Preliminary Findings of Heavy Metal Contents from Road Dust and Health Risk Assessments Towards a More Sustainable Future in Macao

Abstract

Road dust contains a variety of heavy metals and is a widely used sustainability indicator for monitoring pollution and assessing environmental and health risks in sustainable development. Heavy metals in road dust mainly originate from worn-off particles from vehicles, such as tires, brake pads, road dust, and emissions from exhaust pipes. These heavy metal particles could remain on the road surface for a long period and cause environmental pollution. In this preliminary study, road dust was collected from 8 representative areas in Macao. The heavy metal content from road dust in Macao was extracted from each of the collected samples for an assessment of the heavy metal pollution and its potential threat to human health. The results show that heavy metals primarily originate from human activities, including transportation emissions (Mn: 67.37%, Zn: 57.01%, Sb: 54.1%) and industrial activities (Al: 84.70%, Fe: 76.71%, Pb: 65.32%). The metal-specific non-carcinogenic risk ranges from 1.17 × 10⁻⁷ to 2.65 × 10⁻⁵ and the total carcinogenic risk is 6.91 × 10⁻¹⁰, showing minimum health effects from heavy metals in road dust. Furthermore, there is a significant correlation between the total vehicle counts and the heavy metal contents such as Al, Si, As, V, and Fe (r = 0.50 to 0.82). This work represents the first characterization of heavy metal contents and risks of urban road dust in Macao.

1. Introduction

In urban areas with high population density and intense human activities, heavy metals may originate from multiple sources [1,2]. Road dust is one of the main sources of heavy metal pollution in urban settings. Common heavy metals found in road dust include lead (Pb), cadmium (Cd), mercury (Hg), copper (Cu), chromium (Cr), zinc (Zn), arsenic (As), and nickel (Ni) [3,4]. The presence of heavy metals in road dust not only causes pollution to the environment but also poses a threat to human health [5,6]. Furthermore, road dust may contain organic matter, mold spores, animal dander, and pollen [6,7].

Heavy metals are released by fuel combustion and brake/tire wear during the normal driving cycle [8,9]. For example, lead was once widely applied in gasoline for enhanced performance, and it has been banned in many countries after studies showed the negative impact on health [10,11]. The wear of tires, brake pads, and road surfaces produces small particles that may contain heavy metals [12,13]. These particles travel with the wind and settle on the city’s roads and environment [14,15] and are later resuspended due to vehicle movement and wind.

Exhaust gases and wastewater from industrial facilities near roads (such as smelters, chemical plants) may also contain heavy metals, which are washed into the sewage system by rain [16]. In the process of house construction and demolition, the materials used (such as paint and concrete) may contain heavy metals, and dust generated during the construction process can also be introduced to the road environment [17,18].

Previous studies identified influencing factors such as heavy loads of traffic during rush hours, the daily commute, and special events in Macao. Since the economy of Macao depends mainly on tourism and the mass transit system is not the most sophisticated, most tourists and residents rely on buses, taxis, motorcycles, and private vehicles for transportation [19,20].

This study aims to characterize the heavy metal contents in road dust across different urban roads and their collective health risks in Macao, China. Through scientific monitoring, effective governance, and public participation, the threat to the environment and human health can be mitigated. Strengthening management and policy formulation will help improve the quality of the urban environment and protect public health.

2. Materials and Methods

2.1. Road Dust Sampling Locations

The heavy metal contents in road dust from eight sampling locations were studied. Figure 1 shows the locations across Macao: G1 is the outer harbor terminal; G2 is the south customs entry and exit port in the south of Macao (Hengqin Port); G3 is the southern city center of Macao; G4 is Macao Industrial Zone; G5 is the Macao Sai Van Bridge; G6 is the bus transportation hub of Macao; G7 is the Macao Ruins of St. Paul, a scenic attraction; G8 is the north customs entry and exit port in the north of Macao (Qingmao Port). These eight locations are representative urban functional areas of Macao. In this study, the heavy metal content values in the collected road dust were measured in these eight different locations. Since the research period was only half a year (from July to December 2024), only one sample was collected and analyzed at each location.

Different types of roads were sampled, including urban main roads (G2, G3, G5 and G6), sub-roads (G1 and G7), and industrial roads (G4 and G8). Roads close to factories and construction sites may be affected by heavy metal pollution. Transportation hub roads (G2 and G6) near the border crossing ports with a heavy traffic flow were selected to compare with roads with less traffic G7). The traffic flow information (motorcycles, private cars, buses, taxis, trucks passing by the road where each sample was collected) was recorded during a 10 min interval.

2.2. Collection Procedure

The collection tools for this study include a hard brush, a soft brush, a plastic spatula, a collection dish, a 200-mesh nylon screen, and a ruler, as shown in Figure 2. A 200-mesh screen has 200 openings per inch with a pore size of about 74 microns, commonly used in heavy metal assessments [21]. Metallic equipment was not considered to prevent the adsorption of micro-heavy metals, which could impact measurement accuracy. In addition, the samples were collected when there was no rainfall for more than seven days to ensure representativeness of community exposure scenarios.

A new collection dish was used each time to hold the road dust. The different collection dishes were pre-weighed, because there is a weight difference for each collection dish. Subsequently, the weight of the collected road dust was determined by subtracting the weight of the collection dish from the total weight. The weight of the dust after screening was at least 0.20 g. Figure 3 shows the different road dust samples after processing.

2.3. Analysis Procedure

To extract heavy metals, the sample was first soaked in 5 mL of ultra-pure water, ensuring that no impurities were introduced. Next, the samples were processed using ultrasonic extraction technology for a duration of 45 min. Through the vibration of sound waves, the generation of tiny bubbles can effectively enhance the extraction efficiency of the target substance in the sample, improving the sensitivity and accuracy of extraction.

After the extraction is complete, the filtration step follows. Filtration using a 0.22-micron filter head removed solid impurities and particles from the sample, ensuring that the liquid sample for subsequent analysis is clear and free of disturbing substances. The filtered filtrate was acidified with concentrated nitric acid (HNO₃). This process stabilizes the metal ions in the sample by lowering the pH value, preventing them from settling during storage and analysis.

The volume of each filtrate was then standardized to 5 mL to ensure consistency and repeatability for subsequent analysis. Finally, heavy metals were measured by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). This method of analysis is known for its high sensitivity and high resolution and can accurately determine the concentration of various elements in the sample.

Through the above steps, the experiment ensures the integrity of sample processing and the accuracy of analysis results, providing a reliable basis for subsequent research and application.

2.4. Positive Matrix Factorization (PMF 5.0) Model

The US EPA Positive Matrix Factorization (PMF 5.0) model has been valuable in analyzing environmental pollution, specifically for source attribution and apportionment [16,22]. Using the chemical composition of environmental samples (such as sediments and air particles), PMF 5.0 identifies major sources, quantifies their contribution to each component of the samples, and estimates uncertainties in the source apportionment using bootstrap (BS) and/or displacement (DISP) methods. Traffic, industrial, and natural emissions are commonly identified as air pollution sources.

This study applied the PMF 5.0 and BS method to elementally speciated road dust samples. By providing clear information on pollution sources and components over road dust in Macao, the model helps policymakers develop more effective environmental management and pollution control strategies that promote sustainable development.

2.5. Non-Carcinogenic Health Risk Assessment

Heavy metals (such as cadmium, lead, arsenic, and mercury) can enter the human body through the food chain accumulation (from crop absorption to animal/human intake), direct contact (skin penetration), or inhalation (soil dust), causing acute and chronic health problems. To estimate health risk in Macao upon road dust exposure via three pathways, namely ingestion, inhalation, and dermal contact, the framework of Suvetha et al. [23,24] was followed:

$$\mathrm{D}\mathrm{I}\mathrm{n}=\mathrm{M}\times {\displaystyle \frac{\mathrm{I}\mathrm{R}\times \mathrm{P}\mathrm{E}\mathrm{F}\times \mathrm{F}\times \mathrm{D}}{\mathrm{W}\times \mathrm{T}}}\times {10}^{-6}$$

(1)

$$\mathrm{D}\mathrm{I}\mathrm{h}=\mathrm{M}\times {\displaystyle \frac{\mathrm{I}\mathrm{h}\mathrm{R}\times \mathrm{F}\times \mathrm{D}}{\mathrm{P}\mathrm{E}\mathrm{F}\times \mathrm{W}\times \mathrm{T}}}$$

(2)

$$\mathrm{D}\mathrm{d}=\mathrm{M}\times {\displaystyle \frac{\mathrm{L}\times \mathrm{A}\times \mathrm{D}\mathrm{A}\mathrm{F}\times \mathrm{F}\times \mathrm{D}}{\mathrm{W}\times \mathrm{T}}}\times {10}^{-6}$$

(3)

DIn = daily dust particles ingestion; DIh = daily dust particles inhalation; Dd = daily dermal absorption; M = metal concentration; IR = ingestion rate (100 mg/day); IhR = inhalation rate (20 m³/day); PEF = particle emission factor (1.36 × 109 m³/kg); L = skin adherence factor (0.07 mg/cm² day); A = area of skin exposure (5700 cm²); DAF = dermal absorption factor (0.001); F = frequency of exposure (180 day/year); D = duration of exposure (24 years); W = average body weight (70 kg); and T = average exposure time (8760 days).

In order to determine the overall non-cancer toxic risk, the average daily dose value of the specific pathway is divided by a metal-specific reference dose (RfD) to yield the health quotient (HQ) (Equation (4)). Hazard index (HI) is the sum of HQs due to different exposure pathways (Equation (5)). A value of HI ≤ 1 denotes no adverse health risks, while HI > 1 denotes possible occurrence of adverse health risks.

$$\mathrm{H}\mathrm{Q}={\displaystyle \frac{\mathrm{D}}{\mathrm{R}\mathrm{F}\mathrm{d}}}$$

(4)

$$\mathrm{H}\mathrm{I}=\sum \mathrm{H}\mathrm{Q} (\mathrm{D}\mathrm{I}\mathrm{n},\mathrm{D}\mathrm{I}\mathrm{h},\mathrm{D}\mathrm{d})$$

(5)

2.6. Carcinogenic Risk Assessment

For carcinogenic risk upon road dust exposure in Macao, the calculation formula is as follows [23,24]:

$$\mathrm{L}\mathrm{A}\mathrm{D}\mathrm{D}={\displaystyle \frac{\mathrm{C}\times \mathrm{E}\mathrm{F}}{\mathrm{A}\mathrm{T}\times \mathrm{P}\mathrm{E}\mathrm{F}}}\times ({\displaystyle \frac{\mathrm{R} \mathrm{i}\mathrm{n}\mathrm{h} \mathrm{c}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{d} \times \mathrm{E}\mathrm{D} \mathrm{c}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{d}}{\mathrm{B}\mathrm{W} \mathrm{c}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{d}}}+{\displaystyle \frac{\mathrm{R} \mathrm{i}\mathrm{n}\mathrm{h} \mathrm{a}\mathrm{d}\mathrm{u}\mathrm{l}\mathrm{t} \times \mathrm{E}\mathrm{D} \mathrm{a}\mathrm{d}\mathrm{u}\mathrm{l}\mathrm{t}}{\mathrm{B}\mathrm{W} \mathrm{a}\mathrm{d}\mathrm{u}\mathrm{l}\mathrm{t}}})$$

(6)

$$\mathrm{C}\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{i}\mathrm{n}\mathrm{o}\mathrm{g}\mathrm{e}\mathrm{n}\mathrm{i}\mathrm{c} \mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k} \left(\mathrm{C}\mathrm{R}\right)=\mathrm{L}\mathrm{A}\mathrm{D}\mathrm{D}\times \mathrm{S}\mathrm{F}$$

(7)

$$\mathrm{C}\mathrm{R}\mathrm{t}=\sum \mathrm{C}\mathrm{R}$$

(8)

In the formula, C refers to the heavy metal concentrations (average of the 8 sampling sites), EF refers to exposure frequency (180 day/year), AT refers to average exposure time (25,530 day), PEF refers to particle emission factor (1.36 × 10⁹ m³/kg), R inh child refers to rate of inhalation for child (20 m³/day), R inh adult refers to rate of inhalation for adult (7.6 m³/day), ED child refers to exposure duration for child (6 year), ED adult refers to exposure duration for adult (24 year), BW child refers to average body weight of child (15 kg), BW adult refers to average body weight of adult (70 kg), and SF refers metal-specific cancer slope factor [24].

LADD stands for carcinogenic constituents via inhalation. CR is the incremental probability of a person developing cancer over a lifetime. For example, a CR of 10⁻⁵ suggests a possibility of 1 in 100,000 individuals developing cancer.

3. Results and Discussion

3.1. Characteristics of Sampling Sites

Table 1. Data collection summary chart for G1–G8 (SMG, 2024).

	Sampling Weight (g)	Motorcycle	Private Car	Taxi	Bus	Truck	Total Number of Vehicles	Pavement Material	Temperature (°C)	Relative Humidity (%)
G1	0.2	56	121	38	46	17	278	Pitch	28.5	45
G2	0.21	0	16	36	17	0	69	Pitch	29.5	73
G3	0.2	18	48	8	8	1	83	Pitch	30.5	68
G4	0.2	10	46	0	4	37	97	Pitch	28.5	78
G5	0.21	53	306	47	36	18	460	Cement	28.5	80
G6	0.37	102	74	8	34	12	230	Cement	28.5	75
G7	0.2	10	11	1	1	1	24	Gravel	27.5	78
G8	0.2	8	37	24	0	0	69	Pitch	27.5	78

shows a summary of data collected from G1 to G8 sampling sites. Among them, air temperature and relative humidity are obtained from the Macao Meteorological and Geophysical Bureau (SMG). The table shows the environmental characteristics and the number of vehicles by type during sampling campaigns, supporting the analysis of the relationship between traffic patterns and environmental factors.

The sample weights range from 0.20 g to 0.37 g, reflecting different sample collection locations and pavement materials. Pavement materials mainly include asphalt, concrete, and gravel, while the choice of these materials depends on factors such as traffic flow, climatic conditions, and maintenance costs. Asphalt is the most common pavement material due to its durability and adaptability, making it widely used in urban roads.

In terms of the type of vehicles, it shows that the number of motorcycles varies significantly, ranging from 0 to 56. The frequency of motorcycles can be affected by a variety of factors, such as traffic flow and weather conditions, in different environments or time periods. The relatively high number of private cars, up to 121, indicates that in some areas, private cars are the main mode of transport, depending on urban planning and accessibility of public transport. In contrast, the number of taxis, buses, and trucks was relatively low, ranging from 0 to 46, 0 to 17, and 0 to 18, respectively.

The air temperature data shows that the temperature range of each sample is between 27.5 °C and 30.5 °C, which may affect the use of vehicles. For example, higher temperatures may lead to a greater preference for air-conditioned private cars, while the use of motorcycles and public transport may increase in cooler weather. The relative humidity level was between 45 and 80%. Changes in humidity can affect road surface conditions and subsequently the safety and efficiency of vehicles. A high-humidity environment can lead to slippery roads, increasing the risk of traffic accidents.

3.2. Heavy Metal Concentrations in Road Dust

Table 2. G1–G8 concentrations of important metals (µg/g).

9	Na	Mg	Al	Si	K	Ca	Ni	As	Cu	Pb	V	Zn	Mn	Fe	Cr	Be	B
G1	114.56	15.49	2.8	32.32	42.67	341.47	0.303	0.02	0.64	0.007	0.029	1.2	0.059	1.96	0.113	6.27 × 10−4	0.754
G2	313.8	33.19	1.16	13.24	54.072	333.43	0.256	0.01	0.53	0.005	0.015	1.67	0.195	1.53	0.076	9.04 × 10−4	0.379
G3	522.05	68.66	1.16	26.23	167.2	508.15	0.316	0.03	0.65	0.009	0.015	2.76	0.906	2.28	0.089	9.54 × 10−4	0.424
G4	462.3	45.42	2.65	31.62	95.46	528.45	0.256	0.03	1.01	0.011	0.027	1.39	0.127	3.06	0.099	8.25 × 10−4	0.903
G5	617.53	75.27	55.22	99.83	58.16	291.27	0.088	0.05	0.54	0.077	0.057	0.94	0.723	38.23	0.116	2.91 × 10−3	0.265
G6	235.02	29.62	0.96	12.53	34.38	237.59	0.124	0.01	0.35	0.003	0.006	0.66	0.13	0.87	0.057	7.37 × 10−4	0.293
G7	314.52	32.95	1.68	12.27	62.28	226.74	0.17	0.02	0.39	0.005	0.009	0.71	0.255	1.7	0.06	8.77 × 10−4	0.214
G8	491.1	58.98	8.11	27.31	95.27	351.95	0.417	0.03	0.85	0.067	0.022	4.04	0.747	7.71	0.131	1.50 × 10−3	0.551

summarizes heavy metal components in micrograms per gram (µg/g) after performing chemical analysis on G1–G8 samples. With respect to sodium (Na) content, the G5 sample had the highest content, reaching 617.53 µg/g, while the G6 sample had the lowest content, only 114.56 µg/g. For magnesium (Mg), G5 also peaked, reaching 75.27 µg/g, showing its enrichment in this sample. The G1 sample had the lowest Mg content at 15.49 µg/g. The aluminum (Al) content is the highest for the G5 sample, at 55.22 µg/g, while the lowest is for the G6 sample, at 0.96 µg/g. The G2 sample had the highest silicon (Si) content of 99.83 µg/g, while the G7 sample had the lowest Si content of 12.27 µg/g. The concentration of potassium (K) is highest in G3, at 167.2 µg/g, while the lowest is in G6, at 34.38 µg/g. The concentration of calcium (Ca) is the highest in the G4 sample at 528.45 µg/g, and the lowest in the G7 sample at 226.74 µg/g.

The nickel (Ni) content in the G8 sample is highest at 0.417 µg/g, while the lowest is in G5 at 0.088 µg/g. Arsenic (As) concentrations are highest in G5 at 0.05 µg/g, and the lowest in G2 and G6 with 0.01 µg/g. The concentration of copper (Cu) is the highest in G4 with 1.01 µg/g, while it is the lowest in G6 with 0.35 µg/g. The concentration of lead (Pb) was the highest at G5 with 0.077 µg/g, and the lowest at G6 with 0.003 µg/g. The concentration of Vanadium (V) was the highest at G5 with 0.057 µg/g, while it was the lowest at G6 with 0.006 µg/g. The zinc (Zn) content was the highest at G8 with 4.04 µg/g, while it was the lowest at G6 with 0.66 µg/g. The manganese (Mn) content was the highest at G3 with 0.906 µg/g, while it was the lowest at G1 with 0.059 µg/g. The iron (Fe) content was the highest at G5 with 38.23 µg/g, while it was the lowest at G6 with 0.87 µg/g.

The analysis of road dust showed a high loading of Zn that could be identified as brake and tire wear, as Zn is a major component in tire manufacturing, while the high loadings of manganese (Mn) from fuel additives might be identified as tailpipe emissions, and the very high loading of sodium (Na) would likely be identified as road salt. The differences in the content of metal elements in each road dust sample reflect the complexity of its geological background, mineral composition, and environmental impact. These data provide an important basis for subsequent source assessment.

3.3. Result of PMF 5.0 Model

As shown in Figure 4, in factor 1, the loadings of Mn and Zn are the largest, accounting for 67.37% and 57.01%, respectively. Mn mainly comes from brake pad wear (manganese alloy materials), vehicle exhaust emissions, and waste incineration, while Zn mainly comes from tire wear (containing zinc oxide), brake pad wear, and waste incineration [25,26,27,28]. The passage of cross-border vehicles (such as cross-border private cars, buses, and trucks) at observation point G5 (Sai Van Bridge and other cross-border transportation facilities) has a heavy traffic load. The pollutants from their exhaust emissions and tire wear are transported and diffused to the local road network in Macao.

In factor 2, the load of B is the largest, with a contribution rate of 41.82%. Boron is likely linked to the industrial processes and coal combustion from the power plants, which will enter the atmosphere as dust and be transported by wind. Although Macao is a highly urbanized area, it is surrounded by factories, industries, and coal power plants from the Greater Bay Area (GBA), and the dust in the form of PM₁₀ is a transboundary pollutant, which may travel up to 1000 km from its source [29,30,31].

In factor 3, the loadings of Sb and Co are the largest, with contribution rates of 54.11% and 42.60%, respectively. These two metal elements are widely used in the traffic emissions from brake pads, fossil fuel combustion from coal power plants, and waste incinerators. Due to the high density of vehicles at transportation hubs such as the Inner Harbor and Amizade Bridge in Macao, frequent acceleration and braking during the driving cycle may lead to increased wear of the vehicle surface coating. Studies have shown that the content of heavy metals in road dust near traffic arteries is significantly higher than in other areas, partly due to the physical degradation of vehicle materials. The coal power plants in the surrounding area in the GBA and the waste incinerator in Macao are likely to be contributors of the heavy metal [32,33,34,35].

In factor 4, the loading of Ba, Mo, and Se is the greatest, with Ba having the highest contribution rate of 35.07%. These three elements are likely related to the traffic emissions from brake pads, industrial processes, and fossil fuel combustion. Observation points G1, G2, and G8 are the Outer Harbor and the customs entry and exit ports in the south (Hengqin Port) and north of Macao (Qingmao Port) [36,37,38].

In factor 5, the loadings of Al, Fe, and Pb are relatively large, with contribution rates of 84.70%, 76.71%, and 65.32%, respectively. These three elements are likely related to coal combustion, vehicle emissions, and waste incineration. Observation point G4 is near the Macao Industrial Zone, where heavy industrial facilities (such as waste treatment and incinerator facilities) may emit dust containing heavy metals. These particles enter road dust through atmospheric deposition [36,37,38].

3.4. Result of Health Risk Assessment

Table 3. Health Risk Assessment on Daily Ingestion, Inhalation, and Dermal Exposure.

Compound	Din	Dih	Dd	HQin	HQih	HQd	HI
Be	8.23 × 10−10	1.21 × 10−13	3.28 × 10−12
Cr	6.55 × 10−8	9.64 × 10−12	2.61 × 10−10	2.18 × 10−5	3.32 × 10−7	4.36 × 10−6	2.65 × 10−5
Ni	1.70 × 10−7	2.50 × 10−11	6.77 × 10−10
Cu	4.36 × 10−7	6.41 × 10−11	1.74 × 10−9	1.09 × 10−5	1.60 × 10−9	1.45 × 10−7	1.10 × 10−5
Zn	1.17 × 10−6	1.73 × 10−10	4.69 × 10−9	3.92 × 10−8		7.82 × 10−8	1.17 × 10−7
As	1.83 × 10−8	2.69 × 10−12	7.31 × 10−11
Cd	4.31 × 10−10	6.34 × 10−14	1.72 × 10−12	4.31 × 10−7		1.72 × 10−7	6.04 × 10−7
Pb	1.61 × 10−8	2.36 × 10−12	6.41 × 10−11	4.59 × 10−6	7.88 × 10−12	1.21 × 10−7	4.71 × 10−6

presents the health risk assessment results of eight heavy metals to Macao residents based on an average of 8 sampling sites. Cr, Cu, Zn, Cd, and Pb show a possibility of adverse health risks through ingestion, inhalation and dermal contact, while the risks of Be, Ni, and As cannot be determined due to the lack of a suitable RfD value for inhalation and dermal contact.

The cancer risk is calculated for the metals Cd, Pb, and Cr (

Table 4. Health Risk Assessment Values of Heavy Metals.

Compound	LADD	Carcinogenic Risk (CR)	Total CR
Be	5.99 × 10−14
Cr	4.75 × 10−12	2.00 × 10−10
Ni	1.24 × 10−11
Cu	3.17 × 10−11
Zn	8.56 × 10−11
As	1.32 × 10−12
Cd	3.14 × 10−14	1.98 × 10−13
Pb	1.17 × 10−12	4.91 × 10−10
Total CR			6.91 × 10−10

) that pose a higher risk of causing cancer in humans. The overall cancer risk (CR) was calculated to be 6.91 × 10⁻¹⁰, which is way below the acceptable cancer risk of 1.0 × 10⁻⁶ to 1.0 × 10⁻⁴. The CR value for Macau indicates that presently there is no threat of cancer risks from road dust.

3.5. Discussion

Table 5. Correlation Coefficient of Heavy Metals and Total Vehicle Counts.

Heavy Metals	Total Vehicle Counts
Na	0.11
Mg	0.19
Al	0.79
Si	0.82
K	−0.41
Ca	−0.27
Ni	−0.54
As	0.50
Cu	−0.21
Pb	0.47
V	0.77
Zn	−0.41
Mn	0.07
Fe	0.77
Cr	0.33

shows that there is a strong correlation between the total vehicle counts and the content of heavy metals such as Al, Si, As, V, and Fe (r = 0.50 to 0.82), which are heavy metals commonly found in traffic emissions and industrial sources.

In the analysis of important metal concentrations, the concentrations of sodium (Na) and magnesium (Mg) varied significantly. The concentrations of heavy metals such as arsenic (As) and lead (Pb) were relatively low, indicating that the degree of heavy metal pollution in the sample area was mild. The health risk assessment showed there is no cancer risk from heavy metals and the possible occurrence of adverse non-cancer health risks in Macao [39,40,41,42].

A comparison with the nearby region of Hong Kong revealed that many of the heavy metal contents originate from anthropogenic sources such as vehicle exhaust, marine transportation, and industrial activities, which is also aligned with the findings of this study [43,44,45,46].

This situation suggests that in environmental planning and management, it is necessary to strengthen the monitoring and control of heavy metal pollution. The study recommends that governments take measures to reduce traffic emissions and industrial pollution to reduce the concentration of heavy metals in the environment, thereby protecting the health of vulnerable populations [47,48].

4. Conclusions

This study analyzed the content of heavy metals in road dust from different locations in Macao, revealing the current situation of heavy metal pollution and its potential threat to human health. The preliminary results of this work show that heavy metals primarily originated from human activities such as transportation emissions and industrial activities, causing a degradation in air quality [49,50]. The sampling weight and environmental characteristics have a significant impact on the frequency of vehicle usage, especially the frequency of motorcycle and private car usage, which is closely related to traffic flow and climatic conditions.

The concentration differences in important metal elements reflect the complexity of sample sources, especially the changes in sodium (Na), magnesium (Mg), aluminum (Al), and zinc (Zn) elements, revealing the pollution characteristics of different areas [51,52]. The health risk assessment results showed that there is a non-carcinogenic risk through ingestion, inhalation, and dermal exposure, but no carcinogenic risks from heavy metals. The use of water trucks to clean the road surfaces may effectively remove the suspended road dust on the road as a short-term mitigation strategy, while a regional cooperation in the GBA to reduce the emission of heavy metals will be the long-term strategy to reduce road dust and improve air quality for a double-win.

The health risk assessment indicates that long-term exposure to heavy metal pollution may pose risks to human health, especially among children and the elderly. High concentrations of heavy metals are associated with respiratory diseases and neurological damage, highlighting the importance of controlling and monitoring heavy metal pollution.

The transition to electric vehicles from traditional fossil-fuel-powered vehicles may significantly reduce the emission of heavy metals and improve the ambient air quality [53,54]. Nevertheless, limitations of this work include the limited sample size and the duration of this study. Future studies will consider increasing the sampling locations and extending the sampling durations, and also include more exposure pathways of heavy metals.