Asian Journal of atmospheric environment
Asian Journal of atmospheric environment Asian Journal of atmospheric environment
Asian Journal of atmospheric environment

Journal Archive

Asian Journal of Atmospheric Environment - Vol. 12 , No. 2

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 12, No. 2, pp.109-126
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 30 Jun 2018
Received 30 Nov 2017 Revised 10 Jan 2018 Accepted 04 Feb 2018
DOI: https://doi.org/10.5572/ajae.2018.12.2.109

Variability of the PM10 concentration in the urban atmosphere of Sabah and its responses to diurnal and weekly changes of CO, NO2, SO2 and Ozone
Jackson CHANG, Hian Wui1), * ; CHEE Fuei, Pien2) ; Steven KONG, Soon Kai3) ; Justin, SENTIAN4)
1)Preparatory Center for Science and Technology, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
2)Energy, Vibration and Sound Research Group (e-VIBS), Faculty Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
3)Department of Atmospheric Sciences, National Central University, Taoyuan 32001, Taiwan
4)Climate Change Research Group (CCRG), Faculty Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia

Correspondence to : *Tel: +60-088-320 000, E-mail: jacksonchw@ums.edu.my

Funding Information ▼

Abstract

This paper presents seasonal variation of PM10 over five urban sites in Sabah, Malaysia for the period of January through December 2012. The variability of PM10 along with the diurnal and weekly cycles of CO, NO2, SO2, and O3 at Kota Kinabalu site were also discussed to investigate the possible sources for increased PM10 concentration at the site. This work is crucial to understand the behaviour and possible sources of PM10 in the urban atmosphere of Sabah region. In Malaysia, many air pollution studies in the past focused in west Peninsular, but very few local studies were dedicated for Sabah region. This work aims to fill the gap by presenting the descriptive statistics on the variability of PM10 concentration in the urban atmosphere of Sabah. To further examine its diurnal and weekly cycle pattern, its responses towards the variations of CO, NO2, SO2, and ozone were also investigated. The highest mean value of PM10 for the whole study period is seen from Tawau (35.7±17.8 μg m-3), while the lowest is from Keningau (31.9± 18.6 μg m-3). The concentrations of PM10 in all cities exhibited seasonal variations with the peak values occurred during the south-west monsoons. The PM10 data consistently exhibited strong correlations with traffic related gaseous pollutants (NO2, and CO), except for SO2 and O3. The analysis of diurnal cycles of PM10 levels indicated that two peaks were associated during the morning and evening rush hours. The bimodal distribution of PM10, CO, and NO2 in the front and at the back of ozone peak is a representation of urban air pollution pattern. In the weekly cycle, higher PM10, CO, and NO2 concentrations were observed during the weekday when compared to weekend. The characteristics of NO2 concentration rationed to CO and SO2 suggests that mobile sources is the dominant factor for the air pollution in Kota Kinabalu; particularly during weekdays.


Keywords: Particulate Matter, Urban atmosphere, Air quality, Malaysia, Diurnal cycle, Weekly cycle

1. INTRODUCTION

Particulate matter (PM10) consists of very small solid and liquid particles suspended in air. It is of greatest concern to public health because these particles are small enough to be inhaled into the deepest parts of lung and settled there to cause adverse health effects. Particles less than 10 microns in diameter are known as PM10. It is a mixture of substances that include smoke, soot, dust, salt, acids, and metals. Particulate matter also forms when gases emitted from motor vehicles and industry undergo chemical reactions in the atmosphere. It is a major component of air pollution that threatens both our health and our environment. Sources of PM10 in both urban and rural areas are not limited to motor vehicles, but also dust from construction, landfills, and agriculture, wildfires and brush/waste burning, industrial sources, windblown dust from open lands. Epidemiological studies indicated that the fine particle fractions e.g. PM2.5, and PM1.0 have considerable impacts on human health even at concentrations below the Malaysia Ambient Air Quality Standards PM2.5 of 75 μg/m3 in daily 24-hour average (Gomiscek et al., 2004). These particles when inhaled by human can evade the respiratory system’s natural defences and lodge deep in the lungs. Health problems begin as the body reacts to these foreign particles. PM10 can increase the number and severity of asthma attacks, cause or aggravate bronchitis and other lung diseases, and reduce the body’s ability to fight infections (Lelieveld et al., 2015; Nirmalkar and Deb, 2015). Certain people who include children, the elderly, exercising adults, and those suffering from asthma or bronchitis are especially vulnerable to PM10’s adverse health effects (Thurston et al., 2016).

The issue of PM10 pollution in the urban atmosphere has received much attentions in recent years as an increasing share of the world’s population lives in urban areas. Previous studies reported that traffic-related emission accounted at least 50% of the total PM emission in the urban centres (Wrobel et al., 2000). Vehicles produce exhaust and non-exhaust emissions. Aerosol exhaust emissions can be emitted directly as particles such as soot and carbonaceous aggregates that formed during the fuel combustion in the engine or formed during the emission, dilution, mixing and cooling of the vehicle exhaust gases in ambient air (Adame et al., 2014; Pérez et al., 2010). Non-combustion traffic-related PM sources are the result of the resuspension of road dust originating from the mechanical wear and degradation of tires, brakes, and pavement abrasion (Bathmanabhan et al., 2010). Therefore, the contributions to the PM10 concentration in ambient air are segregated into two groups: (1) primary particles emitted by vehicle exhausts, and (2) nucleation particles in ambient air (Rodríguez and Cuevas, 2007). The primary particles emitted by vehicles exhausts tend to exhibit a size distribution with a nucleation mode (<30 nm) and a carbonaceous mode (50 to >100 nm). The gaseous exhaust emissions may also contribute to new particle formation in ambient air by in situ nucleation occurring sometime after emission (hours to days). This process frequently occurs in transit from the road to the urban background especially when the exhaust gas is fully diluted within the ambient air and is photo-oxidised by reactive species.

In Malaysia, the government routinely publicized the air pollution index (API) readings throughout the mainstream media to alert the public of any looming smoke haze episode. Severe haze episode often occurs during the Southeast Asian haze resulted by smoke from forest fires in Kalimantan and Sumatra (Aouizerats et al., 2015; Harrison et al., 2009). The location and background of a station, as well as wind speed, seasonal (monsoon) and weekdays-weekend variations also play important role in influencing PM10 anomalies (Shaadan et al., 2015). Diurnal patterns, rationed between major air pollutants and sensitivity analysis, indicate that the influence of local traffic emissions on urban air quality is critical (Talib et al., 2014). A case study at three selected stations in Malaysia e.g. Petaling Jaya, Melaka, and Kuching, also revealed that the major source of air pollution over urban air is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities (Norsukhairin et al., 2013). A prediction study by PCA had further identified that CH4, NmHC, THC, O3, and PM10 are amongst the most significant factors deteriorating air quality in Malaysia (Azid et al., 2014). A critical understanding of the behaviour of major pollutants in urban atmosphere of big city is important on health concerns. It is also an essential contingency for policy implementation especially when the elevated PM concentration has caused enormous impacts on economical values (Othman et al., 2014).

The paper aims to present the descriptive statistics on the PM10 concentration in the urban atmosphere of Sabah. Understanding the variation of PM10 concentrations is crucial for proper health risk assessment and risk management. PM10 was chosen because it is one of the major air pollutants listed in the Recommended Malaysian Air Quality Guideline (RMAQG) and it is also one of the five air pollutants included in the calculation of Malaysian Air Quality Index (MAPI) together with ozone (O3), carbon moNOxide (CO), sulfur dioxide (SO2) and nitrogen dioxide (NO2). This paper also presents the results of the variability of PM10 concentration owing to the diurnal evolution and weekly cycles of CO, NO2, SO2, and ozone, particularly in Kota Kinabalu.


2. MEASUREMENT AND SITE

The PM10 monitoring sites are located at five different stations in Sabah. Fig. 1 shows the regional site map of the five monitoring stations. The location details of each station is depicted in Table 1. The town of Kota Kinabalu is the busiest urban center in Sabah, located in the eastern part of Malaysia with a population of approximately 460,000 and area 351 km2. Its industry is not very active and the precursor emissions are mainly due to traffic exhaust (Dominick et al., 2012; Sansuddin et al., 2011). Kota Kinabalu is at 5°58ʹ17ʺN 116°05ʹ43ʺE and with an altitude of 5 m. The area is topographically homogenous and the city lies on a narrow flatland between the Crocker Range to the east and the South China Sea to the west. The average temperature is 27°C with April and May are the hottest months while January is the coolest. The average annual rainfall is around 2,400 mm and varies remarkably throughout the year. February and March are typically the driest months while rainfall peaks in the inter-monsoon period in October. The wind speed ranges from 5.5 to 7.9 m/s during the Northeast Monsoon (NEM) but is significantly lower to 0.3 to 3.3 m/s during Southwest Monsoon (SWM).


Fig. 1. 
Regional site map of the CAQM monitoring stations in Sabah. The five stations are labelled as CA0030: Kota Kinabalu; CA0039: Tawau; CA0042: Labuan; CA0049: Keningau; CA0050: Sandakan.

Table 1. 
Location details of monitoring site in Sabah.
Site Site ID Location Latitude Longitude Type
Kota Kinabalu CA0030 SM Putatan, Kota Kinabalu, Sabah N05°53.623 E116°02.596 Residential
Tawau CA0039 Pejabat JKR, Tawau, Sabah N04°15.016 E117°56.166 Residential
Labuan CA0042 Taman Perumahan MPL, Labuan, Sabah N05°19.980 E115°14.315 Residential
Keningau CA0049 SMK Gunsanad, Keningau, Sabah N05°20.313 E116°09.769 Residential
Sandakan CA0050 Pejabat JKR Sandakan, Sandakan, Sabah N05°51.865 E118°05.479 Residential

Tawau is on the south-east coast of Sabah surround by the Sulu Sea in the east and located at 540 kilometres south-east of Kota Kinabalu. It has tropical rainforest climate under the Köppen climate classification. The climate is relatively hot and wet with average shade temperature about 26°C, with 29°C at noon and falling to around 23°C at night. The town sees precipitation throughout the year, with a tendency for November, December and January to be the wettest months, while February and March are the driest months. Tawau’s mean rainfall varies from 1,800 mm to 2,500 mm.

Labuan’s area comprises the main island (Labuan Island - 91.64 square kilometres) and six other smaller islands namely Burung, Daat, Kuraman, Big Rusukan, Small Rusukan and Papan island with a total area of 91.64 square kilometres. It has a tropical rainforest climate with no dry season. Over the course of a year, the temperature typically varies from 25 to 32°C and is rarely below 24°C or above 33°C. Thunderstorms are the most severe precipitation observed in Labuan during 60% of those days with precipitation. They are most likely around October, when they occur very frequently. Meanwhile, the relative humidity for Labuan typically ranges from 63% (mildly humid) to 96% (very humid) over the course of the year.

Keningau is the capital of the Keningau District in the Interior Division of Sabah, Malaysia. This city has a tropical climate. The temperatures are highest on average in May, at around 26.4°C. The lowest average temperatures in the year occur in January, at around 25.3°C The average annual temperature is 25.8°C in Keningau. The least amount of rainfall occurs in August and the greatest amount of precipitation occurs in May, with an average of 203 mm. The average annual rainfall is 1,825 mm.

Sandakan is located on the eastern coast of Sabah facing the Sulu Sea, with the town is known as one of the port towns in Malaysia. The town is located approximately 1,900 kilometres from 319 kilometres from Kota Kinabalu, the capital of Sabah. It has a tropical rainforest climate under the Köppen climate classification. The climate is relatively hot and wet with average shade temperature about 32°C, with around 32°C at noon falling to around 27°C at night. The town sees precipitation throughout the year, with a tendency for October to February to be the wettest months, while April is the driest month. Its mean rainfall varies from 2,184 mm to 3,988 mm.

All selected air quality monitoring stations are operated by Alam Sekitar Sdn. Bhd. (ASMA) under the administration of Department of Meteorology (DOE). The Department of Environment (DOE) monitors the country’s ambient air quality through a network of Continuous Air Quality Monitoring (CAQM). Automatic monitoring is designed to collect and measure data continuously 24 hours a day during the monitoring period. Automatic Continuous Air Monitoring Stations typically include: (a) measurement instrumentation (for both pollutant gases and meteorological parameters); (b) support instrumentation (support gases, calibration equipment); (c) instrument shelters (temperature controlled enclosures); and (d) data acquisition system for data collection and storage. The concentration levels of pollutants in the ambient air such as sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon moNOxide (CO) are measured hourly and analysed in time-weighted average (TWA) in this work. The near-surface atmospheric aerosols are regularly monitored using continuous particulate matter (PM) monitoring instrument with the adoption of Met-One Beta Attenuation Method (BAM) and manual 24-h monitoring system High Volume Air Samplers (HVAS). Instruments from the Teledyne Technologies Inc., USA, such as Teledyne API Model 100A/100E, Teledyne API Model 300/300E, Teledyne API Model 400/400E and Teledyne API Model 200A/200E are used to monitor SO2, CO, O3 and NO2 respectively. The gases SO2, NO2 and O3 are determined using the UV fluorescence method, chemiluminescence detection method and UV absorption (Beer Lambert) method respectively.


3. RESULTS AND DISCUSSION
3. 1 Variability of PM10 in the Urban Atmosphere of Sabah

Fig. 2 shows the histogram of PM10 concentration measured at five distinct study areas in Sabah (a) Kota Kinablu, (b) Tawau, (c) Labuan, (d) Keningau, (e) Sandakan from Jan to Dec 2012. Within the measurement period, the average hourly PM10 measured at Tawau was 35.7±17.8 μg/m3, Kota Kinabalu (35.0±18.4 μg/ m3), Labuan (34.7±18.1 μg/m3), Sandakan (32.8±11.5 μg/m3), and Keningau (31.9±18.6 μg/m3). Dispersity of the data was the least at Sandakan with the nominal min and max at 8.0 μg/m3 and 104 μg/m3, respectively. The variability of PM concentrations observed at different sites are quite different where out of the five monitoring stations, Kota Kinabalu measured the highest range (KK>KEN>LBN>TWU>SDK), the highest maxima (KK>KEN>LBN>TWU>SDK), and the second highest average (TWU>KK>LBN>SDK> KEN). This could be explained by their geographical environmental conditions and population density. Undoubtedly, Kota Kinabalu has the highest PM10 concentration because it is the state capital of Sabah with population of 462,963 for total area 352 meter-sq and population density 1,315/km2 (Department of Statistics Malaysia, 2010). Of all monitoring stations studied in this work, Kota Kinabalu is the most urbanized area with high concentration of cities, industrial and economic activities. Population density is a fundamental amplifier of air pollution problems, and urban population growth has been an important factor in worsening air quality (Yara et al., 2017). The EPA data also indicates a strong association both between higher population densities and higher traffic densities, higher population densities and higher road vehicle nitrogen oxides (NOx) emission intensities. The second highest population density is remarked in Labuan for 948.4/km2, putting it the mid of all monitoring stations. Besides, Labuan is also a federal territory of Malaysia that best known as being an offshore support hub for deepwater oil and gas activities in the region. On the other hand, Sandakan and Tawau have relatively lower population density at 179.8/km2 and 67.06/km2, respectively. The lowest population density is remarked in Keningau (50.12/km2) which explains its lowest hourly-averaged PM10 concentration among all other stations. However, it has the second highest range and maxima due to its geographical location near to Kota Kinabalu. The local source from Kota Kinabalu and the advection due to wind could be one of the reasons responsible for transboundary aerosol between these two regions.


Fig. 2. 
Histogram of PM10 concentration for (a) CA0030, (b) CA0039, (c) CA0042, (d) CA0049, and (e) CA0050 from Jan 2012 to Dec 2012.

The highest maximum ~317 μg/m3 was measured at Kota Kinabalu on Jan 17th during the morning hours 0700h-0800h, while the PM10 concentration of other study areas remained low <80 μg/m3 on the same day and time. The exact reason for the unusual high PM10 occurred only on Jan, 17th from 0700h-0800h was unknown but we ruled out the regional or transboundary effects as the wind speed and wind direction was quite low at 1.7 km/h (153°N) and showed no unusual pattern throughout the past three days (see Fig. 3). Not only that, CO and NO2 also showed no irregular pattern and fluctuated within the normal range between 0.10-1.60 ppm and 0.005 to 0.020 ppm, respectively. Besides, the month of January is also not the open biomass burning period in Kalimantan and Sumatera, which is usually the main cause for low visibility and haze episode in Sabah. Therefore, the transboundary effect in this case is unlikely to cause this phenomenon. In fact, it could be due to other causes but not limited to local emissions related to traffic and open burning. Unfortunately, the local VOC data measured at the same site during the episode day was unavailable and this unusual incident was also not widely archived due to the TWA-24 h of the day was still within the EPA standard of less than 150 μg/m3.


Fig. 3. 
Hourly variation in WS, PM10, CO, NO2 concentration measured in Kota Kinabalu from Jan, 16th to 18th, 2012.

Table 2. 
Summary statistics of PM10 concentration measured in μg/m3 at five distinct study areas in Sabah from Jan 2012 to Dec 2012.
Statistics CA0030 CA0039 CA0042 CA0049 CA0050
Average 35.0 35.7 34.7 31.9 32.8
Standard Deviation 18.4 17.8 18.1 18.6 11.5
Skew 1.90 1.10 2.40 2.70 0.90
Excess Kurtosis 11.4 1.8 11.5 13.3 1.8
Median 32.0 33.0 31.0 27.0 31.0
Minimum 5.0 5.0 5.0 5.0 8.0
Maximum 317.0 148.0 218.0 237.0 104.0
1st Quartile 23.0 23.0 23.0 21.0 24.0
3rd Quartile 43.0 45.0 41.0 37.0 39.0

Fig. 4 shows the seasonal variation of PM10 concentration measured at the five distinct study areas in Sabah from Jan 2012 to Dec 2012. The temporal distribution indicated that the PM10 levels remain highly concentrated during the south-west monsoon (SWM) and post south-west monsoon (POST-SWM) which is the hot and dry season in Sabah. From the statistics summary, the highest PM10 concentration measured was always during the POST-SWM for all five study areas. Besides, no significant difference was found in the highest average PM10 among the sites, ranging from 37 to 40 μg/m3. After the SWM, the PM10 levels started to decline during the north-east monsoon (NEM) and post north-east monsoon (POST-NEM) for all areas. The lowest PM10 concentration was measured at Keningau (27±13 μg/ m3) during the POST-NEM. This finding is similar to another local study in Klang Valley reported that PM10 level was persistently high during the south-west monsoon and low during the north-east monsoon (Rahman et al., 2015; Sansuddin et al., 2011). During the northeast monsoon, the precipitation of rain will carry the pollutants to the earth; hence, reducing the level of pollutants in the atmosphere. However, during the southwest monsoon, the warmer air near the surface area rises to higher latitude, which results in turbulence and causes the pollutants to become unstable; thus resulting in a high level of pollutants in the atmosphere (Barmpadimos et al., 2011). During the wet seasons, precipitation reduces considerably PM10 concentration by wet deposition. In addition, if it is frontal precipitation, some polluted boundary layer air is replaced by clean air from aloft. This feature may not be possible to depict by a linear relationship, but the pattern is significant and obvious for case-by-case comparisons. For example, as shown in Fig. 4d, the PM10 concentration remained positively correlated with the dry seasons during SWM which peaked in the POST-SWM at an average value 37 μg/m3, and negatively correlated with the wet season during NEM which based in the POST-NEM at an average value 27 μg/m3. Nevertheless, the seasonal variation of PM10 concentration in Sabah has no drastic change but limit to an extent of less than <10 μg/m3 for all study areas. This attribute could be due to the small seasonal variation of planetary boundary layer (PBL) in tropics. Previous study revealed that the influence of planetary boundary layer height stands the highest absolute correlation on the PM10 level in Poznan, Poland (Czernecki et al., 2017). Notably, increasing values of net irradiance are associated with strong decrease of PM10 concentration due to large sensible heat flux which in turn drives the formation of deeper atmospheric boundary layers (Barmpadimos et al., 2011). In contrast, the relationship between the PM10 concentration and net irradiance is reversed for decreasing solar irradiance. All five study areas are in tropic where receive fairly constant amount of solar irradiance throughout the year. Hence, it is possible that through this mechanism, relatively small difference of <10 μg/m3 between seasonal PM10 concentration was remarked for all study areas in Sabah.


Fig. 4. 
Seasonal variation of PM10 concentration measured at (a) CA0030, (b) CA0039, (c) CA0042, (d) CA0049, and (e) CA0050 from Jan 2012 to Dec 2012.

3. 2 Diurnal Evolution of PM10 in Kota Kinabalu

Fig. 5 presents the diurnal variation of (a) PM10, (b) CO, (c) Ozone, (d) NO2, and (e) SO2 concentration measured for the total period of 2012 over the study area, Kota Kinabalu in the form of box plot. Each box is determined by the 25th and 75th percentiles, and the whiskers are marked at 5th and 95th percentiles. The median value at 50th percentile are marked with solid lines inside the boxes, while the maximum and minimum values are marked with solid lines outside the boxes. The box plot gives a good insight into the dynamics of the PM10 concentration for each hour. The averaged diurnal variation of PM10 showed pronounced variations with two significant peaks during the morning hours at 7 h and the evening hours at 19 h (see Fig. 5a). Fig. 5b shows that the hourly averaged CO concentration rose abruptly at 7 h and gradually decreased to the normal background concentration <0.50 ppm at 10 h and then started to hike up again at 18 h and persisted until 23 h. This bimodal distribution was also found in the hourly averaged NO2 concentration which peaks at 7 h and 19 h (see Fig. 5d). Nevertheless, the hourly maxima for both parameters CO and NO2 was still far beyond the hourly Recommended Malaysia Air Quality Guidelines (RMAQG) of less than 30 ppm and 0.17 ppm, respectively. In contrast, the diurnal evolution of ozone and SO2 has no direct effect on the PM10 trend. The ozone profile was in accordance with the photochemical effects where it peaks at the noon hours for high intensity of UV solar light. Fig. 5c clearly exemplifies this pattern on its unimodal distribution which has a bell-curve shape peaks at 13 h. Among all parameters, SO2 has the least variability throughout the day where the fluctuation shows no significant anomalies and far behind the hourly RMAQG of <0.13 ppm.


Fig. 5. 
Hourly averaged boxplot for (a) PM10, (b) CO, (c) O3, (d) NO2, and (e) SO2 concentration measured in Kota Kinabalu for the total period in 2012.

The two rushing hours at 7 h and 19 h are associated with the heavy-traffic hours where the number of vehicles on road is the direct reflection of the related emission of CO, NO2, and PM10. The morning peak hour is when people are going to work and sending their children to school, thus causing traffic congestion and increasing the pollutant levels. Similar findings were also reported in a study at Klang Valley where the daily concentration of PM10, CO, NO2 and SO2 was clearly recorded as being the highest during traffic congestion, particularly during the morning peak between 7:00 am-9:00 am, led to the higher amount of CO in the atmosphere (Azmi et al., 2010). In the latter part of the afternoon, PM10 concentration peaked from 7:00 pm in line with the evening rush hour when people started returning home from work. The late evening peak can also be attributed to meteorological conditions, particularly atmospheric stability and wind speed (Rahman et al., 2015). The PM levels were lower between 0800h and 1400h because of low traffic volumes and hence low emission rates. Another possible reason could be the increase in the mixing height that favors dispersion conditions (Bathmanabhan et al., 2010). However, during the night time between 2000h and 2300h, the PM concentrations were slightly higher than the daytime (see Fig. 5). The probable reason for this may be the builtup of particles under inversion conditions. Gomiscek et al. (2004) have also reported a similar trend for three urban sites in Austria.

More specifically, we present two scenarios: (1) the morning effect and (2) the evening effect in the urban atmosphere at Kota Kinabalu on Fig. 6. The left panel Fig. 6a shows the spikes on PM levels due to the evening effect and the right panel Fig. 6b shows the spikes of the morning effect. On the evening effect, the rise of PM level started at 1800h and fall abruptly after 2000h. On the morning effect, the PM level started to rise at 0700h reaching maxima 190 μg/m3 and then fall abruptly to reaching 190 μg/m3 at 0800h. These two effects are highly related to the traffic exhaust gaseous emission such as CO and NO2. Nitrogen dioxides have a substantial impact on PM10 through their atmospheric oxidation to aerosol nitrate and the CO formed from oxidation of VOCs (Wang et al., 2015). The concentrations and compositions of nitrate aerosols are determined primarily by their precursor emissions, which are mainly of anthropogenic origin. Among the major pollutants, CO, nitrogen oxides (NOx=NO+NO2), PM10, and some types of VOCs (e.g., BTEX: benzene, toluene, ethylbenzene, and ortho-, meta-, and para-xylenes) are primarily traffic-induced, while O3 and NO2 are secondary trace gases formed from precursors in photochemical reactions (Yoo et al., 2015). The increase of CO was substantially driven by the road traffic emissions of the day. Similar findings are also reported in Masiol et al. (2014) where species mainly linked to road vehicle exhaust emissions such as CO, and NO2 have a bimodal structure due to the peaks in traffic at 7-9 am and 6-8 pm.


Fig. 6. 
The hourly mean PM10 concentration at Kota Kinabalu from 1-Jan to 2-Jan, 2012 (left) and 15-Jan to 16-Jan, 2012 (right). Left panel shows spikes on the evening effect and right panel shows the morning effect.

Fig. 7a and 7c shows the diurnal changes of PM10 with comparison to CO and NO2, respectively. The morning peak on PM10 level at 0700h was observed concurrently with the rise on CO and NO2, and the abrupt fall on PM10 between 0900h and 1500h was also concurrent with the decrease on both parameters. Similar pattern was observed for the evening peak on PM10 level at 1800h where the peak was corresponding to the rise on CO and NO2 and the abrupt fall of PM10 after 2000h was consistent to the decrease of CO and NO2 (see Fig. 8a and 8c). This pattern was further confirmed by the correlation analysis on the scatter plot PM10 against CO and NO2 on Fig. 7b and 7d, respectively. Positive correlation R2 of 0.46 and 0.23 was remarked on PM10 against CO and NO2, respectively. Same goes to the correlation analysis for the evening effect where positive correlation R2 of 0.45 and 0.42 was remarked for CO and NO2 (see Fig. 8b and 8d). The higher correlative strength of CO further implies that PM10 level in Kota Kinabalu is more dominantly dependent on the diurnal evolution of CO concentration. This finding was in good agreement with other studies reporting that the daily patterns of each pollutant for NO, NO2, CO and PM10 peak during the morning rush hours, and fall abruptly in the noon owing to the decrease in the intensity of emission and the rapid growth of the convective boundary layers (Adame et al., 2014).


Fig. 7. 
Hourly mean concentration of (a) CO, (c) NO2, (e) SO2, (g) O3 plotted on secondary axes with comparison to PM10 plotted on primary axes (Left panel) on Jan, 15-16 for morning effect. Scatter analysis of (b) CO, (d) NO2, (f) SO2, (h) O3 plotted against PM10 (Right panel).


Fig. 8. 
Hourly mean concentration of (a) CO, (c) NO2, (e) SO2, (g) O3 plotted on secondary axes with comparison to PM10 plotted on primary axes (Left panel) on Jan, 1-2 for evening effect. Scatter analysis of (b) CO, (d) NO2, (f) SO2, (h) O3 plotted against PM10 (Right panel).

The profile of SO2 showed no pronounced changes for both the morning and evening effect. The trend is rather irregular and has no significant pattern against the changes of PM10 level (see Figs. 7e and 8e). Both figures show no concurrent rise of PM10 and SO2, neither the significant drop of both parameters occurred at the same time. Therefore, the effects of SO2 on the evolution of PM10 were somewhat less reactive compared to other gaseous e.g. NO2 and CO. A very weak correlative strength of 0.09 and 0.01 was found in the scatter plot between PM10 and SO2 for morning effect and evening effect respectively (see Figs. 7f and 8f), indicating the changes of both parameters are not entirely related and significant. Another possible reason owing to the low variability of SO2 is due to SO2 itself has a very short lifetime and can easily transform into sulphate SO4 through oxidation and interaction with particles and water vapour (Kai et al., 2007).

Ozone is a reactive oxidant gas that plays an essential role in the photochemical air pollution and atmospheric oxidation processes. Although in the upper atmosphere it acts as a barrier for UV-rays, in the troposphere it is a secondary air pollutant induced through a series of complex photochemical reactions involving solar radiation and ozone-precursors gaseous such as NO2 (Masiol et al., 2014). When the concentration of NO2, CO, and PM10 started to increase in the morning hours, surface ozone level decreased due to titration reaction with NO and the rupture of the nocturnal inversion layer (Talib et al., 2014). Figs. 7g and 8g supported this findings by showing the daily pattern of ozone decreased for increasing PM10 and vice versa. Ozone concentrations continued to increase during the noon hours due to photochemical reaction and or horizontal and vertical transport processed and reached to a daily maxima 0.035 ppm at 1330h. The decreased in ozone levels was followed by a reduction in solar radiation when reaching to evening hours at 1700h. This was also associated with the increase of NO2 and PM10 during the evening hours when emission of traffic started to mount up. Over the Borneo, the air mass evolution during the sunset is faster compared to the Peninsular. In other words, solar intensity was dimmed earlier at 1800h in Borneo regions. This is the reason why the ozone level reached to the daily minima 0.005 ppm at earlier time 1900h where the solar activity induced photochemical reaction was no longer active. Hence, a negative correlative R2 -1.35 (see Fig. 7h) and -2.77 (see Fig. 8h) was observed between the ozone and PM10 concentration, indicating an increase on PM10 concentration was most likely associated with the decreased on ozone levels and vice versa. In general, CO, NO, and PM10 shows two typical patterns for urban atmosphere: two daily peaks correspond to morning mode and evening mode at 0700h and 1800h, respectively. The modes are divided by a minimum extend from 1100h to 1700h, which is assumed to the results of three factors: (i) lower emission of traffic exhaust, (ii) large availability of ozone inducing the photolysis of NOx and oxidation of CO, and (iii) enhanced dipersion potential. The last effect is more pronounced in the tropics when ozone levels are always higher during the noon hours and the local atmospheric circulation during daytime is dominated by the sea breezes, which have a key role in blowing air masses from the sea (Masiol et al., 2014).

3. 3 Weekday and Weekend Effect of PM10 in Kota Kinabalu

Fig. 9 shows the daily average PM10 concentration measured in the urban atmosphere of Kota Kinabalu in Jan 2012. In this figure, two effects are highlighted: the weekend effect and the weekday effect. The weekend effect and the weekday effect are separated by the symbol “M” which denotes Monday. It is emphasized the abrupt fall in PM10 concentration is often observed during the weekend and gradual rise is often observed during the weekday. Within the measurement period, the PM level never exceeded 100 μg/m3 over the weekend. The PM concentration was significant low <50 μg/m3 on Sunday and slightly higher on Saturday. Meanwhile, high PM10 concentration >150 μg/m3 was observed on two Mondays of the month on Jan, 2nd and 16th. A harmonic pattern was observed on the PM10 levels where the peaks are often during the first or second day of the week and the lows are during the last two days of the week.


Fig. 9. 
Daily distribution of hourly PM10 concentration in Jan 2012 over the urban atmosphere of Kota Kinabalu, Sabah. Alphabet symbol M denotes Monday.

Fig. 10 shows the average diurnal changes of PM10, CO, ozone, NO2, and SO2. The weekend-weekday differences of PM10 varied greatly at the peak hour 0700h. The highest values ~160 μg/m3 was remarked on weekday and ~40 μg/m3 on weekend (see Fig. 10a). Similar pattern is observed for CO where the highest difference is observed in the morning peak hours from 0700h to 0800h (see Fig. 10b). The increase in PM10 concentration during morning hours as a result of direct road traffic emission is observed but this increase is more marked in the cases of CO which reflects direct exhaust emissions. Daily cycles of traffic-related exhausts NO2 is also marked by road traffic evolution on Fig. 10d where the weekday concentration is consistently higher than that of weekend. This effect has been discussed elsewhere that the pollutant number concentration increases with road traffic intensity in the morning is due to the ultrafine particle vehicle emission and formation of new particles during dilution and cooling of the vehicle exhaust (Pérez et al., 2010). Besides, the second peak observed during the noon hours was attributed to new formation of aerosols by photochemically induced nucleation by solar radiation intensity, and by dilution of aerosols due to the growth of the mixing layer (Rodríguez and Cuevas, 2007). During the night hours, the PM10 concentration started to decline (see Fig. 10a) owing to the decrease of the traffic congestion and also probably due to the thinning of the boundary layer depth that favors condensation and coagulation processes that sink the aerosol particles (Minoura and Akekawa, 2005). As a whole, during the weekday the levels of vehicle exhaust emission such as CO and NO2 follow the traffic intensity with maxima during the morning hours, decreasing during the day because of dilution processes and increasing again the evening. During the night hours, the concentrations remained high owing to the reduction of the boundary layer height and probably lower wind speeds that prevent the dispersion of pollutants (Pérez et al., 2010).


Fig. 10. 
Average diurnal changes of (a) PM10, (b) CO, (c) Ozone, (d) NO2, (e) SO2 by the day of the week. Weekday is represented by blue color line and weekend is represented by orange color line.

The average diurnal ozone volume fraction is higher during the weekend compared to weekdays (Fig. 10c). This finding is similar to that report from Gvozdic et al. (2011). This behaviour is contrary to other air pollutants such as PM10, NO2, CO, and SO2 where the weekend concentration of traffic pollutions were lower than weekday concentration (see Fig. 11). Ozone level increased in the afternoon during both weekdays and weekends, following the solar radiation intensity and its formation is dependent on the photochemistry process. However, during the weekends, the ozone concentration was higher. This is due to the effect of the lower general pollution levels observed during the weekends, thus reducing the interaction of ozone with other species such as nitrate or carbonaceous compounds (Pérez et al., 2010). Similar results were also reported in Qin et al. (2004) that low emission of NO during the weekend could be the reason for high ozone concentration at night.


Fig. 11. 
Average diurnal variations of ozone, NO2, SO2, PM10, and CO during (a) weekday and (b) weekend.

Finally, we present two types of average diurnal variations during the weekday (see Fig. 11a). Firstly, the diurnal variation with a single maxima for ozone and secondly, diurnal variations with double maxima for CO, NO2, and PM10. The single maxima for ozone occurred during the noon hours from 1300h to 1400h while the double maxima for CO, NO2, and PM10 occurred during the morning hours and afternoon hours at 0700h and 1900h, respectively. The peak of CO, NO2, and PM10 concentration are in the front and at the back of the peak for ozone is a representative pattern of urban atmosphere in tropical climatic region (Gvozdic et al., 2011). It is corresponding to the increase of traffic density in the morning hours from 0600h to 0800h, and afternoon hours from 1800 to 2000h. However, this pattern is somewhat less reflective during the weekend (see Fig. 11b). The bimodal distribution of CO during the weekend is still legibly observable but less significant when compared to weekday. The peak of CO during the weekend was observed at 0700h and 1900h, which is similar to that of weekday. However, the peak of PM10 had no extreme variations during the weekend. Same pattern was observed for NO2 levels. The hourly average PM10 concentration was constantly capped below 50 μg/m3 (see Fig. 11b) which is an indicator of good air quality. Meanwhile, the evolution pattern for SO2 during the weekdays and weekend have no much difference. This indicates that the traffic-related emissions is less influential on the changes of SO2. Therefore, the sources of SO2 are somewhat not directly related to traffic exhausts but possibly due to the industrial emissions. This can be explained by the characteristics of NOx concentration rationed to CO and SO2. High CO/NOx and low SO2/NOx ratios values indicate mobile sources while high SO2/NOx and low CO/NOx ratio values typically indicate point sources, e.g. from industrial activities (Talib et al., 2014). Fig. 12 shows that the air quality level in the study area was dominantly influenced by the CO/NO2 ratio value with values ranging from 80 to 140 for weekday and 40 to 80 for weekend. For SO2/NO2 ratio, the values range from 0.10 to 0.25 for weekdays and 0.15 to 0.35 for weekend. These ratio values indicate that air pollution in the study area of Kota Kinabalu, is more seriously influenced by mobile sources rather especially during the weekday period.


Fig. 12. 
Hourly distribution of SO2/NO2 ratio and CO/NO2 ratio during weekdays and weekend over the urban atmosphere of Kota Kinabalu. Clustered column represents SO2/NO2 ratio and line with marker represents CO/NO2 ratio.


4. CONCLUSION

In the present study, the seasonal, diurnal and weekly cycles of PM10 concentration in the urban atmosphere of Sabah was investigated. Five distinct study areas were selected to represent the urban air of Sabah, namely Kota Kinabalu, Labuan, Sandakan, Tawau, and Keningau. The major sources of elevated PM10 over the region was high likely related to the emissions of traffic related gaseous such as NOx and CO. On the temporal distribution, PM10 levels remain highly concentrated during the south-west monsoon (SWM) and post south-west monsoon (POST-SWM) which is the hot and dry season in Sabah. On the contrary, the PM10 levels were slightly lower during the north-east monsoon (NEM) which is the cold and wet season. However, the variance of change was not extremely large but relatively small difference of <10 μg/m3 between seasonal PM10 concentration was remarked for all study areas in Sabah.

To further scrutinize the variability of PM10 concentration due to the changes of CO, NO2, SO2, and ozone, Kota Kinabalu site was selected as it is the most urbanized central city in Sabah and has the most variability among all sites. The analysis of PM10 concentration measured between Jan 2012 to Dec 2012 in Kota Kinabalu showed clear diurnal and weekly cycles. In the diurnal cycles, two peaks were observed at the morning and evening hours which is corresponding to the heavy traffic emissions. Similar pattern was also observable on the diurnal cycles of CO and NO2, which indicate that the major precursors of elevated PM10 were the traffic and mobile sources. The profile of SO2, however has no much significant variability throughout the day. Ozone is photochemical reactive gaseous that peaks during the noon hours but starts to decline after the absence of intense solar radiance. Besides, the correlative analysis further highlights the positive proportionality between PM10 and CO, PM10 and NO2, while negative relationship between PM10 and ozone.

In the weekly cycles, higher PM10, CO, and NO2 concentrations were observed during the weekday when compared to weekend. The ozone concentration was somehow higher during the weekend especially during afternoon hours. It is owing to the lower general pollution levels observed during the weekends reducing the interaction of ozone with other species such as nitrate or carbonaceous compounds. Besides, two types of average diurnal variations were highlighted during the weekday: (a) diurnal variation with a single maxima for ozone and (b) diurnal variations with double maxima for CO, NO2, and PM10. The peak of CO, NO2, and PM10 concentration are in the front and at the back of the peak for ozone is a representative pattern of urban atmosphere in this climatic region. This pattern is less reflective during the weekend except for the unimodal distribution of ozone during noon hours. The PM10 and NO2 levels have no extreme variations during the weekend except for the bimodal distribution CO was still legibly observable but less significant. The evolution pattern for SO2 during weekdays and weekend have no much difference. Finally, the characteristics of NO2 concentration rationed to CO and SO2 suggests that air pollution in Kota Kinabalu is more seriously influenced by mobile sources rather especially during weekday period.


Acknowledgments

This research was supported by the Malaysian Ministry of Education under the research grant number SBK0352-2017 and was greatly acknowledged.


REFERENCES
1. Adame, J.A., Hernández-ceballos, M.Á, Sorribas, M., Lozano, A., Arturo, B., Morena, D., (2014), Weekendweekday effect assessment for O3, NOx, CO and PM10 in Andalusia, Spain (2003-2008), Aerosol and Air Quality Research, 14, p1862-1874.
2. Aouizerats, B., Werf, G.R., Van Der, Balasubramanian, R., Betha, R., (2015), Importance of transboundary transport of biomass burning emissions to regional air quality in Southeast Asia during a high fire event, Atmospheric Chemistry and Physics, 15, p363-373.
3. Azid, A., Juahir, H., Toriman, M.E., Kamarudin, M.K.A., Saudi, A.S.M., Hasnam, C.N.C., Aziz, N.A.A., Azaman, F., Latif, M.T., Zainuddin, S.F.M., Osman, M.R., Yamin, M., (2014), Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: a case study in Malaysia, Water, Air, & Soil Pollution, 225(8), p2063, Available at: http://link.springer.com/10.1007/s11270-014-2063-1.
4. Azmi, S.Z., Latif, M.T., Ismail, A.S., Juneng, L., Jemain, A.A., (2010), Trend and status of air quality at three different monitoring stations in the Klang Valley, Malaysia, Air Quality, Atmosphere and Health, 3(1), p536-4.
5. Barmpadimos, I., Hueglin, C., Keller, J., Henne, S., Prevot, A.S.H., (2011), Influence of meteorology on PM10 trends and variability in Switzerland from 1991 to 2008, Atmospheric Chemistry and Physics 11, p1813-1835.
6. Bathmanabhan, S., Nagendra, S., Madanayak, S., (2010), Analysis and interpretation of particulate matter - PM10, PM2.5 and PM1 emissions from the heterogeneous traffic near an urban roadway, Atmospheric Pollution Research, 1, p184-194.
7. Czernecki, B., Półrolniczak, M., Kolendowicz, L., Marosz, M., Kendzierski, S., Pilguj, N., (2017), Influence of the atmospheric conditions on PM10 concentrations in Poznań, Poland, Journal of Atmospheric Chemistry, 74, p115-139.
8. Dominick, D., Talib, M., Zain, S.M., Zaharin, A., (2012), Spatial assessment of air quality patterns in Malaysia using multivariate analysis, Atmospheric Environment, 60, p172-181.
9. Gomiscek, B., Hauck, H., Stopper, S., Preining, O., (2004), Spatial and temporal variations of PM1, PM2.5, PM10 and particle number concentration during the AUPHEP - project, Atmospheric Environment, 38, p3917-3934.
10. Gvozdic, V., Kovac-Andric, E., Brana, J., (2011), Influence of meteorological factors NO2, SO2, CO and PM10 on the concentration of O3 in the urban atmosphere of eastern Croatia, Environmental Modeling and Assessment, 16(5), p491-501.
11. Harrison, M.E., Page, S.E., Limin, S.H., (2009), The global impact of Indonesian forest fires, Biologist, 56(3), p156-163.
12. Kai, Z., Yuesi, W., Tianxue, W., Yousef, M., Frank, M., (2007), Properties of nitrate, sulfate and ammonium in typical polluted atmospheric aerosols (PM10) in Beijing, Atmospheric Research, 84, p67-77.
13. Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A., (2015), The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525(7569), p367-371.
14. Masiol, M., Agostinelli, C., Formenton, G., Tarabotti, E., Pavoni, B., (2014), Thirteen years of air pollution hourly monitoring in a large city: Potential sources, trends, cycles and effects of carf-ree days, Science of the Total Environment 494-495, p84-96.
15. Minoura, H., Akekawa, H., (2005), Observation of number concentrations of atmospheric aerosols and analysis of nanoparticle behavior at an urban background area in Japan, Atmospheric Environment, 39, p5806-5816.
16. Nirmalkar, J., Deb, M.K., (2015), Impact of intense field burning episode on aerosol mass loading and its possible health implications in rural area of eastern central India, Air Quality, Atmosphere and Health.
17. Norsukhairin, S., Abdul, S., Azid, A., Sharif, S.M., Latif, T., Aris, Z., Zain, S.M., Dominick, D., (2013), Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia, Environmental Science Processes & Impacts, 15, p17171-728.
18. Othman, J., Sahani, M., Mahmud, M., Sheikh, K., (2014), Transboundary smoke haze pollution in Malaysia: Inpatient health impacts and economic valuation, Environmental Pollution, 189, p194-201.
19. Pérez, N., Pey, J., Cusack, M., Reche, C., Querol, X., Alastuey, A., Viana, M., (2010), Variability of particle number, black carbon, and PM10, PM2.5, and PM1 levels and speciation: Influence of road traffic emissions on urban air quality, Aerosol Science and Technology, 44, p487-499.
20. Qin, Y., Tonnesen, G.S., Wang, Z., (2004), Weekend/weekday differences of ozone, NOx, Co, VOCs, PM10 and the light scatter during ozone season in southern California, Atmospheric Environment, 38, p3069-3087.
21. Rahman, S.R.A., Ismail, S.N.S., Ramli, M.F., Latif, M.T., Abidin, E.Z., Praveena, S.M., (2015), The assessment of ambient air pollution trend in Klang Valley, Malaysia, World Environment, 5(1), p1-11.
22. Rodríguez, S., Cuevas, E., (2007), The contributions of “minimum primary emissions” and “new particle formation enhancements” to the particle number concentration in urban air, Journal of Aerosol Science, 38, p1207-1219.
23. Sansuddin, N., Azam, N., Ahmad, R., Yahaya, S., Faizah, N., Yusof, F., Adyani, N., (2011), Statistical analysis of PM10 concentrations at different locations in Malaysia, Environmental Monitoring and Assessment, 180, p573-588.
24. Shaadan, N., Jemain, A.A., Latif, M.T., Deni, S.M., (2015), Anomaly detection and assessment of PM10 functional data at several locations in the Klang Valley, Malaysia, Atmospheric Pollution Research, 6(2), p365-375.
25. Talib, M., Dominick, D., Ahamad, F., Khan, F., Juneng, L., Hamzah, F.M., Nadzir, M.S.M., (2014) Long term assessment of air quality from a background station on the Malaysian Peninsula, Science of the Total Environment, 482(483), (2), p336-348.
26. Thurston, G.D., Burnett, R.T., Turner, M.C., Shi, Y., Krewski, D., Lall, R., Ito, K., Jerrett, M., Gapstur, S.M., Diver, W.R., Iii, C.A.P., (2016), Ischemic heart disease mortality and long-term exposure to sourcerelated components of U.S. fine particle air pollution, Environmental Health Prespectives, 124(6), p785-795.
27. Wang, Y., Zhang, Q.Q., He, K., Zhang, Q., Chai, L., Wang, C.Y., (2015), Sulfate-nitrate-ammonium aerosols over China: response to 2000-2015 emission changes of sulfur dioxide, nitrogen oxides, Atmospheric Chemistry and Physics, 13, p2635-2652.
28. Wrobel, A., Rokita, E., Maenhaut, W., (2000), Transport of traffic-related aerosols in urban areas, The Science of the Total Environment, 257, p199-211.
29. Yara, C., Viegas, M., Lemos, P., Cavalcanti, B., Pais, R., Oliveira, E., De, Gil, R., Neto, D.A., (2017), Atmospheric distribution of organic compounds from urban areas near Olympic games sites in Rio de Janeiro, Brazil, Microchemical Journal, 133, p638-644.
30. Yoo, J., Jeong, M., Kim, D., Stockwell, W.R., Yang, J., Shin, H., Lee, M.I., Song, C.K., Lee, S., (2015), Spatiotemporal variations of air pollutants (O3, NO2, SO2, CO, PM10, and VOCs) with land-use types, Atmo - spheric Chemistry and Physics, 15, p10857-10885.