Sheng-Lun Lin  This email address is being protected from spambots. You need JavaScript enabled to view it.1, Hongjie Zhang1, Ming-Yeng Lin2, Shih-Wei Huang This email address is being protected from spambots. You need JavaScript enabled to view it.3,4

1 School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
2 Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
3 Institute of Environmental Toxin and Emerging Contaminant, Cheng Shiu University, Kaohsiung 83347, Taiwan
4 Center for Environmental Toxin and Emerging-contaminant Research, Cheng Shiu University, Kaohsiung 83347, Taiwan

Received: September 23, 2022
Revised: January 28, 2023
Accepted: February 22, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Lin, S.L., Zhang, H., Lin, M.Y., Huang, S.W. (2023). The Unignorable Near-ground PM2.5, UFP, PAHs, and BC Levels around a Traffic Prohibited Night Market. Aerosol Air Qual. Res. 23, 220331.


  • The source apportionment of background PM2.5 in a night market was evaluated.
  • The traffic restriction has less contribution on the background PM2.5 levels.
  • A mobile monitoring system analyzed the spatiotemporal variation of near-ground pollutants.
  • The near-ground UFP, BC, and PAHs were reduced by the traffic restriction.
  • Near-ground PM2.5 was not improved by only traffic prohibition but night market activity.


In some special densely populated areas, the background atmospheric fine particulate matter (PM2.5) concentration is very high, which makes near-ground (NG) exposure a major problem endangering human health. In our study, the night market in Chiayi City was selected as the research object and collected the 24-hour PM2.5 samples through the federal reference method (FRM), characterizing the mass concentration, water-soluble ionic components, carbon specious, metal compositions and source contributions of PM2.5. To better analyze the contribution of traffic sources under different sampling conditions, the mobile real-time monitoring system was used to analyze the quality of NG-PM2.5, the number of ultra-fine particles (UFP), the concentration of black carbon (BC) and total polycyclic aromatic hydrocarbons (PAH) before and after the traffic restriction. Results indicated the concentration of PM2.5 was 7.26–58.6 mg m–3. In chemical analysis, secondary contents e.g., carbonaceous and ionic components accounted for ~60% of the PM2.5, supporting the importance of long-range transport. However, the traffic contribution accounted for ~30% and hardly changed between different samples, which was not conducive to source apportionment. Through traffic restriction, it was found that all kinds of pollutants increased significantly before restriction, and even after restriction, the concentrations of PM2.5 and BC increased 131% and 151% in low concentration season. In the high concentration season, the traffic restriction significantly reduced the NG-UFP and NG-PAH concentration by 27% and 55%, respectively, but NG-BC and NG-PM2.5 was almost unaffected. Therefore, besides the contribution of traffic source, emissions from cooking activities are very important for the increase of NG-PM2.5 levels in the night market area.

Keywords: PM2.5, Source apportionment, Traffic restriction, Night market, Mobile monitoring


The suspended particle with aerodynamic diameter less than 2.5 µm, called fine particulate matter or abbreviated as PM2.5, which attracts more health concern in recent years (Cassee et al., 2013; De Marco et al., 2018). The World Health Organization export estimates that about 800000 people die each year due to fine suspended particulates air pollution, ranking 13th in the world. When PM2.5 enters the systemic blood circulation, it will not only increase the prevalence of lung cancer (Turner et al., 2011; Mutuku et al., 2020), asthma (Kim et al., 2020), cardiovascular disease (Lee et al., 2014), but also increase the mortality rate due to long-term exposure to PM2.5 (Wang et al., 2022). Therefore, PM2.5 emission has become the focus of many researchers. PM2.5 mainly composed of carbonaceous species (elemental and organic carbon), ions (sulfate, nitrate, and ammonium), metal elements and semi-volatile organic compounds, such as PAH. The formation of these components has natural and artificial sources, such as metal elements, which are usually produced by human activities (Fang et al., 2010). Our controllable anthropogenic activities mainly include combustion of fossil fuels (Chow et al., 2002; Lin et al., 2020) and biomass (Cayetano et al., 2014), garbage incineration (Gao et al., 2002), road dust resuspension (Yu et al., 2013), and kitchen smoke (Zhao and Lin, 2010). The primary aerosols formed by the direct discharge of these pollution sources into the atmosphere and the secondary aerosols formed by the primary pollution in the atmosphere without precursor reaction will form PM2.5 through the primary and secondary reaction pathways respectively (Zhang et al., 2018). To control and reduce the harmful effects of high atmospheric PM2.5 concentration, it is very important to identify the source and control the man-made source.

Chiayi City has the second highest PM2.5 concentration in Taiwan (Tseng et al., 2021), whose 24-h averaging concentrations of PM2.5 could reach 27.2 mg m–3 from 2015 to 2017 observed by Chiayi Air Quality Observation Station. Except for the waste incineration plant, there is no other large-scale industrial emissions in the area under the jurisdiction of Chiayi City, but the concentration of PM2.5 in the air is higher than that in other counties and cities. Due to the high residential density and the gradual establishment and improvement of industrial zones near the county and city, the proportion of commerce in Chiayi City exceeds that of industry, so it is necessary to focus on the impact of commercial activities on air pollution. The night market in Chiayi City is one of the top 10 night-markets in Taiwan with high dense population and there are over 300 night-markets with different sizes in Taiwan, which is even far less than the actual existence (Amesho et al., 2021). The night market is a typical area with high population density and many outdoor emission sources, but the research on PM2.5 in such areas is relatively lacking. From the research on the pollution of outdoor cooking activities in Taiwan night markets, Ngo et al. (2019) found that the mass concentration of PM2.5 reached 28.3 mg m–3, and the organic extracts induced positive genotoxicity in the UMU (UV mutable) test. Zhao and Lin (2010) investigated the PM2.5 level at about 1 to 1.5 meters above the ground with a portable mobile detector of the four major night markets in Kaohsiung City and found that PM10 and PM2.5 exceeded all the limits recommended by the Taiwan Environmental Protection Administration and may have adverse effects on health. An industrial Source Complex Short-Term (ISCST3) air quality model simulating the diffusion of PM2.5 found that the concentration of PM2.5 during the opening period of Kaohsiung night market was 1.6 times higher than usual, and the carbonaceous species were more affected by the opening traffic of the night market (Amesho et al., 2021). Chiayi City is located in the southwest of Taiwan. There are many industrial areas in the north and northwest, and the monsoon is mostly northwest and north wind. The open burning of industrial areas and nearby agriculture may cause PM2.5 pollution sources through long rang transport (Wang et al., 2015). In addition, the high vehicle density of the night market is also a source of contribution (Zhao and Lin, 2010). In complex contribution sources of PM2.5, it is also a challenge to assess the impact degree and perform effective emission reduction control.

This study has two main objectives. The background atmospheric PM2.5 characteristics in a night market and the potential sources were first evaluated by their chemical compositions (carbonaceous species, 8 ions, and 14 metal elements) and chemical mass balance (CMB) model. Second, the spatiotemporal near-ground levels of PM2.5, ultrafine particles, PAH, and BC were analyzed by a mobile monitoring system for more real exposure. Moreover, the effectiveness of traffic restriction was evaluated, as well as other unignorable sources.


2.1 Sample Collection

There are two large night markets in Chiayi City in the downtown and southwest, respectively. To investigate the exposure environment of the PM2.5 and related pollutants on the citizens’ health in urban area, the night market A in downtown area was selected for the current research. The upwind stationary site was located at the north intersection to the night market A, while the post office in the middle of market A was noted as the exposure site (see Fig. 1).

 Fig. 1. The stationary sampling sites and mobile monitoring route around the night market.Fig. 1. The stationary sampling sites and mobile monitoring route around the night market.

Since the night market activities are varied on weekday (Monday–Friday) and weekend (Saturday and Sunday), the samples were collected at both periods of conditions. In addition, there are significant seasonal climate variations at Chiayi City in southern Taiwan. The atmospheric temperature inversion occurs compress the mixing layer height, inhibits the pollutant diffusion, and thus increases their concentration in autumn and winter. On the other hand, more precipitation in summer scavenges the atmospheric PM2.5 and leads to lower concentrations in the ambient air. Therefore, we defined July and November as the low- and high-concentration season, respectively. Therefore, there were four cases in each high and low concentration season: weekdays at upwind (WY-UP), weekdays at exposure (WY-EX), weekends at upwind (WD-UP), weekends at exposure (WD-EX). Due to the time limit of activities in the night market, the samples were only collected during the business hours (18:00–00:00), accumulating 6 hours a day, lasting for 4 days to complete 24 hours of sampling days. Each case collected two samples with 16 samples of total cases. In fixed-point sampling and mobile monitoring, it mainly conducted PM2.5 mass concentration, chemical components (water-soluble ions, carbons and metal elements), UFP number, PAH and BC analysis.

2.2 Sample Analyses

2.2.1 PM2.5 concentrations

During stationary sampling, the samples were collected by two BGI PQ200 suspended particle samplers, which can determine the 24-hour average mass concentration of PM2.5. The filter papers in two samplers were 47 mm PTFE membrane (for analyzing ionic and metal contents) and quartz filter paper (for determining the carbonaceous species), respectively. The PM mass provided from the PTFE filter paper was used for the PM2.5 concentration. The manual sampling complied with the FRM standards: (1) the flow rate is 16.7 L min1, of which 16.7 L min1 is equivalent to the air intake of one hour, (2) the value is measured once every 5 minutes for 24 hours and the average value of the flow rate in 24 hours should be within 5% of the specified value, (3) within 24 h sampling time, the reading of 5 minutes relative standard deviation shall not exceed 2%. The measuring process was non-destructive, and the sample can be used for subsequent physical or chemical analysis. In the mobile detection process, the electric tricycle equipped with GPS high-precision satellite positioning system was selected as the mobile device. The model TSI DustTrak (Model 8530) with a measurable particle size range of 0.1–10 mm was selected for sample collection. The concentration detection limit is 0.001–150 mg m–3 and the sampling flow is 3 L min1. The flow of shear air is 4 L min–1 with sampling frequency of 1 s.

2.2.2 Water soluble ions

There are five cations (NH4+, Ca2+, K+, Mg2+, and Na+) and four anions (F, Cl, SO42, and NO3) analyzed according to the standard method NIEA W415 established by the Environmental Protection Administration, Taiwan. A quarter of the PTFE filter paper was weighted, and then put into the plastic bottle cleaned by ultrasonic vibration of deionized water. The bottle with 10 mL of deionized water was shaken by ultrasonic vibration for 90 minutes to extract the water-soluble ions on the filter paper. After the extraction was completed, the extraction solution was introduced into a 10-mL syringe, filtered with a 0.45 mm filter membrane to remove particles, and then the filtrate was quantitatively measured with an ion-chromatography (Dionex, Model DX-120) in series with a conductivity detector. The standard solution used in our study was the ionic standard solution prepared by Merck company with the concentration of 1000 mg L1, and mixed and diluted by absorbing the same amount of standard solution. There were four samples added for each batch or every ten samples, a blank, a check, a duplicate and an additional sample. The blank analysis values were less than twice the detection limit of the method, when the recovery rate of check and additional samples were 85–115% and 80–120% respectively. Finally, the relative difference percentage of duplicate samples were less than 20%.

2.2.3 Carbonaceous species

The carbonaceous species contents in PM2.5 were analyzed by a conventional thermal analytical instrument, Elementar Vario MIRCO Cube in cooperation with AS200 automatic sampler and DP 700 integrator. The quartz filter papers were heated at 900°C in a high-temperature furnace for 1.5 hours to remove the background carbon impurities before sampling. Before the component analysis, one eighth of the filter paper used to analyze the carbon component was divided into two equal parts, one of which was heated in an oven at 340–345°C for 30 minutes to remove the organic carbon. The other piece of heated and unheated filter paper was cut into about 0.6 mg small pieces, weighed and loaded into tin capsule sample dishes respectively, and the results were recorded. Then, put the treated filter paper together with tin capsule sample dish into the sampler, and measure the carbon composition with an element analyzer. The carbon content measured by the heated filter paper is the elemental carbon content, the total carbon content measured by the unheated filter paper, and the total carbon content minus the elemental carbon content is the organic carbon content.

2.2.4 Trace metal elements

There are 25 metal elements (Li, Be, Na, Ng, Al, K, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Cd, Sb, Ba, Pt, and Pb) quantified following the standard method NIEA W105 established by the Environmental Protection Administration, Taiwan. One quarter PTFE filter paper samples were first pretreated by a microwave digestion process with the concentrated nitric acid and hydrochloric acid (1:3 v/v). Subsequently, the metal contents product solution was determined by a high-resolution inductively coupled plasma mass spectrometry (ICP-MS, Jobin Yvon ULTIMA 2000). The standard solution was used for automatic wavelength search to select the most suitable wavelength for element analysis. At least 5 standard solutions within the concentration range of the analytical sample were prepared to make the calibration curve with the absolute error less than 10%. The recovery rate was checked every 10 samples by an additional standard solution and received the assurance levels raged from 80% to 120%. All blank sample tests indicated low levels less than the limit of detection and ensured the sample purity.

2.2.5 UFP number

Ultrafine particle counter (Model PTRAK 8525) was used to measure UFP quantity. The particles first pass through the saturated column filled with isopropyl alcohol solution and fully mix with it. Then the isopropyl alcohol will be condensed on the particles through the condenser to form larger particles. When large particles pass through laser light, they are counted after being received by optical detector through light reflection. The device can measure the number concentration of particles with a particle size range of 0.02–1 mm, and the concentration measurement range is 0.5–100000 # cm–3. The sampling flow is 0.7 L min1, and the sampling time can be as short as one value per second.

2.2.6 PAH analysis

PAH concentration was measured by Photoelectric Aerosol Sensor (Model PAS2000; EcoChem Analytics., USA). The principle is to use an electric field to ionize the particles in the outer layer of PAHs and release electrons. The concentration of PAH can be obtained by measuring the charge of particles with only positive charges. The concentration measurement range is 0.3–1 mg m–3, and the sampling flow is 2 L min1. Moreover, the shortest sampling time can be 6 s to measure a value.

2.2.7 BC analysis

The BC particle concentration adopted the pocket type particle carbon black detector (microAeth® Model AE51, AethLabs, USA). Its principle is to measure the light absorption intensity of carbon black aerosol through the light path measurement with a wavelength of 880 nm screened by a grating using a light-emitting diode light source. The measuring range of the instrument was 0–1 mg m–3, and the detection limit is 0.001 mg m–3. The sampling flow is 50–150 mL min–1, and the shortest sampling time can be 1 s to measure a value.

2.3 Chemical Mass Balance Model

This study used a receptor model called the CMB to evaluate the source contribution. The model was first proposed by Miller et al. (1972), and formally named as the chemical mass balance method by Cooper and Watson (1980). The CMB model established by this method is the most widely studied and applied receptor model in the practical work of source apportionment of atmospheric particles. It requires speciated profiles of potentially contributing sources and the corresponding ambient data from analyzed samples collected at a single receptor site.

The basic principle of the model is the conservation of mass. It is assumed that there are several emission sources that contribute to the atmospheric particulates in the environmental receptors, and the assumptions are: (1) the chemical composition of the particulates emitted by each source type is significantly different, (2) the chemical composition of the particulates emitted by each source type is relatively stable, there is no interaction between the chemical components, and the changes in the transmission process can be ignored, (3) the component spectra of all pollution sources are linearly independent, (4) the type of pollution source is lower than or equal to the type of chemical component, (5) the measurement uncertainty is random, independent and follows normal distribution.

2.4 Calculation Method of Secondary Pollutants

2.4.1 SOR and NOR of Ionic compositions

The SOR (Sulfur Oxidation Ratio) which is the ratio of sulfate (SO42) and sulfur oxide (SO2) and the NOR (Nitrogen Oxidation Ratio) which is the ratio of nitrate (NO3) and nitrogen oxide (NO2) are used to evaluate the oxidation intensity of SO2 and NO2 in the atmosphere (Colbeck and Harrison, 1984). When the limit of SOR and NOR are 0.25 and 1 respectively, we can judge whether sulfate and nitrate are the local pollution sources, or the secondary pollutants generated by other pollution sources. The calculation formula of SOR and NOR are:


The nss·SO42 is the concentration of non-sea salt sulfate in the atmosphere (mg m–3). SO2 refers to the concentration in the gaseous phase (mg m–3). NO3 and NOxrepresent the nitrate concentration in the particles (mg m–3) and gaseous phase concentration (mg m–3).

2.4.2 SOC in carbonaceous species

The carbonaceous elements in PM2.5 include organic carbon (OC) and elemental carbon (EC). EC is mainly produced by incomplete combustion of fossil fuels or biomass such as wood and directly discharged by pollution sources, while OC includes direct emissions from pollution sources and secondary organic carbon (SOC) generated by hydrocarbons through photochemical reaction. The purpose of quantifying SOC is to pertinently reduce carbonaceous species (EC or OC) concentrations. However, there are real difficulties in directly distinguishing primary organic carbon (POC) from SOC. An indirect method for estimation of SOC has been usually employed using EC as the tracer for POC, since EC is essentially emitted from combustion sources together with primary organic components (Turpin and Huntzicker, 1995), and the equation is:


The (OC/EC)primary refers to the ratio of primary sources contributing to the sample. Most times, (OC/EC)primary was represented by the observed minimum ratio (OC/EC)min, and assumptions regarding the use of this procedure as were discussed by (Castro et al., 1999).


3.1 Atmospheric PM2.5 Concentrations around Night Market

The PM2.5 concentrations at two sampling sites during weekday and weekend were presented at Fig. 2. In high concentration season, the PM2.5 concentrations on average ranged from 25.6 to 58.6 mg m–3. The upwind and exposure sites reported 25.6 and 29.2 mg m–3 of PM2.5, on weekday, and 47.1 and 58.6 mg m–3 on weekend, respectively. The higher concentration may be affected by the increase of background concentration or the heavy traffic on weekends. Moreover, the PM2.5 level was 7.26–17.0 mg m–3 in low concentration season. Amesho et al. (2021) also found the similar concentration of PM2.5 was between 29 and 61 mg m–3 during the opening hours of the night market in Kaohsiung City. In the guideline of World Health Organization (WHO), the PM2.5 concentrations were all higher than the latest AQG (Air Quality Guideline) level of 15 mg m–3 which is also the annual air quality PM2.5 concentration standard set by the Taiwan Environmental Protection Agency (Yang et al., 2017). The PM2.5 concentrations in high concentration season were over 50% higher than the latest AQG level, indicating that the control of PM2.5 emission still has a long way to go.

Fig. 2. The 24 h-accumulated PM2.5 concentrations and compositions in the night market.Fig. 2. The 24 h-accumulated PM2.5 concentrations and compositions in the night market.

The average concentration of PM2.5 in high concentration season was about three times that in low concentration season. For seasonal differences, Juda-Rezler et al. (2020) has reported that in low concentration season, the temperature and wind speed related to the height of the mixing layer were higher, and the precipitation was more frequent, which would more effectively clean the aerosol particles in the atmosphere, resulting in a lower concentration. The concentration of PM2.5 increased from upwind to exposure area, especially the rise of rate was up to 134% on a weekend in low concentration season. The PM2.5 concentration at the upwind on weekend (7.26 mg m–3) was too low even lower than weekday (13.0 mg m–3), resulting in such a significant increase. However, there were various possibilities made the concentration of PM2.5 unstable, including traffic flow, air flow and night market business status, etc. For example, Santosh (2001) has reported that the atmospheric stability in summer was less stable than in winter, which may lead to a large difference in concentration between upwind and the exposure site at a certain time. The average mass ratios of PM2.5 to PM10 (PM2.5/PM10) in high concentration season were higher than low concentration season. The change trend of PM2.5/PM10 ratio was like that of PM2.5 concentration, and from the ratio value the results show that PM2.5 is the main component of PM10. A study of PM2.5 and PM10 ambient levels in Chiayi County showed that the PM2.5 fraction of PM10 accounted for 48% (Lee et al., 2019), relatively lower than the fraction in our study. Zhao and Lin (2010) also found that in the night market, the PM2.5 fraction of PM10 was higher than ambient.

3.2 Chemical Properties of Particulate Matter

3.2.1 Ionic compositions

Our results showed that water soluble ions made up about one third of the PM2.5 mass and the most abundant compositions of water soluble ions were NO3, SO42 and NH4+ (see Fig. 3(a)). Ngo et al. (2019) also found that SO42, NH4+, NO3, were the dominant ionic species in night market samples. In the high concentration season, the ion compositions were relatively not affected by differences of upwind and exposure area, weekdays and weekends. In high concentration season, the average proportion order was NO3 > SO42 > NH4+. While in low concentration season, the most abundant compositions of water-soluble ions were still NO3, SO42 and NH4+, but the average proportion order was SO42 > NH4+ > NO3. This exchange of concentration order occurred because nitrate and sulfate precursors were likely to compete for ammonia (Lei and Wuebbles, 2013).

Fig. 3. The (a) ionic, (b) carbonaceous, and (c) metal compositions of PM2.5 in Night Market.Fig. 3. The (a) ionic, (b) carbonaceous, and (c) metal compositions of PM2.5 in Night Market.

The analysis of the high concentration season indicated that on weekday, the SOR and NOR at the upwind site were 0.155 and 0.064, and at exposure site were 0.154 and 0.067 respectively. On weekend, the derivative potential of sulfate and nitrate was higher, with SOR and NOR of 0.338 and 0.169 at upwind, and 0.318 and 0.153 at exposure site. It was speculated that the secondary air aerosol was mainly affected by the overall atmospheric background. Our sampling season was winter, which was dominated by secondary aerosols, leading to this high derivatization. However, in the low concentration season, the potential of secondary salts on weekday at upwind (SOR: 0.281; NOR: 0.020) and exposure (SOR: 0.290; NOR: 0.027) site and weekend at the same upwind (SOR: 0.135; NOR: 0.007) and exposure (SOR: 0.107; NOR: 0.006) site was completely opposite to that of high concentration season. Tseng et al. (2016) has also found that the lower SOR and NOR values in summer which indicated the lower potential for secondary formation of NO3 and SO42 than winter. Previous study has reported that losing NH4NO3 under high temperature led to low nitrogen abundance (Wu et al., 2015), so the NOR value in summer was low. In our study, at the exposure site on weekend in summer, no NH4+ was detected, which may contribute to favorable conditions of NOR.

3.2.2 Carbonaceous species

As shown in Fig. 3(b), the minimum of SOC/OC was 4% occurring on weekdays at exposure to high concentration season. The maximum value was 58.2%, and the difference with minimum was the upwind location. Compared to the modeling results by CMB, the SOC/OC percentage at Nanzi, Daliao, and Chaozhou in Taiwan was 22.9%, 15.4%, 17.6% respectively (Shen et al., 2020), but our data was much higher, which may be attributed to the special emissions in the night market area. Que et al. (2019) have detected a carbonyl compound concentration of up to 1840 ppb in the edible oil smoke from the night market stall exhaust gas. Moreover, the transformation of VOCs emitted from the traffic exhausts in the night market may also contribute to secondary aerosols in PM2.5 (Sato et al., 2010).

3.2.3 Metal compositions

Among the determined metallic elements, the order of top four concentration proportions was K > Na > Fe > Zn (see Fig. 3(c)). In the high concentration season, the proportion of metal elements has changed significantly, mainly because the K element has increased from 34.1% to 52.7%, while in the low concentration season, the element composition was relatively stable. There was no significant change in the element composition at the upwind and exposure site. High concentrations of K, Na and Fe are all indicative elements of crustal dust (Yang et al., 2005). Moreover, the sources of K include dust, combustion sources, and automobile emissions (Hsu et al., 2004; Song et al., 2006). Rosales et al. (2021) has found that K+, Cl, Na+ constituted the major ionic mass concentration in the dilution chamber test of cooking activities of the carbon burning cement stove and kerosene burning metal stove. Zhang et al. (2017) reported that the usage of salt would cause high Na concentration in PM2.5 emissions, while there were many open-air barbecues and snack stalls, and the air was filled with the smell of lampblack in the night market area. Fe, Zn would also be released with the cookers of raw materials during the cooking process. The study of in a typical Chinese food stall using gas stove to stir-frying found that from non-cooking to cooking, the mass concentration of Na increased by 20 times, while the mass concentration of K also increased by over 10 times (See and Balasubramanian, 2006). Zn is usually used as motor oil additive, tire manufacturing and brake lining, so the high emission of Zn may be due to automobile exhaust and wear of brakes and tires (Thorpe and Harrison, 2008; Gonzalez et al., 2017).

3.3 PM2.5 Source Apportionment

The pollution source of fine suspended particles using CMB 8.2 were summarized in Fig. 4. In all sampling cases, the main pollution source was traffic source emission, followed by secondary nitrate, secondary sulfate and soli dust. The species contributing to the traffic sources included OC, EC, SO42, NH4+, K, Na, Fe, and Zn (Zíková et al., 2016), which was consistent with our chemical analysis in front. The results of CMB calculation showed that on weekends, the contribution of traffic source at exposure site was 2.2% higher than upwind, while the contribution of secondary aerosols (secondary sulfate and nitrate) was 3.6% higher than upwind. However, on weekdays, there was no significant difference in the contribution of traffic and secondary aerosols, indicating that the night market area on weekdays had no significant contribution in the area emission due to fewer tourists, which was subsequently corroborated by the monitoring results of regional hot spots.

Fig. 4. The atmospheric PM2.5 contribution rates from various sources.Fig. 4. The atmospheric PM2.5 contribution rates from various sources.

The wind track at the sampling point of Chiayi Station (see Fig. 5) showed that the northeast monsoon prevailed in winter. On the one hand, the high concentration was due to the low mixing height and poor dispersion in winter (Lotteraner and Piringer, 2016), and the local crustal materials may be resuspended into the atmosphere under strong winds (Hsu et al., 2016). On the other hand, it may cause by long-distance transportation. The pollutants can flow southward from the sea of Yilan City through Taichung City and enter the urban area of Chiayi. In the study of the cross-regional transport of secondary particles in Weihai area, it was also found that the prevalence of the monsoon was conducive to the cross-regional transport of pollutants (Ning et al., 2021). While in the low concentration season, southeast wind prevailed in the city, and the atmospheric mixed layer was relatively high. The main source of PM2.5 may be long-distance transportation. For example, the particulate matter emitted by the Kaohsiung power industry existed in the polluted air mass flowing from the outer sea of Kaohsiung to the north and reached the Chiayi City after long-distance transmission.

Fig. 5. The 24 h wind trajectories pushed back on (a)weekdays in high concentration season (b) weekends in high concentration season (c) weekdays in low concentration season (d) weekends in low concentration season.Fig. 5. The 24 h wind trajectories pushed back on (a)weekdays in high concentration season (b) weekends in high concentration season (c) weekdays in low concentration season (d) weekends in low concentration season.

3.4 Mobile Monitoring

In the high concentration season, the mass concentration of PM2.5 before restriction 1 h was 84 mg m–3, and NG-UFP number was about 7.6 × 104 # cm–3 (see Fig. 6). The NG-PAH and NG-BC concentrations were 33.1 and 2316 ng m–3, respectively. The PM2.5 mass concentration under restriction 1 h and 3 h was 100 and 102 mg m–3, with 19.0% and 21.4% more than before restriction, respectively. In addition, the growth rate of BC decreased with restriction 1h to 3h, from 8.81% 6.26%. However, the under restriction 3h concentrations of NG-UFP number and NG-PAH were 5.0 × 104 #·cm–3 and 15.2 ng m–3, which were lower than all those before restriction and under restriction 1h and reached a statistically significant difference (P < 0.001). The spatial distribution map before and after restriction showed that the mass concentration of NG-PM2.5 was about 50–160 mg m–3 when no control measures were implemented. Some peaks of NG-UFP number concentration appeared in some sections, and the maximum can reach 80000–100000 # cm–3. However, under restriction, the NG-PM2.5 mass concentration was still between 12 and 80 mg m–3, and more obviously there was no significant peak of number concentration (40000–60000 # cm–3), indicating that the limitation of cars and motorcycles was necessary in the night market.

Fig. 6. Near-ground (a) temporal analysis of pollutant concentrations and (b) the spatial hotspot distributions of PM2.5 mass and UFP number concentration in higher concentration season.Fig. 6. Near-ground (a) temporal analysis of pollutant concentrations and (b) the spatial hotspot distributions of PM2.5 mass and UFP number concentration in higher concentration season.

In the low concentration season (see Fig. 7), the mass concentration of PM2.5 before restriction was 29 mg m–3, and the NG-UFP number concentration was about 3.8 × 104 # cm–3. The NG-PAH and NG-BC concentration were 18.5 and 2030 ng m–3, respectively. However, the concentration of NG-PM2.5, UFP number, NG-PAH and NG-BC under 1-h restriction all increased by 39.5% to 171%. Under restriction 3 h, the concentrations of NG-PM2.5 and NG-BC continued to rise, reaching 67 mg m–3 and 5094 ng m–3, respectively. Differently, the concentrations of NG-UFP number and NG-PAH decreased compared with under restriction 1 h, and their values were 4.8 × 104 # cm–3 and 38.3 ng m–3 respectively, which was still higher than that under before restriction.

Fig. 7. Near-ground (a) temporal analysis of pollutant concentrations and (b) the spatial hotspot distributions of PM2.5 mass and UFP number concentration in lower concentration season.Fig. 7. Near-ground (a) temporal analysis of pollutant concentrations and (b) the spatial hotspot distributions of PM2.5 mass and UFP number concentration in lower concentration season.

Table 1 compares the various monitoring conditions in the more and low concentration seasons and finds that all kinds of pollutants have significantly increased without traffic restriction. In the high concentration season, the concentration of NG-UFP and NG-PAH were 19.7–34.2% and 54.1–55.6% reduced, respectively, while the concentrations of NG-PM2.5 and NG-BC still increased with time after the start of traffic restriction and night market activity. This phenomenon might because near-ground atmosphere temperature is lower than that of the upper atmosphere in cold season, stabilize atmospheric structure, inhibit the vertical convection, and amplify the effectiveness of removal of NG-emission. However, the traffic control effect was not obvious in the low concentration season. The concentration of all pollutants still increased with time compared with that before restriction. Because the temperature is relatively higher in summer, the ground is a heat source to the atmosphere and drives the convection and pollutant diffusion. Therefore, the traffic control had less effect on the NG-pollutant level since there were other larger emissions, e.g., cooking, in the night market. The study of PAH inhalation at night market in Taiwan found that during cooking hours, total PAHs in the gas phase and PM2.5 ranged from 233995 to 44166 ng m–3, which was much higher than other time (Zhao et al., 2011).

Table 1. The changes of near-ground pollutant levels around the night market after traffic restriction.


The concentration of PM2.5 in the night market area was 7.26–58.6 mg m–3, which composed of 28.3 ± 13.0% ions, 15.5 ± 5.2% carbonaceous species, and 5.8 ± 2.4% metals. The main water-soluble ionic components were SO42, NH4+, NO3, and the SOR and NOR in low concentration season were lower. The secondary content accounts for approximately 60% of the PM2.5 mass, meaning that long range transportation was very important for the background air quality of such a densely populated city. The high content of Na, Cl and K in metal elements highlighted the contribution of night market cooking activities to PM2.5. The local traffic contribution rate was approximately 30%, but during the traffic restriction period, there was no significant difference between the upwind or exposure areas. During the mobile monitoring, the whole PM2.5 concentration increased with time, but implementing traffic restrictions reduced the increment amplitude of NG-UFP, NG-PAH and NG-BC. It was noteworthy that in the high concentration season, the reduction rate of NG-UFP and NG-PAH can reach 27% and 55% respectively. However, the restricted NG-PM2.5 still increased to 131%. The reduction of major mobile emissions (NG-UFP, NG-PAH and NG-BC) verified the effectiveness of traffic restrictions. In addition, the cooking activities in the night market also led to the concentration of pollutants, especially PM2.5, which should be a part of public exposure and health.


The authors acknowledge the Environmental Protection Bureau of Chiayi City in Taiwan for the financial support under project No. 1050944. The authors also appreciate the professional consultation from Prof. Guo-Ping Chang Chien and the technical supports by Mrs. Tzu-Ying Wu, Ms. Ya-Jing Fu, and Mr. Kun-Hui Lin in the Center for Environmental Toxin and Emerging-contaminant Research (CENTER), Cheng Shiu University, Taiwan.


  1. Amesho, K.T.T., Lin, Y.C., Gong, Z.W., Chou, F.C., Cheng, P.C., Wang, T.N., Chen, P.S., Chen, K.S., Chang, K.L., Lee, C.H. (2021). Assessment of PM2.5 chemical composition and air quality monitoring: Implications of air pollutants emissions from a night market in Kaohsiung City, Taiwan. Aerosol Air Qual. Res. 21, 210206.

  2. Cassee, F.R., Heroux, M.E., Gerlofs-Nijland, M.E., Kelly, F.J. (2013). Particulate matter beyond mass: Recent health evidence on the role of fractions, chemical constituents and sources of emission. Inhalation Toxicol. 25, 802–812.

  3. Castro, L.M., Pio, C.A., Harrison, R.M., Smith, D.J.T. (1999). Carbonaceous aerosol in urban and rural European atmospheres: Estimation of secondary organic carbon concentrations. Atmos. Environ. 33, 2771–2781.

  4. Cayetano, M.G., Hopke, P.K., Lee, K.H., Jung, J., Batmunkh, T., Lee, K., Kim, Y.J. (2014). Investigations of the particle compositions of transported and local emissions in Korea. Aerosol Air Qual. Res. 14, 793–790.

  5. Chow, J.C., Watson, J.G. (2002). Review of PM2.5 and PM10 apportionment for fossil fuel combustion and other sources by the chemical mass balance receptor model. Energy Fuels 16, 222–260.

  6. Colbeck, I., Harrison, R.M. (1984). Ozone—secondary aerosol—visibility relationships in North-West England. Sci. Total Environ. 34, 87–100.

  7. Cooper, J.A., Watson, J.G. (1980). Receptor oriented methods of air particulate source apportionment. J. Air Pollut. Control Assoc. 30, 1116–1125.​00022470.1980.10465157

  8. De Marco, A., Amoatey, P., Khaniabadi, Y.O., Sicard, P., Hopke, P.K. (2018). Mortality and morbidity for cardiopulmonary diseases attributed to PM2.5 exposure in the metropolis of Rome, Italy. Eur. J. Intern. Med. 57, 49–57.

  9. Fang, G.C., Huang, Y.L., Huang, J.H. (2010). Study of atmospheric metallic elements pollution in Asia during 2000-2007. J. Hazard. Mater. 180, 115–121.​2010.03.120

  10. Gao, Y., Nelson, E.D., Field, M.P., Ding, Q., Li, H., Sherrell, R.M., Gigliotti, C.L., Van Ry, D.A., Glenn, T.R., Eisenreich, S.J. (2002). Characterization of atmospheric trace elements on PM2.5 particulate matter over the New York-New Jersey harbor estuary. Atmos. Environ. 36, 1077–1086.

  11. Gonzalez, L.T., Rodriguez, F.E.L., Sanchez-Dominguez, M., Cavazos, A., Leyva-Porras, C., Silva-Vidaurri, L.G., Askar, K.A., Kharissov, B.I., Chiu, J.F.V., Barbosa, J.M.A. (2017). Determination of trace metals in TSP and PM2.5 materials collected in the Metropolitan Area of Monterrey, Mexico: A characterization study by XPS, ICP-AES and SEM-EDS. Atmos. Res. 196, 8–22.

  12. Hsu, C.Y., Chiang, H.C., Lin, S.L., Chen, M.J., Lin, T.Y., Chen, Y.C. (2016). Elemental characterization and source apportionment of PM10 and PM2.5 in the western coastal area of central Taiwan. Sci. Total Environ. 541, 1139–1150.

  13. Hsu, S.C., Liu, S.C., Lin, C.Y., Hsu, R.T., Huang, Y.T., Chen, Y.W. (2004). Metal compositions of PM10 and PM2.5 aerosols in Taipei during Spring, 2002. Terr. Atmos. Ocean. Sci. 15, 925–948.

  14. Juda-Rezler, K., Reizer, M., Maciejewska, K., Blaszczak, B., Klejnowski, K. (2020). Characterization of atmospheric PM2.5 sources at a central European urban background site. Sci. Total Environ. 713, 136729.

  15. Kim, J.B., Prunicki, M., Haddad, F., Dant, C., Sampath, V., Patel, R., Smith, E., Akdis, C., Balmes, J., Snyder, M.P., Wu, J.C., Nadeau, K.C. (2020). Cumulative lifetime burden of cardiovascular disease from early exposure to air pollution. J. Am. Heart Assoc. 9, e014944.​10.1161/jaha.119.014944

  16. Lee, B.J., Kim, B., Lee, K. (2014). Air pollution exposure and cardiovascular disease. Toxicol. Res. 30, 71–75.

  17. Lee, Y.Y., Hsieh, Y.K., Chang-Chien, G.P., Wang, W.W. (2019). Characterization of the air quality index in Southwestern Taiwan. Aerosol Air Qual. Res. 19, 749–785.​aaqr.2019.02.0080

  18. Lei, H., Wuebbles, D.J. (2013). Chemical competition in nitrate and sulfate formations and its effect on air quality. Atmos. Environ. 80, 472–477.​08.036

  19. Lin, Y.C., Li, Y.C., Amesho, K.T.T., Chou, F.C., Cheng, P.C. (2020). Filterable PM2.5, metallic elements, and organic carbon emissions from the exhausts of diesel vehicles. Aerosol Air Qual. Res. 20, 1319–1328.

  20. Lotteraner, C., Piringer, M. (2016). Mixing-height time series from operational ceilometer aerosol-layer heights. Boundary Layer Meteorol. 161, 265–287.​s10546-016-0169-2

  21. Miller, M.S., Friedlander, S.K., Hidy, G.M. (1972). A chemical element balance for the Pasadena aerosol. J. Colloid Interface Sci. 39, 165–176.

  22. Mutuku, J.K., Hou, W.C., Chen, W.H. (2020). Two-phase flow dynamics and PM2.5 deposition in healthy and obstructed human airways during inhalation. Aerosol Air Qual. Res. 20, 1094–1110.

  23. Ngo, T.H., Tsai, P.C., Ueng, Y.F., Chi, K.H. (2019). Cytotoxicity assessment of PM2.5 collected from specific anthropogenic activities in Taiwan. Int. J. Environ. Res. Public Health 16, 5043.

  24. Ning, W.T., Xing, Z.W., Wei, M., Li, M.Y., Zhang, M.G., Liu, H.F. (2021). Study on the influence of regional transportation on PM2.5 based on the RAMS-CMAQ model in Weihai, a typical coastal city of northern China. Aerosol Air Qual. Res. 21, 200266.

  25. Que, D.E., Chao, H.R., Hsu, Y.C., Cui, K.P., Chen, S.D., Tayo, L.L., Arcega, R.D., Tsai, Y.I., Lu, I.C., Wang, L.C., Young, L.H., Yu, K.L.J., Lai, C.Y., Hou, W.C., Lin, S.L. (2019). Emission of carbonyl compounds from cooking oil fumes in the night market areas. Aerosol Air Qual. Res. 19, 1566–1578.

  26. Rosales, C.M.F., Jung, J.S., Cayetano, M.G. (2021). Emissions and chemical components of PM2.5 from simulated cooking conditions using traditional cookstoves and fuels under a dilution tunnel system. Aerosol Air Qual. Res. 21, 200581.

  27. Santosh, B.C.J. (2001). A Methodology to Determine Minimum Stack Height for Thermal Power Plants Based on Dispersion Capacity. Thesis, Indian Institute of Technology Kanpur, India.

  28. Sato, K., Takami, A., Isozaki, T., Hikida, T., Shimono, A., Imamura, T. (2010). Mass spectrometric study of secondary organic aerosol formed from the photo-oxidation of aromatic hydrocarbons. Atmos. Environ. 44, 1080–1087.

  29. See, S.W., Balasubramanian, R. (2006). Risk Assessment of exposure to indoor aerosols associated with Chinese cooking. Environ. Res. 102, 197–204.

  30. Shen, H.Z., Yang, T.M., Lu, C.C., Yuan, C.S., Hung, C.H., Lin, C.T., Lee, C.W., Ling, G.H., Hu, G.R., Lo, K.C. (2020). Chemical fingerprint and source apportionment of PM2.5 in highly polluted events of southern Taiwan. Environ. Sci. Pollut. Res. 27, 6918–6935.

  31. Song, Y., Zhang, Y., Xie, S., Zeng, L., Zheng, M., Salmon, L.G., Shao, M., Slanina, S.J.A.E. (2006). Source apportionment of PM2.5 in Beijing by positive matrix factorization. Atmos. Environ. 40, 1526–1537.

  32. Thorpe, A., Harrison, R.M. (2008). Sources and properties of non-exhaust particulate matter from road traffic: A review. Sci. Total Environ. 400, 270–282.​2008.06.007

  33. Tseng, C.H., Chen, L.L., Tseng, G.H. (2021). Applying low-cost air-pollution sensors’ information to explore PM2.5 concentration with an emphasis on spatial and temporal analysis: A case in Chiayi city. Res. Sq.

  34. Tseng, C.Y., Lin, S.L., Mwangi, J.K., Yuan, C.S., Wu, Y.L. (2016). Characteristics of atmospheric PM2.5 in a densely populated city with multi-emission sources. Aerosol Air Qual. Res. 16, 2145–2158.

  35. Turner, M.C., Krewski, D., Pope, C.A., Chen, Y., Gapstur, S.M., Thun, M.J. (2011). Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never-smokers. Am. J. Respir. Crit. Care Med. 184, 1374–1381.

  36. Turpin, B.J., Huntzicker, J.J. (1995). Identification of secondary organic aerosol episodes and quantitation of primary and secondary organic aerosol concentrations during SCAQS. Atmos. Environ. 29, 3527–3544.

  37. Wang, L.L., Liu, Z.R., Sun, Y., Ji, D.S., Wang, Y.S. (2015). Long-range transport and regional sources of PM2.5 in Beijing based on long-term observations from 2005 to 2010. Atmos. Res. 157, 37–48.

  38. Wang, Y.F., Xiao, S.Y., Zhang, Y.H., Chang, H., Martin, R.V., Van Donkelaar, A., Gaskins, A., Liu, Y., Liu, P.F., Shi, L.H. (2022). Long-term exposure to PM2.5 major components and mortality in the southeastern United States. Environ. Int. 158, 106969.​106969

  39. Wu, S.P., Schwab, J., Yang, B.Y., Zheng, A., Yuan, C.S. (2015). Two-years PM2.5 observations at four urban sites along the coast of southeastern China. Aerosol Air Qual. Res. 15, 1799–1812.

  40. Yang, F.M., Ye, B.M., He, K.B., Ma, Y.L., Cadle, S.H., Chan, T., Mulawa, P.A. (2005). Characterization of atmospheric mineral components of PM2.5 in Beijing and Shanghai, China. Sci. Total Environ. 343, 221–230.

  41. Yang, T.T., Hsu, C.Y., Chen, Y.C., Young, L.H., Huang, C.H., Ku, C.H. (2017). Characteristics, sources, and health risks of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons in Hsinchu, Taiwan. Aerosol Air Qual. Res. 17, 563–573.

  42. Yu, L.D., Wang, G.F., Zhang, R.J., Zhang, L.M., Song, Y., Wu, B.B., Li, X.F., An, K., Chu, J.H. (2013). Characterization and source apportionment of PM2.5 in an urban environment in Beijing. Aerosol Air Qual. Res. 13, 574–583.

  43. Zhang, J., Yang, L., Mellouki, A., Chen, J., Chen, X., Gao, Y., Jiang, P., Li, Y., Yu, H., Wang, W. (2018). Diurnal concentrations, sources, and cancer risk assessments of PM2.5-bound PAHs, NPAHs, and OPAHs in urban, marine and mountain environments. Chemosphere 209, 147–155.

  44. Zhang, N., Han, B., He, F., Xu, J., Zhao, R.J., Zhang, Y.J., Bai, Z.P. (2017). Chemical characteristic of PM2.5 emission and inhalational carcinogenic risk of domestic chinese cooking. Environ. Pollut. 227, 24–30.

  45. Zhao, P., Lin, C.C. (2010). Air quality at night markets in Taiwan. J. Air Waste Manage. Assoc. 60, 369–377.

  46. Zhao, P., Yu, K.P., Lin, C.C. (2011). Risk assessment of inhalation exposure to polycyclic aromatic hydrocarbons in Taiwanese workers at night markets. Int. Arch. Occup. Environ. Health 84, 231–237.

  47. Zíková, N., Wang, Y.G., Yang, F.M., Li, X.H., Tian, M., Hopke, P.K. (2016). On the source contribution to Beijing PM2.5 concentrations. Atmos. Environ. 134, 84–95.​atmosenv.2016.03.047

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