Field Study on Online Monitoring Network of Air Toxics and

Tracking near a Petrochemical Industrial Park

Yu-Cheng Chen, Chin-Yu Hsu, Yue-Liang Leon Guo

National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan

A field study on onsite-continuous monitoring networks of air toxics was performed for

exposure assessment and source tracking near a petrochemical industrial park with mixtures of

manufacturing facilities and residential, urban and rural areas. The automatic monitoring networks of

air toxics utilizing high-performance portable VOC analyzers and anemometers were deployed at four

sites. More than 14,000 data sets were collected from the three-month continuous onsite monitoring.

The source directions were observed using the receptor model in cooperating with ambient VOCs and

wind information in each monitoring site. Our developed monitoring technique can be used to capture

the peak concentration of targeted VOCs and identity the geographical directions of pollutants, which

serves the purpose of exposure assessment of air toxics in the community level and tracking pollutants

for emission reduction.

INTRODUCTION

Air quality issues to public health have been attracting concerns globally. According to a World

Health Organization (WHO) report published in 20161, more than 90% of the world’s population is

exposed to air pollutants exceeding recommended limits. Air pollution is mainly from anthropogenic

sources such as traffic exhausts and emissions from various industrial manufacturing processes.

Among air pollutants, volatile organic compounds (VOCs) are one of the primary contributors to

PM2.5 and the formation of photochemical oxidants (monitored by photochemical assessment

monitoring station [PAMS]). Some VOCs are classified as Air Toxics (also known as Hazardous Air

Pollutants [HAPs]), which are associated with adverse effects on human health, including cancer,

reproductive effects, and respiratory illness, even under trace level exposure2. Air Toxics with various

compounds are still measured by offline canister sampling in accordance with laboratory toxic organic

(TO) methods, since diversified local specific sources make online monitoring challenging. A couple

field studies at industrial parks based on the offline method combined with cross-analysis of the

inventory data base were able to depict background VOC concentrations as well as abundant types of

VOCs3,4. However, lacking temporal resolution of the offline method makes it difficult to capture a

full picture of the emission status; especially for random emissions, such as fugitive emissions,

equipment leaks, emissions from malfunctioning processes, which usually generate air pollutant with a

high level.

Recently, an on-site continuous VOC monitoring network along fencelines of a mega steel

manufacturing plant was successfully demonstrated to be a feasible solution enabling effective

pollution source identification, earlier leakage detection, evaluation of VOC reduction control, and

precise exposure assessment5. In this study, the monitoring scope is extended to cover bigger regions

with mixtures of industrial parks, residential areas, urban and rural areas, which presents more

challenges for monitoring technique due to diverse sources, compounds, geography and climate.

EXPERIMENTAL METHODS Site Selection and System Setup for Monitoring Network

The Renda Petrochemical Industrial Park (RPIP) in southern Taiwan consists of over thirty

industrial plants and four waste water treatment facilities. Four sampling sites were selected to monitor

ambient VOCs using a continuous online air toxics monitoring network as shown in Figure 1. Each site

represents its unique geographic location in corresponding to the Industrial Park. Site A is located 3.6

km west of the RPIP. In this study, the site was influenced by winds from sea during winter season.

Site B is located only 1.6 km south of RPIP, which likely receives the highest VOC levels from the

Industrial Park. Site C is located at 30 km north-east of RPIP. The location is far away from both the

industrial park and metropolitan cities. Site C served as reference site (rural area), in which

background VOCs were more related to the natural environment from local farms and forests. Site D is

located in a metropolitan city 8.8 km south of RPIP with several small manufacturing facilities nearby.

As a result, it may receive combination of VOCs from RPIP, traffic emission, and the local

manufacturing facilities.

Each monitoring site was equipped with an anemometer on the roof of the building and a stand-

alone chassis with the online toxic VOCs monitoring system on the ground, as shown in Figure 1. The

online toxic VOCs chassis system consists of an automatic gas chromatography (Auto-GC) analyzer

MiTAP P310 (Tricorntech Corp., Taipei, Taiwan) capable of continuously measuring fourteen specific

VOCs related to industrial and traffic emissions, including Vinyl Chloride Monomer (VCM) , 1,3-

Butandiene, Benzene, Toluene, Ethylbenzene, m,p-Xylene, o-Xylene, Acetone, Propene, Ethyl

Acetate, 2-Butanone, 1,2-Dichlorobenzene and 1,4-Dichlorobenzene. The MiTAP system was

connected with a sampling tube extending to the roof for VOC collection at the same location as the

anemometer. In addition, the chassis was equipped with an automatic calibration system (ACS), which

served as a regular quality check for the MiTAP analyzer. Finally, a mini-computer was used to collect

the VOC analysis results from MiTAP and wind field information from the anemometer. In this study,

the automatic air toxic monitoring system was setup to output concentration of targeted 14 VOCs and

corresponding wind field data for every 30 minutes. The full data set was then uploaded to the cloud

through 4G wireless communication as shown in Figure 1. Meanwhile, canister field samples were also

manually collected weekly at each location and analyzed by both MiTAP and lab GC/MS for parallel

comparison.

Figure 1. Locations and site setups of online air toxics monitoring network.

Quality Assurance

Prior to onsite deployment, the MiTAP auto-GC analyzer was calibrated with targeted standard

VOC gases, with concentrations ranging from 2 ppbv to 50 ppbv respectively to ensure system

performance as results depicted in Figure 2 (a). To ensure data quality during monitoring period, the

ACS system was used to automatically perform daily checks on MiTAP response with the same

targeted standard VOC gas mixture (10 ppbv) as the results shown in Figure 2 (b). The system resumed

field sample monitoring once passing the ACS quality test. Under the circumstance that the on-site

ACS check result is out of targeted specification (e.g., > 30% concentration deviation), a notification

message will be sent to designated personnel via internet for immediate response.

Figure 2. Illustration of quality checks. a) In-house calibration, b) On-site daily auto check with ACS.

RESULTS

In this study, a total of 14,765 data sets were collected from the monitoring network (Nov. 1,

2016 ~ Feb. 28, 2017). Figure 3 shows the three-month average concentrations of targeted VOCs

measured from four sites respectively. The average concentrations for most VOCs are below 2 ppbv,

which is comparable to weekly off-line GC-MS measurements with canister samplings. Slightly higher

concentrations of industrial specific VOCs were observed at Sites A and B near the industrial park as

compared to Site C (reference area). However, since random or unexpected VOC emissions may

occur only within a day or several hours from a nearby industrial sources, the average concentration

measure tends to mask the importance of actual VOC excursion events.

Figure 3. Three-month average concentrations of VOCs as measured from 4 different sites.

In contrast to the average concentration measure (which leads the similar conclusions from the

off-line method), the advanced analysis with a temporal concentration trends combining information

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from continuous monitoring data, all individual targeted VOCs, wind speed/direction, and network

layout, is able to depict more comprehensive patterns of ambient air pollutants regarding source,

location, transportation, background level, and even episode. Here, Figure 4 illustrates several

examples observed in various scenarios.

Figure 4(a) shows that the acetone concentration at reference Site C has a periodic fluctuation

in concentrations with consistently higher values during daytime (~2 ppbv) than nighttime (~1 ppbv).

It is suspected due to natural environment emissions from plants, trees, or the breakdown of organics

due to higher temperatures during the daytime. Although there is a slightly higher concentration for

acetone at other sites, no apparent periodic trend is observed, possibly due to random local emission

activities. Figure 4(b) shows that an excursion of the high concentration for VCM (~20 ppbv) occurred

during the evening of Dec. 13 at Site B, which is closest to the industrial park. The same VCM

excursion can also be found at Site A with a relatively lower concentration (~7 ppbv). Since the

excursion occurs in the early evening, the west wind at Site A could have potentially assisted pollutant

propagation to this location. Figures 4(c) and (d) show several events of propene and 1,3-butadien

excursions at both Sites B and D. The wind field at Site B is mostly from the northwest, with

significantly higher concentrations than those observed by Site D in a farther south location, indicating

that the emission sources are from the Industrial Park in the north of Site B. Figures 4(e) and (f) show

the temporal concentration trends of benzene and toluene, which are associated with both industrial

and traffic emissions. Site C shows much lower concentrations compared to other sites, which is due to

limited human activity at the corresponding location. Sites B and D show higher concentrations but do

not present apparent correlation between the locations. The benzene at Site D tends to have higher

concentrations during early morning and evening, suggesting more traffic emission is relevant. In

addition, Site B shows random higher concentrations during different times, indicating the combination

of industrial and traffic emission sources.

Figure 4. Example of VOC concentration excursions observed by the monitoring network.

The receptor model in cooperation with VOC concentration and wind-rose analysis is used to

trace the emission sources as illustrated in Figure 5. It can be seen that VMC with the highest

concentration at Site B was measured in the north, clearly indicating the emission source is directly

from the north facility, inside the industrial park. While VCM with a relatively lower concentration

was also observed at Site D, the wind-rose plot shows local high concentrations from different wind

directions. It is suspected that the emission sources are from local manufacturing plants near Site D.

For the case of 1,3-butadien, the highest concentration is also obtained from Site B. However, the

wind-rose shows that 1,3-butadien is from a north-north west direction, indicating a different emission

source from the VCM plant inside the industrial park. The benzene concentration shows the similar

result as 1,3-butadien observed at Site B, indicating the benzene concentration at Site B is dominated

by industrial emission from the industrial park. On the contrary, the benzene concentrations measured

at the other three sites do not show apparent emission sources from industrial park, which suggests that

benzene is dominated by local activities such as traffic emission.

Figure 5. Concentration wind-rose plots for toxic VOCs source tracking (Receptor model).

SUMMARY

An online continuous toxic VOC monitoring network over a large region of mixed industrial

and residential areas is demonstrated to be effective in providing comprehensive status for emission

patterns, source tracking, excursion types, general background concentration, and transportation. With

objective selections of site locations and VOC species, the monitoring network provides solutions,

enabling effective assessments of human exposure to air toxic pollution as well as air toxic impacts on

the environment.

ACKNOWLEDGMENT

This study was funded under the project No. EM-105-PP-13 supported by the National Institute

of Environmental Health Sciences, National Health Research Institutes (NHRI) in Taiwan. We would

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also thank the TricornTech Corporation (Ching-Lin Hsiao, Li-Peng Wang, and Tsung-Kuan A. Chou)

supporting the monitoring network with a MiTAP auto-GC analyzer.

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