Asian Journal of Atmospheric Environment 109 Vol. 12, No. 2, pp. 109-126, June 2018 Ozone Concentration in the Morning in Inland Kanto Region doi: https://doi.org/10.5572/ajae.2018.12.2.109 ISSN (Online) 2287-1160, ISSN (Print) 1976-6912

Variability of the PM10 concentration in the urban atmosphere of 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) and Justin SENTIAN4)

1)Preparatory Center for Science and Technology, Universiti Sabah, Jalan UMS, 88400 , 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 *Corresponding author. Tel: +60-088-320 000, E-mail: [email protected]

rationed to CO and SO2 suggests that mobile sourc­ Abstract es is the dominant factor for the air pollution in Kota Kinabalu; particularly during weekdays. This paper presents seasonal variation of PM10 over five urban sites in Sabah, Malaysia for the period of Key words: Particulate Matter, Urban atmosphere, January through December 2012. The variability of Air quality, Malaysia, Diurnal cycle, Weekly cycle

PM10 along with the diurnal and weekly cycles of CO, NO2, SO2, and O3 at Kota Kinabalu site were also dis­ cussed to investigate the possible sources for incre­ ased PM10 concentration at the site. This work is cru­ cial to understand the behaviour and possible sourc­ 1. INTRODUCTION es of PM10 in the urban atmosphere of Sabah region. Particulate matter (PM10) consists of very small solid In Malaysia, many air pollution studies in the past and liquid particles suspended in air. It is of greatest focused in west Peninsular, but very few local studies concern to public health because these particles are were dedicated for Sabah region. This work aims to small enough to be inhaled into the deepest parts of fill the gap by presenting the descriptive statistics lung and settled there to cause adverse health effects. on the variability of PM10 concentration in the urban atmosphere of Sabah. To further examine its diurnal Particles less than 10 microns in diameter are known as and weekly cycle pattern, its responses towards the PM10. It is a mixture of substances that include smoke, variations of CO, NO , SO , and ozone were also in­ soot, dust, salt, acids, and metals. Particulate matter 2 2 also forms when gases emitted from motor vehicles and vestigated. The highest mean value of PM10 for the whole study period is seen from (35.7±17.8 industry undergo chemical reactions in the atmosphere. μg m-3), while the lowest is from (31.9± It is a major component of air pollution that threatens -3 both our health and our environment. Sources of PM10 18.6 μg m ). The concentrations of PM10 in all cities exhibited seasonal variations with the peak values in both urban and rural areas are not limited to motor occurred during the south-west monsoons. The PM10 vehicles, but also dust from construction, landfills, and data consistently exhibited strong correlations with agriculture, wildfires and brush/waste burning, industri­ traffic related gaseous pollutants (NO2, and CO), al sources, windblown dust from open lands. Epidemi­ except for SO2 and O3. The analysis of diurnal cycles ological studies indicated that the fine particle fractions of PM10 levels indicated that two peaks were associ­ e.g. PM2.5, and PM1.0 have considerable impacts on ated during the morning and evening rush hours. human health even at concentrations below the Malay­ 3 The bimodal distribution of PM10, CO, and NO2 in the sia Ambient Air Quality Standards PM2.5 of 75 μg/m in front and at the back of ozone peak is a representa­ daily 24-hour average (Gomiscek et al., 2004). These tion of urban air pollution pattern. In the weekly particles when inhaled by human can evade the respira­ cycle, higher PM10, CO, and NO2 concentrations were tory system’s natural defences and lodge deep in the observed during the weekday when compared to lungs. Health problems begin as the body reacts to these weekend. The characteristics of NO2 concentration foreign particles. PM10 can increase the number and 110 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018 severity of asthma attacks, cause or aggravate bronchi­ of fossil fuel in motor vehicles and industrial activities tis and other lung diseases, and reduce the body’s abil­ (Norsukhairin et al., 2013). A prediction study by PCA ity to fight infections (Lelieveld et al., 2015; Nirmalkar had further identified that CH4, NmHC, THC, O3, and and Deb, 2015). Certain people who include children, PM10 are amongst the most significant factors deterio­ the elderly, exercising adults, and those suffering from rating air quality in Malaysia (Azid et al., 2014). A cri­ asthma or bronchitis are especially vulnerable to PM10’s tical understanding of the behaviour of major pollutants adverse health effects (Thurston et al., 2016). in urban atmosphere of big city is important on health The issue of PM10 pollution in the urban atmosphere concerns. It is also an essential contingency for policy has received much attentions in recent years as an in­ implementation especially when the elevated PM con­ creasing share of the world’s population lives in urban centration has caused enormous impacts on economical areas. Previous studies reported that traffic-related emis­ values (Othman et al., 2014). sion accounted at least 50% of the total PM emission in The paper aims to present the descriptive statistics the urban centres (Wrobel et al., 2000). Vehicles pro­ on the PM10 concentration in the urban atmosphere of duce exhaust and non-exhaust emissions. Aerosol ex­ Sabah. Understanding the variation of PM10 concentra­ haust emissions can be emitted directly as particles such tions is crucial for proper health risk assessment and as soot and carbonaceous aggregates that formed during risk management. PM10 was chosen because it is one the fuel combustion in the engine or formed during the of the major air pollutants listed in the Recommended emission, dilution, mixing and cooling of the vehicle Malaysian Air Quality Guideline (RMAQG) and it is exhaust gases in ambient air (Adame et al., 2014; Pérez also one of the five air pollutants included in the calcu­ et al., 2010). Non-combustion traffic-related PM sour­ lation of Malaysian Air Quality Index (MAPI) together ces are the result of the resuspension of road dust orig­ with ozone (O3), carbon monoxide (CO), sulfur dioxide inating from the mechanical wear and degradation of (SO2) and nitrogen dioxide (NO2). This paper also pre­ tires, brakes, and pavement abrasion (Bathmanabhan et sents the results of the variability of PM10 concentra­ al., 2010). Therefore, the contributions to the PM10 con­ tion owing to the diurnal evolution and weekly cycles centration in ambient air are segregated into two groups: of CO, NO2, SO2, and ozone, particularly in Kota Kina­ (1) primary particles emitted by vehicle exhausts, and balu. (2) nucleation particles in ambient air (Rodríguez and Cuevas, 2007). The primary particles emitted by vehi­ cles exhausts tend to exhibit a size distribution with a 2. MEASUREMENT and SITE nucleation mode (<30 nm) and a carbonaceous mode

(50 to >100 nm). The gaseous exhaust emissions may The PM10 monitoring sites are located at five differ­ also contribute to new particle formation in ambient air ent stations in Sabah. Fig. 1 shows the regional site map by in situ nucleation occurring sometime after emission of the five monitoring stations. The location details of (hours to days). This process frequently occurs in tran­ each station is depicted in Table 1. The town of Kota sit from the road to the urban background especially Kinabalu is the busiest urban center in Sabah, located when the exhaust gas is fully diluted within the ambi­ in the eastern part of Malaysia with a population of ent air and is photo-oxidised by reactive species. approximately 460,000 and area 351 km2. Its industry In Malaysia, the government routinely publicized is not very active and the precursor emissions are main­ the air pollution index (API) readings throughout the ly due to traffic exhaust (Dominick et al., 2012; San­ mainstream media to alert the public of any looming suddin et al., 2011). Kota Kinabalu is at 5°58ʹ17ʺN smoke haze episode. Severe haze episode often occurs 116°05ʹ43ʺE and with an altitude of 5 m. The area is during the Southeast Asian haze resulted by smoke from topographically homogenous and the city lies on a nar­ forest fires in Kalimantan and Sumatra (Aouizerats et row flatland between the to the east and al., 2015; Harrison et al., 2009). The location and back­ the to the west. The average tempera­ ground of a station, as well as wind speed, seasonal ture is 27°C with April and May are the hottest months (monsoon) and weekdays-weekend variations also play while January is the coolest. The average annual rain­ important role in influencing PM10 anomalies (Shaadan fall is around 2,400 mm and varies remarkably through­ et al., 2015). Diurnal patterns, rationed between major out the year. February and March are typically the dri­ air pollutants and sensitivity analysis, indicate that the est months while rainfall peaks in the inter-monsoon influence of local traffic emissions on urban air quality period in October. The wind speed ranges from 5.5 to is critical (Talib et al., 2014). A case study at three sel­ 7.9 m/s during the Northeast Monsoon (NEM) but is ected stations in Malaysia e.g. , Melaka, significantly lower to 0.3 to 3.3 m/s during Southwest and , also revealed that the major source of air Monsoon (SWM). pollution over urban air is mostly due to the combustion Tawau is on the south-east coast of Sabah surround Variability of the PM10 Concentration in Urban Atmosphere of Sabah 111

114°0.000000000ʹE 116°0.000000000ʹE 118°0.000000000ʹE

6°0.000000000ʹN 6°0.000000000ʹN

° ʹ 4°0.000000000ʹN 4 0.000000000 N

114°0.000000000ʹE 116°0.000000000ʹE 118°0.000000000ʹE

50 0 50 100 150 200 km

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

Table 1. Location details of monitoring site in Sabah. Site Site ID Location Latitude Longitude Type Kota Kinabalu CA0030 SM , 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 by the in the east and located at 540 kilometres likely around October, when they occur very frequently. south-east of Kota Kinabalu. It has tropical rainforest Meanwhile, the relative humidity for Labuan typically climate under the Köppen climate classification. The ranges from 63% (mildly humid) to 96% (very humid) climate is relatively hot and wet with average shade over the course of the year. temperature about 26°C, with 29°C at noon and falling Keningau is the capital of the in to around 23°C at night. The town sees precipitation the of Sabah, Malaysia. This city has throughout the year, with a tendency for November, a tropical climate. The temperatures are highest on ave­ December and January to be the wettest months, while rage in May, at around 26.4°C. The lowest average tem­ February and March are the driest months. Tawau’s peratures in the year occur in January, at around 25.3°C mean rainfall varies from 1,800 mm to 2,500 mm. The average annual temperature is 25.8°C in Keningau. Labuan’s area comprises the main island (Labuan The least amount of rainfall occurs in August and the Island - 91.64 square kilometres) and six other smaller greatest amount of precipitation occurs in May, with islands namely Burung, Daat, Kuraman, Big Rusukan, an average of 203 mm. The average annual rainfall is Small Rusukan and Papan island with a total area of 1,825 mm. 91.64 square kilometres. It has a tropical rainforest cli­ Sandakan is located on the eastern coast of Sabah mate with no dry season. Over the course of a year, the facing the Sulu Sea, with the town is known as one of temperature typically varies from 25 to 32°C and is the port towns in Malaysia. The town is located approx­ rarely below 24°C or above 33°C. Thunderstorms are imately 1,900 kilometres from 319 kilometres from the most severe precipitation observed in Labuan during Kota Kinabalu, the capital of Sabah. It has a tropical 60% of those days with precipitation. They are most rainforest climate under the Köppen climate classifica­ 112 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018 tion. The climate is relatively hot and wet with average min and max at 8.0 μg/m3 and 104 μg/m3, respectively. shade temperature about 32°C, with around 32°C at The variability of PM concentrations observed at differ­ noon falling to around 27°C at night. The town sees ent sites are quite different where out of the five moni­ precipitation throughout the year, with a tendency for toring stations, Kota Kinabalu measured the highest October to February to be the wettest months, while range (KK>KEN>LBN>TWU>SDK), the highest April is the driest month. Its mean rainfall varies from maxima (KK>KEN>LBN>TWU>SDK), and the 2,184 mm to 3,988 mm. second highest average (TWU>KK>LBN>SDK> All selected air quality monitoring stations are oper­ KEN). This could be explained by their geographical ated by Alam Sekitar Sdn. Bhd. (ASMA) under the environmental conditions and population density. Un­ administration of Department of Meteorology (DOE). doubtedly, Kota Kinabalu has the highest PM10 concen­ The Department of Environment (DOE) monitors the tration because it is the state capital of Sabah with pop­ country’s ambient air quality through a network of Con­ ulation of 462,963 for total area 352 meter-sq and pop­ tinuous Air Quality Monitoring (CAQM). Automatic ulation density 1,315/km2 (Department of Statistics monitoring is designed to collect and measure data con­ Malaysia, 2010). Of all monitoring stations studied in tinuously 24 hours a day during the monitoring period. this work, Kota Kinabalu is the most urbanized area Automatic Continuous Air Monitoring Stations typical­ with high concentration of cities, industrial and eco­ ly include: (a) measurement instrumentation (for both nomic activities. Population density is a fundamental pollutant gases and meteorological parameters); (b) sup­ amplifier of air pollution problems, and urban popula­ port instrumentation (support gases, calibration equip­ tion growth has been an important factor in worsening ment); (c) instrument shelters (temperature controlled air quality (Yara et al., 2017). The EPA data also indi­ enclosures); and (d) data acquisition system for data cates a strong association both between higher popula­ collection and storage. The concentration levels of pol­ tion densities and higher traffic densities, higher popu­ lutants in the ambient air such as sulphur dioxide (SO2), lation densities and higher road vehicle nitrogen oxides nitrogen dioxide (NO2), ozone (O3) and carbon monox­ (NOx) emission intensities. The second highest popu­ ide (CO) are measured hourly and analysed in time- lation density is remarked in Labuan for 948.4/km2, weighted average (TWA) in this work. The near-surface putting it the mid of all monitoring stations. Besides, atmospheric aerosols are regularly monitored using con­ Labuan is also a federal territory of Malaysia that best tinuous particulate matter (PM) monitoring instrument known as being an offshore support hub for deepwater with the adoption of Met-One Beta Attenuation Method oil and gas activities in the region. On the other hand, (BAM) and manual 24-h monitoring system High Vol­ Sandakan and Tawau have relatively lower population ume Air Samplers (HVAS). Instruments from the Tele­ density at 179.8/km2 and 67.06/km2, respectively. The dyne Technologies Inc., USA, such as Teledyne API lowest population density is remarked in Keningau Model 100A/100E, Teledyne API Model 300/300E, (50.12/km2) which explains its lowest hourly-averaged Teledyne API Model 400/400E and Teledyne API Model PM10 concentration among all other stations. However, 200A/200E are used to monitor SO2, CO, O3 and NO2 it has the second highest range and maxima due to its respectively. The gases SO2, NO2 and O3 are determin­ geographical location near to Kota Kinabalu. The local ed using the UV fluorescence method, chemilumines­ source from Kota Kinabalu and the advection due to cence detection method and UV absorption (Beer Lam­ wind could be one of the reasons responsible for trans­ bert) method respectively. boundary aerosol between these two regions. The highest maximum ~317 μg/m3 was measured at Kota Kinabalu on Jan 17th during the morning hours 3. RESULTs AND DISCUSSION 0700h-0800h, while the PM10 concentration of other study areas remained low <80 μg/m3 on the same day

3. 1 ‌Variability of PM10 in the Urban and time. The exact reason for the unusual high PM10 Atmosphere of Sabah occurred only on Jan, 17th from 0700h-0800h was un­ Fig. 2 shows the histogram of PM10 concentration known but we ruled out the regional or transboundary measured at five distinct study areas in Sabah (a) Kota effects as the wind speed and wind direction was quite Kinablu, (b) Tawau, (c) Labuan, (d) Keningau, (e) San­ low at 1.7 km/h (153°N) and showed no unusual pattern dakan from Jan to Dec 2012. Within the measurement throughout the past three days (see Fig. 3). Not only period, the average hourly PM10 measured at Tawau that, CO and NO2 also showed no irregular pattern and was 35.7±17.8 μg/m3, Kota Kinabalu (35.0±18.4 μg/ fluctuated within the normal range between 0.10-1.60 m3), Labuan (34.7±18.1 μg/m3), Sandakan (32.8±11.5 ppm and 0.005 to 0.020 ppm, respectively. Besides, the μg/m3), and Keningau (31.9±18.6 μg/m3). Dispersity month of January is also not the open biomass burning of the data was the least at Sandakan with the nominal period in Kalimantan and Sumatera, which is usually Variability of the PM10 Concentration in Urban Atmosphere of Sabah 113

12% 8% (a) Frequency (b) Frequency 7% 10% Normal Normal 6% 8% 5%

6% 4%

3% 4% 2% 2% 1%

0% 0% 6.58 6.52 12.88 19.18 25.48 31.79 38.09 44.39 50.70 57.00 63.30 69.61 75.91 82.21 88.52 94.82 12.61 18.69 24.78 30.86 36.95 43.03 49.12 55.20 61.29 67.37 73.46 79.54 85.63 91.71 97.80 113.73 101.12 107.42 116.05 103.88 109.97 122.14

12% 18% Frequency Frequency (C) 16% (d) 10% Normal Normal 14% 8% 12% 10% 6% 8% 4% 6% 4% 2% 2% 0% 0% 6.54 6.59 62.11 15.80 25.07 34.33 43.59 52.85 71.37 80.63 89.89 99.15 16.12 25.66 35.19 44.73 54.26 63.79 73.33 82.86 92.40 117.67 108.41 126.93 136.20 145.46 154.72 163.98 173.24 182.50 111.47 101.93 121.00 130.53 140.07 149.60 159.14 168.67 178.21

8% (e) Frequency 7% Normal 6%

5%

4%

3%

2%

1%

0% 8.98 14.86 20.73 26.61 32.49 38.37 44.24 50.12 56.00 61.88 67.76 73.63 79.51 85.39 91.27 97.14 103.02

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 main cause for low visibility and haze episode in 150 μg/m3. Sabah. Therefore, the transboundary effect in this case Fig. 4 shows the seasonal variation of PM10 concen­ is unlikely to cause this phenomenon. In fact, it could tration measured at the five distinct study areas in Sabah be due to other causes but not limited to local emissions from Jan 2012 to Dec 2012. The temporal distribution related to traffic and open burning. Unfortunately, the indicated that the PM10 levels remain highly concentrat­ local VOC data measured at the same site during the ed during the south-west monsoon (SWM) and post episode day was unavailable and this unusual incident south-west monsoon (POST-SWM) which is the hot was also not widely archived due to the TWA-24 h of and dry season in Sabah. From the statistics summary, the day was still within the EPA standard of less than the highest PM10 concentration measured was always 114 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018

45 350

TWA-24 h PM10 3 Wind speed (km/h) 16-Jan: 56 μg/m × 40 3 CO ( 10 ppm) 17-Jan: 71 μg/m 300 3 NO2 (×1,000 ppm) 18-Jan: 52 μg/m 3 35 PM10 (μg/m ) 250 30 2

25 200 10 PM 20 150 WS | CO NO 15 100 10 50 5

0 0 1100 1100 1100 0100 0300 0500 0700 0900 1300 1500 1700 1900 2100 2300 0100 0300 0500 0700 0900 1300 1500 1700 1900 2100 2300 0100 0300 0500 0700 0900 1300 1500 1700 1900 2100 2300 16-Jan 17-Jan 18-Jan

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

3 Table 2. Summary statistics of PM10 concentration measured in μg/m 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 during the POST-SWM for all five study areas. Besides, in a high level of pollutants in the atmosphere (Barm­ no significant difference was found in the highest aver­ padimos et al., 2011). During the wet seasons, precipi­ 3 age PM10 among the sites, ranging from 37 to 40 μg/m . tation reduces considerably PM10 concentration by wet After the SWM, the PM10 levels started to decline dur­ deposition. In addition, if it is frontal precipitation, some ing the north-east monsoon (NEM) and post north-east polluted boundary layer air is replaced by clean air from monsoon (POST-NEM) for all areas. The lowest PM10 aloft. This feature may not be possible to depict by a concentration was measured at Keningau (27±13 μg/ linear relationship, but the pattern is significant and m3) during the POST-NEM. This finding is similar to obvious for case-by-case comparisons. For example, another local study in Valley reported that PM10 as shown in Fig. 4d, the PM10 concentration remained level was persistently high during the south-west mon­ positively correlated with the dry seasons during SWM soon and low during the north-east monsoon (Rahman which peaked in the POST-SWM at an average value et al., 2015; Sansuddin et al., 2011). During the north­ 37 μg/m3, and negatively correlated with the wet sea­ east monsoon, the precipitation of rain will carry the son during NEM which based in the POST-NEM at an pollutants to the earth; hence, reducing the level of pol­ average value 27 μg/m3. Nevertheless, the seasonal vari­ lutants in the atmosphere. However, during the south­ ation of PM10 concentration in Sabah has no drastic west monsoon, the warmer air near the surface area change but limit to an extent of less than <10 μg/m3 rises to higher latitude, which results in turbulence and for all study areas. This attribute could be due to the causes the pollutants to become unstable; thus resulting small seasonal variation of planetary boundary layer Variability of the PM10 Concentration in Urban Atmosphere of Sabah 115

350 350 (a) (b) 300 300

250 250

200 200

150 150

100 100

50 50

0 0 NEM POST-NEM SWM POST-SWM NEM POST-NEM SWM POST-SWM

350 350 (c) (d) 300 300

250 250

200 200

150 150

100 100

50 50

0 0 NEM POST-NEM SWM POST-SWM NEM POST-NEM SWM POST-SWM

350 (e) 300

250

200

150

100

50

0 NEM POST-NEM SWM POST-SWM

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.

(PBL) in tropics. Previous study revealed that the influ­ ve fairly constant amount of solar irradiance throughout ence of planetary boundary layer height stands the high­ the year. Hence, it is possible that through this mecha­ 3 est absolute correlation on the PM10 level in Poznan, nism, relatively small difference of <10 μg/m between

Poland (Czernecki et al., 2017). Notably, increasing seasonal PM10 concentration was remarked for all study values of net irradiance are associated with strong de­ areas in Sabah. crease of PM10 concentration due to large sensible heat flux which in turn drives the formation of deeper atmos­ 3. 2 ‌Diurnal Evolution of PM10 in pheric boundary layers (Barmpadimos et al., 2011). In Kota Kinabalu contrast, the relationship between the PM10 concentra­ Fig. 5 presents the diurnal variation of (a) PM10, (b) tion and net irradiance is reversed for decreasing solar CO, (c) Ozone, (d) NO2, and (e) SO2 concentration irradiance. All five study areas are in tropic where recei­ measured for the total period of 2012 over the study 116 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018

350.0

) 300.0 (a) 3 - 250.0 200.0 (μg m

10 150.0 100.0 PM 50.0 0.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 3.00 2.50 (b) 2.00 (ppm)

1.50

CO 1.00 0.50 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.050 0.040 (c) 0.030 (ppm)

3 0.020 O 0.010 0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0.030 0.025 (d) 0.020 (ppm)

2 0.015

NO 0.010 0.005 0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0.008 0.007 (e) 0.006 0.005 (ppm) 0.004 2 0.003 SO 0.002 0.001 0.000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h)

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. area, Kota Kinabalu in the form of box plot. Each box hours at 7 h and the evening hours at 19 h (see Fig. 5a). is determined by the 25th and 75th percentiles, and the Fig. 5b shows that the hourly averaged CO concentra­ whiskers are marked at 5th and 95th percentiles. The tion rose abruptly at 7 h and gradually decreased to the median value at 50th percentile are marked with solid normal background concentration <0.50 ppm at 10 h lines inside the boxes, while the maximum and mini­ and then started to hike up again at 18 h and persisted mum values are marked with solid lines outside the until 23 h. This bimodal distribution was also found in boxes. The box plot gives a good insight into the dyna­ the hourly averaged NO2 concentration which peaks at mics of the PM10 concentration for each hour. The aver­ 7 h and 19 h (see Fig. 5d). Nevertheless, the hourly max­ aged diurnal variation of PM10 showed pronounced vari­ ima for both parameters CO and NO2 was still far be­ ations with two significant peaks during the morning yond the hourly Recommended Malaysia Air Quality Variability of the PM10 Concentration in Urban Atmosphere of Sabah 117

200 200

180 180

160 160 ) ) 3 3 - - 140 140 (μg m (μg m 120 120

100 100

80 80 concentration concentration 10 60 10 60 PM PM 40 40

20 20

0 0 2000 2200 2400 0200 0400 0600 0800 1000 1200 1400 1600 1800 2000 2200 2400 0100 0400 0700 1000 1300 1600 1900 2200 0100 0400 0700 1000 1300 1600 1900 2200 1-Jan 2-Jan 15-Jan 16-Jan

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.

Guidelines (RMAQG) of less than 30 ppm and 0.17 increase in the mixing height that favors dispersion con­ ppm, respectively. In contrast, the diurnal evolution of ditions (Bathmanabhan et al., 2010). However, during ozone and SO2 has no direct effect on the PM10 trend. the night time between 2000h and 2300h, the PM con­ The ozone profile was in accordance with the photo­ centrations were slightly higher than the daytime (see chemical effects where it peaks at the noon hours for Fig. 5). The probable reason for this may be the built- high intensity of UV solar light. Fig. 5c clearly exem­ up of particles under inversion conditions. Gomiscek plifies this pattern on its unimodal distribution which et al. (2004) have also reported a similar trend for three has a bell-curve shape peaks at 13 h. Among all para­ urban sites in Austria. meters, SO2 has the least variability throughout the day More specifically, we present two scenarios: (1) the where the fluctuation shows no significant anomalies morning effect and (2) the evening effect in the urban and far behind the hourly RMAQG of <0.13 ppm. atmosphere at Kota Kinabalu on Fig. 6. The left panel The two rushing hours at 7 h and 19 h are associated Fig. 6a shows the spikes on PM levels due to the eve­ with the heavy-traffic hours where the number of vehi­ ning effect and the right panel Fig. 6b shows the spikes cles on road is the direct reflection of the related emis­ of the morning effect. On the evening effect, the rise of sion of CO, NO2, and PM10. The morning peak hour is PM level started at 1800h and fall abruptly after 2000h. when people are going to work and sending their child­ On the morning effect, the PM level started to rise at ren to school, thus causing traffic congestion and incre­ 0700h reaching maxima 190 μg/m3 and then fall abrupt­ asing the pollutant levels. Similar findings were also ly to reaching 190 μg/m3 at 0800h. These two effects reported in a study at Klang Valley where the daily are highly related to the traffic exhaust gaseous emis­ concentration of PM10, CO, NO2 and SO2 was clearly sion such as CO and NO2. Nitrogen dioxides have a recorded as being the highest during traffic congestion, substantial impact on PM10 through their atmospheric particularly during the morning peak between 7:00 am- oxidation to aerosol nitrate and the CO formed from 9:00 am, led to the higher amount of CO in the atmo­ oxidation of VOCs (Wang et al., 2015). The concentra­ sphere (Azmi et al., 2010). In the latter part of the after­ tions and compositions of nitrate aerosols are determin­ noon, PM10 concentration peaked from 7:00 pm in line ed primarily by their precursor emissions, which are with the evening rush hour when people started return­ mainly of anthropogenic origin. Among the major pol­ ing home from work. The late evening peak can also lutants, CO, nitrogen oxides (NOx =NO+NO2), PM10, be attributed to meteorological conditions, particularly and some types of VOCs (e.g., BTEX: benzene, toluene, atmospheric stability and wind speed (Rahman et al., ethylbenzene, and ortho-, meta-, and para-xylenes) are 2015). The PM levels were lower between 0800h and primarily traffic-induced, while O3 and NO2 are second­ 1400h because of low traffic volumes and hence low ary trace gases formed from precursors in photochemi­ emission rates. Another possible reason could be the cal reactions (Yoo et al., 2015). The increase of CO was 118 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018 substantially driven by the road traffic emissions of the it acts as a barrier for UV-rays, in the troposphere it is a day. Similar findings are also reported in Masiol et al. secondary air pollutant induced through a series of com­ (2014) where species mainly linked to road vehicle plex photochemical reactions involving solar radiation exhaust emissions such as CO, and NO2 have a bimod­ and ozone-precursors gaseous such as NO2 (Masiol et al structure due to the peaks in traffic at 7-9 am and al., 2014). When the concentration of NO2, CO, and 6-8 pm. PM10 started to increase in the morning hours, surface Fig. 7a and 7c shows the diurnal changes of PM10 ozone level decreased due to titration reaction with NO with comparison to CO and NO2, respectively. The mor­ and the rupture of the nocturnal inversion layer (Talib ning peak on PM10 level at 0700h was observed con­ et al., 2014). Figs. 7g and 8g supported this findings by currently with the rise on CO and NO2, and the abrupt showing the daily pattern of ozone decreased for incre­ fall on PM10 between 0900h and 1500h was also con­ asing PM10 and vice versa. Ozone concentrations con­ current with the decrease on both parameters. Similar tinued to increase during the noon hours due to photo­ pattern was observed for the evening peak on PM10 chemical reaction and or horizontal and vertical trans­ level at 1800h where the peak was corresponding to the port processed and reached to a daily maxima 0.035 rise on CO and NO2 and the abrupt fall of PM10 after ppm at 1330h. The decreased in ozone levels was fol­

2000h was consistent to the decrease of CO and NO2 lowed by a reduction in solar radiation when reaching to (see Fig. 8a and 8c). This pattern was further confirm­ evening hours at 1700h. This was also associated with ed by the correlation analysis on the scatter plot PM10 the increase of NO2 and PM10 during the evening hours against CO and NO2 on Fig. 7b and 7d, respectively. when emission of traffic started to mount up. Over the Positive correlation R2 of 0.46 and 0.23 was remarked , the air mass evolution during the sunset is faster­ on PM10 against CO and NO2, respectively. Same goes compared to the Peninsular. In other words, solar inten­ to the correlation analysis for the evening effect where sity was dimmed earlier at 1800h in Borneo regions. positive correlation R2 of 0.45 and 0.42 was remarked This is the reason why the ozone level reached to the for CO and NO2 (see Fig. 8b and 8d). The higher corre­ daily minima 0.005 ppm at earlier time 1900h where the lative strength of CO further implies that PM10 level in solar activity induced photochemical reaction was no Kota Kinabalu is more dominantly dependent on the longer active. Hence, a negative correlative R2 -1.35 diurnal evolution of CO concentration. This finding (see Fig. 7h) and -2.77 (see Fig. 8h) was observed bet­ was in good agreement with other studies reporting that ween the ozone and PM10 concentration, indicating an the daily patterns of each pollutant for NO, NO2, CO increase on PM10 concentration was most likely associ­ and PM10 peak during the morning rush hours, and fall ated with the decreased on ozone levels and vice versa. abruptly in the noon owing to the decrease in the inten­ In general, CO, NO, and PM10 shows two typical pat­ sity of emission and the rapid growth of the convective terns for urban atmosphere: two daily peaks correspond boundary layers (Adame et al., 2014). to morning mode and evening mode at 0700h and The profile of SO2 showed no pronounced changes 1800h, respectively. The modes are divided by a mini­ for both the morning and evening effect. The trend is mum extend from 1100h to 1700h, which is assumed to rather irregular and has no significant pattern against the results of three factors: (i) lower emission of traffic the changes of PM10 level (see Figs. 7e and 8e). Both exhaust, (ii) large availability of ozone inducing the figures show no concurrent rise of PM10 and SO2, nei­ photolysis of NOx and oxidation of CO, and (iii) enhanc­ ther the significant drop of both parameters occurred ed dipersion potential. The last effect is more pronounc­ at the same time. Therefore, the effects of SO2 on the ed in the tropics when ozone levels are always higher evolution of PM10 were somewhat less reactive com­ during the noon hours and the local atmospheric circu­ pared to other gaseous e.g. NO2 and CO. A very weak lation during daytime is dominated by the sea breezes, correlative strength of 0.09 and 0.01 was found in the which have a key role in blowing air masses from the scatter plot between PM10 and SO2 for morning effect sea (Masiol et al., 2014). and evening effect respectively (see Figs. 7f and 8f), indi­cating the changes of both parameters are not 3. 3 ‌Weekday and Weekend Effect of PM10 in entirely related and significant. Another possible rea­ Kota Kinabalu son owing to the low variability of SO2 is due to SO2 Fig. 9 shows the daily average PM10 concentration itself has a very short lifetime and can easily transform measured in the urban atmosphere of Kota Kinabalu in into sulphate SO4 through oxidation and interaction Jan 2012. In this figure, two effects are highlighted: the with parti­cles and water vapour (Kai et al., 2007). weekend effect and the weekday effect. The weekend Ozone is a reactive oxidant gas that plays an essential effect and the weekday effect are separated by the sym­ role in the photochemical air pollution and atmospheric bol “M” which denotes Monday. It is emphasized the oxidation processes. Although in the upper atmosphere abrupt fall in PM10 concentration is often observed dur­ Variability of the PM10 Concentration in Urban Atmosphere of Sabah 119

(a) 200 1.4 (b) 200 PM10 180 CO 1.2 160 150 1 ) ) 140 3 3 0.8 120 2 (μg/m (ppm) (μg/m

= 100 100 R 0.4629 10 0.6 10 CO 80 PM PM 0.4 60 50 40 0.2 20 0 0 0 0 0.5 1 1.5 0100 0400 0700 1000 1300 1600 1900 2200 0100 0400 0700 1000 1300 1600 1900 2200 CO (ppm) Jan-15 Jan-16

(c) 200 0.025 (d) 200 180 PM10 180 NO 160 2 0.02 160

) 140 ) 140 3 3 120 0.015 120 R2 =0.2314 (ppm) (μg/m (μg/m

100 100 2 10 80 0.01 10 80 NO PM PM 60 60 40 0.005 40 20 20 0 0 0 0 0.005 0.01 0.015 0.02 0.025 0100 0400 0700 1000 1300 1600 1900 2200 0100 0400 0700 1000 1300 1600 1900 2200 NO (ppm) Jan-15 Jan-16 2

(e) 200 0.0045 (f) 200 180 PM10 0.004 180 SO2 160 0.0035 160 )

) 140 140 3 3 0.003 120 120 2 0.0025 R =0.0922 (μg/m

(ppm) (μg/m

100 100 2 10

10 0.002 80 80 SO PM PM 60 0.0015 60 40 0.001 40 20 0.0005 20 0 0 0 0 0.001 0.002 0.003 0.004 0.005 0100 0400 0700 1000 1300 1600 1900 2200 0100 0400 0700 1000 1300 1600 1900 2200 SO (ppm) Jan-15 Jan-16 2

(g) 200 0.04 (h) 200 180 PM10 180 O 0.035 160 3 160 0.03

) 140 ) 140 3 3 120 0.025 120 (μg/m (μg/m 100 0.02

(ppm) 100

10 10 3

80 0.015 O 80 R2 = -1.35 PM 60 PM 60 0.01 40 40 20 0.005 20 0 0 0 0 0.01 0.02 0.03 0.04 0100 0400 0700 1000 1300 1600 1900 2200 0100 0400 0700 1000 1300 1600 1900 2200 O (ppm) Jan-15 Jan-16 3

Fig. 7. Hourly mean concentration of (a) CO, (c) NO2, (e) SO2, (g) O3 plotted on secondary axes with comparison to PM10 plot­ ted 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). 120 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018

(a) 200 1.2 (b) 200 180 180 1 160 160 ) ) 140 140 3 3 0.8 120 120 R2 =0.4505 (μg/m (μg/m (ppm)

100 0.6 100 10 10

80 CO 80 PM PM 0.4 60 60 40 40 PM10 0.2 20 CO 20 0 0 0 0 0.2 0.4 0.6 0.8 1 1.2 2000 2200 2400 0200 0400 0600 0800 1000 1200 1400 1600 1800 2000 2200 2400 CO (ppm) Jan-01 Jan-02

(c) 200 0.018 (d) 200 180 PM10 0.016 180 NO2 160 0.014 160 )

140 )

3 140 0.012 3 2 = 120 120 R 0.4187 0.01 (ppm) (μg/m

(μg/m

100

2 100 10

0.008 10 80 NO 80 PM 60 0.006 PM 60 40 0.004 40 20 0.002 20 0 0 0 0 0.005 0.01 0.015 0.02

2000 2200 2400 0200 0400 0600 0800 1000 1200 1400 1600 1800 2000 2200 2400 NO2 (ppm) Jan-01 Jan-02

(e) 200 0.0025 (f) 200 180 PM10 180 SO 160 2 0.002 160

) 140 ) 140 3 3 2 = - 120 0.0015 120 R 1.563 (ppm)

(μg/m (μg/m

100 2 100 10 10

80 0.001 SO 80 PM 60 PM 60 40 0.0005 40 20 20 0 0 0 0 0.0005 0.001 0.0015 0.002 0.0025

2000 2200 2400 0200 0400 0600 0800 1000 1200 1400 1600 1800 2000 2200 2400 SO2 (ppm) Jan-01 Jan-02

(g) 200 0.018 (h) 200 180 PM10 0.016 180 O3 160 0.014 160

) 140

) 140 3

0.012 3 120 120 0.01 (μg/m

100 (μg/m 100

(ppm) 2 10 3 0.008 10 R = -2.766

80 O 80 PM

0.006 PM 60 60 0.004 40 40 20 0.002 20 0 0 0 0 0.005 0.01 0.015 0.02

2000 2200 2400 0200 0400 0600 0800 1000 1200 1400 1600 1800 2000 2200 2400 O3 (ppm) Jan-01 Jan-02

Fig. 8. Hourly mean concentration of (a) CO, (c) NO2, (e) SO2, (g) O3 plotted on secondary axes with comparison to PM10 plot­ ted 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). Variability of the PM10 Concentration in Urban Atmosphere of Sabah 121

350

300

250 ) 3 - (μg m 200 M M

150 concentration 10 PM 100 M M M

50

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Time (day)

Fig. 9. Daily distribution of hourly PM10 concentration in Jan 2012 over the urban atmosphere of Kota Kinabalu, Sabah. Alphabet symbol M denotes Monday. ing the weekend and gradual rise is often observed dur­ ultrafine particle vehicle emission and formation of ing the weekday. Within the measurement period, the new particles during dilution and cooling of the vehi­ PM level never exceeded 100 μg/m3 over the weekend. cle exhaust (Pérez et al., 2010). Besides, the second The PM concentration was significant low <50 μg/m3 peak observed during the noon hours was attributed to on Sunday and slightly higher on Saturday. Meanwhile, new formation of aerosols by photochemically induced 3 high PM10 concentration >150 μg/m was observed on nucleation by solar radiation intensity, and by dilution two Mondays of the month on Jan, 2nd and 16th. A har­ of aerosols due to the growth of the mixing layer (Rod­ monic pattern was observed on the PM10 levels where ríguez and Cuevas, 2007). During the night hours, the the peaks are often during the first or second day of the PM10 concentration started to decline (see Fig. 10a) week and the lows are during the last two days of the owing to the decrease of the traffic congestion and also week. probably due to the thinning of the boundary layer depth Fig. 10 shows the average diurnal changes of PM10, that favors condensation and coagulation processes that

CO, ozone, NO2, and SO2. The weekend-weekday dif­ sink the aerosol particles (Minoura and Akekawa, 2005). ferences of PM10 varied greatly at the peak hour 0700h. As a whole, during the weekday the levels of vehicle 3 The highest values ~160 μg/m was remarked on week­ exhaust emission such as CO and NO2 follow the traf­ day and ~40 μg/m3 on weekend (see Fig. 10a). Similar fic intensity with maxima during the morning hours, pattern is observed for CO where the highest difference decreasing during the day because of dilution process­ is observed in the morning peak hours from 0700h to es and increasing again the evening. During the night

0800h (see Fig. 10b). The increase in PM10 concentra­ hours, the concentrations remained high owing to the tion during morning hours as a result of direct road traf­ reduction of the boundary layer height and probably fic emission is observed but this increase is more mark­ lower wind speeds that prevent the dispersion of pollu­ ed in the cases of CO which reflects direct exhaust emis­ tants (Pérez et al., 2010). sions. Daily cycles of traffic-related exhausts NO2 is The average diurnal ozone volume fraction is higher also marked by road traffic evolution on Fig. 10d where during the weekend compared to weekdays (Fig. 10c). the weekday concentration is consistently higher than This finding is similar to that report from Gvozdic et that of weekend. This effect has been discussed else­ al. (2011). This behaviour is contrary to other air pollu­ where that the pollutant number concentration increases tants such as PM10, NO2, CO, and SO2 where the week­ with road traffic intensity in the morning is due to the end concentration of traffic pollutions were lower than 122 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018

200 200 (a) Weekday (b) Weekday Weekend Weekend

150 150 ) 3 - ppm) 3 - 10 (μg m 100 100 × 10 (

PM CO 50 50

0 0 1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23 Time (h) Time (h)

0.035 0.025 (c) Weekday (d) Weekday 0.030 Weekend Weekend 0.020 0.025

0.020 0.015 (ppm)

(ppm)

2 0.015 NO

Ozone 0.010 0.010 0.005 0.005

0.000 0.000 1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23 Time (h) Time (h)

0.004 (e) Weekday 0.003 Weekend

0.003

0.002 (ppm)

2 0.002 SO

0.001

0.001

0.000 1 3 5 7 9 11 13 15 17 19 21 23 Time (h)

Fig. 10. Average diurnal changes of (a) PM10, (b) CO, (c) Ozone, (d) NO2, (e) SO2 by the day of the week. Weekday is repre­ sented by blue color line and weekend is represented by orange color line. weekday concentration (see Fig. 11). Ozone level in­ thus reducing the interaction of ozone with other spe­ creased in the afternoon during both weekdays and cies such as nitrate or carbonaceous compounds (Pérez weekends, following the solar radiation intensity and et al., 2010). Similar results were also reported in Qin its formation is dependent on the photochemistry pro­ et al. (2004) that low emission of NO during the week­ cess. However, during the weekends, the ozone concen­ end could be the reason for high ozone concentration tration was higher. This is due to the effect of the lower at night. general pollution levels observed during the weekends, Finally, we present two types of average diurnal vari­ Variability of the PM10 Concentration in Urban Atmosphere of Sabah 123

(a) 0.025 140 (b) 0.025 80 Ozone Ozone PM10 PM10 NO2 120 NO2 70 CO CO 0.020 SO2 0.020 SO2 60 100 ) ) 3 3 50 (ppm) (ppm)

0.015 (μg/m 0.015 (μg/m

80 10 10 40 , Ozone , Ozone 2 2 60 , NO , NO

0.010 (ppm), PM 0.010 (ppm), PM 2 2 30 SO SO CO CO 40 20 0.005 0.005 20 10

0.000 0 0.000 0 1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23 Time (h) Time (h)

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

14000 0.4

SO2/NO2 weekday SO /NO weekend 2 2 0.35 12000 CO/NO2 weekdays

CO/NO2 weekends 0.3 10000

0.25 2 2 8000 /NO

0.2 2 CO/NO 6000 SO 0.15

4000 0.1

2000 0.05

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h)

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. ations during the weekday (see Fig. 11a). Firstly, the red during the morning hours and afternoon hours at diurnal variation with a single maxima for ozone and 0700h and 1900h, respectively. The peak of CO, NO2, secondly, diurnal variations with double maxima for and PM10 concentration are in the front and at the back CO, NO2, and PM10. The single maxima for ozone of the peak for ozone is a representative pattern of urban occurred during the noon hours from 1300h to 1400h atmosphere in tropical climatic region (Gvozdic et al., while the double maxima for CO, NO2, and PM10 occur­ 2011). It is corresponding to the increase of traffic den­ 124 Asian Journal of Atmospheric Environment, Vol. 12(2), 109-126, 2018

sity in the morning hours from 0600h to 0800h, and To further scrutinize the variability of PM10 concen­ afternoon hours from 1800 to 2000h. However, this pat­ tration due to the changes of CO, NO2, SO2, and ozone, tern is somewhat less reflective during the weekend (see Kota Kinabalu site was selected as it is the most urba­ Fig. 11b). The bimodal distribution of CO during the nized central city in Sabah and has the most variability weekend is still legibly observable but less significant among all sites. The analysis of PM10 concentration when compared to weekday. The peak of CO during the measured between Jan 2012 to Dec 2012 in Kota Kina­ weekend was observed at 0700h and 1900h, which is balu showed clear diurnal and weekly cycles. In the similar to that of weekday. However, the peak of PM10 diurnal cycles, two peaks were observed at the morning had no extreme variations during the weekend. Same and evening hours which is corresponding to the heavy pattern was observed for NO2 levels. The hourly aver­ traffic emissions. Similar pattern was also observable age PM10 concentration was constantly capped below on the diurnal cycles of CO and NO2, which indicate 3 50 μg/m (see Fig. 11b) which is an indicator of good that the major precursors of elevated PM10 were the traf­ air quality. Meanwhile, the evolution pattern for SO2 fic and mobile sources. The profile of SO2, however during the weekdays and weekend have no much differ­ has no much significant variability throughout the day. ence. This indicates that the traffic-related emissions is Ozone is photochemical reactive gaseous that peaks less influential on the changes of SO2. Therefore, the during the noon hours but starts to decline after the sources of SO2 are somewhat not directly related to traf­ absence of intense solar radiance. Besides, the correla­ fic exhausts but possibly due to the industrial emissions. tive analysis further highlights the positive proportion­ This can be explained by the characteristics of NOx con­ ality between PM10 and CO, PM10 and NO2, while neg­ centration rationed to CO and SO2. High CO/NOx and ative relationship between PM10 and ozone. low SO2/NOx ratios values indicate mobile sources In the weekly cycles, higher PM10, CO, and NO2 con­ while high SO2/NOx and low CO/NOx ratio values typi­ centrations were observed during the weekday when cally indicate point sources, e.g. from industrial activi­ compared to weekend. The ozone concentration was ties (Talib et al., 2014). Fig. 12 shows that the air qual­ somehow higher during the weekend especially during ity level in the study area was dominantly influenced afternoon hours. It is owing to the lower general pol­ by the CO/NO2 ratio value with values ranging from lution levels observed during the weekends reducing 80 to 140 for weekday and 40 to 80 for weekend. For the interaction of ozone with other species such as nit­ SO2/NO2 ratio, the values range from 0.10 to 0.25 for rate or carbonaceous compounds. Besides, two types weekdays and 0.15 to 0.35 for weekend. These ratio of average diurnal variations were highlighted during values indicate that air pollution in the study area of the weekday:­ (a) diurnal variation with a single maxi­ Kota Kinabalu, is more seriously influenced by mobile ma for ozone and (b) diurnal variations with double sources rather especially during the weekday period. 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 4. CONCLUSION atmosphere in this climatic region. This pattern is less reflective during the weekend except for the unimodal In the present study, the seasonal, diurnal and weekly distribution of ozone during noon hours. The PM10 and cycles of PM10 concentration in the urban atmosphere NO2 levels have no extreme variations during the week­ of Sabah was investigated. Five distinct study areas end except for the bimodal distribution CO was still were selected to represent the urban air of Sabah, name­ legibly observable but less significant. The evolution ly Kota Kinabalu, Labuan, Sandakan, Tawau, and Ke­ pattern for SO2 during weekdays and weekend have no ningau. The major sources of elevated PM10 over the much difference. Finally, the characteristics of NO2 region was high likely related to the emissions of traf­ concentration rationed to CO and SO2 suggests that air fic related gaseous such as NOx and CO. On the tem­ pollution in Kota Kinabalu is more seriously influenc­ poral distribution, PM10 levels remain highly concen­ ed by mobile sources rather especially during weekday trated during the south-west monsoon (SWM) and post period. south-west monsoon (POST-SWM) which is the hot and dry season in Sabah. On the contrary, the PM10 lev­ els were slightly lower during the north-east monsoon ACKNOWLEDGEMENT (NEM) which is the cold and wet season. However, the variance of change was not extremely large but rela­ This research was supported by the Malaysian Min­ tively small difference of <10 μg/m3 between season­ istry of Education under the research grant number al PM10 concentration was remarked for all study areas SBK0352-2017 and was greatly acknowledged. in Sabah. Variability of the PM10 Concentration in Urban Atmosphere of Sabah 125

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