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,

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., , 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.

3.00 )

2.50 ppbv 2.00

1.50

1.00

Concentration ( Concentration 0.50

0.00 Site A Site B Location Site C Site D

Benzene Toluene Ethylbenzene mp-Xylene o-Xylene Acetone Propene VCM 1,3-Butadiene 2-butanone Ethyl Acetate 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 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.

max N max max N N max Conc Conc Conc N Conc 5 5 3 Speed 3 . 1.4 . . 1.4 Speed . 4 VCM 2.5 2.5 4 1,3-Butadiene 1 1.2 1 1.2 2 3 WS 2 3 WS 1.5 WS 1.5 WS 0.8 2 1 2 1 0.8 1 1 1 1 W E W 0.5 E 0.8 0.5 W E 0.8 W E 0.6 Meinong 0.6 0.6 0.6 (Site C) 0.4 0.4 (Site C) 0.4 0.4 0.2 0.2 0.2 0.2

0 0 0 Nanzi District 0 S Nanzi District S (Site A, 3.6km) VCM S 1.3-Butadiene S VCM 1.3-Butadiene (Site A, 3.6km)

max max N N max N max Conc Renda Industrial Park Conc N Conc Conc From Park Renda Industrial Park Renwu District 4 . 6 . > 1.4 1.4 6 . 4 . From Park (Site B, 1.6km) Renwu District 5 5 3 WS > 1 (Site B, 1.6km) 3 WS 1 1.2 1.2 4 Fengshan District 4 3 WS (Site D, 8.8km) 2 3 WS Fengshan District 2 1 1 0.8 0.8 2 2 (Site D, 8.8km) 1 1 1 W E 0.8 W E 0.8 W 1 E W E 0.6 0.6

0.6 0.6 0.4 0.4 0.4 0.4

0.2 0.2 0.2 0.2

0 0 0 0

S S S S VCM VCM 1.3-Butadiene 1.3-Butadiene max N max N Conc 5 2 Speed Conc 2 3 . 4 . Benzene 2.5 3 WS 1.5 2 1.5 2 1.5 WS

1 1 W E 1 W 0.5 E 1 Meinong District (Site C) 0.5 0.5

0 0 S Benzene Nanzi District S Benzene (Site A, 3.6km)

max N max Conc N Conc From6 Park > 2 Renda Industrial Park . 2 5 Renwu District 4 .

4 (Site B, 1.6km) 3 WS 1.5 3 WS Fengshan District 1.5 2 2 (Site D, 8.8km)

1 1 W E 1 W E 1

0.5 0.5

0 0 S Benzene S Benzene 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 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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