Journal of the Air & Waste Management Association
ISSN: 1096-2247 (Print) 2162-2906 (Online) Journal homepage: http://www.tandfonline.com/loi/uawm20
A framework for emissions source apportionment in industrial areas: MM5/CALPUFF in a near-field application K. Ghannam & M. El-Fadel To cite this article: K. Ghannam & M. El-Fadel (2013) A framework for emissions source apportionment in industrial areas: MM5/CALPUFF in a near-field application, Journal of the Air & Waste Management Association, 63:2, 190-204, DOI: 10.1080/10962247.2012.739982 To link to this article: http://dx.doi.org/10.1080/10962247.2012.739982
Accepted author version posted online: 13 Nov 2012.
Submit your article to this journal
Article views: 312
View related articles
Citing articles: 3 View citing articles
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=uawm20 Download by: [American University of Beirut]
Date: 28 January 2016, At: 02:18
TECHNICAL PAPER
A framework for emissions source apportionment in industrial areas: MM5/CALPUFF in a near-field application K. Ghannam and M. El-Fadel⁄
Downloaded by [American University of Beirut] at 02:18 28 January 2016
Department of Civil and Environmental Engineering, Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon ⁄Please address correspondence to: M. El-Fadel, Department of Civil and Environmental Engineering, Faculty of Engineering and Architecture, American University of Beirut, Bliss Street, P.O. Box 11-0236 Beirut, Lebanon; e-mail:
[email protected]
This paper examines the relative source contribution to ground-level concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), and PM10 (particulate matter with an aerodynamic diameter 95%. At LO3, point sources contributed up to 36% during some days (Figure 7c), but with significant underpredictions of observations (2–3 orders of magnitude), which are hence not included. Although this could be attributed to underestimation of emissions from point sources, there remains the possibility that other potential unaccounted-for sources in the simulations (domestic heating/generators, arterial roads, open burning, etc.) are contributing to such elevated concentrations. Furthermore, CALPUFF generally captured the temporal fluctuation in the concentration distribution at this receptor (LO3), indicating that
201
Downloaded by [American University of Beirut] at 02:18 28 January 2016
Ghannam and El-Fadel / Journal of the Air & Waste Management Association 63 (2013) 190–204
Figure 6. Spatial distribution of the 24-hr average CO concentration during winter and summer (colour figure avilable in online).
Table 8. Source contribution to the highest weekly average NO2 concentration
Location (Period) LO1 (Jan 01–Jan 11) LO2 (Jan 12–Feb 02) LO3 (Jun 15–Jul 05) LO4 (Jul 06–Aug 10) LO5 (Jun 15–Jul 05)
Observed (µg/m3)
Predicted (µg/m3)
Point Sources (%)
Highway (%)
Quarries (%)
95640 3010 24550 1840 1670
624 1910 100 455 3102
6 1 13 2 0
94 99 87 98 100
0 0 0 0 0
a higher emission rate would result in better matching of observed concentrations. During another period at LO4 (Figure 7d), the simulated concentration was 5 times less than the observed, with highway emissions dominating the contribution (90%), except for July 10–14, when point sources contributed 35%. The spatial distribution of NO2 levels contributed by the two categories is shown in Figure 8. Summer times were characterized by higher accumulation of NO2 concentration. Peak concentrations contributed by point sources increased during summer and occurred at a different location (southwest in winter and north in
summer) (Figure 8b–d). Also, larger areas were affected by pointsource emissions during summer, with relatively higher concentrations. In contrast, highway emissions contributed to larger areas during the winter, indicating longer transport distances. The peak concentration occurred at the same location near LO5, and increased by slightly more than a factor of 2 in the summer.
Particulate matter apportionment Sources contributing to PM10 emissions are area sources (quarrying sites) (197 g/sec) and point sources (2773 g/sec),
Downloaded by [American University of Beirut] at 02:18 28 January 2016
202
Ghannam and El-Fadel / Journal of the Air & Waste Management Association 63 (2013) 190–204
Figure 7. Typical daily source contribution to NO2 concentration at several locations.
Figure 8. Spatial distribution of the 24-hr average NO2 concentration during winter and summer (colour figure avilable in online).
accounting for 7% and 93% of total emissions (2784 g/sec), respectively. Measurements of ambient PM10 concentrations covered 1–2 days at a receptor. The corresponding contribution of each source category to PM10 levels was examined in the context of spatial analysis of the concentration distribution. The
analysis considered one period of the year to reflect on the contribution magnitude of each source category, rather than the spatial distribution (areas affected), which is expected to have similar patterns as that of NO2. Figure 9 shows contour lines of PM10 levels in the study area contributed by point and area
Downloaded by [American University of Beirut] at 02:18 28 January 2016
Ghannam and El-Fadel / Journal of the Air & Waste Management Association 63 (2013) 190–204
203
Figure 9. Spatial distribution of the 24-hr average PM10 concentration during summer (colour figure avilable in online).
sources during summer times (July), which revealed high accumulation levels. Emissions from point sources affected almost all the study area, with concentrations exceeding the 24-hr average standard, with inner areas witnessing higher PM10 levels than coastal ones (Figure 9a). The peak 24-hr average concentration contributed by point sources reached 4186 µg/m3 south of LO4. On the other hand, emissions associated with quarrying (area sources) affected a smaller part of the study area, albeit higher accumulation levels near the sources with peak concentration reaching 17,700 µg/m3 close to the CN quarry, indicating weak dispersion and shorter transport.
Chemical transformations Figure 10 shows time series of the 1-hr average NO2 concentration at several locations simulated with and without considering chemical transformations. Although time series of both underpredicted observed NO2 concentrations, particularly at LO3 (several orders of magnitude and hence not included), they coincided with each other, indicating little to no NOx reactions. Only at LO3 (Figure 10c), simulated NO2 concentrations decreased slightly when chemical transformations (CT) were incorporated, reflecting the significance of longer transport distances on inducing chemical reactions. Note that the statistical parameters calculated upon simulating chemical transformations, and the spatial distribution of the NO2 concentration, exhibited no significant differences compared with base-case simulations.
Conclusion The non-steady-state CALPUFF model was coupled with an emission inventory for source apportionment to CO, NO2, and PM10 levels at several locations in an urban industrial area encompassing emissions from a multistack complex, a highway, and quarrying sites. CALPUFF was tested for its linear response to changes in emissions, and exhibited equivalence in simulating the contribution of a source category using two approaches, namely, the “emissions-in” and “emissions-out” scenarios, which indicates that the model adds the contributions from each source category to the pollutant concentration at a receptor. Source apportionment revealed that ground-level releases (i.e., highway and quarries) extended over large areas dominated the contribution to ambient concentrations of CO, NO2, and PM10 at close locations to the sources, despite the higher cumulative emissions from point sources, indicating that releases at elevated heights experience adequate dilution. Near the highway and quarries, significant accumulation is noticeable during summer times compared with winter times due to higher frequency of calm conditions and unfavorable meteorology, with point sources affecting distant areas the most, but with lower exposure levels compared with area sources. The latter results point to the significance of ground-level area sources in contributing to elevated exposure levels in urban areas and that such sources should be equally targeted for management options. Equally important, the results raise questions in relation to the viability of the prevailing paradigm of point-source emission reduction
Figure 10. Time series of NO2 concentration with and without chemical transformations.
204
Ghannam and El-Fadel / Journal of the Air & Waste Management Association 63 (2013) 190–204
Downloaded by [American University of Beirut] at 02:18 28 January 2016
that encompass excessive costs with little return on investment measured as improvement in air quality or decreased exposure per unit cost invested. To this end, sensitivity analysis to chemical transformations, although limited by the use of model defaults of background ozone and ammonia concentrations, revealed that these were insignificant and subsequently had little effect on the linear response of CALPUFF to NOx emissions, possibly due to short-range plume transport within the study area. Note that for industry-specific pollutants that constitute markers for certain industries and that are not commonly emitted from open area/ground-level sources in urban settings, pointsource emission reduction remains the critical abatement measure, with source apportionment becoming of less significance.
Acknowledgment The air quality and meteorology database was collected by the Lebanese American University (LAU) under a grant from the United States Agency for International Development (USAID). Special thanks are extended to Drs. Gebran Karam and Mazen Tabbara at LAU for making the database available.
References Barna, M.G., B.A. Schichtel, K.A. Gebhart, and W.C. Malm. 2006. Modeling regional sulfate during the BRAVO study: Part 2. Emissions sensitivity simulations and source apportionment. Atmos. Environ. 40:2423–2435. doi:10.1016/j.atmosenv.2005.12.038 Behera, S.N., N. Sharma, O. Dickshit, and S.P. Shukla. 2011. GIS-based emission inventory, dispersion modeling, and assessment for source contributions of particulate matter in an Urban Environment. Water Air Soil Pollut. 218:423– 436. doi:10.1007/s11270-010-0656-x Cheung, H.C., T. Wang, K. Baumann, and H. Guo. 2005. In uence of regional pollution out ow on the concentrations of ne particulate matter and visibility in the coastal area of southern China. Atmos. Environ. 39:6463–6474. doi:10.1016/j.atmosenv.2005.07.033 Crabbe, H., R. Beaumont, and D. Norton. 2000. Assessment of air quality, emissions and management in a local urban environment. Environ. Monit. Assess. 65:435–442. doi:10.1023/A:1006481229246 El-Fadel, M., and L. Abi-Esber. 2012. Simulating industrial emissions using ADMS4: Model performance and source emission factors. J. Air Waste Manage. Assoc. 62:336–349. doi:10.1080/10473289.2011.651556 El-Fadel, M., L. Abi-Esber, and T. Ayash. 2009. Managing emissions from highly industrialized areas: Regulatory compliance under uncertainty. Atmos. Environ. 43:5015–5026. doi:10.1016/j.atmosenv.2009.06.056 European Environment Agency. 2009. EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-Range Transmission of Air Pollutants in Europe)/EEA (European Environment Agency) Air Pollutant Emission Inventory Guidebook 2009. Technical Guidance to Prepare National Emission Inventories. EEA Technical Report No. 9/2009. Copenhagen: EEA. Fushimi, A., H. Kawashima, and H. Kajihara. 2005. Source apportionment based on an atmospheric dispersion model and multiple linear regression analysis. Atmos. Environ. 39:1323–1334. doi:10.1016/j.atmosenv.2004.11.009 Hueglin, C., W. Devos, R. Gehrig, P. Hofer, J. Kobler, W.A. Stahel, M. Wolbers, U. Baltensperger, and C. Monn. 2000. Source apportionment of PM10 in
Switzerland by application of a multivariate receptor model. J. Aerosol Sci. 31:891–892. doi:10.1016/S0021-8502(00)90901-7 Islam, M.A., 1999. Application of a Gaussian plume model to determine the location of an unknown emission source. Water Air Soil Pollut. 112:241–245. doi:10.1023/A:1005047321015 Krishna, T.V., M.K. Reddy, and R.C. Reddy. 2005. Impact of an industrial complex on the ambient air quality: Case study using a dispersion model. Atmos. Environ. 39:5395–5407. Doi: http://dx.doi.org/10.1016/j. atmosenv.2005.06.003 Laupsa, H., B. Denby, S. Larrsen, and J. Schaug. 2009. Source apportionment of particulate matter (PM2.5) in an urban area using dispersion, receptor and inverse modeling. Atmos. Environ. 43:4733–4744. doi:10.1016/j. atmosenv.2008.07.010 Leksmono, N.S., J.W.S. Longhurst, K.A. Ling, T.J. Chatterton, B.E.A. Fisher, and J.G. Irwin. 2006. Assessment of the relationship between industrial and traffic sources contributing to air quality objective exceedences: A theoretical modelling exercise. Environ. Model. Softw. 21:494–500. doi:10.1016/j. envsoft.2004.07.012 Longhurst, J. W., C.I. Beattie, and N.K. Woodfield. 2001. Air quality management: Evolution of policy and practice in the UK as exemplified by the experience of English local government. Atmos. Environ. 35:1479–1490. doi:10.1016/S1352-2310(00)00311-3 Scire, J.S., D.G. Strimaitis, and R.J. Yamartino. 2000. A User’s Guide for the CALPUFF Dispersion Model. Concord, MA: Earth Tech Inc. U.S. Environmental Protection Agency. 1994. Compilation of Air Pollutant Emission Factors, AP-42, 5th ed. Research Triangle Park: Office of Air Quality Planning and Standards, Emission Inventory Branch, U.S. Environmental Protection Agency. U.S. Environmental Protection Agency. 2008. Clarification of Regulatory Status of CALPUFF for Near-Field Applications. Research Triangle Park, NC: U.S. Environmental Protection Agency. Wang, X., G. Carmichael, D. Chen, Y. Tang, and T. Wang. 2005. Impacts of different emission sources on air quality during March 2001 in the Pearl River Delta (PRD) region. Atmos. Environ. 39:5227–5241. doi:10.1016/j. atmosenv.2005.04.035 Watson, J.G., and J.C. Chow. 2004. Receptor models for air quality management. EM 10:15–25. Whyatt, J.D., S.E. Metcalfe, J. Nicholson, R.G. Derwent, T. Page, and J.R. Stedman. 2007. Regional scale modeling of particulate matter in the UK, source attribution and an assessment of uncertainties. Atmos. Environ. 41:3315–3327. doi:10.1016/j.atmosenv.2006.12.040 World Health Organization. 2005. WHO Air Quality Guidelines Global Update 2005. Report on a working group meeting, World Health Organization, Bonn, Germany, 18–20 October 2005. Yim, H.L., C.H. Fung, and K.H. Lau. 2010. Use of high-resolution MM5/ CALMET/CALPUFF system: SO2 apportionment to air quality in Hong Kong. Atmos. Environ. 44:4850–4858. doi:10.1016/j.atmosenv.2010.08.037
About the Authors K. Ghannam is a graduate student at the Department of Civil and Environmental Engineering, Faculty of Engineering and Architecture, American University of Beirut. M. El-Fadel is Professor and Chairperson, Department of Civil and Environmental Engineering, Faculty of Engineering and Architecture, American University of Beirut. He holds the Dar Al-Handsah (Shair & Partners) Chair in Engineering.
