Spatiotemporal Variations and Health Assessment of Heavy Metals and Polycyclic Aromatic Hydrocarbons (PAHs) in Ambient Fine Particles (PM_1.1) of a Typical Copper-Processing Area, China

Abstract

This study investigates the concentrations, health risks, and potential sources of heavy metal elements and polycyclic aromatic hydrocarbons (PAHs) in PM_1.1 particles in Zhuji, a major copper-processing city in China. The ratios of heavy metals (summer: 0.906; winter: 0.619) and PAHs (>0.750 in both seasons) in PM_1.1/PM_2.0 suggest significant accumulation in ultrafine particles. In winter, heavy metal concentrations in PM_1.1 reached up to 448 ng/m³, and PAH concentrations were 13.4 ng/m³—over ten times higher than in summer. Health risk assessments revealed that hazard index (HI) values exceeded 1.00 for five age groups (excluding infants) during winter, indicating chronic exposure risks. Incremental lifetime cancer risk (ILCR) values surpassed the upper acceptable limit (1.0 × 10⁻⁴) for four age groups, with Cr, As, Cd, and Pb as major contributors. PAH-related ILCRs were also elevated in winter, with benzo[a]pyrene (BaP) identified as the most potent carcinogen. Enrichment factor (EF) and principal component analysis (PCA) indicated that industrial activities and traffic emissions were the dominant anthropogenic sources of heavy metals. Diagnostic ratio analysis further showed that PAHs mainly originated from vehicle and coal combustion. These findings provide critical insights into pollution patterns in industrial cities and underscore the importance of targeted mitigation strategies.

1. Introduction

Zhuji City, located in north-central Zhejiang Province, China, is one of the country’s major copper-processing hubs. Since 2020, it has maintained a population of over 1 million and experienced rapid economic growth [1,2]. The city’s annual copper-processing volume has reached approximately 350,000 tons, accounting for about 5% of China’s total output. Copper mining and smelting activities are known to increase environmental concentrations of heavy metals, thereby elevating the risk of exposure for populations living in and around such industrial areas [3]. Heavy metals, such as lead (Pb), zinc (Zn), cadmium (Cd), mercury (Hg), and chromium (Cr), are generally defined as metals and metalloids with densities greater than 5 g/cm³. Metalloids such as arsenic (As) are often included in this category due to their similar environmental behavior and chemical persistence [4]. Heavy metal pollution is typically covert, persistent, and irreversible [5].

The Zhuji region has encountered several significant environmental issues related to heavy metal contamination. For example, Sun reported that Pb, As, Cu, Zn, and Ni in mining-affected soils may originate from common sources [6]. In general, heavy metals have attracted considerable attention due to their detection frequency and toxicity [7,8]. These non-biodegradable elements are recognized as harmful components of atmospheric particulate matter and can adversely impact human health through inhalation, ingestion, and dermal contact [9,10]. Long-term or high-level exposure to such elements has been linked to cardiovascular diseases, cancer, neurological disorders, and other serious health effects [6,9,10,11,12]. Moreover, the presence of heavy metals in ambient particles can serve as useful indicators for identifying pollution sources [5,13]. Similar cases of metal contamination near copper mining and smelting facilities have been documented in regions such as Chile, the United States (e.g., Anaconda and Tacoma), and Sweden. A 2008 study on arsenic speciation in PM_2.5 near a copper smelter revealed significantly elevated levels of toxic elements in the urban atmosphere. Arsenic contamination arises from both natural and anthropogenic sources, and is commonly found in rocks, soils, water bodies, and atmospheric dust [14,15].

In addition to heavy metals, PAHs are also of growing concern among airborne toxic pollutants. PAHs have been identified as potential carcinogens and mutagens, posing considerable threats to human health [16,17,18]. The United States Environmental Protection Agency (USEPA) has classified 16 PAHs as priority pollutants under the “Consent Decree,” including seven known or suspected carcinogens: benz[a]anthracene(BaA), BaP, benzo[b]fluoranthene (BbF), benzo[k]fluoranthene(BkF), chrysene(Chy), dibenz[a,h]anthracene, and indeno [1,2,3-cd]pyrene[17]. The World Health Organization (WHO) set a guideline concentration for BaP at 1 ng/m³, while China's national air quality standard is 2.5 ng/m³ [16,19]. PAH exposure is associated with a variety of adverse health effects, including respiratory and cardiovascular diseases, reduced lung function, myocardial infarction, asthma, immune system dysfunction, and increased cancer risk [18,20,21]. Major emission sources of PAHs include vehicle exhaust, fossil fuel combustion, industrial operations, and residential heating. The toxicity of PAHs depends on factors such as molecular structure, particle size, chemical composition, and regional meteorological conditions.

Despite the established hazards associated with toxic elements and PAHs, few studies have comprehensively assessed their concentrations, sources, and health risks in fine particulate matter in copper mining and processing regions like Zhuji. Given the city’s rapid industrialization and role as a key copper smelting center, it is crucial to investigate the pollution profile and associated health implications in its urban environment. Under specific meteorological conditions, emissions from industrial facilities, including copper smelters, may be transported and dispersed across urban areas, potentially affecting the health of residents.

Therefore, this study focuses on the concentrations, health risks, and sources of heavy metals and PAHs in fine particulate matter—specifically PM_1.1—in the residential areas of Zhuji City. The research objectives are fourfold: (1) to collect ambient particulate matter samples in five size fractions during both summer and winter; (2) to quantify heavy metal concentrations using inductively coupled plasma–mass spectrometry (ICP-MS) and PAH concentrations using gas chromatography–mass spectrometry (GC-MS); (3) to evaluate the non-carcinogenic and carcinogenic health risks of these pollutants; (4) to explore the potential sources of heavy metals and PAHs in PM_1.1, including the role of long-range atmospheric transport. This study aims to provide a detailed understanding of the current state and characteristics of air pollution in Zhuji, and to offer scientific insights for balancing industrial development and environmental health protection in similar rapidly developing industrial regions.

2. Materials and Methods

2.1. Ambient Partilce Sampling Site and Sampling Campaigns

Ambient fine particulate matter was collected on the rooftop of a residential building located near a major arterial road in central Zhuji City (N 29.372, E 120.238; elevation approximately 55 m), as shown in Figure 1. An Andersen high-volume air sampler (AHV-600, Shibata Co., Tokyo, Japan), equipped with a Tissuquartz filter (AHQ-6300, TIS-SUQUARTZ-2500QAT-UP, Tokyo Dylec, Japan), was used to collect airborne particles, which were classified into five aerodynamic diameter ranges [22]: PM_1.1 (<1.1 μm), PM_1.1–2.0 (1.1–2.0 μm), PM_2.0–3.3 (2.0–3.3 μm), PM_3.3–7.0 (3.3–7.0 μm), and coarse particles (>7.0 μm). The sampling procedure followed the methods described in our previous studies [5,16,23].

Sampling was conducted in two distinct seasons: (1) Summer: eight consecutive 23-hour sampling sessions from 16th August to 23rd August 2016; and (2) Winter: eight consecutive 23 h sessions from 25th December 2016 to 1st January 2017. Meteorological data during the sampling periods were obtained from the Tianqihoubao website [24]. During summer, the average maximum and minimum temperatures were 36.5 °C and 27.8 °C, respectively, with a mean relative humidity of 63% (Figure 2a and Table S1). In contrast, during winter, the average maximum and minimum temperatures were 11.8 °C and 8.0 °C, respectively, with a mean relative humidity of 84%.

2.2. Measurement of Heavy Metal Elements and PAHs in Ambient Particles

Elemental species in particulate matter were analyzed using inductively coupled plasma–mass spectrometry (ICP-MS, Model 7700, Agilent Technologies, Santa Clara, CA, USA), following procedures described in our previous studies [9,23]. The suitable mounts AHV filters were extracted using 8 ml acid mixture solution (5.0 ml nitric acid (HNO₃), 1.0 ml hydrogen peroxide (H₂O₂), 2.0 ml hydrofluoric acid (HF)) in a polytetrafluoroethylene (PTFE) vessel with high-pressure acid digestion (105 °C, 30 min; 170 °C, 2 h). Finally, the samples were filled to 10 ml with 2% HNO₃. The analyzed elements included iron (Fe), Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb. Each sample was analyzed in triplicate. The details of chemicals are as follows: HNO₃, Wako 1st grade, Fujifilm, Japan; H₂O₂, super special grade, Fujifilm, Japan; HF, Guaranteed Reagent, Fujifilm, Japan; ICP multi-element standard solution IV, Merck, USA. Each sample was analyzed in triplicate.

Polycyclic aromatic hydrocarbons (PAHs) were extracted and quantified using gas chromatography–mass spectrometry analysis, following the method reported by Wang [16]. PAHs were extracted using dichloromethane (guaranteed reagent, Fijifilm, Tokyo, Japan), and subsequently subjected to solvent exchange and concentrated in Hexane (guaranteed reagent, Fijifilm, Japan). The standard was used Sigma-Aldrich EPA 16 PAHs mixture (Sigma-Aldrich, 4S8743, St. Louis, MO, USA). The extract solution with an internal standard was used in a volume of up to 0.1 mL and measured by GC-MS (GC-MS, Model Agilent 5973N, Agilent Technologies. Inc., Santa Clara, CA, USA). The GC-MS analyses were performed on an InertCap 17 (GL Sciences, Tokyo, Japan) column using an HP6890 GC interfaced into an HP5973 MS detector. The target PAH compounds included naphthalene (NAP), acenaphthylene (ACY), acenaphthene (ACE), fluorene (Flu), phenanthrene (Phe), anthracene (Ant), fluoranthene (FLN), pyrene (Pyr), BaA, Chy, BbF, BkF, BaP, benzo[ghi]perylene (BghiP), indeno [1,2,3-cd]pyrene (IndP), and dibenz[a,h]anthracene (DBahA). Each sample was analyzed in triplicate.

2.3. Health Risk Assessment of Heavy Metal Elements and PAHs

In this study, the human health risks associated with heavy metal elements and PAHs in PM_1.1 were assessed based on inhalation exposure, with a focus on both non-carcinogenic and carcinogenic risks. Non-carcinogenic risks were evaluated for heavy metals including Cr, Ni, Cu, Zn, As, Cd, Pb, and Mn, while carcinogenic risks were assessed for Cr, Ni, As, Cd, and Pb, as well as PAHs. The health risk assessment methodology followed the guidelines of the USEPA [5,25,26].

2.3.1. Non-Carcinogenic Risks of Heavy Metal Elements

The non-carcinogenic risks via inhalation exposure were calculated using the following equation:

EC = (Ch × ET × EF × ED)/BW × AT,

(1)

HQe = EC/(Rfc × 1000 μg/mg),

(2)

HI = ⅀HQe,

(3)

The exposure concentration (EC) was calculated using Equation (1), with parameter definitions for Ch, InhR, ED, ET, EF, BW, and AT provided in

Table 1. Factors of non-carcinogenic risk and carcinogenic risk calculation for 6 age categories.

Parameter	Definition	Values for Age Categories (Years)						References
		Infant (0–1)	Young Children	Children (6–16)	Adult1 (16–31)	Adult2 (31–51)	Adult3 (51–81)
			(1–6)
C(e)	Mean concentration in airborne particle (mg/m3)	C (element)						[5,25,26]
CA(PAHs)	Converted BaP toxic equivalent (TEQ BaP) by the toxic equivalency factors (mg/m3)	CA
InhR	Inhalation rate (m3/day)	5.4	9	13.6	16	16	15
ED	Exposure duration (year)	0.5	3.5	11	23.5	41	66
EF	Exposure frequency (days/year)	350	350	350	350	350	350
ET	Exposure time (hours/day)	1.5	1.5	3	3	3	3
BW	Body weight (kg)	8.8	14.5	43	65	71.2	70
AT	Average time (h)	ED*ET*365
Atn	Average time for carcinogens (h)	70*24*365

.

Table 2. Recommended values of reference doses and inhalation unit risk of elemental content [25].

	Cr	Ni	Cu	Zn	As	Cd	Pb	Mn
RfC (mg/m3)	0.0001	0.000014	0.000402	0.301	2 × 10−5	0	0.00325	0
IUR (ug/m3.day)−1	0.012	0.00026			0.0043	0	1.2 × 10−5

lists the inhalation reference concentrations (RfC) for each target element. The hazard quotient for each element (HQₑ) represents the non-carcinogenic risk from inhalation exposure to a single element. The hazard index (HI), defined as the sum of all HQₑ values, is used to evaluate the overall non-carcinogenic health risk posed by the mixture of elements. An HQₑ or HI value exceeding 1.00 indicates a potential for adverse health effects and warrants further attention, whereas a value below 1.00 suggests that non-carcinogenic risks are unlikely to occur [5,17,25].

2.3.2. Carcinogenic Risks of Heavy Metal Elements

The carcinogenic risks associated with heavy metal elements (Cr, Ni, As, Cd, and Pb) were calculated using the following equation:

LEC = Ce*InhR*EF*ED*ET/(BW*ATn)

(4)

ILCRe = LEC*IUR

(5)

The details of Ce, InhR, ED, ET, EF, BW, and ATn can be found in Table 2. The inhalation unit risk (IUR) shown in Table S3 concerns the inhalation unit risk of exposure to each element. The incremental lifetime cancer risk (ILCR_e) posed by an element is represented as the number of cases of specific types of cancer among a certain number of residents. According to the publication by USEPA, acceptable ILCR_e values are in the range of 1.0 × 10⁻⁶ to 1.0 × 10⁻⁴. When the ILCR is below 1.0 × 10⁻⁶, it is considered not to pose any significant carcinogenic risk; when this value is over 1.0 × 10⁻⁴, it is considered as very dangerous [18,23,27].

2.3.3. Carcinogenic Risks of PAHs

The carcinogenic risks associated with PAHs were calculated using the following equation:

LADDi = CA*InhR*ET*EF*ED/(BW*ATn)

(6)

ILCR_PAHs = 1-e^{(-ICRF*LADDi)}

(7)

The definitions of CA, InhR, ET, EF, ED, BW, and ATₙ are provided in Table 1. Here, CA represents the ambient concentration of benzo[a]pyrene (BaP), which was converted into the BaP toxic equivalent concentration using toxic equivalency factors (TEFs). The TEF values for each PAH were as follows: Flu, 0.001; Phe, 0.001; FLN, 0.001; Pyr, 0.001; BaA, 0.1; Chy, 0.01; BbF, 0.1; BkF, 0.1; BaP, 1.0; BghiP, 0.01; IndP, 0.1; and DBahA, 1.0 [23]. The inhalation cancer risk factor (ICRF) for BaP was set at 3.9 (mg/kg/day)⁻¹ [16,18,19]. ILCRₚₐₕₛ represents the estimated number of cancer cases attributable to PAH exposure via inhalation in a given population.

2.4. Assessment of Potential Sources of Heavy Metal Elements and PAHs

The potential sources of toxic elements were identified using enrichment factor (EF) analysis and principal component analysis (PCA). EFs are commonly employed to distinguish between natural and anthropogenic sources of elements and to assess the degree of human influence on elemental concentrations [5,10,25,28]. In this study, Fe element was selected as the reference crustal element for calculating the EF of each target element. The calculation methods and interpretation criteria followed those outlined in previous studies [5,28].

PCA was used to reduce the dimensionality of the dataset while preserving the majority of its variance. Variables exhibiting similar behaviors were grouped into principal components (factors), each representing a potential source or group of sources and explaining a significant portion of the total variance in the dataset [10].

For PAHs, diagnostic ratio analysis was used to identify potential anthropogenic sources. Several commonly used diagnostic ratios were applied as

Table 3. Commonly used PAH diagnostic ratios and corresponding source implications for source apportionment.

Diagnostic Ratios	Value	Potential Source	References
LMW/HMW	<1	pyrogenic sources	[16,20]
Low (high)-molecular-weight (L/HMW)	>1	petrogenic sources
COMB/TPAHs	~1	dominance of combustion-derived PAHs (COMB)	[26]
BaP/BghiP	<0.6	non-traffic emissions	[29]
	>0.6	traffic-related sources
IndP/(IndP + BghiP)	<0.2	petrogenic sources	[29]
	0.2–0.5	petroleum combustion
	>0.5	biomass or coal combustion
BaA/BaP	~0.5	gasoline exhaust	[30]
	~1.0	diesel exhaust
FLN/(FLN + PYR)	<0.4	petrogenic sources	[18]
	0.4–0.5	fossil fuel combustion
	>0.5	biomass or coal combustion

.

In addition, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was employed to analyze long-range transport pathways. Backward air mass trajectories were generated using meteorological data from the Global Data Assimilation System (GDAS) via the NOAA Air Resources Laboratory web server. Trajectories were calculated at 24-hour intervals, tracing air masses 192 hours backward in time. The arrival height was set at 50 m above ground level, corresponding to the elevation of one of the ambient particulate monitoring sites in Zhuji.

2.5. Quality Assurance, Quality Control (QA/QC), and Statistical Analysis

Descriptive statistics were calculated by using Origin 2021 software (Originlab corporation, New York, NY, USA) and Excel 2013 (Microsoft corporation, Redmond, WA, USA). A statistical analysis was conducted, including a Pearson correlation coefficient (PCC) analysis, and two significance levels of 0.05 and 0.001 were used in the statistics.

3. Results and Discussion

3.1. Variation of Particle Matter (PM), Heavy Metal Elements, and PAHs in PM_1.1 and PM_2.0

3.1.1. PM in PM_1.1 and PM_2.0

Generally, atmospheric fine particles (such as PM_1.1 and PM_1.0) have received more attention due to their association with increased in pneumonia hospitalizations and complex chemical compositions [29]. In this study, mass concentrations of ambient particles during summer and winter are shown in Figure 2b and Table S1. It could be found that mass concentrations of PM_2.0 (PM_1.1 + PM_1.1–2.0) were about 52.53µg/m³ (36.88–83.35 µg/m³) during summer, while they were about 100.74 µg/m³ (39.05–180.92 µg/m³) during winter. Meanwhile,

Table 4. Summary of PM_1.1/PM_2.5(PM_1.1/PM_2.0) of several areas all over the world.

Sampling City	Sampling Season	PM1.0/PM2.5	PM1.1/PM2.0	Sampling Periods	Literature
Zhuji (China)	Summer	0.401 (PM2.0/TSP)	0.664	August 2016	This study
	Winter	0.458 (PM2.0/TSP)	0.587	December 2016	This study
Shanghai (China)	Spring		0.21 (PM1.1/TSP)	21 March–11 April 2017	[5]
Beijing (China)	Winter		0.55	26 December 2018–11 January 2019	[28]
Durg (India)	Annual	0.48		July 2009–June 2010	[35]
Algiers (Algeria)	Annual	0.566		1 January–30 September 2015; 4 March–30 November 2016	[11]
Harbin (China)	Heat supply periods	0.832		November 2014–February 2015	[9]
	No heat supply	0.769		February 2015–October 2015	[9]
Hanoi (Vietnam)		0.676		November 2015–June 2016	[32]
Handan (China)	Winter	0.75		November 2015	[36]
Beijing (China)	Annual	0.533		2013–2017	[19]
Incheon (Korea)	Summer	0.807		August, 2022	[35]
	Winter	0.677		21 November–11 December 2022

shows that the ratio of PM_2.0/TSP (total suspended particles) were about 40.1% (37.3–44.9%) in summer and 45.8% (43.4–49.3%) in winter, indicating that these fine particles accounted for main proportion of TSP. In PM_2.0, PM_1.1 accounted for 66.4% (61.4–74.2%) in summer and 58.7% (50.2–72.5%) in winter. PCC results between PM_1.1 and other four particle sizes were 0.81 (PM_1.1-PM_1.1–2.0, p < 0.05), 0.88 (PM_1.1-PM_2.0–3.3, p < 0.05), 0.88 (PM_1.1-PM_3.3–7.0, p < 0.05), and 0.78 (PM_1.1-coarse particles, p < 0.05), indicating a strong association between PM_1.1 and other size fractions, especially for PM_2.0. PCC result between PM_1.1 and temperature was about −0.52 (p < 0.05), which indicated that the generation of these particle could get the negative effect from temperature. Coincidentally, Table 4 also shows several studies focused on the size distribution of ambient particles. It is obvious to find that PM_1.0/PM_2.5 was over 0.50 in the sampling sites in Algeria, Korea, India, Vietnam, and China, indicating that PM_1.0 is the main component of PM_2.5 [29,30,31,32,33,34]. This mass distribution of PM_1.1 and PM_2.0 in this mining industrial city could be considered consistent with that in other megacities. Moreover, particles have many complex chemical compounds, such as ionic, metal, and organic compounds [23,27,33]. Herein, heavy elements and PAHs with a potentially healthy effect are as the target ingredient to analyze their size distributions and variations.

3.1.2. Heavy Metal Elements (HMEs)

Size distribution of HMEs, including Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb, are displayed in Figure 3a,b and Tables S2 and S3. It shows that the mass concentrations of heavy metal elements in PM_2.0 were about 162.8 ng/m³ in summer and 724.7 ng/m³ in winter, whereas the mass concentrations of HMEs in PM_1.1 were about 147.6 ng/m³ in summer and 448.4 ng/m³ in winter, with higher enrichment of each HME in PM_1.1. In summer, about 90.6% of HMEs were found to be in PM_1.1, in the order of Zn > 50 ng/m³ > Pb > Mn > 10 ng/m³ > Cu > Ni > Cr > As > Cd. In winter, about 61.9% of HMEs were found to be in PM_1.1, with major contributions from the Zn, Pb, and Mn components (Zn > 200 ng/m³ > Fe > 150 ng/m³> Pb >Mn > 50 ng/m³ > Cu). These elements (except for Cr and Ni) increased several times in winter, especially Pb, Mn, and Zn. In summer, 90.6% of the heavy metals within the PM_2.0 size range were concentrated in particles smaller than 1.1 um, while in winter, this proportion was 61.9%. These values are notably higher than the corresponding PM_1.1/PM_2.0 mass ratios, indicating a preferential enrichment of heavy metals in finer particles.

Regarding each element in Figure 3a,b, it was found that Zn had the largest concentration in both summer (PM_1.1, 96.4 ng/m³) and winter (PM_1.1, 256.1 ng/m³; PM_1.1–2.0, 197.5 ng/m³). Compared with the heavy metal contents in PM_2.5 of Ningbo City, which is located in the Yangtze River Delta, with over 8 million people [35], it was found that the concentrations of Pb, Mn, As, and Cd in PM_2.0 of Zhuji in winter were higher, whereas they were lower than those in summer. There is no doubt that the heavy metal elements accumulated more in PM_1.1 than other sizes in both summer and winter. According to the atmospheric studies on Shanghai and Beijing area, it was also observed that the elemental contents in ambient particles were mainly associated with fine aerosol particles [27,34]. Analogical studies have reported that many areas have a higher mean concentration of metals in winter than in summer, such as in China, India, South Africa, and Italy [13,36,37].

3.1.3. PAHs

As shown in the results above, particulate matter and the majority of heavy metal elements were observed to accumulate predominantly in the PM1.1 size fraction [17,21]. Due to their widespread presence and well-documented health risks, PAHs were analyzed to evaluate their size distribution, potential anthropogenic sources, and associated health risks.

Figure 3c,d and Table S4 shows that Chy, BbF, BghiP, and IndP had higher concentrations than other kinds of PAHs during summer and winter. The TPAHs in PM_2.0 (17.81 ng/m³) in winter were about ten times higher than in summer (1.59 ng/m³). Over 75% of PAHs were accumulated in PM_1.1 during both summer and winter. It should be noted that the average concentration of BaP in PM_1.1 was about 1.01 ng/m³, which was over the WHO standard (1 ng/m³) [16,33]. Similarly to the HMEs, the amounts and proportions of PAHs were more obviously enriched in PM_1.1. This PAH enrichment phenomenon in PM_1.1 was also found in a study of 12 sites in China [38]. Moreover, PAH enrichment in PM_1.1 of Shanghai was found during spring, summer, and winter [16]. Lao et al. reported that PAHs were greater in PM_1.8 than in particles over 1.8 µm [39]. It was evident that particles, toxic elements, and PAHs accumulated more in winter, and were more dominant in PM_1.1.

The predominance of BaP and BghiP in fine particles during winter further emphasizes the potential for enhanced toxicological impacts due to their carcinogenic nature and higher alveolar deposition efficiency. These findings underscore the significant influence of seasonal atmospheric dynamics and anthropogenic activities on PAH profiles and associated health risks.

3.2. Health Risk Assessment of HMEs and PAHs in PM_1.1

HMEs and PAHs in the atmosphere have been identified as compounds posing potential health risks through inhalation, ingestion, and dermal exposure routes [23,27]. Herein, Cr, Ni, Cu, Zn, As, Cd, Pb, and Mn as typical HMEs group were evaluated for non-carcinogenic risks via inhalation route. Simultaneously, HMEs (Cr, Ni, As, Cd, and Pb) and the PAH group (BaPeq) were assessed for carcinogenic risks [7,13,39].

3.2.1. Non-Carcinogenic Risk Assessment of HMEs

The HI values for six age groups, evaluated based on toxic elements in PM_1.1, are presented in Figure 4a,b and Table S5. It is easy to find that the HI values of these total six age groups during summer were below 1.00, indicating no significant risk of chronic effects from these toxic elements. For HMEs in winter, the HI values exceeded 1.0 in all age groups except the 0–1 age group, with the following values: 1.85 for the 6–16 age group, 1.82 for the 1–6 age group, 1.44 for the 16–31 age group, 1.32 for the 31–51 age group, and 1.25 for the 51–81 age group. This indicates that these HME element in PM_1.0 could have severe health effects on the residents of these age groups, especially for children (the 1–6 age group) and adolescents (the 6–16 age group). Figure 4b also shows that the HQ values of Mn and Ni in summer were notably high, contributing 43.0% and 28.0% of the total HI, respectively. In winter, the contributions followed the order Mn (approximately 39.0%) >> As > Cd > Pb. Notably, the HQ values of Mn exceeded 1.00 in the 1–6 and 6–16 age groups during winter, indicating that Mn posed the highest non-carcinogenic risk.

3.2.2. Carcinogenic Risk Assessment of HMEs and PAHs

ILCR posed by the inhalation route of HMEs (Cr, Ni, As, Cd, and Pb) and PAHs was evaluated among the six age groups, and the results are shown in Figure 4c–f and Tables S6 and S7. Figure 4c shows that the ILCRₑ values for all six age groups exceeded 1.0 × 10⁻⁶ during both summer and winter. For the four age groups of 6–16, 16–31, 31–51, and 51–81 years, the ILCRₑ values surpassed the acceptable threshold of 1.0 × 10⁻⁴ in both seasons, indicating a significantly elevated carcinogenic risk associated with these elements. In contrast, the 0–1 and 1–6 age groups had ILCRₑ values within the acceptable range (between 1.0 × 10⁻⁶ and 1.0 × 10⁻⁴). Figure 4d further indicates that the ILCRₑ values for individual elements were all above 1.0 × 10⁻⁶, following the order Cr > As > Cd > Ni. These results highlight the substantial carcinogenic risks posed by these HMEs.

For PAHs, the ILCR_PAHs values among each of the six age groups, as shown in Figure 4e,f were over 1.0 × 10⁻⁶ during winter, whereas they were below 1.0 × 10⁻⁶ during summer. During winter, the ILCR_PAHs values of the six age groups were between 1.0 × 10⁻⁶ and 1.0 × 10⁻⁵, indicating that several people out of millions might develop cancer via the inhalation of PAHs during their lifetime. Regarding each PAH, the ILCR values of BaP among the six age groups were over 1.0 during winter. BaP could be considered the most significant carcinogenic PAH. In addition, BkF, BbF, IndP, and BkF, all of which belong to the so-called COMB PAH group [7,8,11,17,40], made a great contribution to ILCR. Furthermore, due to these HMEs and PAHs coexisting in fine particles, the potential health risk might be more severe than that calculated here.

3.3. Assessment of Potential Sources of HMEs and PAHs in PM_1.1

3.3.1. Assessment of Potential Sources of HMEs

The crustal enrichment factor (EF) is commonly used to distinguish whether an element originates from natural or anthropogenic sources [5,28,35,41]. As shown in Table 2, the EF values of elements in PM_1.1 were ranked as follows: Cd > 1000 > Zn > Pb > 100 > As > Cu > Ni > 10 during summer, and Cd > Pb > 1000 > Zn > As > Cu > 100 > Mn > 10 during winter. These high EF values, particularly in winter, suggest that the listed elements likely originate from anthropogenic sources. To further identify potential emission sources, PCA analysis was conducted using the toxic elements as input variables. The loadings and the percentage of variance explained for each component are also presented in

Table 5. EF values of each element and PCA loading factors for toxic elements in PM_1.1 during summer and winter sampling periods.

	Summer PM1.1				Winter PM1.1
Element	EFs	F1	F2	F3	EFs	F1	F2
Cr	6.61	0.03	0.65	−0.03	-	-	-
Mn	5.69	0.10	−0.29	0.76	19.77	0.27	0.82
Ni	17.12	0.00	0.64	0.10	-	-	-
Cu	66.28	0.33	−0.22	−0.48	211.5	0.35	−0.32
Zn	364.6	0.44	0.09	0.08	892.5	0.40	−0.13
As	92.58	0.42	0.02	0.24	316.3	0.40	0.24
Cd	2238.5	0.36	−0.14	−0.31	6727.0	0.38	−0.36
Pb	273.0	0.43	0.07	0.02	1267.4	0.41	−0.11
% of variance	---	54.4	25.0	13.8	---	82.0	11.7
% of cumulative	---	54.4	79.4	93.3	---	82.0	93.7
Source		steel industry activities; vehicle emissions	stainless steel manufacture	coal combustion; road particles		steel industry activities; vehicle emissions	coal combustion

. In summer, three principal components accounted for 93.3% of the total variance in PM_1.1. Factor 1 (F1) explained 54.4% of the variance and was mainly influenced by Cu, Zn, As, Cd, and Pb. The EF values of Zn, Cd, and Pb exceeded 50, indicating strong enrichment from anthropogenic sources. PCCs (

Table 6. PCC results between each element in PM_1.1.

		Cr	Mn	Co	Ni	Cu	Zn	As	Cd	Pb	Fe
Summer PM1.1	Cr	1.000	−0.421	−0.076	0.884	−0.233	0.173	0.108	−0.137	0.190	0.124
	Mn	−0.421	1.000	−0.013	−0.292	−0.135	0.216	0.379	0.002	0.156	0.122
	Co	−0.076	−0.013	1.000	0.141	0.417	0.274	0.018	0.009	0.275	0.141
	Ni	0.884	−0.292	0.141	1.000	−0.358	0.181	0.014	−0.182	0.053	−0.062
	Cu	−0.233	−0.135	0.417	−0.358	1.000	0.608	0.485	0.757	0.673	0.680
	Zn	0.173	0.216	0.274	0.181	0.608	1.000	0.925	0.729	0.921	0.880
	As	0.108	0.379	0.018	0.014	0.485	0.925	1.000	0.639	0.926	0.928
	Cd	−0.137	0.002	0.009	−0.182	0.757	0.729	0.639	1.000	0.623	0.624
	Pb	0.190	0.156	0.275	0.053	0.673	0.921	0.926	0.623	1.000	0.977
	Fe	0.124	0.122	0.141	−0.062	0.680	0.880	0.928	0.624	0.977	1.000
Winter PM1.1	Cr	-	-	-	-	-	-	-	-	-	-
	Mn	-	1.000	0.172	-	0.367	0.541	0.770	0.340	0.570	0.733
	Co	-	0.172	1.000	-	0.504	0.699	0.505	0.810	0.760	0.578
	Ni	-	-	-	-	-	-	-	-	-	-
	Cu	-	0.367	0.504	-	1.000	0.807	0.702	0.752	0.798	0.410
	Zn	-	0.541	0.699	-	0.807	1.000	0.870	0.897	0.966	0.621
	As	-	0.770	0.505	-	0.702	0.870	1.000	0.816	0.906	0.755
	Cd	-	0.340	0.810	-	0.752	0.897	0.816	1.000	0.956	0.669
	Pb	-	0.570	0.760	-	0.798	0.966	0.906	0.956	1.000	0.742
	Fe	-	0.733	0.578	-	0.410	0.621	0.755	0.669	0.742	1.000

Bold font, p < 0.05.

) showed significant correlations among these elements (e.g., Cu–Zn: 0.61, As–Zn: 0.93, Cu–Cd: 0.76, Cu–Pb: 0.67, Cd–Pb: 0.62; all p < 0.05), suggesting a common source. As and Pb are commonly associated with coal combustion used in steel and metallurgical industries [25,36], while Cd is a typical byproduct of metallurgical processes [28,36]. Cu and Zn are primarily attributed to traffic-related emissions, such as diesel exhaust, brake wear, and tire abrasion [28,36]. Therefore, F1 likely represents industrial sources, including steel production and vehicular emissions. Factor 2 (F2), accounting for 25.0% of the total variance, was characterized by high loadings of Cr and Ni. A strong correlation between Cr and Ni (r = 0.88, p < 0.05) suggests a shared source, likely emissions from stainless steel manufacturing [10,36]. Factor 3 (F3), which explained 13.8% of the variance, was dominated by Mn, a known indicator of coal combustion [12,35,42], thus suggesting coal combustion as its primary source.

In winter, only two principal components were identified, explaining 93.7% of the total variance. F1, accounting for 82.0%, was influenced by Cu, Zn, As, Cd, and Pb, with strong correlations (e.g., Cu–Zn: 0.81, Cu–Cd: 0.75, Cu–Pb: 0.80; all p < 0.05) and high EF values for these elements. These results indicate that F1 also represents industrial sources, similarly to summer. F2, contributing 11.7% of the variance, was likely associated with coal combustion. Overall, the sources of heavy metal elements in PM_1.1 were relatively consistent across seasons, mainly stemming from industrial activities and transportation. The absence of Cr and Ni in PM_1.1 during winter suggests they may be linked to seasonal or short-term industrial activities.

3.3.2. Assessment of Potential Sources of PAHs

Based on molecular weights, these 13 identified PAHs can be categorized into three groups: low-molecular-weight (LMW) PAHs, consisting of 2–3-ring compounds such as Flu, Phe, Ant; medium-molecular-weight (MMW) PAHs, composed of 4-ring compounds, including FLN, Pyr, BaA, and Chy; and HMW PAHs, which are typically particle-bound compounds, such as BbF, BkF, BaP, DBahA, IndP, and BghiP [5,39]. Figure 3c,d shows that in summer, approximately 58.4% of LMW, 78.8% of MMW, and 80.7% of HMW PAHs were associated with PM_1.1. In winter, the proportions were 65.7% (LMW), 71.6% (MMW), and 78.4% (HMW), respectively. MMW and HMW PAHs were more concentrated in PM_1.1, particularly during the winter season. To further identify PAH sources in PM_1.1, several diagnostic ratios were employed: combustion-PAHs(COMB)/TPAHs, FLN/(FLN + Pyr), BaA/BaP, BaP/BghiP, BaA/(BaA + Chy), and IndP/(IndP + BghiP)[5,29]. The LMW/HMW ratio is commonly used to distinguish between petrogenic and pyrogenic sources [17,40]. COMB PAHs, including FLN, Pyr, BaA, Chy, BbF, BkF, BaP, IndP, and BghiP, are typically associated with combustion processes such as coal, petroleum, and diesel burning [18].

As shown in Figure 5a, the LMW/HMW ratios in PM_1.1 were low (0.05–0.09 in summer; 0.05–0.17 in winter), while COMB/TPAHs ratios were high (0.92–0.95 in summer; 0.90–0.96 in winter), indicating that combustion processes were the dominant source of PAHs. Additionally, the BaP/BghiP ratio in summer was consistently below 0.60, and the BaA/BaP ratio was below 0.50, suggesting a predominant contribution from traffic-related petroleum combustion. In winter, higher values of IndP/(IndP + BghiP) and FLN/(FLN + Pyr) suggested the influence of both traffic emissions and coal combustion.

To assess long-range transport, backward air mass trajectories during the sampling periods are presented in Figure 5b. In summer, air masses originated from the northeastern marine region, likely affected by emissions from busy shipping activity in the Yellow Sea. In winter, air masses predominantly arrived from northwestern inland regions, where coal combustion is prevalent. These pollutants may have reached Zhuji via long-range atmospheric transport [5,13,21]. Overall, combustion activities—including vehicle emissions, industrial fuel combustion, and coal burning—were the dominant sources of PAHs in PM_1.1 during both summer and winter.

4. Conclusions

To the best of our knowledge, this is the first systematic investigation of atmospheric pollution in Zhuji, a representative city with intensive copper-processing industries in China. This study concentrates on the seasonal variations (summer and winter), source apportionment, and health risk assessment of heavy metals and PAHs in ambient PM_1.1. The main findings are summarized as follows:

Heavy metals and PAHs showed higher accumulation in PM_1.1 than in larger particle fractions. PM_1.1/PM_2.0 ratios (summer: 0.664; winter: 0.587) indicate that PM_1.1 dominates the fine particle fraction. Seasonal variation was pronounced, with winter concentrations of heavy metals and PAHs being 3.04 and 10.6 times higher, respectively, than in summer.

Non-carcinogenic risk was observed in all age groups except infants during winter, with Mn showing the highest risk. ILCR values for four age groups (6–16, 16–31, 31–51, 51–81) exceeded 1.0 × 10⁻⁴, indicating a high lifetime cancer risk from exposure to Cr, Ni, As, Cd, and Pb.

PAHs in winter also posed carcinogenic risks, with ILCR values exceeding 1.0 × 10⁻⁶ for all age groups. BaP was identified as the most critical carcinogen, along with BkF, BbF, and IndP.

Source apportionment analysis revealed that heavy metals predominantly originated from steel production and vehicle emissions. PAHs were mainly derived from combustion sources, especially traffic and coal combustion. Backward trajectory analysis further supported the influence of long-range transport during winter.

Overall, these findings underscore the urgent need for implementing effective emission control measures and enhancing environmental monitoring in industrial cities such as Zhuji. Although some limitations exist in this study, we hope that the insights provided help raise awareness of the environmental challenges faced by small- and medium-sized industrial cities and promote further research. Future work may include approaches such as social survey-based investigations to better understand the health impacts of air pollution on local residents.