pollution in the UNT during the summer remains high every year. This indicates that the current burning control policies are not adequate, and government should implement more effective burning control policies to reduce the air pollution levels.

 
ACKNOWLEDGEMENTS


This research was supported by 1) the National Science, Research and Innovation Fund (NSRF) and Prince of Songkla University (Grant No SAT64050885), 2) the Air Pollution and Health Effect Research Center, Faculty of Engineering and 3) the Graduate School, Prince of Songkla University. We extend our profound appreciation to the Pollution Control Department, Ministry of Natural Resources and Environment of Thailand, for providing the data for this study. We are in gratitude to Emeritus Prof. Don McNeil for his guidance, comments and suggestions throughout this research.


REFERENCES


  1. Abdulmana, S., Lim, A., Wongsai, S., Wongsai, N. (2021). Land surface temperature and vegetation cover changes and their relationships in Taiwan from 2000 to 2020. Remote Sens. Appl.: Soc. Environ. 24, 100636. https://doi.org/10.1016/j.rsase.2021.100636

  2. Aziz, T.A., Xu, R., Fan, C., Shah, M., Boonman, T., Thao, P., Bonnet, S., Garivait, S. (2016). Analysis of spatial and temporal variation of criteria air pollutants in Bangkok Metropolitan Region (BMR) during 2000-2015. 1st International Electronic Conference on Atmospheric Sciences, Paris, France. https://doi.org/10.3390/ecas2016-B006

  3. Brockwell, P.J. (2010). Time series analysis. in: Peterson, P., Baker, E., McGaw, B. (Eds.), International Encyclopedia of Education, 3rd edn. Elsevier, New York, pp 474–481.

  4. 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

  5. Chuang, H.C., Hsiao, T.C., Wang, S.H., Tsay, S.C., Lin, N.H. (2016). Characterization of particulate matter profiling and alveolar deposition from biomass burning in Northern Thailand: The 7-SEAS study. Aerosol Air Qual. Res. 16, 2897–2906. https://doi.org/10.4209/aaqr.2015.08.0502

  6. Dell’Antonia, D., Gubiani, R., Maroncelli, D., Pergher, D. (2012). Gaseous emissions from fossil fuels and biomass combustion in small heating appliances. J. Agri. Eng. 4, 37–46. https://doi.org/10.4081/jae.2010.4.37

  7. Department of Disaster Prevention and Mitigation (2015). National disaster risk management plan. (accessed 10 August 2020).

  8. Fakkaew, N., Rungratanaubon, T., Bualert, S., Karnasuta, S., Thongyen, T., Veesommai, C., Dampin, N. (2019). Generation of volatile organic compounds (VOCs), Oxides of nitrogen (NOx), and ozone (O3) during smog case study: Chiang Rai Province. J. Assoc. Res. 24, 31–40. 

  9. Han, S., Bian, H., Feng, Y., Liu, A., Li, X., Zeng, F., Zhang, X. (2011). Analysis of the relationship between O3, NO and NO2 in Tianjin, China. Aerosol Air Qual. Res. 11, 128–139. https://doi.org/10.4209/aaqr.2010.07.0055

  10. Ito, A., Wakamatsu, S., Morikawa, T., Kobayashi, S. (2021). 30 years of air quality trends in Japan. Atmosphere. 12, 1072. https://doi.org/10.3390/atmos12081072

  11. Janta, R. (2020). Spatial and temporal variations atmospheric PM10 and air pollutants concentration in upper northern Thailand during 2006-2016. Appl. Sci. Eng. Prog. 13, 256–267. https://doi.org/10.14416/j.asep.2020.03.007

  12. Kliengchuay, W., Cooper Meeyai, A., Worakhunpiset, S., Tantrakarnapa, K. (2018). Relationships between meteorological parameters and particulate matter in Mae Hong Son province, Thailand. Int. J. Environ. Res. Public Health 15, 2801. https://doi.org/10.3390/ijerph15122801

  13. Lalitaporn, P. (2018). Long-term assessment of carbon monoxide using MOPITT satellite and surface data over Thailand. Eng. Appl. Sci. Res. 45, 132–139.

  14. Lalitaporn, P., Boonmee, T. (2019). Analysis of tropospheric nitrogen dioxide using satellite and ground based data over northern Thailand. Eng. J. 23, 19–35. https://doi.org/10.4186/ej.​2019.23.6.19

  15. Laothawornkitkul, J., Taylor, J.E., Paul, N.D., Hewitt, C.N. (2009). Biogenic volatile organic compounds in the earth system. New Phytol. 183, 27–51. https://doi.org/10.1111/j.1469-8137.2009.02859.x

  16. Li, X., Yang, Y., Xu, X., Xu, C., Hong, J. (2016). Air pollution from polycyclic aromatic hydrocarbons generated by human activities and their health effects in China. J. Cleaner Prod. 112, 360–1367. https://doi.org/10.1016/j.jclepro.2015.05.077

  17. Matsuda, K., Watanabe, I., Wingpud, V., Theramongkol, P., Ohizumi, T. (2006). Deposition velocity of O3 and SO2 in the dry and wet season above a tropical forest in northern Thailand. Atmos. Environ. 40, 7557–7564. https://doi.org/10.1016/j.atmosenv.2006.07.003

  18. Mohtar, A.A.A., Latif, M.T., Baharudin, N.H., Ahamad, F., Chung, J.X., Othman, M., Juneng, L. (2018). Variation of major air pollutants in different seasonal conditions in an urban environment in Malaysia. Geosci. Lett. 5, 21. https://doi.org/10.1186/s40562-018-0122-y

  19. Mustafić, H., Jabre, P., Caussin, C., Murad, M.H., Escolano, S., Tafflet, M., Périer, M.C., Marijon, E., Vernerey, D., Empana, J.P., Jouven, X. (2012). Main air pollutants and myocardial infarction: A Systematic review and meta-analysis. JAMA 307, 713–721. https://doi.org/10.1001/jama.​2012.126

  20. Orru, H., Ebi, K.L., Forsberg, B. (2017). The interplay of climate change and air pollution on health. Curr. Environ. Health Rep. 4, 504–513. https://doi.org/10.1007/s40572-017-0168-6

  21. Outapa, P., Ivanovitch, K. (2019). The effect of seasonal variation and meteorological data on PM10 concentrations in Northern Thailand. Int. J. GEOMATE 16, 46–53. https://doi.org/​10.21660/2019.56.4558

  22. Pollution Control Department (2021). National ambient air quality standard of Thailand. (accessed 30 May 2021).

  23. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. (accessed 20 July 2020).

  24. Sonkaew, T., Macatangay, R. (2015). Determining relationships and mechanisms between tropospheric ozone column concentrations and tropical biomass burning in Thailand and its surrounding regions. Environ. Res. Lett. 10, 065009. https://doi.org/10.1088/1748-9326/10/​6/065009

  25. Thai Pollution Control Department (2020). Situation and air quality report in Thailand. (accessed 19 July 2020).

  26. Thepanondh, S., Hooper, M., Ayers, G. (2002). Dry deposition of atmospheric sulphur species in northern Thailand: A report on methods and early results. The 16th International Clean Air and Environment Conference, 19-22 August, Christchurch, New Zealand. 

  27. Wiriya, W., Prapamontol, T., Chantara, S. (2013). PM10-bound polycyclic aromatic hydrocarbons in Chiang Mai (Thailand): seasonal variations, source identification, health risk assessment and their relationship to air-mass movement. Atmos. Res. 124, 109–122. https://doi.org/10.1016/​j.atmosres.2012.12.014

  28. Wiwatanadate, P., Liwsrisakun, C. (2011). Acute effects of air pollution on peak expiratory flow rates and symptoms among asthmatic patients in Chiang Mai, Thailand. Int. J. Hyg. Environ. Health 214, 251–257. https://doi.org/10.1016/j.ijheh.2011.03.003

  29. Wongsai, N., Wongsai, S., Huete, A. (2017). Annual seasonality extraction using the cubic spline function and decadal trend in temporal daytime MODIS LST data. Remote Sens. 9, 1254. https://doi.org/10.3390/rs9121254

  30. 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


Share this article with your colleagues 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

6.5
2021CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2021 Impact Factor: 4.53
5-Year Impact Factor: 3.668

Aerosol and Air Quality Research partners with Publons

Aerosol and Air Quality Research partners with Publons

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.