1. Introduction
Road transportation is a common mode of transport in Macao due to the increasing convenience of motor vehicles. Private cars are used to avoid adverse weather conditions during the summer and winter seasons, which is also a known source of urban air pollution. Studies have found that air pollution kills more than 7 million people worldwide every year, with 4.2 million dying from outdoor air pollution and 3.8 million dying from indoor air pollution. In particular, fatalities result from pneumonia (21%), stroke (20%), ischemic heart disease (34%), chronic obstructive pulmonary disease (COPD) (19%), and lung cancer (7%). There are over 2 million people affected by air pollution in Southeast Asia [
1,
2,
3].
A study in Macao identified an episode of high pollution during a week of the Chinese National Holiday of 2019 (1 to 7 October), in which high concentration levels were identified for PM
2.5 and O
3, with a peak of daily levels reaching 55 μg/m
3 and 400 μg/m
3, respectively. In contrast, a low pollution episode for PM
2.5 and O
3 was identified during the COVID-19 pandemic period, with a record low of daily levels of concentration for PM
2.5 and O
3 registered at only 2 μg/m
3 and 50 μg/m
3, respectively [
4]. These pollution episodes are caused by transboundary pollutants causing a regional problem in the Greater Bay Area, not limited to the region of Macao. Another study in Macao shows that the local emissions contributed to at least 35% of PM
2.5 concentrations, and higher black carbon (BC) concentrations were observed during the day than at night, which is consistent with the diurnal variations of traffic flow [
5]. BC is the product of incomplete combustion that is mainly derived from diesel engines and rich-burned gasoline carburetor engines on scooters and light-duty vehicles [
6].
Traffic-related emissions are a significant component of airborne pollution, and the measurement and analysis of real-world vehicle emissions have been used due to the fact that conventional drive cycle testing is not considered representative of vehicles under real-world driving conditions [
7]. A study in South Korea shows that PM
2.5 emissions decreased by 0.6 to 4.1% under the subsidy policy for zero emission vehicles (ZEVs), including electric battery vehicles (BEVs) and fuel cell electric vehicles (FCEVs) for cars, buses, motorcycles, and freight trucks, but it did not reduce diesel freight trucks, which is a major contributor to PM
2.5 and NOx [
8]. The overall average of PM
2.5 mass is slightly higher than those measured in the urban site [
9].
Vehicle emissions are among the major sources of airborne PM
2.5 in urban environments, which adversely impacts the environment and public health [
10]. Vehicular emissions are a major source of carbonaceous aerosols at a heavily trafficked sampling site in Hong Kong [
11]. Traffic is a major source of urban air pollution in developing countries, with PM
2.5 and BC being the primary sources of pollutants at the roadside [
12]. The cause of roadside air pollution is due to a large number of on-road vehicles and traffic jams [
13]. The contribution of roadway sources is about 12 to 17% of PM
2.5 at the near-road site [
14]. The characterization of freshly emitted traffic aerosols in Hong Kong observed high levels of PM
2.5, OC, and EC at the roadside location [
15]. The concentrations of CO and NO
2 were correlated with similar diurnal variations, with meteorological factors driving the diurnal variability more than traffic counts [
16]. The concentration of indoor PM
2.5 was higher than that of outdoor PM
2.5 [
17].
The monitoring of air pollution primarily relies on expensive, high-end static sensor stations, which only produce aggregated information about air pollutants but cannot capture the variations in an individuals’ air pollution exposure [
18]. The average hourly PM
2.5 concentration at a roadside location is approximately twice the concentration at nearby air quality monitoring station (AQMS) [
19]. A monitoring campaign in Kenya successfully applied calibrated low-cost sensors to investigate the concentration of PM
10, PM
2.5, and PM
1 in the urban background and roadside locations [
20]. A study in Yangon also successfully assessed the regional distribution of PM
2.5 using a portable PM
2.5 sensor [
21].
The main challenge of air quality management in most cities is meeting air quality limits in areas with high road traffic [
22]. The assessment of the air quality in metropolitan areas is a major challenge due to the distribution of monitoring stations [
23]. Analysis of PM
2.5 in Tehran reveals that a large fraction of the total mass of PM
2.5 is composed of BC, especially during cold seasons [
6]. There is a dominant percentage of ultra-fine particle (UFP) deposition in the lungs which strengthens the need to incorporate UFP into the current AQI [
24]. A monitoring campaign shows that UFP increases with an increase in the number of flights [
25]. A study in Lisbon identified marine transportation as a significant source of UFP [
26]. Another study in South Korea attempted to identify the influencing factors that affected the concentration of resuspended road dust, and the result shows that further studies are needed to reach a conclusion [
27]. Due to the limited area in Macao, there are no heavy industries, and the largest industry is gaming and tourism.
Thus, monitoring urban roadside air pollution using portable and affordable solutions and better understanding the impact of traffic characteristics on air quality is extremely important to protect the health and well-being of the local community. This study will determine if traffic emissions contribute to the concentration of PMs in the monitored locations of Macao. This is a preliminary study of roadside PM emissions in the high-density roadside area in Macao, which is surrounded by many residential buildings and is very important to the well-being of the nearby residents. This study employed low-cost portable equipment, and pre-recorded footage was used for traffic counting and characterization.
3. Results and Discussion
According to the Macao Environmental Protection Bureau (DSPA), the major emission sources for various suspended particulates (TSP, PM
10, and PM
2.5) are from the construction and land transport sectors, accounting for about 40% and 25%, respectively [
31]. In addition, the major emission sources of NOx are land transport, waste incineration, and construction, with each accounting for more than 20%. The largest emission sources of CO were land transport, wastewater treatment, and organic solvents, while the major emission sources of sulfur oxides (SOx) were waste incineration as well as commercial, domestic, and service industries [
31]. The total estimated emissions of PM
10 and PM
2.5 in 2021 will have decreased by 7.7% and 6.7%, respectively. A detailed breakdown is for PM
10 and PM
2.5, respectively, is as follows, land transport decreased by 0.3% and 9.6%, maritime transport decreased by 18.5% and 18.5%, air transport increased by 8.7% and 8.7%, commercial, domestic and service industries increased by 3.5% and 3.6%, construction increased by 18.1% and 18.1%, the industrial sector increased by 28.4% and 28.4%, local production of electricity decreased by 69.4% and 67.6%, and waste incineration increased by 1.6% and decreased by 0.1% respectively [
31].
Nevertheless, the emission of PM
10 on the road is often associated with road dust and particle emissions from brake pad wear [
32,
33]. In contrast, the emission of PM
2.5 on the road is often associated with the tailpipe emission of fossil-fuel-powered motor vehicles, and the switch to battery electric vehicles (BEVs) is an ideal measure to reduce the emission of PM
2.5 [
34,
35].
3.1. Observation Results
The levels of PM10 and PM2.5 concentrations were collected in each of the four roadside locations, including Av. do Coronol Mesquita (S1), R. do Campo (S2-A and S2-B), R. da Ribeira do Patane (S3-A and S3-B), and Av. de Almeida Ribeiro (S4-A and S4-B). Each location consists of 48 non-consecutive hours of monitoring.
Table 2 shows the obtained average and standard deviation (SD) of PM
10 and PM
2.5 on traffic-related sites in µg/m
3. The roadside location with the highest average of PM
10 and PM
2.5 is recorded at Av. do Almeida, Ribeiro, with 40.8 µg/m
3 and 19.2 µg/m
3, respectively, while the lowest average of PM
10 and PM
2.5 is recorded at Av. do Coronol Mesquita, with 38.4 µg/m
3 and 18.1 µg/m
3, respectively (see
Table 2).
3.2. Distribution Analysis
Figure 6a shows the boxplot of PM
10 average distribution by traffic site, including the first quartile, average (represented by x), median (represented by -), third quartile, and outliers (represented by dots). For Av. do Coronol Mesquita, the average is 38.4 µg/m
3 and the median is 39.2 µg/m
3. For R. do Campo, the average is 39.8 µg/m
3 and the median is 36.0 µg/m
3. For R. do Ribeira do Patane, the average is 40.2 µg/m
3 and the median is 39.8 µg/m
3. For Av. de Almeida Ribeiro, the average is 40.8 µg/m
3 and the median is 40.4 µg/m
3. Overall, the average and median values for PM
10 mean distribution are within a close range for all four locations (
Figure 6a).
Figure 6b shows the boxplot of PM
2.5 average distribution by traffic site, including the first quartile, average (represented by x), median (represented by -), third quartile, and outliers (represented by dots). For Av. do Coronol Mesaquita, the average is 18.1 µg/m
3 and the median is 16.0 µg/m
3. For R. do Campo, the average is 18.4 µg/m
3 and the median is 16.4 µg/m
3. For R. do Ribeira do Patane, the average is 19.1 µg/m
3 and the median is 15.7 µg/m
3. For Av. de Almeida Ribeiro, the average is 19.2 µg/m
3 and the median is 18.1 µg/m
3. Overall, the average and median values for PM
2.5 mean distribution are within a close range for all four locations (
Figure 6b).
3.3. Traffic Characterization
Figure 7a shows the traffic characterization by type of vehicles in Av. do Coronol Mesquita over two sampling periods. The most popular type of vehicle is the motorcycle (48%), while the least popular type of vehicle is the heavy-duty vehicle (2%).
Figure 7b shows the traffic characterization by type of vehicles in R. do Campo over two sampling periods. The most popular type of vehicle is the motorcycle (40%), while the least popular type of vehicle is the heavy-duty vehicle (2%).
Figure 7c shows the traffic characterization by type of vehicle in R. da Ribeira do Patane over two sampling periods. The most popular type of vehicle is the motorcycle (56%), while the least popular type of vehicle is the heavy-duty vehicle (5%) and taxi (5%).
Figure 7d shows the traffic characterization by type of vehicle in Av. de Almeda Ribeiro over two sampling periods. The most popular type of vehicle is the motorcycle (42%), while the least popular type of vehicle is the heavy-duty vehicle (3%) (
Figure 7a–d).
Table 3 shows the average traffic flow in each sampling location per hour. Av. do Coronol Mesquita has the highest traffic flow, with 370 vehicles per hour, while Av. de Almeida Ribeiro has the lowest traffic flow, with 110 vehicles per hour (see
Table 3).
Table 4 shows the traffic characterization by Euro Emission Standard and types of vehicles in Av. do Coronol Mesquita, R. do Campo, R. da Riberia do Patane, and Av. de Almeida Ribeiro, with vehicles characterized by different Euro Emission Standards and different types observed at each of the collection points and converted and presented in percentage as listed below (see
Table 4).
In Av. do Coronol Mesquita, the most popular type of emission standard is Euro 6, with 54% for the private car, 61% for the light-duty vehicle, 100% for the taxi, 57% for the bus, 56% for the heavy-duty vehicle, and 71% for the motorcycle. In R. do Campo, the most popular type of emission standard is Euro 6, with 53% for the private car, 62% for the light-duty vehicle, 100% for the taxi, 56% for the bus, 56% for the heavy-duty vehicle, and 69% for the motorcycle. In R. da Ribeira do Patane, the most popular type of emission standard is Euro 6, with 54% for the private car, 60% for the light-duty vehicle, 100% for the taxi, 65% for the bus, 54% for the heavy-duty vehicle, and 69% for the motorcycle. In Av. de Almeida Ribeiro, the most popular type of emission standard is Euro 6, with 52% for the private car, 61% for the light-duty vehicle, 100% for the taxi, 65% for the bus, 56% for the heavy-duty vehicle, and 71% for the motorcycle. With the majority of the vehicles observed at the four roadside locations in Macao complying with EU 6 emission standards, it is expected that roadside emissions will continue to improve in Macao.
3.4. Correlation between Observed PM Concentration and the Number of Vehicles
Table 5 shows the statistical output for MLR analysis with a 95% confidence level between PM
10 and PM
2.5 1-h averages and the number of vehicles (hourly traffic flow). The result shows a weak relationship between the number of vehicles and the level of PM
10 concentration, with a correlation of determination (R
2) of (R
2 = 0.001 to 0.024). The result also shows a weak relationship between the number of vehicles and the level of PM
2.5 concentration (R
2 = 0.015 to 0.122). The result shows that the traffic flow has an insignificant contribution to the concentration of PM
10 and PM
2.5 in the monitored locations (see
Table 5).
In order to test the null hypothesis and alternative hypothesis below, the significance level is set at 0.05 to validate this hypothesis. The
p-Value is shown in
Table 5 (see
Table 5).
Null Hypothesis (H0): There is no relationship between PM concentrations and hourly traffic flow in the monitored locations in Macao.
Alternate Hypothesis (H1): There is a relationship between PM concentrations and hourly traffic flow in the monitored locations in Macao.
The result shows since the p-Value of all monitored locations except Av. Do Coronol Mesquita exceed the significance level, we failed to reject the null hypothesis. Thus, no relationship between the PM concentrations and hourly traffic flow can be established in this study.
3.5. Correlation between Observed PM Concentration and Traffic Characterization
Table 6 shows the statistical output for MLR analysis with a 95% confidence level between PM
10 and PM
2.5 1-h averages and the types of vehicles. The result shows a weak relationship between the types of vehicles and the level of PM
10 concentration, with a correlation of determination (R
2) of (R
2 = 0.000 to 0.007). The result also shows a weak relationship between the types of vehicles and the level of PM
2.5 concentration (R
2 = 0.001 to 0.043). The result shows that the vehicle types have an insignificant contribution to the concentration of PM
10 and PM
2.5 in the monitored locations (see
Table 6).
In order to test the null hypothesis and alternative hypothesis below, the significance level is set at 0.05 to validate this hypothesis. The
p-Value is shown in
Table 6 (see
Table 6).
Null Hypothesis (H0): There is no relationship between PM concentrations and the types of vehicles in the monitored locations of Macao.
Alternate Hypothesis (H1): There is a relationship between PM concentrations and the types of vehicles in the monitored locations of Macao.
The result show that since the p-Values of all vehicle types except HD and M for PM
2.5 exceed the significance level, we failed to reject the null hypothesis. Thus, no relationship between PM concentrations and vehicle types can be established in this study. In addition, a similar study conducted in Brisbane, Australia, showed that there was a weak linear relationship between the total traffic volume and the roadside particle concentration [
36]. Moreover, another study in Portland, Oregon, USA, showed that local traffic has no relationship with roadside PM
2.5 concentrations [
37]. In addition, a study in Shanghai, China, showed a weak linear relationship between total traffic volume and roadside PM
2.5 concentrations [
38]. Previous studies showed a similar result compared to the study conducted in Macao, which further ensures the robustness and quality of this study. It is not surprising to see that traffic flow and vehicle types have no significant contribution to the levels of PM
10 and PM
2.5 concentrations because PM is generally known to be a regional problem caused by transboundary pollutants rather than a local emission problem.
4. Conclusions
According to The United Nations World Prospects Report, Macao was ranked as the world’s most densely populated region [
39]. It is very important to evaluate the roadside air quality in Macao. The present work aimed to evaluate the impact of road transportation on the levels of PM
10 and PM
2.5 concentrations and to fulfill the lack of monitoring at several important roadside locations in Macao. Although the TSI Sidepak Monitor is simple and reliable equipment, it is necessary to calibrate it against the US EPA equivalent reference method to ensure the data reported is accurate. The calibration results show a correlation coefficient (r) of 0.79 between the field-calibrated equipment and the EPA equivalent reference method. The highest PM
10 and PM
2.5 averages are recorded at Av. do Almeida, Ribeiro, with 40.8 µg/m
3 and 19.2 µg/m
3, respectively. The lowest PM
10 and PM
2.5 averages are recorded at Av. do Coronol Mesquita, with 38.4 µg/m
3 and 18.1 µg/m
3, respectively. The traffic characterization provides an important perspective on the vehicle types that currently travel on the road in Macao. The result shows that the most popular type of vehicle in Macao is the motorcycle (>40%), while the least popular type of vehicle in Macao is the heavy-duty vehicle (<5%), during the monitoring campaign at the four roadside locations. The majority of private cars (>50%) on the road in Macao comply with the EU 6 emission standard. In addition, there is a weak relationship between the observed PM
10 and PM
2.5 concentrations and the hourly traffic flow (R
2 = 0.001 to 0.122). Moreover, there is a weak relationship between the observed PM
10 and PM
2.5 concentrations and the vehicle types (R
2 = 0.000 to 0.043).
This result could be explained by the primary roadside pollutants being CO and NO
x instead of PM
10 and PM
2.5, as mentioned by the DSPA report [
31], and the high levels of PM
10 and PM
2.5 concentrations being a regional problem caused by transboundary pollutants brought by the northern monsoon from inland, as shown in
Figure 1 [
40,
41]. As shown, there is little to no relationship between local traffic volume and roadside PM concentration in the monitored locations of Macao, leading us to conclude that PM concentration is more likely tied to regional sources and meteorological conditions. Nevertheless, the complex geographical setting of Macao is also likely an influential factor in this study. Future studies may consider exploring the presence of heavy metals in the roadside and residential areas of Macao [
42,
43]. The limitation of this study may include the PM measured being influenced by external factors presented in the environment, such as the smoking cigarettes from walking pedestrians and the exhaust fan from a nearby restaurant’s kitchen.