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1 January 2020 Prediction of Ambient Nitrogen Dioxide Concentrations in the Vicinity of Industrial Complex Area, Thailand
Supitchaya Tunlathorntham, Sarawut Thepanondh
Author Affiliations +
Abstract

The AERMOD dispersion model was evaluated for its performance in predicting 1-hour average nitrogen dioxide (NO2) concentrations in the vicinity of the largest petrochemical industrial complex in Thailand during the period between January 2012 and December 2013. Measured data from 10 ambient air monitoring stations were intensively used to compare with modeled results. Model results indicated that the tier 1 approach (full conversion of NOx to NO2) provided the most accurate results compared with other tiers. It also performed very well in predicting the extreme end of NO2 concentrations. With an absence of emission data from mobile sources, tier 1 was concluded as the most appropriate scheme for prediction of ambient NO2 ground-level concentrations in this study.

Introduction

A steady change in the atmospheric composition since the commencement of the industrial revolution is mainly due to the combustion of fossil fuels used for the generation of energy and transportation.1 In Thailand, industrial activities have been promoted by the government with the aim to move the Thai economy forward from developing to developed country. Map Ta Phut industrial area (MA) located in Rayong Province in the eastern region is the largest industrial complex in Thailand. It serves as one of the most important complexes for heavy industries in terms of the production capacities in the Southeast Asia region. Various industrial manufacturing and production processes in this complex include petrochemical industry (48%), metal processing (10%), gas separation (10%), oil refining (2%), electricity generation (5%), chemical product (17%), and other industries (9%).2 The development of MA has brought out local environmental concerns, particularly air pollution problems. Major air pollutants emitted from this industrial area include sulfur dioxide (SO2), particulate matter, volatile organic compounds, and nitrogen dioxide (NO2).3 To support air quality management in this area, the Thai government declared the MA as a pollution control zone in 2009. This designation requires all the relevant organizations to seek proper measures to limit and control emissions to the environment. NO2 and SO2 are air pollutants required by the government for consideration when assessing the impacts of an industrial facility to acquire a permit for operation in this pollution-controlled zone. Furthermore, they are also the parameters that are required to be assessed when planning for future expansion of industrial activities in the MA.4,5

Nitrogen oxides (NOx) occur naturally and are also produced by man’s activities. In nature, they are a result of bacterial processes, biological growth and decay, lightning, and forest and grassland fires. The primary source of artificial nitrogen oxides is from the burning of fossil fuels.6 Most of the NOx emissions are in the form of nitric oxide (NO). The amount of nitrogen oxides emitted varies with the temperature of combustion; as temperature increases, so does the level of nitrogen dioxide.7

The chemical mechanism of NOx (NO and NO2) formation during combustion results from hundreds of elementary chemical reactions. Depending on the temperature range, stoichiometric ratio, and type of nitrous species present in the combustion zone, it is possible to distinguish predominant groups of chemical reactions, which are called the mechanisms of nitrogen oxide formation. Usually, the type of flame determines the conditions of the predominant mechanism of NOx formation.8 Major sources of NOx formation during combustion have 3 recognized mechanisms on NOx formation—thermal, fuel, and prompt.9 Thermal NOx is produced by the reaction of atmospheric oxygen and nitrogen at elevated temperatures and is reputed to contribute about 20% of the total NOx emission in pulverized coal firing, but is the dominant mechanism when the fuel contains little or no inherent nitrogen (ie, gas firing). Where high air preheat temperatures are used, for example, in cement kilns, thermal NOx can also contribute considerably to the overall NOx emission. Prompt NOx is formed by the reaction of hydrocarbon radicals with atmospheric nitrogen to produce HCN and hence NOx via a complex series of gas phase reactions. The contribution of the prompt NOx to the total emission in pulverized coal combustion is small (about 5%). Measures, which are effective in minimizing thermal and fuel NOx, are also effective in minimizing prompt NOx. Fuel NOx arises from the reaction of the organically bound nitrogen in the fuel with oxygen. The process is complex (reaction schemes typically consider the order of 50 intermediate species and several hundred separate reversible reactions, there is still considerable uncertainty as to the true value of the various rate constants, etc).10

This study presents the method to determine the choices to predict ambient NO2 concentration emitted as NOx from combustion sources. At ambient temperature and excess oxygen, NO primarily released from thermal process is subsequently oxidized to NO2. In the presence of tropospheric O3, NOx can also react with this oxidant and change to NO2. In this study, 3 different tiers, recommended by US Environmental Protection Agency (US EPA), are tested and evaluated for their ability in predicting NO2 ambient concentration. Performance of each calculated tiers was assessed by comparing predicted data with intensively measured data from continuous ambient air quality monitoring stations located in the surrounding area of the industrial complex.

Material and Methods

Evaluation of AERMOD performance in this study was conducted by comparing predicted NO2 concentrations with those measured data in the vicinity of the industrial complex. Hourly NO2 concentrations measured from 10 ambient air quality monitoring stations were compared with hourly predicted data during the period from January 1, 2012, to December 31, 2013 (2 years).

Study area

The Map Ta Phut industrial complex is located in Rayong Province about 185 km from Bangkok on the Gulf of Thailand’s coast (Figure 1). This area consists of 117 industrial plants which include 45 petrochemical factories, 8 coal-fired power plants, 12 chemical fertilizer factories, and 2 oil refineries.11 The National Economic and Social Development Board of Thailand reported that Rayong is a province with a relatively high gross domestic product (GDP) per capita among all 76 provinces in the country. This high GDP is mainly due to the presence and growth of a large industrial sector.3

Figure 1.

Study area and locations of air quality monitoring stations.

HMTP, health promotion hospital maptaphut; FCRC, field crops research center; BTKH, ban ta kuan public health center; WNFS, wat nong fap school; MMTP, muang mai maptaphut; KKYC, krok yai cha; MCLT, map chalut temple; TKTP, ta kuan temple; HBGD, herbal garden; CCIL, chum chon islam.

10.1177_1178622117700906-fig1.tif

Emission data

Emission data used in this study were obtained from the Office of Natural Resources and Environmental Policy and Planning (ONEP) database. These data consisted of emission source coordinates, stack heights, exit temperatures, exit velocities, and NOx emission rates. Totally, there were 292 point sources used as emission inputs in this model simulation. It should be noted that NOx emissions in this study were solely derived from stack combustion sources. Emissions from mobile sources in the study area were not included.

Meteorological data

Due to unavailability of upper air data, both of the surface and upper air data were simulated using the MM5 meteorological model for the years 2012 and 2013. Meteorological data contained included hourly wind speed, temperature, cloud cover, ceiling height, surface pressure, and relative humidity. The upper air data included vertical profile of wind speed, wind direction, elevation, temperature, and pressure, which were also simulated using the MM5 model. Data periods read from meteorological data files were started from the first hour in January 1, 2012, to the 24th hour in December 31, 2013.

Tiering options

Three-tier approaches were evaluated for their ability to predict NO2 ground-level concentrations. Assumptions of each tier can be described as follows:

  • Tier 1. Total conversion or all NOx = NO2 (the entire NO component of NOx emission is assumed to instantaneously react and convert to NO2).

  • Tier 2. A default NO2/NOx ratio of 0.60 is applied.12 This approach assumed that 60% of the NOx emitted from a source are converted to NO2.

  • Tier 3. In this approach, O3 ground-level concentrations are used for calculation of NO2 ambient concentrations. The calculations were based on the Ozone Limiting Method (OLM) and the Plume Volume Molar Ratio Method (PVMRM). The OLM involves an initial comparison of the estimated maximum NOx concentration and the ambient O3 concentration to determine the limiting factor of NO2 formation.13 The PVMRM determines the conversion rate of NOx to NO2 based on a calculation of the NOx moles emitted into the plume and the amount of O3 moles contained within the volume of the plume between the source and receptor.14 Ambient O3 concentrations intensively measured in the study areas were used as input data for this approach.

Model configurations

AERMOD dispersion model was used in this study. This model was developed by the American Meteorology Society and the US EPA. AERMOD is designated as a regulatory model by the US EPA. It is a steady-state plume model which assumes that concentrations at all distances during a modeled hour are governed by the temporally averaged meteorology of the hour.15 The horizontal and vertical distributions in the convective boundary layer are assumed to be Gaussian and bi-Gaussian probability density function, respectively. Using a relatively simple approach, AERMOD incorporates current concepts about flow and dispersion in complex terrain. Where appropriate, the plume is modeled as either affecting or following the terrain. All terrain is handled in a consistent and continuous manner while considering the dividing streamline concept in stably stratified conditions. One of the major improvements that AERMOD brings to the applied dispersion modeling is its ability to characterize the planetary boundary layer through both surface and mixed layer scaling. AERMOD constructs vertical profiles of required meteorological variables based on measurements and extrapolations of those measurements using similarity (scaling) relationships.16

The modeling system includes of a key program (AERMOD) and 3 preprocessors: AERMET, AERMAP, and AERSURFACE.17 The modeling domain in this study was designed for a radius of 10 km (outer grid spacing of 20 × 20 km2) with the finest grid spacing of 200 m. Urban dispersion coefficient was selected together with the regulatory modeling options for model simulation in this research. Hourly average of NOx emissions and NO2 concentrations was calculated on elevated terrain height options.

The MM5 model was used to simulate surface and upper meteorological parameters. The gridded data required by AERMET were selected from the Digital Elevation Model (DEM) data. Terrain characteristics were derived from the Shuttle Radar Topography Mission (SRTM3) database.

Evaluation of the model performance

Model performance was evaluated to ensure that the modeling results were accurate.18 The US EPA proposed a tool to determine the ability of model to ensure that the best model is properly used for each regulatory application. It is also used to confirm that the model is not arbitrarily imposed. Model performance in this study was evaluated through the measures of difference and correlation. Measures of difference quantitatively estimate the size of the differences between observed and modeled data. The association between modeled and observed data is quantitatively determined using the measures of correlation.19

To serve this purpose, evaluation of model was conducted for each case using the following statistical parameters: observed mean (Omean), predicted mean (Pmean), observed SD/sigma (Ostd), predicted SD/sigma (Pstd), fractional bias (Fb), fractional variance (Fs), root mean square error (RMSE), Pearson correlation coefficient (r2), index of agreement (IOA), and the robust highest concentration (RHC).

Results and Discussion

In this study, the performance of the AERMOD dispersion model in predicting 1-hour average concentration of NO2 in the vicinity of the largest petrochemical industrial complex in Thailand was conducted for the years 2012-2013. Hourly average ambient ground-level concentrations of NO2 at each of the monitoring sites were computed. Results were compared with those measured data at the same sites. Statistical analysis of model performance evaluation for each tier is presented in Table 1.

Table 1.

Performance evaluation using statistical measures.

10.1177_1178622117700906-table1.tif

Results from statistical evaluation indicated that there were differences between the predicted and observed values. However, these differences were much lower than their respective standard deviations (sigma) (RMSE < standard deviation), indicating that accurate results were being shown by the model. Generally, AERMOD performed well for the prediction of average concentration at every monitoring site, at least within the accuracy of the observations (standard deviation) for every tiering options.

The Fb and Fs values varied between −2 (extreme overprediction) and +2 (extreme underprediction). It was found that Fb and Fs values for all tiers were negative, indicating overprediction. The good model performance can be interpreted when the values of these parameters are near 0. The highest Fb and Fs values were calculated for modeled data at every stations in tier 3_PVMRM (Fb = 0.56), whereas the best model performances were found in tier 1 (Fb = 0.05), as shown in Figure 2. The lowest value of RMSE (7.26 µg/m3) was also obtained from simulation results under tier 1 (Figure 3). These findings supported the ability of tier 1 in predicting overall concentrations of NO2 in this study.

Figure 2.

Performance evaluation of fraction bias (tiers 1-3).

10.1177_1178622117700906-fig2.tif

Figure 3.

Performance evaluation of root mean square error (tiers 1-3).

10.1177_1178622117700906-fig3.tif

The RHC is preferred to the actual peak value and represents a rounded estimate of the highest concentrations, based on a tail exponential fit to the upper end of the distribution. With this procedure, the effect of extreme values on model comparison is reduced.20 Results from the RHC revealed that tier 1 gave the best result in predicting the extreme end of NO2 ambient concentration (Figure 4). The robust highest concentration of measured data (combining all receptors) was 60.24 µg/m3, whereas predicted results from tier 1, tier 2, tier 3_OLM, and tier 3_PVMRM are 58.26, 37.97, 50.48, and 32.98, respectively. This finding indicated that tier 1 provided the best result in an attempt to predict episodes of air pollution in this study.

Figure 4.

Annual mean, maximum, robust highest concentration (RHC), and percentile statistics for modeled and observed NO2 for all sites.

10.1177_1178622117700906-fig4.tif

The maximum ground-level concentrations of NO2 within the modeling domain were also predicted for each tier. It should be noted that the values at each receptor (10 monitoring sites) did not exceed the Thai’s ambient air quality standards (NO2 < 320 µg/m3 for 1-hour average). However, the maximum NO2 concentrations predicted within modeling domain were greater than the standard values for all simulated tiers. This finding depicts the importance of siting an appropriate location of ambient air monitoring station by considering not just only individual factory but also as an area-based emission for better management of air pollution in this industrial area. Spatial distributions of NO2 simulated map are as presented in Figures 5 to 8.

Figure 5.

Plot file of the first highest 1-hour averages of tier 1 in the years (A) 2012 and (B) 2013.

10.1177_1178622117700906-fig5.tif

Figure 6.

Plot file of the first highest 1-hour averages of tier 2 in the years (A) 2012 and (B) 2013.

10.1177_1178622117700906-fig6.tif

Figure 7.

Plot file of the first highest 1-hour averages of tier 3_OLM in the years (A) 2012 and (B) 2013.

10.1177_1178622117700906-fig7.tif

Figure 8.

Plot file of the first highest 1-hour averages of tier 3_PVMRM in the years (A) 2012 and (B) 2013.

10.1177_1178622117700906-fig8.tif

Conclusions

The AERMOD dispersion model was used to predict the concentrations of ambient NO2 emitted from stack combustion sources in the MA, Thailand. Evaluation of the model’s performances was conducted by comparing predicted data with those measured concentrations over the period of 2 years (from January 1, 2012, to December 31, 2013). AERMOD was simulated using local emission sources, terrains, and meteorological characteristics within the study domain. The model predicted 1-hour average concentrations of ambient NO2 at 10 receptors where there were intensive NO2 monitoring stations installed to serve the model’s validation purpose. A total of 292 stack combustion sources were accounted as emission inputs for the simulation of the model. The models were tested for its performance under 3 different scenarios to evaluate the most appropriate approach for further application in environmental impact assessment both in this industrial zone and in other areas. The performance of the model was evaluated through the measures of difference and correlation between observed and modeled data using statistical analysis.

The tier 1 approach (100% conversion of NOx to NO2) resulted in the highest predicted value of NO2 concentrations. Overall predicted results obtained from tier 1 were shown to have less bias with those measured results compared with other tiers. It is also the best option to determine the maximum ground-level concentration as depicted by its ability to predict the extreme end concentration of NO2 in this study. This finding indicated that tier 1 can be considered as the most appropriate simulation scheme in the prediction of annual concentration of NO2 in this industrial area. Results from this study revealed the fact that emission inventory of oxide of nitrogen may be underestimated. NO2 concentrations are contributed by emissions from both industrial and mobile source emissions. However, lack of emission data for mobile sources occurring in many areas constrains the application of air dispersion models in such areas. Efforts in using the background concentration of NO2 to compensate mobile source contribution still do not overcome this problem. In the presence of O3 data, the behavior of NO2 should be more refined. Therefore, tier 3 which involves chemistry of O3 and NOx has been developed to explain the characteristics of atmospheric chemistry of those pollutants once emitted from emission sources. However, this latest tier cannot perform well when emissions of NOx are underestimated. Therefore, availability of input data is the most crucial factor when considering types and options of model simulated in each area. The other approach to support this limitation is the use of both NO and NO2 ambient concentrations measured at the receptors to compare with predicted data rather than using only NO2 ambient concentrations. This analysis could assist to determine the extent of NO2/NOx concentration which is the specific value in each area.

Acknowledgements

The authors thank the Center of Excellence on Environmental Health and Toxicology (EHT), Office of Natural Resources and Environmental Policy and Planning (ONEP), the Industrial Estate Authority of Thailand, and the Pollution Control Department and BLCP Power Plant.

REFERENCES

1.

Bharali. Health impacts of air pollution.  http://edugreen.teri.res.in/explore/air/health.htm. Accessed June 19, 2015. Google Scholar

2.

Chusai, C Carrying capacity assessment for Map Ta Phut industrial area using air quality modeling.  http://library.senate.go.th/document/Ext8014/8014095_0002.PDF. Accessed June 19, 2015. Google Scholar

3.

Chusai, C Manomaiphiboon, K Saiyasitpanichc, P Thepanondh, S. NO2 and SO2 dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand. J Air Waste Manag Assoc. 2012;62:932–945. Google Scholar

4.

Jittra, N Pinthong, N Thepanondh, S. Performance evaluation of AERMOD and CALPUFF air dispersion models in industrial complex area. Air Soil Water Res. 2015;8:87–95. Google Scholar

5.

Khamyingkert, L Thepanondh, S. Analysis of industrial source contribution to ambient air concentration using AERMOD dispersion model. Environ Asia. 2016;9:28–36. Google Scholar

6.

Maleny Weather. Nitrogen oxides (NOx).  http://www.malenyweather.com/2013/12/16/clean-air-thought-week-12/. Accessed July 5, 2015. Google Scholar

7.

Ministry for the Environment Manatū Mō Te Taiao. Nitrogen dioxide (NO2).  http://www.mfe.govt.nz/issues/air/breathe/nitrogen-dioxide.html. Accessed October 23, 2015. Google Scholar

8.

Wroclawska, P . Nitrogen oxides in combustion processes.  http://fluid.wme.pwr.wroc.pl/~spalanie/dydaktyka/combustion_en/NOx/NOx_formation.pdf. Accessed September 2, 2015. Google Scholar

9.

Hesselmann, G Rivas, M . What are the main NOx formation processes in combustion plant?  http://www.handbook.ifrf.net/handbook/cf.html?id=66. Accessed May 18, 2014. Google Scholar

10.

Hesselmann, G Rivas, M . What are the main NOx formation processes in combustion plant.  http://www.handbook.ifrf.net/handbook/cf.html?id=66. Accessed October 12, 2015. Google Scholar

11.

Janmaimoo, P Watanabe, T . Environmental health risk management based on stakeholder’ qualitative risk assessment: a case Map Ta Phut municipality.  http://kutarr.lib.kochi-tech.ac.jp/dspace/bitstream/10173/1213/1/sms13-1056.pdfGoogle Scholar

12.

Ruangkawsakun, J Thepanondh, S. Air assimilative capacity for sulfur dioxide and nitrogen dioxide: case study the Eastern Region of Thailand. Int J Environ Sci Develop. 2014;5:187–190. Google Scholar

13.

Hendrick, EM Tino, VR Egan, BA Hanna, SR. Evaluation of NO2 predictions by the plume volume molar ratio method (PVMRM) and ozone limiting method (OLM) in AERMOD using new field observations. J Air Waste Manag Assoc. 2013;63:844–854. Google Scholar

14.

Bange, P Jannsen, L Nieuwstadt, F Visser, H Erbrink, J. Improvement of the modelling of daytime nitrogen oxide oxidation in plumes by using instantaneous plume dispersion parameters. Atmos Environ A: Gen. 1991;25:2321–2328. Google Scholar

15.

Mohan, M Bhati, S Sreenivas, A Marrapu, P. Performance evaluation of AERMOD and ADMS-urban for total suspended particulate matter concentrations in Megacity Delhi. Aerosol Air Qual Res. 2011;11:883–894. Google Scholar

16.

USEPA. AERMOD: description of model formulation.  https://www3.epa.gov/scram001/7thconf/aermod/aermod_mfd.pdf. Accessed September 2, 2015. Google Scholar

17.

USEPA. AERMOD implementation guide.  https://www3.epa.gov/ttn/scram/models/aermod/aermod_implementation_guide.pdf. Accessed July 22, 2015. Google Scholar

18.

Venkatram, A. Model predictability with reference to concentrations associated with point sources. Atmos Environ. 1967;15:1517–1522. Google Scholar

19.

Kumar, A Dixit, S Varadarajan, C Vijayan, A Masuraha, A. Evaluation of the AERMOD dispersion model as a function of atmospheric stability for an urban area. Environ Prog Sustain. 2006;25:141–151. Google Scholar

20.

Thepanondh, S. A Study of Wet and Dry Deposition Processes for Regional Air Pollution and Atmospheric Deposition Modeling. Australia: Monash University; 2004. Google Scholar

Notes

[1] Five peer reviewers contributed to the peer review report. Reviewers’ reports totaled 989 words, excluding any confidential comments to the academic editor.

[2] Financial disclosure The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was partially supported for publication by the China Medical Board (CMB) and the Faculty of Public Health, Mahidol University, Thailand.

[3] Conflicts of interest The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

[4] ThS conceived and designed the experiments, contributed to the writing of the manuscript, agree with manuscript results and conclusions, and jointly developed the structure and arguments for the paper. TuS analyzed the data and wrote the first draft of the manuscript. ThS made critical revisions and approved the final version. All authors reviewed and approved the final manuscript.

[5] Conflicts of interest As a requirement of publication, authors have provided to the publisher signed confirmation of compliance with legal and ethical obligations including, but not limited to the, following: authorship and contributor ship, conflicts of interest, privacy and confidentiality, and (where applicable) protection of human and animal research subjects. The authors have read and confirmed their agreement with the ICMJE authorship and conflict of interest criteria. The authors have also confirmed that this article is unique and not under consideration or published in any other publication, and that they have permission from rights holders to reproduce any copyrighted material. Any disclosures are made in this section. The external blind peer reviewers report no conflicts of interest.

© The Author(s) 2017 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(https://us.sagepub.com/en-us/nam/open-access-at-sage).
Supitchaya Tunlathorntham and Sarawut Thepanondh "Prediction of Ambient Nitrogen Dioxide Concentrations in the Vicinity of Industrial Complex Area, Thailand," Air, Soil and Water Research 10(1), (1 January 2020). https://doi.org/10.1177/1178622117700906
Received: 15 July 2016; Accepted: 30 January 2017; Published: 1 January 2020
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