Concentration and Potential Sources of Total Gaseous Mercury in a Concentrated Non-Ferrous Metals Smelting Area in Mengzi of China

Abstract

To investigate concentration and potential sources of total gaseous mercury (TGM) in a concentrated non-ferrous metals smelting area in southwest China, a high temporal resolution automatic mercury meter was used to measure TGM in the environment and the emissions from major sources of Mengzi city. The average concentration of TGM in urban air was 2.1 ± 3.5 ng·m⁻³ with a range of 0.1~61.1 ng·m⁻³ over the study period. The highest TGM concentration was in fall (3.3 ± 4.3 ng·m⁻³). The daytime TGM concentration (2.8 ± 3.5 ng·m⁻³) was significantly higher than that in the nighttime (1.6 ± 1.1 ng·m⁻³), which may be attributed to the increased emissions of mercury from the high volume of vehicle activity during the day. To discuss the contributions of local sources and long-range transport, eight pollution events were identified based on the ratio of ΔTGM/ΔCO (Carbon Monoxide), which can be found that local sources are a key contributor to the major TGM pollution events. Concentrations of TGM in flue gases from eight non-ferrous industrial sources were also measured in Mengzi, which were found that the highest TGM emission concentration was up to 4.6 mg·m⁻³. Simultaneously, the concentrations of TGM in ambient air around these industries and Xidu Tunnel were also detected, the concentrations were 1 to 4 times higher than that in the urban air sampling site. Based on the analysis of air mass and PSCF, when northwest wind happened, these emissions of industries and vehicles can be identified as the primary sources of TGM in urban air of Mengzi.

1. Introduction

Mercury (Hg), as a highly toxic heavy metal widely present in the air, can exist in gaseous form at ambient temperature [1]. It is classified as a global pollutant due to its ability to undergo long-range atmospheric transport and bioaccumulate in organisms [2,3]. Gaseous elemental mercury (GEM), reactive gaseous mercury (RGM), and particulate-bound mercury (PBM) are the three main forms of atmospheric mercury [4]. Among them, gaseous singlet mercury and reactive gaseous mercury are called TGM. GEM constitutes over 90% of TGM, due to its low chemical reactivity, poor water solubility, and limited deposition rate, can persist in the atmosphere for a duration ranging from 0.5 to 2 yr [5,6].

The majority of mercury emitted into the atmosphere from anthropogenic and natural sources is in inorganic forms [7]. Natural sources encompass volcanoes, geothermal sources and the re-emission from land, water bodies, vegetation surfaces [8]. Fossil fuel combustion, mining, non-ferrous metal smelting, iron steel production, chloroalkali plants, cement production and miscellaneous category are main anthropogenic mercury sources [9,10,11]. Natural sources globally release 2100 tons of mercury into the atmosphere each year, while anthropogenic emission sources can release 3000 tons into the atmosphere annually [12]. Anthropogenic emissions contribute approximately 1.5 times more than natural sources. Atmospheric mercury transfers to other environmental media through direct industrial emissions or atmospheric deposition (e.g., the methylation process in aquatic ecosystems) [13,14]. Methylmercury accumulates in the human body through biomagnification, ultimately posing a threat to human health, even at low concentrations [15,16,17].

After the Industrial Revolution around 1840, increased industrial activities generated significant anthropogenic mercury emissions, which contributed to a swift rise in mercury accumulation in the environment [18]. Research has indicated that notable fluctuations in atmospheric mercury concentrations near urban and industrial areas are primarily attributed to local anthropogenic emission sources [19,20]. Direct mercury emissions from industrial processes significantly affect soil mercury concentrations, increasing them by three orders of magnitude compared to areas affected solely by atmospheric deposition [13]. Compared with non-industrial sources, the average mercury concentrations were 2~3 times higher and individual mercury concentrations up to 12 times higher in songbirds from a forest located near industrial sources [21]. Measurements of total mercury concentrations in crops cultivated near coal-fired power plants revealed that two-thirds of the samples exceeded food safety standards [22].

The global mercury emissions increased at a rate of 1.8% per year (2010–2015), East Asia remaining the largest emitting region [23]. Due to the rapid economic and industrial growth in China, 500~800 tons of mercury can be emitted annually, accounting for 30% of global emissions, and makes China as the largest contributor to mercury emissions in the world [24,25]. The global inventory of anthropogenic mercury emissions in 2000 indicated that two-thirds of these emissions were attributable to the combustion of fossil fuels [26]. However, the situation regarding mercury emissions in China could be distinct from those observed elsewhere. Mercury emissions from industrial sources rank second only to those from coal combustion in total emissions [27,28]. According to data on mercury emissions from anthropogenic activities in China, nearly half of the mercury originates from the smelting of non-ferrous metals [29]. Approximately 54% of the non-coal mercury emissions mercury emissions came from zinc smelting, followed by lead, gold, and copper smelting, contributing 18%, 11%, and 4%, respectively [30]. In 2007, the total emissions from global non-ferrous metal smelting amounted to 310 tons, of which 230 tons were contributed by China [31]. Non-ferrous metal smelting, particularly zinc, lead, and copper smelting, has become one of the primary contributors to anthropogenic mercury emissions in China [32].

China’s vast territory results in significant differences among provinces in terms of economic development, energy types, technological capabilities, etc., [33]. Atmospheric mercury demonstrates significant spatial and temporal variability depending on emission source types, physicochemical characteristics, geographic factors, and meteorological conditions [34]. Anthropogenic mercury emissions are mainly concentrated in the southwestern, western, and central regions near industrial sources, with non-ferrous metal smelting and cement plants in the southwestern region being a major source of mercury [5].

CO and TGM have Comparable Contaminant sources and similar residence time in the atmosphere, so CO is often used as mercury tracer [35,36]. Hybrid receptor models have been widely employed to identify potential source areas and assess their impact on the ambient environment [37,38]. Total potential source contribution function (PSCF) is commonly employed to identify pollutant sources associated with long-distance transport, however, some studies suggest that it can also be used to pinpoint local sources [39].

Yunnan, located in Southwest China, which is referred to as “kingdom of non-ferrous metals” for its ample reserves of non-ferrous metals [40]. Mengzi has the lowest air quality among the 16 prefecture-level administrative regions in Yunnan Province. However, research on atmospheric pollution in this region is relatively scarce, and the mechanisms of TGM pollution in this area remain unclear. Therefore, this study focuses on Mengzi, Yunnan Province, to investigate the variations in TGM concentration. Source types were identified through the analysis of ΔTGM/ΔCO ratio; Conditional probability function (CPF) was employed to ascertain the prevailing wind direction, while Conditional bivariate probability function (CBPF) was utilized to identify the wind direction and wind speed in the source region. Additionally, PSCF, in conjunction with concentration data, was employed to ascertain potential source locations.

2. Materials and Methods

2.1. Regional Research Profiles

Mengzi is located in the southeastern part of Yunnan Province, with an average elevation of 1300 m above sea level. The seasons are not distinct, but there is a significant difference between the dry winter and the rainy summer [41]. The rainy season occurs from May to October, with rainfall accounting for more than 80% of the annual precipitation. The prevailing wind direction throughout the year is predominantly southeast. Mengzi is renowned for its characteristic abundance of lead and zinc mineral deposits and is home to the provincial-level Mengzi Economic Development Zone and the Red River Industrial Park. It represents a typical concentration area for non-ferrous metal smelting within Yunnan Province, with a high regional mercury emission rate.

2.2. Testing Locations and Times

2.2.1. Urban Air Sample Site

The richness of mineral resources and urban planning characteristics may cause densely populated cities located upwind to be affected by surrounding industrial enterprises. Therefore, the selected atmospheric mercury detection sites in this study are located on the 3rd floor of the Red River State Monitoring Station in the main urban area of Mengzi (103°23′40″ E, 23°21′11″ N), as shown in Figure 1. The monitoring site is located at an elevation of approximately 10 m above ground level, with a high population density and significant vehicular traffic in the surrounding area. The measurements were conducted in August 2017, October 2017, and January 2018. The sampling duration for each month is as follows: 10 days in August, 5 days in October, and 7 days in January.

2.2.2. Source Site

Simultaneous source sampling was conducted. In July and August 2017, eight industrial enterprises including nonferrous metal smelting, chemical plant, and cement plant in the vicinity of Mengzi were selected as sampling site for TGM anthropogenic source. Xidu Tunnel serves as a key transportation artery for smelting industrial logistics between Gejiu and Mengzi. It has a total length of 6900 m and experiences significant vehicular traffic within the tunnel. Therefore, Xidu Tunnel was selected as a sampling point for TGM in the context of vehicle emissions.

2.3. Testing Methods

2.3.1. TGM in Urban Air Sample Site and Source Site

TGM concentration measurements were taken with a high temporal resolution atmospheric mercury analyzer (model: UT-3000, Mercury Instruments, Karlsfeld, Germany). The working principle of this instrument involves the use of a gold amalgamation trap in the device, which utilizes the strong adsorption between mercury and gold at room temperature to capture mercury. Following the enrichment of mercury, the gold trap is rapidly heated to facilitate thermal desorption of the mercury, which then enters the optical sample chamber for detection using atomic absorption spectroscopy. The instrument detection limit is 0.1 ng·m⁻³, with a sampling volume of 1 L, and the sampling interval was 4 min. Urban air sample site conducted continuous 24-h observations each day, resulted in a total of 6863 valid data sets. Additionally, no fewer than 7 h of continuous observations were carried out for the environmental air around 8 factories and Xidu Tunnel, leading to a total of 1032 valid data sets.

2.3.2. TGM in Sources Flue Gas

TGM samples of flue gas emitted by anthropogenic source were collected using gas bags. The samples were diluted 8 times through a dilution channel (provided by Dekati, Kangasala, Finland), and then drawn into a gold-coated quartz sand sampling tube (17 cm long, 6 mm inner diameter, with 1.5 cm gold-coated quartz sand inside) using an atmospheric sampling pump (model: QC-1S, Beijing Institute of Labor Protection Science, Beijing, China). The collection of TGM was achieved by exploiting the property of mercury to easily form an amalgam on the gold surface. The sampling flow rate was set at 1.0 L·min⁻¹ (at standard temperature and pressure). The sampling tube for TGM capture is heated using the FJ-2 Thermal Decomposition System (provided by Jinfen Instrument Co., Ltd., Jintan, China) and subsequently analyzed for TGM content using a cold atomic absorption mercury meter (provided by Jinfen Instrument Co., Ltd., Jintan, China).

2.3.3. Air Pollutants and Meteorological Data

Meteorological hourly data such as wind speed, wind direction, temperature, and rainfall during the monitoring period were obtained by downloading from the website of the China Meteorological Administration (http://www.cma.gov.cn/, accessed on 4 February 2018). The synchronized data on hourly concentrations of five conventional air pollutants is obtained from the National Air Quality Monitoring and Management Platform. The average mass concentrations of conventional air pollutants and meteorological information during the monitoring period in Mengzi are obtained through data collection and processing (

Table 1. Concentration air pollutants, meteorological data in Mengzi.

		CO (ppb)	NO2 (μg·m−3)	O3 (μg·m−3)	SO2 (μg·m−3)	PM2.5 (μg·m−3)	Wind Speed (m·s−1)	Temperature (°C)	Rainfall (mm)
Summer	Average	515 ± 281	12.1 ± 6.5	49.8 ± 22.1	19.2 ± 33.5	22.5 ± 14.8	2.3 ± 1.0	20.5 ± 2.41	0.7 ± 1.3
	Median	437	10	46	19	7	2.0	18.9	0.14
Fall	Average	787 ± 488	15.9 ± 7.1	73.1 ± 29.5	30.5 ± 42.3	63.2 ± 25.3	2.1 ± 1.1	14.3 ± 4.3	0.1 ± 0.2
	Median	611	14	72	40	13	2.0	14.8	0
Winter	Average	563 ± 75	10.3 ± 5.7	67.7 ± 15	11.2 ± 23.8	36.2 ± 10.6	3 ± 1.4	13.7 ± 3.4	0 ± 0
	Median	524	9	70	36	7	2.6	12.4	0
Total	Average	594 ± 324	12.5 ± 6.7	60.9 ± 24.5	19.4 ± 34	34.1 ± 16.8	2.4 ± 1.2	16.7 ± 4.7	0.3 ± 0.9
	Median	524	11	60	30	7	2.1	18.0	0

).

2.4. Quality Assurance/Quality Control

In order to ensure the accuracy and reliability of the results, the instrument was calibrated using standard saturated mercury vapor prior to sampling. This calibration process is performed three times to ensure that the calibration error is less than 5%, thus ensuring data quality. Each time an analysis of mercury samples from anthropogenic source emissions is conducted, it is necessary to prepare a standard curve. The reusable sampling tube is preheated using the sample analyzer (CG-1C cold atomic absorption mercury meter, provided by Jinfen Instrument Co., Ltd., Jintan, China) according to the analysis procedure, in order to thermally desorb the residual mercury in the sampling tube before each sampling. The CG-1C cold vapor atomic absorption spectrometer is periodically cleaned by wiping the interface of the tubing with ethanol reagent, followed by blowing with high-purity nitrogen gas to achieve drying and cleaning.

3. Analysis Method

3.1. Conditional Probability Function

Using CPF to ascertain the prevailing wind direction during high-concentration events for the purpose of assessing the impact of local sources [42]. Several studies have demonstrated its effectiveness in evaluating the environmental impact of emission sources and to identify source direction [43,44,45,46,47]. CPF is applied to assess the likelihood that the measured concentration surpasses a set threshold under assigned wind direction [48]. The CPF is characterized by the equation provided below:

$${CPF}_{∆\theta }={\displaystyle \frac{m_{∆\theta }{|}_{c\ge x}}{n_{∆\theta }}}$$

(1)

where $m_{∆\theta }$ refers to the number of samples in wind sector $\theta$ where the concentration $c$ equal to or exceeding the threshold value x, and $n_{∆\theta }$ indicates the aggregate number of samples collected from the wind sector $∆\theta$. This study divided the wind sectors into 32 sections ($∆\theta$ = 11.25°). The CPF outcomes are typically presented using a polar coordinate graph. Due to the uniform characteristics of the wind vane, calm wind (<1 m·s⁻¹) were omitted from the analysis. The continental background value for the Northern Hemisphere (1.7 ng·m⁻³) was set as the threshold. Therefore, CPF can be used to determine the probability of wind directions originating from regions with high pollutant levels.

3.2. Conditional Bivariate Probability Function

CBPF incorporates wind speed as a third variable in conjunction with CPF, assigning observed pollutant concentration to units defined by wind direction and speed ranges [37]. The CBPF is characterized by the equation provided below:

$${CBPF}_{∆\theta ,∆u}={\displaystyle \frac{m_{∆\theta ,∆u}{|}_{c\ge x}}{n_{∆\theta ,∆u}}}$$

(2)

where $m_{∆\theta ,∆u}$ refers to the number of samples in wind sector $∆\theta$ where the concentration $c$ equal to or exceeding the threshold value x, and $n_{∆\theta ,∆u}$ indicates the aggregate number of samples. The continental background value for the Northern Hemisphere (1.7 ng·m⁻³) was set as the threshold. The CBPF method compensates for the limitations of the CPF approach, which relies exclusively on wind direction to identify pollution sources, by incorporating both wind direction and speed in the analysis of pollutant concentrations. This combination offers deeper insights into the characteristics of the sources [49]. Because different types of sources may exhibit varying dependencies on wind speed, such as long-range transport sources and ground-level sources [48]. Bivariate polar plots effectively illustrate the relationship between a species’ concentration and its variation with wind speed and direction within a polar coordinate system [37]. Hence, utilizing a third variable in polar coordinate plotting can offer more insights regarding the characteristics of the source types in question [37,49,50,51].

3.3. Total Potential Source Contribution Function

PSCF is a method that combine pollutants concentrations with the backward trajectories of the air mass based on conditional probability functions to qualitatively identify potential pollution sources [50,52]. PSCF is defined as the conditional probability that an air mass passing through a specific region will result in the observed values of a certain parameter exceeding a predefined threshold when it reaches the monitoring point [53]. The PSCF was defined as follows:

$${PSCF}_{ij}={\displaystyle \frac{m_{ij}}{n_{ij}}}\times W$$

(3)

In this study, $n_{ij}$ denotes the number of instances in which trajectories intersect the cell (i, j), and $m_{ij}$ refers to the number of occurrences when the source concentration is high at these cell during trajectory passage [54]. In these cells, elevated PSCF values are interpreted as indicative of potential source areas [48]. The PSCF threshold is set above the Northern Hemisphere continental background level (1.7 ng·m⁻³). The geographical area covered by calculated trajectories is partitioned into an array consisting of 0.05° × 0.05° cells. To calculate trajectories starting from different heights (10, 50, and 100 m above ground level) for 24 h and then combine the probabilities from these different starting heights, PSCF should be weighted using the factor $W_{ij}$. The weight W_ij is defined as follows:

$$W_{ij}=\left\{\begin{array}{l}1.0, N_{ij}\ge 3N_{ave}\\ 0.7, {3N_{ave}>N}_{ij}\ge 1.5N_{ave}\\ 0.4, {1.5N_{ave}>N}_{ij}\ge N_{ave}\\ 0.2, N_{ave}\ge N_{ij}\end{array}\right.$$

(4)

4. Results

4.1. Overall Description of TGM Concentration

Figure 2 illustrates the time series of TGM concentrations across different seasons. The average concentration of TGM during the complete sampling period was 2.16 ± 3.46 ng·m⁻³, exceeded the Northern Hemisphere background concentration of 1.7 ng·m⁻³ [55]. From Figure 2, it can be seen that TGM exhibited it can be seen that TGM exhibited varying degrees and durations of pollution, with occasional high concentrations (61.1 ng·m⁻³), and exhibiting short-term variations on the scale of a few hours, indicating that the air in Mengzi has been affected to some extent by anthropogenic mercury emissions [6,56]. From Table 1, it can be seen that the median TGM concentration was 1.4 ng·m⁻³, significantly lower than the mean concentration, suggesting that there were some extreme pollution events with substantially elevated TGM levels throughout the sampling period.

4.2. Seasonal Variations of TGM Concentration

During the observation period, the seasonal concentrations of TGM in Mengzi were observed to follow the order from highest to lowest as follows: fall (3.25 ± 4.30 ng·m⁻³), summer (2.19 ± 4.05 ng·m⁻³), and winter (1.41 ± 0.31 ng·m⁻³). From

Table 2. TGM measurements in China and other countries.

Locations	Type	Year	Method	Season	TGM (ng·m−3)	Reference
Northern Hemisphere	Background points				1.5~1.7	[55]
Changbai, China	Background points	2008–2010	Tekran 2537A	Summer	1.52 ± 0.56	[58]
				Fall	1.64 ± 0.64
				Winter	1.61 ± 0.47
Kunming, China	Urban	2018	UT-3000	Summer	0.7 ± 0.2	[57]
				Fall	1.6 ± 0.5
				Winter	1.0 ± 0.4
Lanzhou, China	Urban	2016–2017	Tekran 2537B	Summer	4.45 ± 2.10	[61]
				Fall	4.15 ± 1.78
				Winter	5.06 ± 2.45
Guiyang, China	Urban	2001–2002	Tekran 2537A	Summer	7.51 ± 4.80	[63]
				Fall	8.20 ± 9.58
				Winter	10.54 ± 10.26
Guangzhou, China	Urban	2010–2011	Tekran 2537B	Summer	4.16	[64]
				Fall	4.3
				Winter	4.44
Shanghai, China	Urban	2008–2010	AFS-930	Summer	7.78 ± 2.33	[62]
				Fall	9.30 ± 2.48
				Winter	8.32 ± 2.79
Windsor, Canada	Urban	2007–2011	Tekran 2537A	Summer	2.4 ± 2.0	[59]
				Fall	1.8 ± 0.81
				Winter	1.9 ± 1.4
Seoul, Korea	Urban	2005–2006	Tekran 2537A	Summer	2.59 ± 1.32	[65]
				Fall	3.16 ± 1.82
				Winter	3.89 ± 2.70
Lichwin, Poland	Rural	2003	MA-2 CVAAS	Summer	1.63 ± 0.35	[60]
				Winter	4.15 ± 1.33
Mengzi, China	Urban	2017–2018	UT-3000	Summer	2.19 ± 4.05	This study
				Fall	3.25 ± 4.30
				Winter	1.41 ± 0.31

, it can be seen that the seasonal TGM concentration in Mengzi is generally higher than those measured in Kunming [57], Changbai [58], Windsor in Canada [59], and Lichwyn in Poland [60], but lower than those measured in Lanzhou [61], Shanghai [62], Guiyang [63], Guangzhou [64] and other locations in Korea [48]. Mengzi is rich in mineral resources, particularly with a high number of non-ferrous metal smelting enterprises; therefore, this may be attributed to industrial emissions in Mengzi.

In addition, the seasonal variation of TGM differed from most of the observed results, where TGM concentrations in most monitoring sites are typically higher in winter than in summer. In contrast, TGM concentrations in Mengzi show a higher seasonal variation in fall and summer compared to winter (Table 2) [60,63,66]. This seasonal variation may be influenced by meteorological conditions and oxidation processes. The Higher O₃ concentration lead to increased levels of OH concentrations generated through O₃ photolysis, resulting in a faster rate of oxidation removal rates [63]. According to Table 1, we can determine that the concentration of O₃ in winter is higher than that in summer. The elevated levels of O₃ in winter lead to higher concentrations of OH, which accelerate the rate of oxidative removal. Consequently, the TGM levels are lower in winter. Notably, coal combustion is an important source category of anthropogenic mercury emissions to the atmosphere worldwide [67]. Seasonal fluctuations in TGM concentrations are attributed to the periodic nature of coal and biomass combustion for heating purposes, with the greatest impact observed during the winter [68]. The average winter temperature in Mengzi is above 13.7 °C, and there is no significant demand for heating, which may also contribute to the lower TGM concentrations observed during winter. Moreover, several studies have demonstrated a significant correlation between TGM concentrations and wind speed, indicating that TGM from local emission sources may disperse more rapidly with increasing wind speeds [59,64]. Thus, high winter wind speeds also facilitate the diffusion of TGM, leading to a decrease in TGM concentration.

Figure 3 shows the relative frequency distribution of TGM across three seasons. From this figure, we can be observed that the distribution frequencies of TGM concentrations exceeding the Northern Hemisphere background value of 1.7 ng·m⁻³ are 26.8%, 40.4%, and 11.8%, respectively. TGM concentrations exceeding 10 ng·m⁻³ were primarily observed during the fall season, representing 8.1% of all high-concentration events, whereas nearly all instances of the lowest concentrations (TGM < 1.7 ng·m⁻³), 88.3%, were detected during the winter months (Figure 3).

Rain has a scouring effect on TGM in the air [69], As shown in Table 1, the reduction in rainfall during fall diminishes this washout effect, which is unfavorable for mercury deposition. Among them, the concentrations of other pollutants in fall are the highest among the seasons, indicating that high concentrations of TGM are generated from other emission sources. Additionally, the lowest wind speeds in fall hinder the timely dispersion of TGM emitted from these other sources, resulting in the accumulation of TGM concentrations. Therefore, the TGM concentration in fall is the highest among the three seasons.

4.3. Hourly Variations of TGM Concentration

Figure 4 and Figure S1 shows the hourly variation of TGM, conventional air pollutants concentrations and meteorological data. TGM, CO, O₃, SO₂, NO₂, PM_2.5 and temperature were higher at daytime (06:00–18:00) than that at nighttime, except for wind speed (Figure S2). The daytime TGM concentration (2.80 ± 3.47 ng·m⁻³) was found to be higher than the nighttime (1.56 ± 1.10 ng·m⁻³), indicating a hourly control pattern. Studies have shown that this difference is attributed to local sources near the sampling site [70,71,72]. There are several known mercury sources around Mengzi, such as industrial sources and vehicle emissions, including non-ferrous metal smelting and Xidu Tunnel in the northwest directions of the sampling site. In addition, the nighttime wind speed (2.55 ± 0.96 m·s⁻¹) was higher than that in the daytime (2.29 ± 1.12 m·s⁻¹), and there exists a negative correlation between TGM concentration and wind speed (r = −0.21) (Figure 5). The increase in wind speed enhances atmospheric turbulence, which is conducive to the diffusion of pollutants, resulting in lower daytime TGM concentrations than at night [59,73].

Figure 5 shows the hourly data and 06:00–08:00 correlation coefficients between TGM and other factors. The atmospheric boundary layer is a crucial meteorological factor that influences the formation, development, and dissipation of pollution [73]. The daytime temperature (18.04 ± 3.78 °C) is higher than the nighttime temperature (15.30 ± 4.17 °C). The increase in daytime ambient temperature increases the height of the boundary layer [74,75], leading to a decrease in TGM concentration. Typically, an increase in temperature leads to an increase in boundary layer thickness, which can dilute TGM concentration to some extent. However, as shown in Figure 4, the TGM concentration reaches its maximum value when the temperature rises from 06:00 to 08:00.. There is positively correlation between TGM concentration and traffic emission tracers [64] NO₂ (r_s = 0.35), CO (r_s = 0.53), and PM_2.5 (r_s = 0.21) during 06:00 to 08:00. When PM_2.5 concentration increased, the divalent mercury (Hg⁺ and Hg²⁺) on the surface of aerosol particles could be reduced to elemental mercury (Hg⁰) being released to the atmosphere by photo-reduction which increase TGM concentration [76]. In addition the contribution of PM_2.5 emissions from industrial and vehicle emissions in Mengzi exceeds 71% [77] which indicates that during this period, industrial and vehicle emissions are the main sources of TGM emissions.It is noteworthy that winter is the only type that belongs to nighttime control. According to Figure S2, only the daytime wind speed in winter is higher than that at night, and the TGM concentration is negatively correlated with wind speed (r = −0.25) (Figure S3), resulting in a higher TGM concentrations at night during winter.

In summary, the hourly variation of TGM concentration are due to a combination of (1) variations in ambient temperature, (2) the effects of different wind speeds, and (3) localized emissions associated with industrial and vehicle emissions. To supplement these conclusions, we sampled industrial and vehicle emissions near the sampling site to determine their impact on TGM concentration in Mengzi. In addition, we adopt ΔTGM/ΔCO to evaluate the possibility of local sources. CPF and CBPF methods are used to determine the source direction, while the TPSCF is employed to identify potential source locations.

4.4. TGM in Tunnels, Industrial Environments, and Flue Gas

This study conducted flue gas mercury collection at eight industrial exhaust outlets located around Mengzi and found that industrial mercury emissions exceeded the emission limit of 0.015 mg·m⁻³ in the Emission Standards of Pollutants for Stannum, Antimony and Mercury Industries [78]. By combining the TGM content of flue gas with the investigated industrial exhaust emissions, the hourly mercury emissions are calculated as emission rate. In addition, TGM concentration in industrial emissions was also detected. Figure 6 shows that the emission rate in the non-ferrous metal smelting process are the most significant, followed by the cement production and chemical industry. Within non-ferrous metal smelting, the emission rate are highest in aluminum smelting, steel smelting and copper smelting. TGM concentration in the industrial sources of non-ferrous metal smelting is significantly higher than that in urban air. Mengzi industrial is primarily centered on non-ferrous metal smelting. According to statistics, non-ferrous metal smelting enterprises account for 58% of the total number of enterprises in the Mengzi, with lead smelting enterprises accounting for a large proportion of 36% [79]. This indicates that the emissions of mercury from the non-ferrous metal smelting industry significantly contribute to the concentration of TGM.

In the concentrated area of non-ferrous metal smelting, the substantial transportation of raw materials and products has led to a sharp increase in the volume of freight vehicles. The detection of TGM concentration in Xidu Tunnel is required, where there is a large traffic volume and often large trucks transporting ores and slag pass through. It was found that the TGM concentration in the tunnel (5.64 ± 4.07 ng·m⁻³) was 2.6 times higher than that in urban air (2.16 ± 3.46 ng·m⁻³).

4.5. Analysis of High TGM Concentration Events

A period during which the average TGM concentration exceeds 1.7 ng·m⁻³ continuously for 10 h is defined as a high TGM concentration events. Additionally, instances where exceptionally high TGM values are recorded are also defined as separate high TGM concentration events (

Table 3. Summary of pollution events.

Event.	Date/Time	Duration (h)	TGM conc. (ng·m−3)	CO conc. (ppb)
A1	2017082308	10	3.80 ± 1.63	812 ± 233
A2	2017082320	22	4.87 ± 2.11	873 ± 292
A3	2017082805	22	3.30 ± 0.49	738 ± 352
A4	2017082907	5	18.43 ± 16.36	1275 ± 443
A5	2017102707	10	2.03 ± 0.25	707 ± 120
A6	2018011700	13	1.90 ± 0.23	651 ± 150
B1	2017102904	26	6.87 ± 6.05	1208 ± 747
B2	2017103106	11	4.53 ± 3.39	698 ± 432

). Throughout the monitoring period, a total of eight high TGM concentration events were observed.

During all high concentration events, a significant difference in low wind speeds (2.6 ± 1.3 m·s⁻¹) was observed compared with the average wind speed over the non-pollution events (1.9 ± 0.8 m·s⁻¹), indicating that variations in wind speed are important factors in reducing TGM concentrations. In addition, the concentrations of CO (831 ± 512 ppb), NO₂ (17.3 ± 7.4 μg·m⁻³), SO₂ (39.8 ± 44.1 μg·m⁻³), PM_2.5 (43.8 ± 19.0 μg·m⁻³) during high concentration events were about 1.6 to 3 times higher than those during non-pollution events (CO: 505 ± 174 ppb, NO₂: 10.7 ± 5.6 μg·m⁻³, SO₂: 12.2 ± 26.1 μg·m⁻³, PM_2.5: 27.1 ± 13.5 μg·m⁻³), which indicates that industrial and vehicle emissions generally affected the enhancement of the TGM concentrations (Table S1).

TGM has the highest correlation with CO (r = 0.63) among the representative conventional air pollutants: PM_2.5, CO, SO₂ and NO₂. CO is generated from fossil fuel combustion and its atmospheric residence time is similar to that of TGM [36]. Additionally, it shares common emission sources with TGM [35]. Therefore, CO is often employed as a mercury tracer and serves as an indicator of anthropogenic emissions [48]. These high concentration events were classified as one of two phenomena using enhancement ratios (ERs) between TGM and CO concentrations (i.e., the slopes of the linear regression): long-range transport or local events [80]. Research indicates that long-range transport events were defined by high values of TGM/CO ratio (ΔTGM/ΔCO) (0.0046~0.0158 ng·m⁻³·ppb⁻¹) and high correlations (r² > 0.5), while local events are characterized by a lower TGM/CO ratio (0.0005 ng·m⁻³·ppb⁻¹) in average and weak correlations (r² < 0.5) [48,65,67,80,81,82]. Consequently, among the eight instances of mercury pollution events, six were identified as local events (with ΔTGM/ΔCO is markedly lower than the standard value for long-range transport from Asia), and of the events were identified as long-range transport events (Figure 7). This implies that, within this study, the contribution of local sources to pollution was greater than that of long-range transport sources. It is important to note that defining pollution events using a 10 h elevated concentration standard may introduce biases, as local events typically occur in durations less than 10 h [80], such as event A4.

4.6. Potential Source of TGM in Urban Air of Mengzi

Figure 8a presents the conventional CPF for TGM, emphasizing sources where concentration exceed the Northern Hemisphere background, which is equivalent to a TGM concentration of 1.7 ng·m⁻³. This Figure clearly indicates that the concentrations are more likely to originate from the northwest and northeast, but no further information is provided. In Figure 8b the same TGM data is presented in the form of a bivariate polar coordinate plot. CBPF corroborates that the highest probability source group for CPF originates from the northwest and northeast sectors. The findings from CBPF reveal that elevated probabilities from the northwest and northeast occurred under low wind speed (≤3 m·s⁻¹) indicative of non-buoyant ground level sources as well as high wind speed (>3 m·s⁻¹) indicative of emissions from stacks [37,48,51]. Pollutants typically remain confined to a localized area under low wind speeds, whereas high wind speeds enhance dispersion and facilitate long-range transport. Thus, low wind speeds and high wind speeds that correspond to high CBPF probabilities are indicative of local emission sources and non-local emission sources, respectively [50]. CBPF indicates that local sources are relatively more significant in this study.

TPSCF (Figure 8c) analysis identified potential sources of TGM to be the industrial sources located to the northwest of the sampling site. As discussed in Section 4.4, there are metal smelting facilities in Mengzi that emit relatively high concentrations of mercury. Areas to the south and east of the sampling point have also been identified as possible sources of TGM. According to Figure 1, there is a cement factory in the southern region, where mercury from raw materials can evaporate during high-temperature calcination and be released into the atmosphere with the flue gas [83]. As described in Section 4.4, the zinc smelting facility in the eastern region is among the highest regional emitters of mercury among industrial sampling site, making it another potential source of TGM.

5. Discussion

The TGM emissions from industrial sources can have a significant impact on the surrounding urban areas. The TGM concentration in Mengzi exceeds the background value in the northern hemisphere and exhibits short-term fluctuations over several hours, indicating that the air quality in Mengzi is influenced by anthropogenic mercury emissions to a certain extent. Mengzi is rich in mineral resources, particularly with numerous non-ferrous metal smelting enterprises, and the large transportation of raw materials and products will also lead to a sharp increase in the number of freight vehicles. TGM concentrations collected in the industrial environment and tunnels are higher than those in the urban air, with the most significant TGM emissions occurring during the smelting processes of non-ferrous metals. The largest potential sources of TGM in the study area basically coincide basically coincide with the location of the industrial enterprises. Although Mengzi is in the downwind of most industrial enterprises, the northwest winds blowing from the direction of these industries transport TGM from the source areas into the city, thereby increasing the local TGM concentrations. The potential source, PSCF, and CBPF results confirm these conclusions. TGM concentrations in Mengzi are lowest in winter and highest in fall, which is contrary to the seasonal variations observed at of the observed results. This phenomenon can be attributed to several factors: the relatively high winter temperatures in Mengzi reduce the demand for coal heating, and the combination of higher wind speeds and decreased rainfall in winter contributes to the dispersion of TGM. The novelty of this article lies in the analysis of meteorological factors, which can reveal the impact of wind direction on the TGM of surrounding urban air.

However, this study has some limitations. First, there are relatively few sample site, which may affect its representative. Second, we only examined the correlation between the data and did not employ tracer methods to demonstrate that industrial and mobile sources directly influence local urban air quality.

6. Conclusions

The average concentration of TGM in Mengzi was significantly higher than the background value in the northern hemisphere, which were the highest in fall and lowest in winter. The positive correlation with CO, SO₂, NO₂ and PM_2.5 proved that the elevated daytime concentration of TGM is influenced by industrial and vehicle emissions. Eight industrial emission rates confirm that compared to other industrial enterprises in Mengzi, non-ferrous metal smelting and release enterprises have released more TGM. The concentration of TGM in Xidu Tunnel is 2.6 times that of urban air. The higher emission levels and emission rates from industrial enterprises and tunnels are important sources of TGM in the air of Mengzi. According to ΔTGM/ΔCO analysis found that local sources of TGM are more outstanding effect than that of long-range transport. Through potential source analysis, it was found that local industrial and vehicle sources have significant contributions to TGM in urban air of Mengzi. Especially during the northwest wind, the probability of pollution incidents occurring in Mengzi increases. In summary, it is necessary to strengthen the management of industrial emissions in the northwest direction.