Salwa Naidin1, Justin Sentian This email address is being protected from spambots. You need JavaScript enabled to view it.1, Farrah Anis Fazliatul Adnan1, Franky Herman1, Siti Rahayu Mohd Hashim2 

1 Climate Change Research Lab (CCRL), Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Sabah, Malaysia
2 Department of Mathematics and Economics, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia


Received: July 24, 2023
Revised: November 16, 2023
Accepted: November 16, 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.230176  

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Cite this article:

Naidin, S., Sentian, J., Adnan, F.A.F., Herman, F., Mohd Hashim, S.R. (2024). Decade-Long Analysis: Unravelling the Spatio-Temporal Dynamics of PM10 Concentrations in Malaysian Borneo. Aerosol Air Qual. Res. 24, 230176. https://doi.org/10.4209/aaqr.230176


HIGHLIGHTS

  • Analysis spatial and temporal variations of PM10 across Malaysian Borneo from 2006 to 2016.
  • The highest frequency of exceedance was observed in year 2006, 2015, and 2009.
  • Intense biomass burning is the primary source of transboundary PM10 pollution.
  • The variation in PM10 concentration is influenced by the monsoon season and El Nino.
 

ABSTRACT


High levels of particulate matter in the air, caused by air pollution from neighbouring countries, have always been a major problem in Malaysia. For many years, Malaysia has experienced a hazy atmosphere due to high levels of particulate matter (PM10) from regional biomass burning and other human activities. This study aims to analyse the changes in PM10 levels over time in Malaysian Borneo. Data collected from air quality monitoring stations over a 10-year period (2006–2016) were obtained from the Malaysian Department of Environment (DOE). Statistical analyses were conducted using the Mann Kendall test to examine trends in PM10 concentrations. The study divided Sabah and Sarawak into three regions: Northern Malaysian Borneo, Central Malaysian Borneo, and Southern Malaysian Borneo. Throughout the studied period, the highest levels of PM10 were primarily found in Southern Malaysian Borneo, with the highest concentration recorded in Sibu (434.38 µg m–3). The monitoring stations in Miri, Limbang, and Kota Kinabalu showed an increasing pollution trend, while Kuching, Sri Aman, Bintulu, Kapit, Sandakan, Tawau, and Keningau showed a significant decreasing trend. No significant trend was observed in Kota Samarahan and Sarikei. The highest annual PM10 exceedances, surpassing the Recommended Malaysian Ambient Air Quality Guideline (RMAAQG) of 150 µg m–3, occurred in 2015 and 2009 with 80 and 65 days respectively in 2006. Biomass burning is identified as the primary source of emissions, contributing to the significant monthly and seasonal variations in this region. Meteorological conditions and the El Niño phenomenon were observed to exert a significant influence on the concentration and distribution of PM10 in this area. In order to improve air quality in Malaysian Borneo, it is necessary to take a multifaceted approach encompassing source emissions reduction, inter-country collaboration, region-wide strategies for land and forest management improvement, and reinforced cooperation on pollution monitoring, reporting and reduction efforts.


Keywords: Biomass burning, El Nino, Monsoonal effect, PM10, Transboundary pollution


1 INTRODUCTION


In Malaysia, the pollution caused by ambient particulate matter remains a significant threat to the health of the public and the sustainability of the environment. Over the years, Malaysia has regularly experienced periods of high pollution where there were elevated levels of particulate matter in the air. These episodes are known to occur during specific seasons and are heavily influenced by pollution originating from neighbouring countries. Previous studies, conducted by Md Yusof et al. (2010), Azmi et al. (2010), Ghani et al. (2017), Latif et al. (2014), and Sentian et al. (2019), have shown that transboundary pollution from the burning of biomass in Indonesia has been linked to these episodes of high particulate matter concentration. These elevated concentrations are frequently observed in the southern and western regions of Peninsular Malaysia during the southwest monsoon season, which lasts from June to October.

In Peninsular Malaysia, between 1997 and 2001, several monitoring sites recorded PM10 concentrations that exceeded the Recommended Malaysian Air Quality Guideline (RMAAQG) (150 µg m–3). The highest concentration was recorded at Seremban Station (central region), at 353.83 µg m–3. The main cause of these high monthly concentrations of particulate matter over Peninsula Malaysia was found to be long-range transboundary pollution from Sumatera, Indonesia. This coincides with the arrival of large air masses originating from Indonesia's Sumatera region, where extensive biomass burning takes place (Anwar et al., 2010; Juneng et al., 2011; Latif et al., 2014; Sentian et al., 2019). The central region of Peninsular Malaysia, which is the most urbanised area in the country, experienced the highest concentration of pollutants and had the greatest variability in PM10 levels, with coefficient variations ranging from 29.17% to 37.61% (Sentian et al., 2019). In contrast, the southern region showed a decreasing trend in pollution levels but had a high degree of spatial variance, with variation coefficients ranging from 25.39% to 31.61%.

Local anthropogenic sources, primarily from transportation and industry, are a significant source of pollution in heavily urbanised regions. These activities contribute to high levels of particulate matter and other harmful gases such as nitrogen oxide (NOx) and carbon monoxide (CO). This pollution is especially prominent in the southern, central, and northern areas of the Malaysian Peninsular, as reported by Azmi et al. (2010), Latif et al. (2014), and Sentian et al. (2019). The haze phenomenon, which causes poor visibility and elevated levels of particulate matter over the Malaysian Peninsular, particularly affects densely populated areas like Klang Valley and the southern part of the peninsula during the southwest monsoon season. Numerous studies have extensively investigated this issue, focusing on the deterioration of air quality (Juneng et al., 2011; Latif et al., 2014; Khan et al., 2016; Sentian et al., 2019) and potential health effects (Omar et al., 2006; Latif et al., 2014; Othman et al., 2014; Ab Manan et al., 2016; Wan Yaacob et al., 2016).

The Miri monitoring station (CA0028) in Malaysian Borneo, Sarawak, recorded the highest Air Pollution Index (API) ever recorded in the history of air quality in Malaysia in September 1997 (Sentian et al., 2019; Khan et al., 2020). This station also recorded the highest concentration of PM10 at 526 µg m–3 in the same month. Several studies have examined the issue of air pollution in this area and have concluded that the deterioration in air quality can be attributed to transboundary air pollution caused by extensive biomass burning in Kalimantan, Indonesia, on the Island of Borneo (Mahmud, 2013; Sentian et al., 2018, 2019; Khan et al., 2020). Similarly, just like the occurrences in Peninsula Malaysia, this high episodic pollution was most noticeable between June and October, during the southwest monsoon. Given the significant threats to public health and the economy in this region, exacerbated by the glaring inequalities in healthcare facilities and infrastructure development compared to the Malaysian Peninsula, there's a need for a more detailed analysis of the degree and variability of particulate pollution. Analysing the seasonal trends of increased pollutant levels utilising extensive datasets is crucial for evaluating regional air quality. Moreover, understanding the spatial and temporal variations in pollution levels, particularly of fine particles, is beneficial in this region during monsoon seasons or under unusual atmospheric circumstances, like during an El-Nino event. This data also holds significant value for urban planning and air quality control, especially in rapidly growing cities like Kuching, Kota Kinabalu, and Miri.

The present study aimed to investigate the long-term spatio-temporal distribution of surface PM10 mass concentration over Malaysian Borneo (Sabah and Sarawak) over a decade (2006–2016), utilising considerable ground-based PM10 monitoring data acquired from the Malaysian Department of Environment's (DOE) monitoring network. The primary objectives of this investigation were to describe long-term variations and trends in PM10 pollution levels, as well as to determine how frequently these levels exceeded the RMAAQG. Furthermore, the potential sources of pollution in areas with monitoring stations that consistently recorded high levels of exceedance were evaluated using HYSPLIT models. Additionally, a potential connection between the heightened PM10 pollution levels and weather conditions during monsoonal periods and El-Nino events was explored to determine its significance in relation to air quality in the region.

The present study investigated the long-terms (2006–2016) spatio-temporal distribution of surface PM10 mass concentration over Malaysian Borneo (Sabah and Sarawak) based on the extensive ground-based PM10 monitoring data from a Malaysian Department of Environment (DOE) monitoring network. The investigation's primary goals are to describe long-term spatio-temporal variations and trends in PM10 pollution levels, as well as to examine how frequently those levels are exceeded in comparison to the RMAAQG. The potential sources of pollution in areas with monitoring stations that have recorded a high frequency of exceedance were further evaluated using HYSPLIT models. A potential link between the high levels of PM10 pollution and the weather conditions during the monsoonal periods and El-Nino event was also investigated in order to determine its significance for the air quality in the region.

The findings and analysis of this study are divided into four sections. The first section covers the regional daily variations and trend analysis of the observed PM10 datasets, and this should provide insights into the day-to-day changes and any overarching trends in the recorded PM10 values. This section would ideally delve into the specific daily variations, which could shed light on any repeating patterns over long-term periods. In the second section, the frequency analysis of exceedance to the RMAAQG is analysed to identify hot spots area of particulate pollution. In the third section, will highlight the monsoonal effect and discussion on the potential origin of pollutant based on the airmass trajectory analysis. Lastly, this study focuses on the PM10 long terms variations and its connections to the El-Nino phenomenon. The conclusion, which gathers key findings, will be pivotal in creating actionable recommendations for stakeholders. It seems this study not only aims to understand the complexities of regional air pollution but goes a step further in connecting it to transboundary pollution and extreme atmospheric events. Such information can be invaluable for decision-makers, guiding them towards well-informed, practical solutions to deal with pollution and its broader impacts.

 
2 METHODS


 
2.1 Study Area

Malaysian Borneo, which consists of Sabah and Sarawak, is situated to the north and northwest of the Borneo Island. It is separated from Peninsula Malaysia by the South China Sea. Malaysian Borneo covers a total land area of approximately 198,354 square kilometres, which is nearly 60% of Malaysia's total land area. It is a region with relatively low population density. The landscape largely consists of lowland rainforests, with mountain rainforests in the inland areas, particularly in the northern part of the region. This area is characterised by a rugged chain of mountains, with an average height of 1,800 meters. Malaysian Borneo has a typical tropical climate, influenced by the regional wind systems that result from atmospheric pressure distribution. The seasonal fluctuation of the inter-tropical convergence zone (ITCZ) and the associated trade wind fields in the region produces two monsoonal seasons, namely the Northeast Monsoon (NEM) (December to March) and the Southwest Monsoon (SWM) (June–October), which greatly influences regional climatic variations. The two monsoons are separated by transitional periods, when the wind conditions are generally light and variable. For the purpose of the study, Malaysian Borneo is divided into three regions: Southern Malaysian Borneo (southern Sarawak), and Central Malaysian Borneo (northern Sarawak) Northern Malaysian Borneo (Sabah) (Fig. 1).

 
2.2 Particulate Matter (PM10) Dataset

The Department of Environment (DOE) in Malaysia has provided daily air quality data from 13 monitoring sites across Malaysian Borneo. These monitoring stations are equipped with automated equipment that collects and measures data continuously. The use of a β-ray attenuation mass monitor (BAM-1020) allows for the monitoring of PM10 to be fully automated. The selection of these 13 stations was based on the availability of long-term PM10 data from January 2006 to December 2016, as well as their representation of the southern, central, and northern regions of Malaysian Borneo. PM10 has been identified as the primary pollutant in this area, harmful to human health, and frequently exceeding the RMAAQG (150 µg m3) at several monitoring stations. The selection of these stations also took into account the surrounding land use settings, including urban, sub-urban, industrial, and rural areas. There are 5 stations in Southern Malaysian Borneo, 4 stations in Central Malaysian Borneo, and 4 stations in Northern Malaysian Borneo. The location and details of the 13 monitoring sites are shown in Fig. 1 and tabulated in Table 1.

 Fig. 1. Location of the 13 air quality monitoring stations at the three regions in Malaysian Borneo (Note: Southern Malaysian Borneo (pink); Central Malaysian Borneo (yellow); Northern Malaysian Borneo (blue)).Fig. 1. Location of the 13 air quality monitoring stations at the three regions in Malaysian Borneo (Note: Southern Malaysian Borneo (pink); Central Malaysian Borneo (yellow); Northern Malaysian Borneo (blue)).

Table 1. Location and coordinates (longitude (N) and latitude (E)) of the 13 air quality monitoring stations across Malaysian Borneo. 


2.3 Spatio-Temporal and Trends Analysis of PM10

A statistical analysis was conducted on the daily and monthly variations in PM10 based on a dataset of PM10 concentration from 2006 to 2016. Daily average PM10 concentrations on an inter-annual basis were used to carry out trend analysis over the specified period. From the hourly PM10 dataset, a total of 51,582 data points were utilised in determining the daily average concentration. To identify constant trends in the time series, while discarding seasonal or other cyclical factors, the non-parametric Mann-Kendall (MK) test was applied (Sentian et al., 2018; Sulong et al., 2017; Chaudhuri and Dutta, 2014; Emami et al., 2018; Ma et al., 2011). While parametric methods, which require a normal distribution of independent data, are generally more accurate (Watthanacheewakul, 2011), the non-parametric Mann-Kendall Trend test is deemed more suitable as it prioritises significant differences over randomness, thus yielding trend determinations less influenced by atypical data points (Jamaludin and Sayang, 2010; Ahmad et al., 2015). In assessing the extent of inter-annual variability relative to the pollutant mean concentrations over the period of investigation, the coefficient of variation (CV), which is the ratio of the standard deviation (σ) to the mean (µ), was also determined.

The alternate hypothesis (Ha) for the trend analysis stated that a trend (either increasing or decreasing) over time. This is in contrary to the null hypothesis (H0), which suggested no trend in PM10 concentration over time. The Mann Kendall Trend Test was used to evaluate these hypotheses, with the following equations:

 

In these equations, Xi and Xj are the time series observations in chronological order, n is the length of the time series, tp is the number of ties for p-th value, and q is the number of ties values. Positive Z values indicate an increasing trend in the air quality time series, while negative Z values indicate a decreasing trend. The null hypothesis is rejected, and there is a statistically significant trend in the PM10 time series if the absolute value of Z is greater than |Z| > Z1-α/2.

 
2.4 Frequency of Exceedance and Pollutant Trajectory Analysis

The generated statistical values are important for explaining the distribution of the variables and also evaluating the level of air pollution. In this study, we counted the days on which the daily average PM10 concentration was higher than 150 µg m–3, of the RMAAQG threshold. In this study, we analysed the days when the average daily concentration of PM10 exceeded 150 µg m–3, which is the threshold set by the RMAAQG. Additionally, to determine the origin of the polluted air masses and their transportation paths, we used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. This model has the ability to generate backward trajectories of airmass from any starting point. This feature makes it particularly valuable for analysing and understanding an air quality event over a specific time period. The model was provided by the US National Oceanographic and Atmospheric Administration (NASA), and it can be accessed at http://www.arl.noaa.gov/ready.html. It is a hybrid model that combines the Lagrangian and Eulerian approaches. It uses a moving frame of reference to calculate the trajectories of an airmass for advection and diffusion calculations. It also utilises a fixed three-dimensional grid to compute air pollutant concentrations (Draxler and Hess, 1998; Stohl, 1998; Su et al., 2015).

Information on regional fire maps was gathered from FIRMS (Fire Information for Resource Management System), which can be downloaded at https://firms.modaps.eosdis.nasa.gov/firemap/, to assess potential sources of pollution from biomass burning emission. The detection of areas with high temperatures due to biomass burning on Borneo Island was based on data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) observation instrument on board NASA's Terra and Aqua EOS satellites. These satellites pass over the region at least four times each day (Giglioa et al., 2003). In order to determine the likely influence of monsoon seasons and El Nino events, we examined the air quality data and El Nino events from the years 2006 to 2016.

 
3 RESULTS AND DISCUSSION


 
3.1 Spatio-Temporal Variations and Trends of PM10

During the investigation period from 2006 to 2016, there were significant variations in the daily levels of PM10 in Southern Malaysian Borneo and Central Malaysian Borneo (Fig. 2). In contrast, Northern Malaysian Borneo exhibited comparatively less variability (Fig. 3). It is worth noting that higher concentrations of PM10 were observed in 2006, 2009, and 2015 throughout Malaysia Borneo. Additionally, all monitoring stations in Southern Malaysian Borneo, as well as Bintulu station and Miri station in Central Malaysian Borneo, displayed higher concentrations of PM10 in 2012, 2013, and 2014, although the magnitude was relatively lower in these years across all stations. Furthermore, significant peaks were predominantly observed in 2012 and 2013 in other monitoring stations.

Fig. 2. The daily mean concentration of PM10 in Southern Malaysian Borneo (left panel) and Central Malaysian Borneo (right panel) (Sarawak) from 2006 to 2016. The red line shows the threshold limit (150 µg m–3) for PM10 as specified under RMAAQG.Fig. 2. The daily mean concentration of PM10 in Southern Malaysian Borneo (left panel) and Central Malaysian Borneo (right panel) (Sarawak) from 2006 to 2016. The red line shows the threshold limit (150 µg m3) for PM10 as specified under RMAAQG.

Fig. 3. The daily mean concentration of PM10 in Northern Malaysian Borneo (Sabah) from 2006 to 2016. The red line shows the threshold limit (150 µg m–3) for PM10 as specified under RMAAQG.Fig. 3. The daily mean concentration of PM10 in Northern Malaysian Borneo (Sabah) from 2006 to 2016. The red line shows the threshold limit (150 µg m3) for PM10 as specified under RMAAQG.

In order to understand the spatial and temporal variations and trends of PM10 in these regions, it is important to examine these basic statistical values. Spatially, Southern Malaysian Borneo and Central Malaysian Borneo exhibited significant variations, with coefficient variations (CV) ranging from 45% to 61% and 39% to 54% respectively (Table 2). In contrast, Northern Malaysian Borneo showed relatively smaller variations, with a coefficient variation of less than 40%. All air quality monitoring stations in Southern Malaysian Borneo recorded the highest daily maximum mean of PM10 compared to the other monitoring stations in Malaysian Borneo. Among the monitoring stations, Sibu had the highest daily maximum concentration at 434.38 µg m–3, followed by Sri Aman (336.50 µg m–3), Kota Samarahan (329.29 µg m–3), Sarikei (321.58 µg m–3), and Kuching (316.00 µg m–3).

Table 2. Descriptive analysis of daily average and Coefficient of Variations (CV) of PM10 (µg m–3) for all 13 air quality monitoring stations across Malaysian Borneo from 2006 to 2016.

In Central Malaysian Borneo and Northern Malaysian Borneo, all air quality monitoring stations showed a daily maximum concentration ranging from 128.35 to 283.71 µg m–3 and 129.58 to 256.42 µg m–3 respectively. Comparatively, Northern Malaysian Borneo experienced less particulate pollution during this period. As explained later in this paper, the large variability in particulate concentrations across these three regions can be attributed to the distance from identified emission sources and the trajectory of air masses transporting pollutants from these sources.

In terms of pollution trends over this period, four air quality monitoring stations specifically Kuching (p-value of < 0.0001; Kendall’s value of –0.063), Sri Aman p-value of < 0.0001; Kendall’s value of –0.130), and Sibu (p-value of < 0.0001; Kendall’s value of –0.142) have demonstrated significant decreases. On the other hand, two other stations, Kota Samarahan (p-value of 0.620; Kendall’s value of 0.005) and Sarikei (p-value of 0.492; Kendall’s value of 0.007) did not show any significant trend (Table 3).

Table 3. Results of Man Kendall and Sen’s slope estimator tests on PM10 data for the years 2006 to 2016.

In the Central Malaysian Borneo region, there were noticeable increases in pollution levels at the Miri (p-value of less than 0.0001; Kendall's value of 0.173) and Limbang (p-value of less than 0.0001; Kendall's value of 0.115) monitoring stations. However, the Bintulu (p-value of less than 0.0001; Kendall's value of –0.114) and Kapit (p-value of < 0.0001; Kendall’s value of –0.053) monitoring stations showed significant decreases in pollution. In the Northern Malaysian Borneo region, the Kota Kinabalu monitoring station had a significant increase in pollution (p-value of less than 0.0001; Kendall's value of 0.043). On the other hand, pollution levels decreased at the Sandakan (p-value of less than 0.0001; Kendall's value of –0.101), Tawau (p-value of less than 0.0001; Kendall's value of –0.402), and Keningau (p-value of less than 0.0001; Kendall's value of –0.276) monitoring stations. A previous study conducted by Sentian et al. (2018), which analyzed long-term air quality data for the entire Malaysian Borneo, showed significant decreases in PM10 levels at all monitoring stations with 95% confidence. Therefore, based on the current study, Miri, Limbang, and Kota Kinabalu are among the areas experiencing a gradual increase in pollution. These areas are characterised by urbanisation, industrialisation, and development, leading to high levels of pollution due to human activities in the transportation, oil, and gas industries.

 
3.2 Frequency of Exceedance of RMAAQG

By comparing it to the maximum levels permitted according to the Recommended Malaysian Ambient Air Quality Guideline (RMAAQG) of 150 µg m–3, an analysis of the average daily concentration of PM10 would allow us to assess the level of pollution. In Malaysian Borneo, the concentration of PM10 ranged from 7.25 µg m–3 to 434.38 µg m–3, with an average concentration between 27.93 µg m–3 and 45.83 µg m–3 (Table 4). Between 2006 and 2016, the Southern Malaysian Borneo region had the highest frequency of exceeding the RMAAQG limit, with a total of 205 days (85.8%). This was followed by the Central Malaysian Borneo region with 27 days (11.3%) and the Northern Malaysian Borneo region with only 7 days (2.9%). In the Southern Malaysian Borneo region, the highest frequency was observed in 2015 with 71 days (34.6%), followed by 2009 (66 days; 32.2%), 2006 (54 days; 26.3%), 2014 (12 days; 5.9%), and 2011 and 2012 with 1 day (0.5%) each, respectively. In terms of location, Kota Samarahan recorded the highest frequency of exceedance with 61 days, followed by Sri Aman (59 days), Kuching (32 days), Sarikei (23 days), and Sibu (32 days). The regions with the highest occurrence of surpassing limits were primarily located in the southern part of the region, in proximity to the border of Kalimantan, Indonesia. These regions can be recognized as hot spot areas with a concentrated level of particulate pollution.

Table 4. Annual frequency of exceedance of the daily average of PM10 against the Recommended Malaysian Ambient Air Quality Guideline from 2006 to 2016.

The highest frequency of particulate pollution was observed in 2009 over Central Malaysian Borneo, lasting for 14 days (51.9%). This was followed by 10 days in 2006 (37.0%), and 3 days in 2015 (11.1%). The Bintulu area had the highest recorded frequency of exceedance with 14 days, followed by Miri with 7 days, and the remote area of Kapit with 6 days. Interestingly, the Limbang area was the only monitoring station in this region that was considered clean in terms of particulate pollution during the years of investigation. The results obtained from Kapit Station, one of the background sampling stations in Malaysian Borneo, are particularly valuable. They not only help assess air quality levels on a regional scale and allow for the comparison of pollutant levels in areas directly impacted by pollution sources, but also provide data for comparing the status of an area before and after modernisation. The results of this study show that, even though this station is situated in a remote area of Central Malaysian Borneo and is not affected by emissions from human activities like industry and motor vehicles, it recorded a frequency of exceedance of 6 days (4 days in 2006 and 2 days in 2015). The instances of high PM10 concentrations at this specific station are similar to the most severe pollution incidents observed in all monitoring stations in Malaysian Borneo. This strongly indicates that there is a significant occurrence of transboundary air pollution happening on a large scale.

In contrast, the Northern Malaysian Borneo region was considered less polluted as it recorded the lowest number of days exceeding the RMAAQG threshold. The Tawau monitoring station, located on the east coast and closer to the northern region of Kalimantan, Indonesia, exhibited a total frequency of exceedance of 7 days, with the highest number in 2015 at 6 days. When compared to the Malaysian Peninsular region, the Malaysian Borneo region was significantly less polluted. However, in Malaysian air quality history since 1997, the Miri air quality monitoring station, located in Central Malaysian Borneo, recorded the highest level of particulate pollution in September 1997 at 436.8 µg m–3 (Sentian et al., 2018). This high level was mainly attributed to transboundary pollution from intense biomass burning emissions in Kalimantan, Indonesia (Mahmud, 2013; Sahani et al., 2014; Sulong et al., 2017; Sentian et al., 2018).

 
3.3 Monsoonal Variations of PM10 and Trajectory Analysis

The particulate pollution in the air quality monitoring in Malaysian Borneo was higher at certain times between 2006 and 2016. This was indicated by high PM10 concentration and a higher frequency of exceeding the RMAAQG. It is crucial to investigate the cause and source of this pollution. Previous studies have identified transboundary pollution as the main source of pollution in Malaysian Borneo, resulting from intense biomass burning in Kalimantan, Indonesia (Mahmud, 2013; Sentian et al., 2018). As this region is strongly influenced by tropical monsoon, therefore it is important to characterize the seasonality of the pollution episodes. Consequently, by analyzing the backward airmass trajectory, we should be able determine and assess the transport and potential source of particulate pollution over this region. For the trajectory analysis, we focused our investigation at high pollution episodes in 2006, 2009 and 2015 to the three hot spot areas over Southern Malaysian Borneo (Kota Samarahan, Sri Aman, and Sibu) and one hot spot areas in the Northern Malaysian Borneo (Tawau).

The monsoon season has a significant impact on the transboundary pollution in Borneo. In most air quality monitoring stations, the PM10 profiles clearly demonstrate seasonality, with PM10 concentrations typically higher during the dry monsoon season (June–October) and lower during the wet monsoon season (December–March) (Fig. 4 and Fig. 5). A previous study reported that the PM10 concentration is influenced by the southwest monsoon wind and the occurrence of biomass burning (Abas et al., 2004). As mentioned earlier, this high PM10 level during that period was considered unhealthy, based on the frequency of exceeding the RMAQG standards. During this monsoon season, the prevailing winds change direction, resulting in the transport of air pollution across borders. This phenomenon has been well-demonstrated in Borneo, with the monsoonal winds and high particulate pollution episodes. The transport of particulate pollutant from Kalimantan, Indonesia, has contributed significantly to the increased pollution levels in Southern Malaysian Borneo (specifically in Kuching, Kota Samarahan, Sri Aman, and Sibu). This contribution was particularly notable in 2006, 2009, and 2015, as explained further in the analysis of the airmass back trajectory.

Fig. 4. The daily average of PM10 concentrations in Southern Malaysian Borneo (left panel) and Central Malaysian Borneo (right panel) in 2006 (top panel), 2009 (middle panel) and 2015 (bottom panel), and during monsoonal periods is indicated in a black dotted box.Fig. 4. The daily average of PM10 concentrations in Southern Malaysian Borneo (left panel) and Central Malaysian Borneo (right panel) in 2006 (top panel), 2009 (middle panel) and 2015 (bottom panel), and during monsoonal periods is indicated in a black dotted box.

Fig. 5. The daily average of PM10 concentrations in Northern Malaysian Borneo in 2006 (top left panel), 2009 (top right panel) and 2015 (bottom panel), and during monsoonal periods is indicated in a black dotted box.Fig. 5. The daily average of PM10 concentrations in Northern Malaysian Borneo in 2006 (top left panel), 2009 (top right panel) and 2015 (bottom panel), and during monsoonal periods is indicated in a black dotted box.

The air mass trajectories were calculated using the HYSPLIT Model for the periods of high particulate pollution in 2006, 2009, and 2015 at all air quality monitoring stations in Southern Malaysian Borneo. These stations include Kuching, Kota Samarahan, Sri Aman, Sibu, and Sarikei. These areas were chosen because they exhibited the highest concentrations and significant variation in pollution levels, as shown in Fig. 6, Fig. 7, and Fig. 8. These trajectories often came from the same direction and sometimes originated from the same locations in Kalimantan, Indonesia. During the monsoonal months, the weather conditions were very hot and dry. Satellite images have shown large areas of biomass burning in the southern region of Kalimantan in October 2006. In August 2009, there were large areas of biomass burning observed in the western part of Kalimantan and at the border area between Kalimantan and Southern Malaysian Borneo. All the airmass trajectories in 2006, 2009, and 2015 passed through these hot spot areas of biomass burning. The intense biomass burning in these areas released thick smoke and a large amount of particulate matter into the atmosphere, which was then carried away by the monsoonal wind across the border and caused high concentrations of PM10 over the southern part of Southern Malaysian Borneo region. This study concurs earlier findings (see Sundarambal et al., 2010; Dotse et al., 2016; Sentian et al., 2018) that found transboundary pollution of particulate matter was responsible for pollution episodes in Malaysian Borneo. However, it is also important to note that local biomass burning, particularly in the Southern Malaysian Borneo region, also played a significant role in the pollution episodes in 2009. It is also important to add that local biomass burning mostly in Southern Malaysian Borneo region is equally important in attributing to the pollution episodes in 2009 and 2015.

Fig. 6. The backward air mass trajectories were conducted for five specific monitoring sites (Kuching, Sibu, Kota Samarahan, Sri Aman, and Sarikei) in the Southern Malaysian Borneo region during episodes of high pollution concentration in 2006. The regional satellite image (Sources: https://firms.modaps.eosdis.nasa.gov/map) shows areas with high biomass burning activity during the same period (01–08 October, 2006) (right bottom panel).Fig. 6. The backward air mass trajectories were conducted for five specific monitoring sites (Kuching, Sibu, Kota Samarahan, Sri Aman, and Sarikei) in the Southern Malaysian Borneo region during episodes of high pollution concentration in 2006. The regional satellite image (Sources: https://firms.modaps.eosdis.nasa.gov/map) shows areas with high biomass burning activity during the same period (01–08 October, 2006) (right bottom panel).

Fig. 7. The backward air mass trajectories were conducted for five specific monitoring sites (Kuching, Sibu, Kota Samarahan, Sri Aman, and Sarikei) in the Southern Malaysian Borneo region during episodes of high pollution concentration in 2009. The regional satellite image (Sources: https://firms.modaps.eosdis.nasa.gov/map) shows areas with high biomass burning activity during the same period (October 05–12 August, 2009) (right bottom panel).Fig. 7. The backward air mass trajectories were conducted for five specific monitoring sites (Kuching, Sibu, Kota Samarahan, Sri Aman, and Sarikei) in the Southern Malaysian Borneo region during episodes of high pollution concentration in 2009. The regional satellite image (Sources: https://firms.modaps.eosdis.nasa.gov/map) shows areas with high biomass burning activity during the same period (October 05–12 August, 2009) (right bottom panel).

 Fig. 8. The backward air mass trajectories were conducted for five specific monitoring sites (Kuching, Sibu, Kota Samarahan, Sri Aman, and Sarikei) in the Southern Malaysian Borneo region during episodes of high pollution concentration in 2015. The regional satellite image (Sources: https://firms.modaps.eosdis.nasa.gov/map) shows areas with high biomass burning activity during the same period (15–22 September 2015) (right bottom panel).Fig. 8. The backward air mass trajectories were conducted for five specific monitoring sites (Kuching, Sibu, Kota Samarahan, Sri Aman, and Sarikei) in the Southern Malaysian Borneo region during episodes of high pollution concentration in 2015. The regional satellite image (Sources: https://firms.modaps.eosdis.nasa.gov/map) shows areas with high biomass burning activity during the same period (15–22 September 2015) (right bottom panel).

 
3.4 El Nino Events and Variations of PM10

El Niño is a phenomenon that involves an increase in the temperatures of the sea surface in the central and eastern equatorial Pacific Ocean, leading to disruptions in weather patterns, most notably in the equatorial Pacific Ocean (Ashok and Yamagata, 2009). It is characterised by warmer-than-average sea surface temperatures at least 0.5 degrees Celsius above average for three consecutive months. However, it's important to note that El Niño is a complex and dynamic phenomenon influenced by various factors, and its exact temperature conditions can vary. Its effects can vary, but in regions like Borneo, it can lead to prolonged dry seasons and reduced rainfall (Sentian et al., 2019). During an El Niño event, Borneo may experience drier conditions, which can exacerbate the occurrence and severity of forest fires. Forest fires release large amounts of smoke and pollutants into the air, contributing to high particulate pollution levels. These fires are often deliberately started to clear land for agriculture and palm oil plantations, but dry conditions associated with El Niño can make them spread more easily and intensify the resulting pollution. Furthermore, El Niño can also affect the wind patterns in the region, potentially transporting smoke, and pollutants over longer distances, which can impact air quality at larger scale (Latif et al., 2018; Addiena A Rahim et al., 2023). This phenomenon can lead to increased health risks, especially for those with respiratory issues (Li et al., 2023; Mazeli et al., 2023), as well as ecological and economic impacts (Ho et al., 2023; Zhang and Zhou, 2020).

The study analysed the occurrences of El Nino between 2006 and 2016 in Malaysian Borneo. During this 10-year period, El Nino events were observed three times in Borneo, specifically in 2006, 2009, and a particularly severe event known as the super El Nino in 2015 (Fig. 9). During these El Nino events, higher concentrations of PM10 than usual were observed in all air quality monitoring stations in Malaysian Borneo, but with significantly notable levels observed in Southern Malaysian Borneo (Kuching, Sibu, Sarikei, Kota Samarahan, and Sri Aman), Central Malaysian Borneo (Bintulu, Miri, and Kapit), and Northern Malaysian Borneo (Tawau) (see also Fig. 2). The PM10 concentrations at Sarikei and Kuching were found to be strongly correlated (R > 0.5) during the El Nino events in 2006, 2009, and 2015. The higher concentrations of pollutants during the El Nino events were due to the amplifying effects of this phenomenon. This study also supports the role of El Nino events in causing high pollution episodes in Malaysian Borneo (Sentian et al., 2018, 2019), the Southern region and Central region of Peninsular Malaysia (Tangang et al., 2010; Sulong et al., 2017; Sentian et al., 2018; Addiena A Rahim et al., 2023).

Fig. 9. The daily average of PM10 concentrations at four selected air quality monitoring with high frequency of exceedance in Southern Malaysian Borneo (Kuching, Sibu, Kota Samarahan, Sri Aman and Sarikei) with the RMAAQG (2006–2015) and the Oceanic Nino Index (shaded with pink) (Source: https://www.ncei.noaa.gov/access/monitoring/enso/sst#oni).Fig. 9. The daily average of PM10 concentrations at four selected air quality monitoring with high frequency of exceedance in Southern Malaysian Borneo (Kuching, Sibu, Kota Samarahan, Sri Aman and Sarikei) with the RMAAQG (2006–2015) and the Oceanic Nino Index (shaded with pink) (Source: https://www.ncei.noaa.gov/access/monitoring/enso/sst#oni).


4 CONCLUSIONS


This paper analysed the spatial and temporal variations of PM10 over Malaysian Borneo using air quality data from 2006 to 2016. The concentration of PM10 varies with the seasons and is strongly influenced by monsoonal factors and abnormal atmospheric conditions like the El Nino phenomenon. The pollution level in the Southern Malaysian Borneo region is higher compared to the Central and Northern Malaysian Borneo. All the air quality monitoring stations in Southern Malaysian Borneo have recorded a higher frequency of exceeding the Recommended Malaysian Ambient Air Quality Guideline (RMAAQG) threshold limit of 150 µg m–3. Thus, these regions can be identified as hot spot areas in Malaysian Borneo with a significant level of particulate pollution. Based on the long-term database, Miri, Limbang, and Kota Kinabalu have shown a significant increase in PM10 pollution. On the other hand, Kuching, Sri Aman, Sandakan, Tawau, Keningau, and Keningau have shown a significant decreasing trend, while Kota Samarahan and Sarikei have shown no significant trend.

This study has provided evidence suggesting that PM10 concentration in Malaysian Borneo is affected by El Nino phenomena and the monsoonal effect. The concentration of PM10 was usually higher in the dry monsoon season from June to October, and lower in the wet monsoon season from December to March at all monitoring stations. The highest frequency of exceedance with the RMAAQG was observed in 2015 and 2009, with 80 days and 65 days respectively in 2006. Transboundary air pollution also has a significant impact on PM10 pollution in Malaysian Borneo, particularly in Southern Malaysian Borneo due to its proximity to the hotspots of biomass burning in Kalimantan, Indonesia. Understanding the transboundary atmospheric pollution issue and the significant role that the natural dynamics of atmospheric conditions play in the regional dispersion of pollutants, managing emissions at the source is crucial for any effort to improve air quality in Malaysian Borneo.

In this note, it requires a collaborative effort from different countries to manage effectively by developing comprehensive regional strategies to improve their land and forest management practices, as well as increase cooperation on monitoring, reporting, and reduction efforts. This could involve agreements on emissions standards for industries, sharing of data and information, and collaboration on research and development of cleaner technologies. It's important to continue to raise awareness and prioritise action to manage transboundary air pollution as it is entrenched in the preservation of air quality, which has countless positive consequences for human health, ecosystems, and global climate stability. It's also worth noting that there are international frameworks and agreements that can help guide countries in managing transboundary air pollution, such as the Association of Southeast Asian Nations (ASEAN) Agreement on Transboundary Haze Pollution.

This study concludes that atmospheric particulate pollution in Malaysian Borneo is heavily impacted by transboundary pollution and influenced by atmospheric conditions such as monsoon and intensified by El Nino events. In the future, research should focus on investigating the impact of pollution on human health, specifically respiratory diseases. It is also important to study how pollution affects other economic activities such as tourism and agriculture, with a specific focus on crop yields. Additionally, it would be beneficial to explore the consequences of biomass burning on the acidity of the atmosphere, as well as the contribution of emissions containing black carbon, organic carbon, and other pollutants to atmospheric warming. In addition to scientific research, it is important to prioritize the study of the effectiveness and challenges of implementing regional cooperation and agreements to address transboundary pollution. However, improving air quality in Malaysian Borneo by managing emissions from their original sources may face significant challenges due to the collaborative efforts required from different countries in implementing international frameworks and agreements, such as the Agreement on Transboundary Haze Pollution.

 
ACKNOWLEDGEMENT


The authors would like to express their gratitude to the Department of Environment Malaysia (DOE) for providing the datasets for long-term period monitoring on PM10 over Malaysian Borneo at 13 monitoring stations. The authors also extend their appreciation to Universiti Malaysia Sabah for the Research University Grant (GUG0541). Furthermore, the authors are thankful to the NOAA Air Resources Laboratory (ARL) for supplying the HYSPLIT transport and dispersion model (http://www.arl.noaa.gov/ready.html), which was utilised in this publication. Lastly, the authors would like to give a special thanks to Rose Norman for proofreading the manuscript.


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