of pm from mobile sources

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em • feature

A study of best management practices to reduce the contribution of coarse particulates in the atmosphere.Mobile sources are the largest single contributor of coarse particulate matter (PM10) in the atmosphere for many jurisdictions. A 2000 study by Watson and Chow1 outlined the typical presence of chemicals and nutrients at various particle sizes and showed that PM10 or fugitive dust encompasses most parti-cle sizes found in the atmosphere. While, Environ-ment Canada’s 2012 Emission Summary2 estimated that paved and unpaved roads emit 861,477 tons

annually of PM10 in Ontario, positioning it as the largest source in the province. The AP-42 paved and unpaved road emission equations developed by the U.S. Environmental Protection Agency (EPA) were used to estimate road emissions within the Lake Simcoe, Ontario, airshed by both Weiss et al.3 and Environment Canada’s 2012 Emission Summary.2 Brown et al.4 analyzed the Lake Simcoe bulk collector data and found a seasonal and spatial

Lee Weiss is a Ph.D. candidate, and Bahram Gharabaghi, Ph.D., P.Eng, is an associate professor of engineering, both with the University of Guelph, Ontario, Canada; and Jesse Thé, Ph.D., P.Eng, is president and founder of Lakes Environmental Software Inc. and a professor at the University of Waterloo, Canada. E-mail: [email protected].

by Lee Weiss, Bahram Gharabaghi, and Jesse Thé MODELING

& MANAGEMENTof PM10 from Mobile Sources

©iStock.com/DonFord1

Barrie, Ontario, a waterfront city adjacent to Lake Simcoe, located between Oro and Innisfi l.

24_EM0415-FT4-Weiss.indd 24 3/23/15 12:25 PM

Copyright 2015 Air & Waste Management Association

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pattern to emissions within the airshed. Weiss et al.5 followed this work by mapping and quantifying key agricultural emission sources within the airshed. This study was then continued by Weiss et al.3 to add PM10 transport and deposition components to the existing PM10 airshed emissions map and also included paved and unpaved road emissions.

Paved and Unpaved Road—Best Management Practices (BMPs)In 2009, Johnson and Olson6 evaluated calcium chloride, magnesium chloride, and an organic poly-mer-plus-binder at various rates of application for two years on unpaved roads in Minnesota. They examined these different types of dust suppres-sants, in concert with their rates of application, costs, and types of unpaved roads being managed. They concluded that application frequency needed to increase from the standard application rate (typi-cally once per year) and that the chemical suppres-sants worked better on road beds with a higher silt content in contrast with the high-sand road beds that were relatively effective with the suppressants.

Gillies et al.7 analyzed the effi ciency of several sup-pressants and found that the petroleum emulsion with polymer was 73% effi cient three months after application, but reduced to 49% effective 12 months after application. The Wisconsin Transpor-tation Information Center8 recommended one to two treatments per year for both calcium chlo-ride and oil suppressants and is consistent with the Federation of Canadian Municipalities’ 2005 assessment for application.9 Additionally, Succa-rieh10 determined that reducing vehicular speed from 40 to 20 miles per hour (mph) can reduce dust emissions by 65%. This can be accomplished through signage and speed bumps, along with law enforcement availability and community coopera-tion; however, road modifi cation can only reduce speed within the limited modifi ed distance.

Strategic and dense planting of trees and shrubs along roads, in the form of a shelterbelt, have also been studied extensively as a natural technique to control dust emissions. A recent study by Mao et al.11 modeled and measured the effect of a shel-terbelt on an unpaved road and found that due to the small nature of particles emitted from the

surface, the shelterbelt did not work to suppress the emission and may even increase the suspen-sion of these particles. In contrast, Vezina et al.12

performed an extensive study on the cost-effec-tive options available for shelterbelts in Southern Ontario and outlined benefi ts to shelterbelts in addition to dust suppression and erosion manage-ment. They identifi ed a dust suppression factor of 40% directly attributable to shelterbelts that can be applied to both paved and unpaved roads.

Case Study: Lake Simcoe, OntarioWithin the Lake Simcoe airshed case study area, the Township or Oro-Medonte and the Town of Innisfi l currently use virgin oil for dust suppression, as is the case with several municipalities adjacent to the lake (see Figure 1). However, Tiny Town-ship has stated that calcium chloride is used and is applied once per year in late June.13,14 The Fed-eration of Canadian Municipalities’ 2005 National Guide to Sustainable Municipal Infrastructure, Dust Control for Unpaved Roads9 states that the proper buffers should be used to minimize impact on water quality from the application of calcium chloride on road surfaces. Runoff from oiling the surface can reduce dissolved oxygen conditions in receiving water bodies.

Dust emissions from paved roads are also a func-tion of the volume of vehicular traffi c on the road. Paved roads within the airshed are shown in Figure 2. Dust can be generated and emitted from com-bustion byproducts released through the tailpipe of the car and the application of brake pads and the resuspesion of dust from other sources that deposited on the road. The standard approach to controlling emissions from paved roads includes street sweeping. EPA15 states that an effective street cleaning programs can minimize road pollutants.

BMP Optimization MethodologyGenetic algorithm (GA) is an optimization method-ology that uses the evolutionary process of survival of the fi ttest to fi nd optimal solutions to problems involving large amounts of data. The compo-nents of a GA include the population, which is composed of chromosomes and genes that repre-sent the various solutions possible and randomly set. The fi tness (or optimization) of these possible

MODELING & MANAGEMENT

Mobile sources

are the largest

single contributor

of PM10 for many

jurisdictions.




solutions is determined using a fitness function, which optimizes a parameter based on a set of criteria or constraints. For this study, the fitness function was defined using information from the best management practice (BMP) tool, outlined in Table 1, in conjunction with the results from the emission model for each grid cell.

Each possible solution is evaluated and the fit or optimized solutions that meet the criteria best are duplicated, while those that do not are discarded from further analysis through selection. Most GAs will, however, retain a set of unfit solutions to retain diversity in the population with the hopes that latent desirable genes will later manifest. Cross-over and mutation steps are genetic operators that work to introduce new options into the population. The number of times this process is repeated is called generations and with increasing genera-tions comes increasing accuracy of results. Figure 3 depicts this process.

The objective function consisted of maximizing the calculation of dust suppression/cost to imple-ment the BMP and was calculated per grid cell. It is a straight calculation of the most cost-effec-tive solution based on the parameters outlined in Table 1. Table 2 outlines the model parameters and objective functions used in the GA process.

Results and DiscussionThe potential emissions reductions using the paved and unpaved road BMPs described above, namely applying calcium chloride (CaCl2) to unpaved roads and street sweeping for paved roads, were determined using the GA method. The application of vegetable oil and installing shelter breaks were quickly eliminated by the opti-mization methodology, due to the relatively high cost and relatively lower suppression efficiency. In order to account for the transport and deposition elements, as outlined in Weiss et al.,3 a location factor was developed that is based on the relative

Road BMP Allele Set PM10 Suppression (%)

Cost ($/km) Reference

Vegetable oil ‘Not present’ and ‘Present’ 0, 30 3,000 Addo and Sanders16

Calcium chloride ‘Not present’ and ‘Present’ 0, 60 2,800 Federation of Canadian Municipalities9

Street sweeping (paved roads) ‘Not present’ and ‘Present’ 0, 30 40 Finley17

Shelterbelt ‘Not present’ and ‘Present’ 0, 40 3,761 Vezina et al.17

Table 1. Optimization criteria and BMP tool for BMPs used in the optimi-zation model.

Figure 1. Unpaved roads within the airshed with a 20-km buffer around Lake Simcoe.

Figure 2. Paved roads within the Lake Simcoe airshed.

(1) (2)



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contribution of each cell to the deposition of PM10to the Lake Simcoe airshed. The GA methodology used emissions corrected with a location factor that accounted for the transport and deposition mechanisms within the airshed.

Using the GA results, the total cost to implement the optimal BMPs for each cell were calculated and normalized over the area of the cell. The results were then plotted in a geographic information sys-tem (GIS) for the airshed to show spatially which

Start

Stop

No

Yes

Initializepopulation

Evaluate fitness

EmissionsBMP tool

Gen = gen + 1

Is Gen = max_gen?

Mutation Crossover Non-dominantsorting Selection

Parameter Description Source/Constraints GA Application Formula

Potentiali,nMaximum achievable coverage for

each BMP “n” on grid cell “I”

Fixed value based on potential BMP adoption from OMAFRA

workshop

P i,n for i=1 to 56, n=1 to 7

Percentage of each BMP (1 through 7) applied to grid cell “i” 0 ≤ pi,n ≤ potentiali,n

LiLength of paved or unpaved road

applicable to each BMP Fixed based on input data

SFn Suppression factor for BMP “n” Based on literature

Ei Total emission (tons) from grid cell “i” Based on fi xed input data

SGi Total suppression (tons) for grid cell “i”

(total suppression cannot exceed total emission)

Cn Cost ($/km) for BMP “n” Based on literature

CGi

Total cost ($) for grid cell “i”; based on unit costs (Cn) for each BMP and

associated area / length.(to avoid division by zero in objective function)

Objective Function

Maximizes total suppression and minimizes cost for highest value of

“tons suppressed per dollar”

Table 2. Model param-eters and objective functions used in the GA optimization.

Figure 3. GA methodol-ogy for BMP selection and placement opti-mization adapted from Maringanti et al.19

Note: Authors employed Evolver GA (Palisade, 201418) to solve each optimization.

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cells required more resources in order to efficiently reduce emissions from paved and unpaved roads (see Figure 4).

As expected, the grid cells located in the high risk North–West quadrant of the lake and specifically in the nearshore should be a priority for paved and unpaved roads BMPs. The identified cells located outside of the nearshore (i.e., cells 2, 35, 48, and 54) contribute greatly to the transport and deposition of PM10 (see Table 3) and were, there-fore, highly ranked by the GA with the inclusion of the location factor.

ConclusionsA review of relevant BMPs to control particulate emissions for the Lake Simcoe airshed concluded that the optimal BMPs are those that are cur-rently being implemented in many of the adja-cent municipalities, namely applying salt and oil for unpaved roads and street sweeping on paved roads. Wind breaks can also be effective; how-ever, due to their cost and minimal suppression efficiency, they were effectively excluded by the GA process. The GA methodology was well suited for this application and provided an optimal BMP application for mobile sources within the airshed. The inclusion of location factors provided further insight into the GA results by incorporating the transport and deposition aspects into the GA optimized results. em

Figure 4. Cost ($)/km2 for paved and unpaved road BMPs based on the GA optimization results.

Table 3. Contribution to total mobile PM10 transport and deposition.

Grid Cell % of Total PM10 Deposition

Paved Unpaved

2 3 4

35 10 14

48 31 5

54 9 6

References1. Watson, J.; Chow, J. Reconciling urban fugitive dust emissions inventory and ambient source contribution estimates: Summary of current

knowledge and needed research; Desert Research Institute, Reno, NV, 2000.2. Emission Summary; Environment Canada, 2012; http://www.ec.gc.ca.3. Weiss, L.; Thé, J.; Gharabaghi, B.; Stainsby, E.; Winter, J. A new dust transport approach to quantify anthropogenic sources of atmospheric

PM10 deposition on lakes; Atmos. Environ. 2014, 96, 380-392.4. Brown, L.; Taleban, V.; Gharabaghi, B.; Weiss, L. Seasonal and spatial distribution patterns of atmospheric phosphorus deposition over Lake

Simcoe, ON; J. Great Lakes Res. 2011, 15-25.5. Weiss, L.; Stainsby, E.; Gharabaghi, B.; Thé, J.; Winter, J. Mapping key agricultural sources of dust emissions within the Lake Simcoe airshed;

J. Inland Waters 2013, 3, 153-166.6. Johnson, E.; Olson, R. Best Practices for Dust Control on Aggregate Roads; Minnesota Local Road Research Board Investigation 842; St. Paul:

Minnesota Department of Transportation, 2009.7. Gillies, J.; Watson, J.; Rogers, F.; DuBois, D.; Chow, J. Long-Term Efficiencies of Dust Suppressants to Reduce PM10 Emissions from Unpaved

Roads; J. Air & Waste Manage. Assoc. 1999, 49, 3-16.8. Wisconsin Transportation Information Center, 1997; http://www.wistrans.org.9. Federation of Canadian Municipalities; Dust Control for Unpaved Roads; National Guide to Sustainable Municipal Infrastructure, 2005;

https://www.fcm.ca/Documents/reports/Infraguide/Dust_Control_for_Unpaved_Roads_EN.pdf.10. Succarieh, M. Control of Dust Emissions from Unpaved Roads; Alaska Cooperative Transportation and Public Facilities Research Program,

Fairbanks, Alaska, 1992.11. Mao, Y.; Wilson, J.; Kort, J. Effects of a shelterbelt on road dust dispersion; Atmos. Environ. 2013, 79, 590-598.12. Vezina, A.; Kort, J.; Peterson, B.; Munn, N. Multi-functional Windbreaks: Design Options and Economic Evaluation; Nottawasaga Valley

Conservation Authority, 2012.13. Atmospheric Deposition Control of PM10 to Reduce Phosphorus Loadings to Lake Simcoe. Report prepared by XCG Environmental Engineers

& Scientists, 2013, for Etobicoke: Ministry of the Environment.14. Roubos, B. Aggregate Resources Officer, Tiny Township; Personal Communication (L. Weiss, Interviewer), December 14, 2011.15. Parking Lot and Street Cleaning. In Water: Best Management Practices; U.S. Environmental Protection Agency, 2014; available at

http://water.epa.gov/polwaste/npdes/swbmp/Parking-Lot-and-Street-Cleaning.cfm.16. Addo, J.Q.; Sanders, T.G. Effectiveness and Environmental Impact of Road Dust Suppressants; MPC Report NO. 95-28A; Mountain Plains

Consortium, March 1995.17. Finley, S. Sweeping works; Pavement Maintenance and Reconstruction 1996, October/November, 16-17.18. Palisade, Evolver 4.0, Palisade, Ithaca, NY.19. Maringanti, C.; Chaubey, I.; Popp, J. Development of a multiobjective optimization tool for the selection and placement of BMPs for NPS

control; Water Resources Research 2009, 45, W06406.

