case no. mm120031 ontario municipal board
TRANSCRIPT
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Case No. MM120031
Ontario Municipal Board Commission des affaires municipals de l’Ontario
IN THE MATTER OF subsection 11(5) of the Aggregate Resources Act, R.S.O. 1990, c. A.8, as amended Referred by: Ministry of Natural Resources Objector: Gerald & Janice Brown Objector: Ministry of Natural Resources Applicant: Preston Sand and Gravel Company Limited Subject: Application for a Class A licence for the removal of aggregate Property Address/Description: Part Lot 23 and 24, Concession X Municipality: Township of North Dumfries OMB Case No.: MM120031 OMB File No.: MM120031 IN THE MATTER OF subsection 34(19) of the Planning Act, R.S.O. 1990, c. P.13, as amended Appellant: Gerry Brown, et al Subject: By-law No. 2526-12 Municipality: Township of North Dumfries OMB Case No.: MM123031 OMB File No.: PL121250
WITNESS STATEMENT OF DR. FRANCO DIGIOVANNI
PART 1: BACKGROUND
1.1. Qualifications as an Air Quality Consultant
1. I am a Senior Air Quality Modeller for Airzone One Limited, an air quality consulting company located in Mississauga, Ontario. My position entails conducting air quality assessments using dispersion modelling for permitting purposes and also for general air assessments. I have been in this position since 1989 and during my time I have worked on a wide range of air regulatory approvals in Ontario and a number of air assessments using dispersion modelling. As part of my experience, I have been involved in reviewing and providing commentary on the regulatory air permitting system in Ontario.
2. I have a BSc(HONS) in Geology from Imperial College (London) and a PhD in Physical Geography from the University of Hull (UK) where my thesis was on modelling airborne
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particle dispersion. I spent four years doing postdoctoral research at the University of Guelph and as an NSERC Visiting Fellow to a Canadian Government Laboratory spent with Environment Canada. During that time I focussed my research on modelling particle dispersion in the air. I have published 12 peer-reviewed scientific articles; all have dealt with airborne particles and five specifically dealt with modelling the dispersion of airborne particles. I have taught Air Quality courses at Conestoga and Sheridan Colleges.
3. I have been retained as an air pollution dispersion modelling expert in approximately a half-dozen litigation (mainly land re-zoning) disputes which have involved peer-reviews. I have been qualified to give opinion evidence before the Ontario Municipal Board (“OMB") on matters of air quality.
4. I have assisted the Town of Oakville develop their Health Protection and Air Quality (HPAQ) Bylaw, specifically aimed at assessing stationary facility emissions of fine particulate matter (“PM2.5”). This involved the setting of air quality threshold standards for Oakville. As part of that work I have also written dispersion modelling and air impact assessment guidance to support their regulatory system.
5. For aggregate pits, specifically, I have carried out an air assessment on behalf of a proponent directly in Ontario and also supervised an assessment of dust emissions from a limestone quarrying operation in the Caribbean as part of an environmental assessment for a waste dump expansion. More recently I have directed the air emission assessment of multiple limestone quarries in the Caribbean in relation to an Environmental Impact Assessment for a garbage incinerator. I have peer-reviewed air assessments for two other Ontario aggregate operations before this. I have reviewed two proposed aggregate operations brought before the OMB and a Joint Review Tribunal. Also, I have carried out an assessment for Environmental Compliance Approval (“ECA”) purposes for a brick manufacturing facility that has ancillary aggregate-like operations. Additionally, I have managed noise impact assessments for asphalt plants which have ancillary aggregate-like operations. I have also conducted dust assessments, or supervised the assessment, for a number of industrial operations which have aggregate operation-like activities on-site. A copy of my Curriculum Vitae is attached as Appendix A.
1.2. Retainer
6. I have been retained by the Concerned Residents Association of North Dumfries (“CRAND”) to review reports prepared for Preston Sand and Gravel Company Ltd. on the potential air quality and dust levels, in the community surrounding the proposed Henning Pit, caused by operation of the proposed pit.
7. I have signed and executed the Acknowledgement of Expert’s Duty for the Board.
1.3. Attachments
APPENDIX A Curriculum Vitae of Dr. Franco DiGiovanni
APPENDIX B IARC Report on Carcinogens
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APPENDIX C AP-42 Chapter 11.19.2 Crushed Stone Processing and Pulverized Mineral Processing
APPENDIX D AP-42 Chapter 13.2.2 Unpaved Roads
APPENDIX E Excerpts from Rosbury, K. D., 1985. Handbook, Dust Control at Hazardous Waste Sites, EPA/540/2-85/003.
APPENDIX F Fitz, D. R. and K. Burmiller (2000). Evaluation of Watering to Control Dust in High Winds. J. A&WMA, 50, pp. 570-577.
APPENDIX G AP-42 Chapter 13.2.1 Paved Roads
APPENDIX H Ontario's Ambient Air Quality Criteria, Ministry of the Environment, April 2012 (PIBS 6570e01)
APPENDIX I Excerpts from Great Lakes Regional Toxic Air Emissions Inventory Steering Committee, Assessment of Benzo(a)pyrene Air Emissions in the Great Lakes Region, March 2007 http://www.glc.org/air/BapReport.pdf
APPENDIX J AP-42 Chapter 2.5 Open Burning
APPENDIX K AP-42 Introduction
APPENDIX L Lall, R., M. Kendall, K. Ito and G. D. Thurston (2004): Estimation of Historical Annual PM2.5 Exposures for Health Effects Assessments, Atmos. Env. , 38, pp. 5217-5226.
APPENDIX M Brook et al. [1997] The Relationship among TSP, PM10, PM2.5 and Inorganic Constituents of Atmospheric Particulate Matter at Multiple Canadian Locations; J. Air & Waste Manage. Assoc. 47, p.2-19
APPENDIX N AP-42 Chapter 13.2.4 Aggregate Handling and Storage Piles
APPENDIX O Parker, M., Diamond, G., Orphan, L. A Summary of Air Monitoring in the Beachville Area, Ministry of Environment, January 2003.
1.4. Issues to be Addressed
8. I will be addressing the following issues in my witness statement:
ISSUE 6
Does the proposal meet the requirements of the Aggregate Resources Act and the Aggregate Resources of Ontario Provincial Standards? In particular: b. Has the proponent demonstrated that dust and air emissions will be mitigated on site? Should dust and air mitigation measures for each component of the operation be specified in more detail in the Site Plans? c. Should material specifications and the permitted volume of imported material for recycling be detailed in the Site Plans? d. Do the Site Plans adequately protect against continuation of the recycling use after the resource has been substantially depleted?
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e. Should the Site Plans specify what type or how much crushing is to take place at the pit face as opposed to the central processing facility? f. Should the specifications for inert fill to be used for rehabilitation and any testing and monitoring requirements be specified in the Site Plans? g. Has consideration been given to the compliance history of the proponent?
ISSUE 10
Air and Dust
a. Whether dust and air emissions that may cause adverse effects have been appropriately assessed, having regard to:
i. Whether the appropriate operating scenarios, equipment locations and emissions rates were assessed in accordance with sections 10 and 11 of O. Reg. 419/05 and MOE Guidance;
ii. Whether all sources of emissions that may cause adverse effects and all airborne contaminants that can cause adverse effects were assessed, using the AERMOD dispersion model, in accordance with the dispersion modeling prescribed in Sections 6 through 17 of O. Reg. 419/05 and MOE dispersion modelling guidance;
iii. Whether sufficient evidence, i.e. appropriate data shown and references cited, to support all relevant assessment inputs, emissions rates and control efficiencies have been provided; and
iv. Whether all relevant outputs or predictions, including with respect to PM 2.5, to determine risk of adverse effects have been provided.
b. Does the proposed Henning Pit adhere to the PPS with respect to
preventing adverse effects from dust and air emissions?
ISSUE 11
Does the proposed Henning Pit adhere to the PPS, and if required the Ministry of Natural Resources and Ministry of Environment Statements of Environmental Values with respect to preventing adverse effects and risk to public health from dust and air emissions, and if required have the cumulative effects of other pits and sources in the area been properly considered?
ISSUE 12
Does the proposed Henning Pit prevent adverse effects from dust and air emissions and if not, what mitigation should be included?
ISSUE 13
Should the proposed Henning pit include conditions that monitor the dust and other relevant air emissions from the proposed operation?
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ISSUE 14
Are sensitive land uses protected from the proposed pit to prevent adverse effects from dust and air emissions?
1.5. Documents Reviewed
1. RWDI Air Quality Assessment Report, RWDI, November 14, 2013
2. Model Input Files received from RWDI on November 19th, 2013, November 26th,
2013, November 27th, 2013 and November 28th, 2013 (partially reviewed)
3. Spreadsheets with sample calculations received from RWDI on November 26th,
2013, November 27th, 2013 and November 28th, 2013 (partially reviewed)
4. Air Expert's Meeting Notes, August 26, 2013
5. Environmental Protection Act, ONTARIO REGULATION 419/05 (O. Reg. 419/05)
AIR POLLUTION — LOCAL AIR QUALITY; Consolidation Period: From February
1, 2013.
6. SUMMARY of STANDARDS and GUIDELINES to support Ontario Regulation
419/05 - Air Pollution – Local Air Quality (including Schedule 6 of O. Reg. 419/05
on UPPER RISK THRESHOLDS); MOE PIBS # 6569e01, April 2012
7. Jurisdictional Screening Level (JSL) List – A Screening Tool for Ontario Regulation
419: Air Pollution – Local Air Quality, Ministry of the Environment (PIBs # 6547e),
February 2008
8. Procedure for Preparing an Emission Summary and Dispersion Modelling Report,
Ministry of the Environment, February 2008 (PIBs # 3614e03)
9. Ontario's Ambient Air Quality Criteria, Ministry of the Environment, April 2012
(PIBS 6570e01)
10. Guidance Document on Achievement Determination Canadian Ambient Air Quality
Standards (CAAQS) for Fine Particulate Matter and Ozone, Canadian Council of
Ministers of the Environment, 2012 PN 1483
11. AP-42: Compilation of Air Pollutant Emission Factors Volume 1: Stationary Point
and Area Sources, U.S. Environmental Protection Agency, Fifth Edition, January
1995
12. Alberta Environment. 2009, Air Quality Model Guideline
13. British Columbia Ministry of Environment, Guidelines for Air Quality Dispersion
Modelling in British Columbia, March 2008.
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14. World Health Organization (WHO) Europe. (2004). Health Aspects of Air Pollution
(2004). Results from the WHO project ‘Systematic Review of Health Aspects of Air
Pollution in Europe’.
15. Toronto Public Health (TPH). (2004). Agenda for Action on Air and Health.
Prepared by Kim Perrotta, Monica Campbell, Angela Li-Muller, Ronald
MacFarlane, Sarah Gingrich. Toronto, Ontario: July 2004
16. Filling the Gaps in the Regulation of Fine Particulate Matter, Office of the Environmental Commissioner of Ontario. Serving the Public; Annual Report, 2012-2013 (Section 5.8)
17. “Canada-wide Standards for Particulate Matter and Ozone: Five Year Report:
2000-2005 (CCME November 2006)
18. US EPA. 2005. 40 CFR Part 51 Revision to the Guideline on Air Quality Models:
Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion
Model and Other Revisions; Final Rule.
19. Ministry of Transportation, Environmental Guide for Assessing and Mitigating the
Air Quality Impacts and Greenhouse Gas Emissions of Provincial Transportation
Projects. January 2012.
20. Rosbury, K. D., 1985. Handbook, Dust Control at Hazardous Waste Sites,
EPA/540/2-85/003.
21. Fitz, D. R. and K. Burmiller (2000). Evaluation of Watering to Control Dust in High Winds. J. A&WMA, 50, pp. 570-577.
22. Golder Associates, 2012. Determination of Natural Winter Mitigation of Road Dust
Emissions from Mining Operations in Northern Canada, Report No. 11-1365-0012-
6050/DCN-091, submitted to De Beers Canada Inc.
23. Great Lakes Regional Toxic Air Emissions Inventory Steering Committee,
Assessment of Benzo(a)pyrene Air Emissions in the Great Lakes Region, March
2007 http://www.glc.org/air/BapReport.pdf
24. Benzo[a]pyrene, 1988 August, Environmental and Workplace Health. Health Canada [Retrieved December 12, 2013, from: http://www.hc-sc.gc.ca/ewh-semt/pubs/water-eau/benzo_a_pyrene/index-eng.php]
25. Benzo[a]pyrene, International Agency for Research on Cancer. World Health Organization [Retrieved December 12, 2013, from: http://monographs.iarc.fr/ENG/Monographs/vol100F/mono100F-14.pdf]
26. Lall, R., M. Kendall, K. Ito and G. D. Thurston (2004): Estimation of Historical Annual PM2.5 Exposures for Health Effects Assessments, Atmos. Env. , 38, pp. 5217-5226.
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27. Brook et al. [1997] The Relationship among TSP, PM10, PM2.5 ; J. Air & Waste
Manage. Assoc. 47, p.2-19
28. United States Environmental Protection Agency (1996). Ambient Levels and
Noncancer Health effects of Inhaled Crystalline Silica and Amorphous Silica:
Health Issue Assessment. EPA/600/R-95-115.
29. ADDENDUM USER'S GUIDE FOR THE AMS/EPA REGULATORY MODEL -
AERMOD (EPA-454/B-03-001), October 2009
30. Parker, M., Diamond, G., Orphan, L. A Summary of Air Monitoring in the Beachville Area, Ontario Ministry of Environment, January 2003.
1.6. Site Visit and Location
9. I visited the location of the proposed Henning Pit and toured the surrounding landscape on July 29 th 2013.
PART 2: ORGANISATION OF WITNESS STATEMENT
10. For my witness statement I will:
(1) provide a general introduction to air assessments;
(2) describe Preston’s air assessment;
(3) peer review Preston’s air assessment; and
(4) provide overall conclusions including a summary of my evidence on issues before the Board related to air quality.
11. I wish to advise the Board that this witness statement is not a complete or sufficient peer review of RWDI Air Inc.’s (“RWDI”) Air Quality Assessment (the “Air Quality Assessment” or AQA). I received RWDI’s Air Quality Assessment on November 14, 2013. I subsequently uncovered a large number of errors and omissions. In response to my queries regarding these errors, I received updated information and corrections from RWDI up to and including December 9, 2013, as I explain further in my statement. By this time, I had completed much of my review and analysis, and did not have the time or capacity for instant re-analysis. As such, I cannot consider my review and analysis of the Air Quality Assessment as a complete peer review at this point.
12. Furthermore, to be completely thorough, I would want a conservative Air Quality assessment without missing information that refrains from non-conservative assumptions.
13. The incomplete RWDI study prejudices my ability to inform the Board, legal counsel, the client and CRAND’s health expert regarding potential human health impacts or other environmental impacts.
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2.1 Review Process
14. On November 14, 2013, I received the Air Quality Assessment on behalf of Preston Sand and Gravel Company Ltd. (“Preston”).
15. However, a number of events have transpired since November 14, 2013, to make it virtually impossible to complete this review in an appropriate fashion and provide a witness statement by December 13, 2013:
15.1. The AERMOD modelling files did not accompany the Air Quality Assessment that we received on November 14, 2013, as they should have and is standard industry practice. Despite more than one request, I was not supplied with all files necessary until Thursday November 28, 2013.
15.2. The report provided incorrect information on the terrain input file used; that was
not clarified, nor the correct file supplied, until November 28, 2013.
15.3. There were numerous citations in the report that were not fully described in the reference list, which is what I would have expected at the outset. I have had to request those references separately; we have still not received all of them.
15.4. Some citations in the Air Quality Assessment refer to specific sections, or secondary references, in those citations. These were not specified in detail in the report and, therefore, it was unclear exactly what reference was used. This required further clarification and additional time to be spent.
15.5. Sample calculations for emissions and cumulative concentrations, were not included in the Air Quality Assessment received on November 14, 2013, as I would have expected. I have had to request those calculations, delaying review further. Some of the sample calculations provided in the Air Quality Assessment, and the example calculations requested, had arithmetic errors and required further correspondence to clarify. In some cases, I have had to request the same sample calculations multiple times. I am still waiting for some information requests to be fulfilled.
15.6. Upon requesting spreadsheets of RWDI’s emission calculations, the request was
declined by RWDI. Having these spreadsheets would ease the burden of error checking of calculations and assumptions.
15.7. Upon requesting verification of truck weight data (required to estimate road dust
emissions), RWDI altered their opinion on truck weights and truck traffic frequency on November 29, 2013 (compared to the values used in the report of November 14, 2013). RWDI explained that this error would lead to modified emission estimate calculations and new model runs. RWDI sent me revised tables for their air assessment report on December 2, 2013 as a Word document and as a pdf document which, I assume, includes their estimates of how the altered truck weights and traffic volumes affected the final results. There was no mention of new sample calculations or of new model files to accompany these revised tables. Since I have already spent a substantial amount of time dealing with what was sent to me in the first place, to redo all of this analysis would take me more time than I now have (in the current procedural order) to re-assess
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results. RWDI claims that the alterations will cause “minimal change” but this must be verified and will require significant additional review time.
15.8. Upon requesting verification of dust emission calculations from the bulk handling
operations, RWDI altered their opinion on emission rates (compared to the values used in the Air Quality Assessment dated November 14, 2013) but did not alter their final air quality results accordingly.
15.9. We only received revised Site Plans on November 28, 2013, which has a bearing
on our analysis. 15.10. Upon requesting verification of background air contaminant levels that RWDI
used in their cumulative assessment, RWDI altered their opinion and provided new data. I have yet to investigate those altered data.
15.11. I was originally informed by RWDI that they estimated existing background
concentrations of Benzo(a)pyrene (BaP), a possible carcinogen, using data from the period 2007-2011. Upon requesting verification of RWDI’s estimates of background BaP, RWDI informed me that, in fact, they used data from the period 2006-2010. I had already spent significant time trying to check the 2007–2011 data in order to compare against their results (without success and hence prompting my questioning). A re-analysis is pending.
15.12. On December 9, 2013, RWDI sent e-mail correspondence to me advising the scenario description table previously provided is incorrect. RWDI also informed me in the email of December 9, 2013 that the spreadsheet of model results provided to me during the week of December 2, 2013, was only partially updated with the latest model results. RWDI informs me it will “shortly provide” an updated spreadsheet to address its errors.
16. In isolation, some of the issues above would not have caused significant delay; however, taken together, the additional review time caused by the above issues is significant. This additional review and wasted effort has added a new and serious additional cost to our work and has caused me to waste over 150 hours on work that is unusable as a result of these errors and omissions.
17. In my opinion, the number and type of errors and missing information also seems to be unusually high.
PART 3: ANALYSIS
3.1. General Introduction to Air Assessments
3.1.1 Introduction
18. One way to determine airborne pollutant levels resulting from emissions from an aggregate facility would be to measure the levels of all substances emitted into the surrounding community. However, actual measurements will not be available for a proposed aggregate project; instead, we have to rely on predicted changes in air quality to assess estimated changes in local air pollution levels.
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19. Although properly taken measurements provide an accurate assessment of air pollutant levels, the results are limited to the times and places where the measurements were taken. So, for example, if we measure airborne dust downwind of an aggregate operation on one day, we will only get a snap-shot in time of the dust levels at the specific location where the measurement took place. We would not know if that measurement is representative of other times or areas in the surrounding community. Nor would we know if that measurement is representative of the worst-case scenario.
20. To determine worst-case air quality levels, with a high degree of certainty, at all locations in the surrounding community, would require many air samplers located around the aggregate operation and also require sampling for an extended period of time. Many assessments utilize air dispersion modelling to determine air quality levels.
3.1.2 Sources of Emissions
21. There are numerous sources of contaminant emissions at an aggregate operation. They include dust sources such as vehicle loading and unloading with extracted material, road dust from on-site traffic, rock crushing and screening operations, wind erosion of stockpiles and also gaseous emissions from on-site combustion equipment (diesel generators, vehicles, gas-fired heaters, etc.).
22. In regards to the dust emissions, dust particles vary in size and composition. The total amount of dust in the air is known as Total Suspended Particles (“TSP”). The size fractions of dust particles can vary from very fine particles, less than 2.5 micrometres (μm) in aerodynamic diameter, through to particles greater than 44 μm in diameter. Dust particles smaller than 10 μm in aerodynamic diameter are known as “PM10.” The finer dusts (especially those smaller than 2.5 μm in aerodynamic diameter, termed “PM2.5”) are known to cause health effects.
23. In Ontario, TSP is regulated by the Ministry of the Environment (“MOE”) as part of Ontario Regulation 419 (“O.Reg.419/05”), which sets out a point-of-impingement (“POI”) standard of 120 μg/m3 averaged over a 24 hour period. The PM10 and PM2.5 size fractions do not have POI standards under O.Reg.419/05. PM10 has a suggested interim ambient air quality criterion (interim Ambient Air Quality Criterion, or “interim AAQC”). PM2.5 has a “Canada Ambient Air Quality Standard” (“CAAQS”).
24. Dust from aggregate operations also varies by composition. For example, pit road dust may contain the same minerals contained in the overburden soil or the aggregate deposit itself. If the road surface material contains quartz (a common mineral in rocks and soils; a form of crystalline silica) the dust raised may be an inhalation hazard since crystalline silica has known health effects if inhaled (Appendix B).
25. Once all contaminants that can be emitted have been identified these become the “contaminants of concern” (CoCs) for an air quality assessment focused on the impacts of a “subject” facility.
26. In any air assessment, one must consider the locations of sources of emissions at the facility (e.g., rock crushers within an aggregate pit producing dust) and how individual dust source emissions vary over time. It is necessary to consider the maximal emissions that could happen (e.g., all dust sources that may emit at the same time) and the worst-
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case locations for dust sources (if they move around – which is common for aggregate machinery). For example, if material handling, causing dust emissions, may occur at the same time as vehicle movement on roads, also causing dust emissions, this combined scenario should be assessed. A proponent is always free to show that the coincidence of certain emissions is not possible or not permitted at their facility, or that the location of certain dust sources never changes.
27. Emissions from most mechanical sources tend to vary according to activity-level. For example, an aggregate crusher will be designed for a maximum throughput rate of aggregate, and will emit maximum dust levels when at its highest level of activity. It is usual to estimate maximum dust emissions from each source at maximum design rates to ensure capturing the worst-case emissions – a proponent is always free to show that a maximum design level of throughput, and therefore emissions, is not possible or is not permitted at their facility.
28. Most of the emission estimates used in the Air Auality Assessment report, prepared for Preston for the proposed Henning pit, are based on average US EPA Air Pollutant (AP-42) emission factors (EFs). EFs are simple relationships (equations) that link pollutant emissions to equipment activity levels. For example, the US EPA EF for PM10 emissions, from screening operations at an aggregate facility, is 0.0043 kg/tonne (as set out in Table 11.19.2-1, AP-42 11.19.2, Crushed Stone Processing, Appendix C). So, if 800 tonnes of aggregate are processed through a screening machine in 1 day, the EF would predict (on average) that:
800 tonnes x 0.0043kg/tonne = 3.44 kg of PM10 would be emitted, or
approximately 0.04 g/s, on average over that 24 hour period.
29. However, it is important to note that the US EPA EFs were developed for a different purpose than addressing worst-case air quality level assessments. They were provided to help development of long-term and regional-scale ambient air quality levels to address U.S. Clean Air Act requirements for State-specific air quality plans. For this U.S. context, there is no requirement that emissions are worst-case. The US EPA EFs represent averages of a series of measurements, which the US EPA has determined is suitable for the purposes of assessing long-term and regional-scale ambient air quality levels, but do not provide worst case EF’s for assessing worst-case air quality levels from a specific facility.
3.1.3 Modelling Air Concentrations
30. To assess the levels of a contaminant surrounding a facility, due to emissions from that facility, Ontario (and most other jurisdictions) requires the use of quantitative computer models that predict the dispersion of contaminants from a discharge point to a receptor in the surrounding community (“dispersion models”).
31. In its simplest form, a dispersion model requires input on (1) the sources of pollution, including the emission rate, and, (2) meteorological data such as wind speed and turbulence. The model then simulates, mathematically, the pollutant’s transport and diffusion through the air. The model output is an air pollutant concentration level for a
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particular time period at one or more specific receptor locations in the surrounding community.
32. For the proposed Henning aggregate pit, the study by RWDI used a model (the US EPA AERMOD model) that predicted contaminant levels on an hourly basis, using publicly available, hourly meteorological data supplied by Environment Canada meteorological stations. The model also generates 24-hour contaminant levels so that these hourly inputs may be assessed against the 24-hour standards set by the Province. The model can also generate levels based on other averaging periods (e.g., monthly or annual) for comparison against different standards.
33. Dispersion modelling represents a simplification of actual events. For a specific location and specific time period, dispersion modelling is not as accurate as specific measurement of airborne contaminants.
34. The most common air dispersion models used for regulatory compliance in North America are generally accurate within a factor of 2 when compared to actual measurements (as ranked comparisons), but may be even more inaccurate when model results are compared to measurements at specific locations and times (paired comparisons). However, modelling does allow a prediction of changes in air pollution levels when an aggregate facility is altered, and does allow estimates to be made at many locations (“receptors”) and for long periods of time (e.g., years). It is also the only way to estimate air quality levels from a proposed facility.
35. Due in part to the issues with the accuracy of dispersion modelling it is normal practice, when performing an air quality assessment, that dispersion modelling should be conducted in a “conservative” manner. The term “conservative” refers to a methodology that ensures air quality levels are not under-estimated. Because of natural variations in meteorology, and variations in human activities (such as production levels at an aggregate quarry), it is vital to ensure that contaminant levels are not under-predicted, especially for assessments where human health is concerned as under-prediction may result in unanticipated health impacts.
36. Over-prediction of air quality levels carries its own problems (usually borne by the proponent) but this is the accepted practice in Ontario air emission impact assessments (and for other jurisdictions) to overcome inaccuracies in emission estimates and dispersion modelling. For instance, this general approach is required by O.Reg.419/05 for modelling to obtain an Environmental Compliance Approval, unless a proponent conducts additional research work to fill data gaps, refine estimates and improve accuracy.
37. Overall, the practice associated with taking a conservative approach, when conducting a modelled air impact assessment, means that an assessment must combine worst-case emissions with worst-case meteorology to ensure that worst-case air quality levels are estimated at all locations in question. This focus on worst-case impacts (rather than average impacts) is the general practice when conducting an air quality impact assessment.
38. A worst-case analysis was agreed to by Mr. Mike Lepage (Principle, RWDI) and me at our meeting on August 26, 2013 to review CRAND’s proposed issues list.
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3.1.4 Estimated Air Quality Levels in the Surrounding Community
39. As pollutants from the proposed facility (“subject” source) disperse through the air, they will add to pre-existing levels of those same pollutants (so-called “background levels”) emitted from other sources. For example, PM2.5 will be emitted by many of the surrounding “non-subject” facilities (e.g., other aggregate pits in the North Dumfries area), from public roads, as well as from other industrial facilities, etc., in the area. This is illustrated in Figure 1 below:
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Figure 1. Illustrative Mapping of Potential PM2.5 Sources in a 5 km Area around the
proposed Henning Pit Site.
40. In Figure 1, above, locations of pre-existing and projected pits and quarries in the surrounding area are shown in red-brown, the site of the proposed Henning Pit in bright red, and the sites of potential industrial PM2.5 emitters are shown in blue.
41. However, background levels of air pollutants are not the same at all locations. For example, closer to a non-subject source background levels will be higher as they will be affected by emissions of CoCs from that non-subject source. A specific example would be consideration of other aggregate pits in the area. These facilities will emit PM2.5 (for example) due to aggregate processing, unpaved road traffic, and diesel engine operation. These facilities will also emit oxides of nitrogen (“NOx“) from diesel engine exhausts. Therefore, locations closer to other aggregate pits will experience higher background levels of PM2.5 and NOx, for example.
42. In theory all sources, no matter how far away, will contribute to air quality levels at locations in North Dumfries; in practice, however, it is found that only sources within a relatively short distance will cause significant variations in background levels. Beyond that short distance, emissions from all non-subject sources will “merge” together.
1 km
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43. This concept, of dividing background air levels into “regional” and “local” components is well established and is formalized in various regulatory modelling guides and regulations around the world. For example, the Province of Alberta Air Quality Model Guideline describes methods dividing background into these two components where section 3.9 (“Cumulative Effects Assessment of Nearby Emission Sources”) describes inclusion of local non-subject sources and section 4.2 (“Baseline Concentrations”) describes the addition of regional background. In addition, the United States regulatory air quality dispersion modelling is guided by the "Guideline on Air Quality Models" and is incorporated by reference in the American regulations for the Prevention of Significant Deterioration of Air Quality, Title 40, Code of Federal Regulations (CFR) sections 51.166 and 52.21 in June 1978 [Federal Register, 43 (118), 26 382-26 388]. Part 51 paragraph 8.2.3 describes division of background into local and distant sources.
44. Emissions from local, anthropogenic (“man-made”), non-subject sources can be divided into mobile (on-public-roads vehicle emissions) and stationary sources. Mobile sources (e.g., on-road vehicles) emit CoCs via tail-pipe emissions and via re-suspension of road dust (causing, for example, emissions of PM2.5).
45. In assessing background concentrations, biogenic (“natural”) emissions should be considered.
46. The Province of British Columbia “Guidelines for Air Quality Dispersion Modelling in British Columbia” section 10.1 (“Model Output – the Need to Add Background”) provides advice on an order of preference among different techniques to estimate background concentrations of CoCs.
47. The BC modelling guide (page 82) indicates the order of preference as, sequentially:
“a network of long-term ambient monitoring stations near the source under
study
long-term ambient monitoring at a different location that is adequately
representative
modelled background”.
3.1.5 Use of Air Quality Assessment Modelling Results
48. In my experience conducting peer reviews of air quality reports, a health impact expert frequently provides their opinion in the form of a human health risk assessment for the CoCs estimated by the modelling.
49. I am not a health impact expert. However, below are a few references indicating the dangers of PM2.5.
50. World Health Organization (WHO) Europe. (2004). Health Aspects of Air Pollution (2004). Results from the WHO project ‘Systematic Review of Health Aspects of Air Pollution in Europe’:
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“Many studies have found that fine particles (usually measured as PM2.5) have serious effects on health, such as increases in mortality rates and in emergency hospital admissions for cardiovascular and respiratory reasons. Thus there is good reason to reduce exposure to such particles.”
51. Toronto Public Health (TPH). (2004). Agenda for Action on Air and Health. Prepared by
Kim Perrotta, Monica Campbell, Angela Li-Muller, Ronald MacFarlane, Sarah Gingrich. Toronto, Ontario: July 2004, referring to PM2.5 as one of the five air pollutants:
“The premature deaths and hospital admissions estimated for the five air pollutants in the Toronto Air Pollution Burden of Illness study are associated with air levels that are well below both, Ontario’s existing ambient air quality criteria (AAQC) and the new Canada-wide Standards (CWS) developed by the Canadian Council of Ministers of the Environment (CCME).”
52. Filling the Gaps in the Regulation of Fine Particulate Matter, Office of the Environmental Commissioner of Ontario. Serving the Public; Annual Report, 2012-2013 (Section 5.8)
Particles less than 10 micrometres (μm) in diameter can be inhaled, and particulate matter smaller than 2.5 μm (PM2.5) is able to penetrate deep into the lungs where there is a diminished capacity to remove contaminants. Anthropogenic emissions of PM2.5 are produced primarily by fuel combustion (e.g., gasoline and diesel engines, wood burning, etc.), industrial activities, and disturbance of open sources, such as dust, during construction, resource extraction, etc. Secondary PM2.5 is produced through reactions between gaseous substances known as precursor emissions. Transboundary emissions from the United States are also a significant source of PM2.5 in Ontario. Evidence shows that exposure to particulate matter is a cause of a number of serious and fatal health effects, including chronic bronchitis and asthma, reduced lung function, and increases in hospitalization and mortality due to cardiorespiratory diseases. Health risk increases with exposure to PM2.5, and there is no known threshold below which adverse health effects are not anticipated.
53. A Canadian document about PM2.5 standards: “Canada-wide Standards for Particulate
Matter and Ozone: Five Year Report: 2000-2005” (CCME November 2006):
The long-term air quality management goal for PM and ozone is to minimize the risks of these pollutants to human health and the environment. There is clear evidence of the harmful effects of these pollutants throughout the range of concentrations to which Canadians are exposed. This means that any reduction in the ambient levels of these pollutants provides a reduction in population health risk. The CWSs for PM and Ozone were endorsed by CCME in June 2000. They represent a balance between the desire to achieve the best health and environmental protection possible in the relative near-term and the feasibility and costs of reducing the pollutant emissions that contribute to elevated levels of PM and ozone in ambient air.
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54. According to the above references, it seems prudent to pass any resulting conclusions of
PM2.5 concentrations on to a health impacts expert, as was my experience in the Nelson Aggregated Case (Re Nelson Aggregates Co. Case No.: 20-030).
55. In my experience of air quality reports, an ecological expert could also provide their opinion in the form of an ecological risk assessment for the CoCs estimated by the modelling.
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3.2. Overview of RWDI's Air Assessment
57. PSG hired RWDI to conduct an air quality assessment. This is reported in the document “Air Quality Assessment,” (“AQA”) dated and supplied to me 14 November 2013.
3.2.1 Air Assessment
58. The report purported to identify all sources of dust but did not include some dust-generating activities such as the removal of overburden or site rehabilitation activities once the quarrying operations have ceased.
59. RWDI used US EPA AP-42 EF’s to attempt to simulate maximal dust emitting conditions. RWDI also attempted to quantify the effect of certain dust control activities such as the watering of roads and watering of material handling.
60. The resultant emission estimates were inserted into the US EPA AERMOD dispersion model.
61. By this means, levels of PM2.5, PM10 and TSP and other CoCs were estimated in the surrounding community for planned operations at the site. RWDI attempted to add background levels of contaminants to provide cumulative levels in the surrounding community.
62. RWDI did not assess cumulative concentrations at all locations, just at (seeming) residences (and one church) in 7 locations. Therefore, experts in ecological impacts may not have information available to assess ecological impacts at areas most highly exposed to air emissions.
63. Since Preston did not initiate an air monitoring program as part of their initial site investigations, RWDI estimated background by attempting to estimate regional background levels (by analyzing data from other monitoring locations) and adding local sources (but only for two, adjacent, non-subject facilities).
64. RWDI attempted to assess the composition of the dust formed of the aggregate in the pit and recycled materials (concrete and asphalt). I believe their compositional analysis to be faulty for reasons I will provide below. In addition, they did not account for the composition of any dust suppressants added to roads and that may become airborne. Nor did they assess asphalt dust as part of the road dust emissions that would occur due to their using crushed asphalt for covering of unpaved traffic areas (AQA, s.5.13 p.14 and s.5.16 p.15)
65. Based on the results obtained RWDI concluded that the proposed aggregate pit will not “significantly” exceed the air quality thresholds for the contaminants they considered, based upon the methods and assumptions used in their work.
3.2.2 Proposed Dust Controls
66. RWDI has proposed various dust control methods cited in different sections of their report. For example, RWDI proposed the following in their report:
Recommended dust control provisions, including those already required by prescribed conditions within the MNR’s Provincial Standards, are as follows:
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- Dust will be mitigated on site;
- Water or another provincially approved dust suppressant will be applied to
internal haul roads and processing areas as often as required to mitigate dust;
- Processing equipment will be equipped with dust suppressing or collection
devices;
- Operation of an auxiliary processing plant will not coincide with that of the
central plant;
- The auxiliary plant will be operated with a maximum extraction and processing
rate of 2200 tonnes/day, and a minimum setback of 125m from the property line for crushers, screens, loader dumping and conveyor transfers;
- Traffic speed control will be encouraged and a speed limit of 35 km/h will be
posted on site;
- Site entrance will be cleaned as often as necessary to control track-out onto the
public road;
- Stripping of overburden will be limited to periods when excavation, processing
and shipping are at levels well below the levels assessed;
- Potential will be visually monitored, and the amount/frequency of water and/or
traffic speeds will be adjusted as needed to prevent visible dust;
- These measures will be incorporated into a Best Management Practices plan for
the site, which would also set out staff training plans, equipment maintenance plans (water sprays, water truck, etc.), and record keeping and monitoring/inspection to verify compliance with the plan.
67. RWDI reviewed road dust control efficiencies obtained by watering, efficiencies noted at other locations (from published studies) and under varying conditions. No calculations were provided to predict or explain how road dust control efficiency will be achieved for the proposed Henning Pit itself.
68. Of the range of dust control efficiencies cited by RWDI, they used a high-end estimate of dust control efficiency. This would not constitute a worst-case assumption for emissions.
69. On p. 15 of the AQA, RWDI claim that a watering rate of 0.5 – 1 L/m2/hour will be required but do not provide calculations to link those watering rates to the claimed dust control efficiency. RWDI claim this is needed when the “weather is hot and dry” but do not define the degree of “hot and dry” necessary.
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3.3. Detailed Review of RWDI's Air Assessments
70. My review of RWDI’s report will generally follow the order of the subject matter presented in the previous section, General Introduction to Air Assessments. Thus I will speak to emissions estimate first, secondly how RWDI estimated that those emissions would disperse into the surrounding community, thirdly how RWDI estimated that those dispersions would combine with pre-existing background levels of CoCs, and finally, how those resultant air quality levels are interpreted in relation to air quality thresholds.
3.3.1 Henning Pit Emissions
71. I compared the emission rates for each of the different sources of (for example) PM2.5 to determine which emission sources were most important. This was based on the information provided to me by RWDI.
72. The average emission rates for bulk material and storage piles (which RWDI assumed varied with wind speed) were used in this comparison. For the purposes of comparison, road dust was assumed to be controlled using a control efficiency of 90% (as assumed by RWDI, Table 1) and of 0% (RWDI AQA, Appendix C).
Table 1: Ranking of RWDI emission rates assuming road dust was controlled with a control efficiency of 90%, for 2 Phases at the proposed Henning Pit.
Table 2: Ranking of RWDI emission rates assuming road dust was not controlled (i.e. control efficiency = 0%), for 2 Phases at the proposed Henning Pit.
73. This clearly demonstrates the dominance of road dust emissions, whether controlled or not controlled, among all dust sources. A similar pattern will be evident for coarser dust fractions (PM10 and TSP) as well as constituents of dust.
Phase 1 Phase 3
road dust-controlled 90% 64% 37%
vehicle exhaust 10% 38%
bulk material 6% 7%
process fugitives 9% 4%
storage piles 2% 3%
diesel generator 10% 10%
Phase 1 Phase 3
road dust-uncontrolled 95% 85%
vehicle exhaust 1% 9%
bulk material (cumulative) 1% 2%
process fugitives 1% 1%
storage piles 0% 1%
diesel generator 1% 2%
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3.3.1.1 Road dust
3.3.1.1.1 Introduction
74. Road dust is emitted from a road when a vehicle drives over the road. The force of the wheels and draft produced by the vehicle causes the finer loose material on the road surface to become suspended in the air. The major variables that affect the amount of road dust generated are: silt levels, traffic frequency, and vehicle weight. These factors are used in the quantitative estimation of emissions by the use of AP-42 emission factors. In general, site specific values for these variables should be determined for use in an air quality assessment.
75. RWDI provided data on truck weights and traffic frequency for the proposed operations at the Henning Pit. However, they altered their opinion on that data and provided new results on December 2nd. I have not had time to review the new results as explained earlier. Also, it would seem that revised dispersion model input and output files and calculations were not provided for review.
3.3.1.1.2 Silt Levels
76. Silt levels on roads can have a significant influence on dust emissions. Silt is dust particles on the road surface that are less than 75 μm in diameter. Essentially, silt levels indicate the “dustiness” of the road. Silt levels are measured in two different ways: as silt loading (measured as g/m2, used for Paved Road emissions calculations), and as silt content (measured as a percentage [%], used for Unpaved Road emissions calculations).
77. Silt levels can vary depending on a number of factors such as the level of dust control and the weather.
78. The AP42 document on unpaved roads (s. 13.2.2, Unpaved Roads, Appendix D) presents typical values for silt content on unpaved roads in the stone quarrying and processing industry and shows them to be highly variable. For sand and gravel processing, the range provided is 4.1 to 7.1% and for stone quarrying and processing the range provided is 2.4 – 16%. However these data are based on samples from a limited number of sites.
79. The following explanation was provided in Appendix C (Unpaved Roads) of the RWDI report:
“For unpaved internal roads, AP-42 gives a silt content range of 4.1%-6.0% (mean value of 4.8%) for sand and gravel processing, based on just 3 samples at one site. For Stone quarrying the range is 2.4% - 16% (mean value of 10%), based on 10 samples at 2 sites. In reality, silt content will vary along a haul route, being low in some places and higher in others. Therefore, it would be unrealistic to assume 16% along an entire haul route and a mean value of 10%, which represents the average of the stone quarrying data set and is conservative compared to the 4.1% - 6% range indicated for sand and gravel processing, were used. RWDI recently analyzed some samples from two other gravel pits in Central Ontario. A haul road sample from one site had silt = 13.6%. A sample
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from the other site was silt = 1.3%. RWDI also previously measured an average value of 7.9% from a limestone quarry operation in Southwestern Ontario. These values are reasonably consistent with the range reported by AP-42 for sand and gravel and stone quarrying (2.4% to 16% within an overall average around 9%). So, the value of S = 10% is a realistic and reasonable assumption.”
80. RWDI used their own data to provide a basis for their estimate of the silt content to be used for the Henning Pit analysis. It is unknown if this data has been published and verified. I would need to review that data to ensure its credibility. I did not have the time to verify RWDI’s data, as indicated above, and therefore, their interpretation remains uncertain.
81. RWDI assumed a silt content of 10% and opined that it is a “reasonable” assumption. However the requirement, where specific data is lacking, is to assume worst-case for emission calculations. It is unknown whether even the upper limit of the silt content provided by the US EPA (16%) would truly represent a worst-case at the proposed site.
82. There was no indication in the report that RWDI attempted to contact any of the many quarries in the region, to request either their silt measurements or to take measurements of the silt content themselves.
83. It is not reasonable to use a silt content value of 10% to represent the worst-case, as required by our agreement; rather they seem to have used an average case.
84. Given the high degree of variability in silt content on a site, there is a risk that silt content has been underestimated, resulting in an underestimate of dust emissions and, in turn, an underestimate of community air quality levels.
85. To illustrate the potential impacts of underestimating silt content, I have displayed (later) RWDI’s modelling results modified by the use of silt content increased from 10% up to 16%, the maximum value provided in the limited dataset provided in AP-42.
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3.3.1.1.3 Dust Control by Watering
86. In order to control dust emissions from unpaved roads, watering (and other chemicals) can be applied but the results are quite variable as discussed in the AP-42 documentation:
“The necessary reapplication frequency varies from several minutes for plain water under summertime conditions to several weeks or months for chemical dust suppressants.
Watering increases the moisture content, which conglomerates particles and reduces their likelihood to become suspended when vehicles pass over the surface. The control efficiency depends on how fast the road dries after water is added. This in turn depends on (a) the amount (per unit road surface area) of water added during each application; (b) the period of time between applications; (c) the weight, speed and number of vehicles traveling over the watered road during the period between applications; and (d) meteorological conditions (temperature, wind speed, cloud cover, etc.) that affect evaporation during the period.” (AP42, 13.2.2-10: Appendix D)
87. A control efficiency of 90% for the watering control of unpaved roads is assumed in the emission calculations by RWDI. This is not the worst-case value for watering control efficiency presented in the review provided by RWDI (AQA p. 16, Table 5) but rather tends to the best-case scenario out of a broad range of values given.
88. Table 5 is RWDI’s compilation of some literature sources that have information on control efficiencies for several methods used to reduce dust emissions. However, the range of control efficiency values shown in the Table 5 does not represent the full range provided in citations given. For example, in the Rosbury (1985) reference (Appendix E), control efficiencies are shown to range from 25-98% for reapplication of water once per hour or more often, rather than the 58-98% range cited in the RWDI report. The table cited therein shows even lower control efficiencies but we have not been able to fully investigate the values shown. Additionally, this particular reference does not directly provide the full description of conditions when those efficiencies were estimated or measured. This missing information is critically important since site conditions can affect dust control efficiency by watering; therefore, it renders the applicability of these estimates to the Henning Pit uncertain.
89. The study of Fitz and Burmiller (2000) (Appendix F), cited by RWDI, suggests a control efficiency of 90% for dust reduction using watering control. However, the measurements were made during windy conditions (wind speeds higher than 9 m/s) and no measurements were conducted at lower wind conditions. RWDI does not indicate whether the wind conditions at the proposed Henning site will always experience these types of wind speeds; therefore, it renders the applicability of these estimates to the Henning Pit uncertain.
90. Table 5 also presents data from a study by Golder Associates (2012). It is unknown if that data has been published and verified. I would need to review that data to ensure its credibility. I did not have the time to fully verify Golder’s data and therefore, its use remains uncertain to me. However I was able to ascertain that the study was conducted in a very different environment (northern climate, different composition of soil) to the site
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of the proposed Henning Pit and therefore, the applicability of those findings would seem questionable at this point.
91. Also, Table 5 presents (for water use on unpaved areas) misleading information in the reference of the 75%-95% control efficiency attributed to the AP-42 chapter for Unpaved Roads [the EPA (2006) reference in the RWDI report] (Appendix D). In the AP-42 for Unpaved Roads, Figure 13.2.2-2 shows the relationship between control efficiency and moisture ratio. The range of 75-95% control efficiency (cited in the RWDI report) corresponds to road surface moisture ratios higher than 2. It is worth mentioning that according to this figure, the control efficiency sharply drops when the moisture ratio is below 2. For example, if the moisture ratio is just slightly higher than 1 one could estimate a control efficiency of 5%. Therefore, without knowledge of the moisture ratio at the site a much broader range of control efficiency of 5-95% is possible, not the range of 75-95% cited by RWDI. The RWDI report does not specify how the soil moisture ratio can be estimated from the watering frequency they specified.
92. As mentioned earlier RWDI have not presented calculations linking on-site conditions to the dust control efficiency claimed. Therefore, their estimate of 90% control efficiency is uncertain and unverifiable.
93. To explore the implications of this, I compare the results of tests for control efficiency of 58% (the worst-case value presented in the RWDI report) and a control efficiency of 25%, the worst-case value presented in the Rosbury (1985) reference (Appendix E), with those obtained by RWDI’s predicted concentrations. These results are presented later.
3.3.1.1.4 Paved Roads
94. Dust “trackout” refers to the spread of dust on to paved roads carried from unpaved areas by vehicles. Trackout can increase dust emissions from a site by increasing the effective length of dusty roads present.
95. RWDI noted in their air assessment report on page iii “Site entrance will be cleaned as often as necessary to control track-out onto the public road” and on page 19 “Track-out minimized (wheel washing if appropriate).” Thus, RWDI are aware of the trackout of dust from the quarry unpaved road surface out on to the paved public roads in the vicinity; however, RWDI did not provide any quantitative assessment of this.
96. Silt loading along the paved roads will vary with distance from the unpaved areas. Silt loading will be high close to unpaved areas and reduce further away. This is because loose material from unpaved areas will be tracked out by vehicles onto the paved roads. This should be considered when taking silt loading measurements as indicated by the US EPA:
“To adjust the baseline silt loadings for mud/dirt trackout, the number of trackout points is required. It is recommended that in calculating PM-10 emissions, six additional miles of road be added for each active trackout point from an active construction site, to the paved road mileage of the specified category within the county. In calculating PM-2.5 emissions, it is recommended that three additional miles of road be added for each
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trackout point from an active construction site.” (AP42 13.2.1-9: Appendix G)
97. There is no indication that RWDI accounted for trackout points as recommended. By following US EPA methods, and adding an extra length of road to account for trackout, the extra amount of silt near the trackout point will be accounted for. This should be done if no values for silt loading are available at the trackout point.
98. Dust trackout should have been assessed. By not assessing it, RWDI will underestimate the worst-case dust emissions in their report and therefore underestimate community-level exposure to dust as a result.
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3.3.1.2 Dust Composition
3.3.1.2.1 Composition of Aggregate
100. Dust is not only classified by size but also by composition. Some of the dusts emitted from the proposed Henning Pit will have a geologic origin and will be composed of the same minerals contained in the aggregate or overburden (soils). Dusts of different composition will also be emitted from the processing of man-made materials such as the concrete and asphalt planned for on-site recycling.
101. RWDI indicates that the aggregate will contain significant quantities of crystalline silica (quartz). Any crystalline silica in the suspended dust can represent an inhalation health hazard to the surrounding community due to potential carcinogenic effects associated with fine particles of crystalline silica (Appendix B). Indeed, the MOE provides an air quality guideline for crystalline silica of 5 μg/m3 averaged over a 24-hour period (Appendix H).
102. RWDI mentions the use of crushed asphalt as cover on areas with vehicular traffic but have not assessed the compositional breakdown of those sources of road dust emissions.
103. As regards aggregate dust, no site specific data is provided, but ample opportunity was available to obtain such data. Site Plans provided on November 28th, 2013 indicate that boreholes were drilled and aggregate samples taken to assess aggregate quality. Analysis should have been conducted on those samples. It is not indicated if those borehole samples are still available for analysis. Even if they are not, there has been opportunity to collect more borehole samples, and have analysis conducted, since the air quality study was commissioned. Instead RWDI relies on studies from other locations.
104. RWDI indicate that the aggregate will consist of unconsolidated limestone and sand. However, no information is provided on the proportions of these two major constituents expected at the site. As regards the limestone portion of the aggregate, RWDI quotes Hewitt (1971); however, the reference was not given nor provided, even though I had requested it. I cannot review nor verify its applicability.
105. As regards the sandy portion of the aggregate, RWDI quotes Hewitt (1963, p. 8 of the AQA); however, the reference was not given nor provided, even though I had requested it. I cannot review nor verify its applicability.
106. In regards to choosing an overall proportion of crystalline silica RWDI admittedly chooses a “middle” value (20% of the composite aggregate) rather than a worst-case value. This is chosen despite the lack of site-specific information, thereby rendering the data used uncertain as well as not worst-case. RWDI would seem to assume (although it is not clear) that all of the aggregate extracted will be formed of equal proportions of limestone and sand whereas different areas of aggregate may have higher portions of sand and therefore contain higher proportions of silica. RWDI have not chosen a conservative, worst-case value, and so does not demonstrate an abundance of caution.
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107. With regard to the sand portion of the aggregate, RWDI oddly claim that silica content of sand is “not high” (AQA, p.8) and yet cite values ranging from 17 to 62%. It is not clear why RWDI indicate this range as “not high.”
108. By not assuming a worst case dust content for silica, RWDI could be underestimating silica content, which may lead to underestimating the exposures to crystalline silica in the surrounding community.
3.3.1.2.2 Composition of Recycled Material
109. PSG intends to recycle two types of man-made materials: asphalt and concrete.
110. With regard to the compositional analysis of concrete RWDI provides some information on the composition of concrete but have not provided information on how the composition of all concrete material to be accepted at the proposed pit varies in composition. RWDI indicate that concrete will consist of a mixture of aggregates and Portland cement.
111. There would seem to be no assessment of the aggregate portion of the concrete; emissions from this source are therefore missing from the assessment.
112. RWDI argue (AQA, p. 11) that certain contaminants are present only in “trace amounts” and were therefore not analyzed explicitly. This includes calcium oxide. However, reference to the composition data sheets provided (“MSDS’s” in Appendix A of the AQA) indicates that this is not the case.
113. Examining the case of calcium oxide in the Portland cement component; the CBM MSDS indicates that Portland cement can make up to 30% of concrete. The cement itself can consist of up to 5% of CaO. Therefore CaO can, according to the composition information provided by RWDI, constitute 1.5% of the concrete. RWDI do not provide a source or reference for their definition for the term “trace amount,” but I would not consider a constituent composing 1.5% as insignificant. Therefore, this constituent should be included in the assessment.
114. Further, RWDI did not provide a full, explicit analysis nor calculations (provided in a clear manner) indicating which constituents they considered “trace amount,” and therefore what they omitted from their assessment.
115. With regard to the compositional analysis of asphalt, RWDI did not provide any information on their compositional analysis of asphalt intended to be recycled and processed at the proposed pit. Furthermore, RWDI indicates (p. 11) that certain constituents are only present in “trace amounts” and therefore decide not to analyze those further. Since the information was not provided I cannot assess the validity of their suppositions. Their analysis therefore remains uncertain and incomplete.
116. RWDI make no mention of whether other nearby sites were vetted for similar recycling activities. If it does occur, and RWDI have not accounted for those non-subject sources of asphalt dust, then pre-existing background levels of those CoCs would be under-estimated.
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117. By not properly assessing the composition of material handled, potential CoC’s may have been missed and therefore, this work will not provide a complete analysis of community-level exposures. In other words, this may lead to underestimating the air quality levels in the surrounding community.
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3.3.1.3 Unassessed Sources
118. RWDI states in their air assessment report (p. 7):
“Other activities that will occur from time to time at the site, that are not included in the worst-case scenarios include stripping of overburden and site preparation. The rationale for not including these operations in the worst-case scenario is as follows. Stripping of overburden typically occurs early or late in the operating season, when excavation, processing and shipping are either not occurring or occurring at levels well below peak levels. The overall truck traffic and equipment activity levels are expected to be lower than those associated with the maximum excavation and production scenario described above. Preston Sand & Gravel should conduct stripping of overburden only at times when excavation, processing and shipping are at levels well below the peak. Stripping of overburden also typically occurs at times of the year when moisture levels in the soil are relatively high and the potential for dust emission is low.”
119. Terms in the RWDI description such as “at levels well below the peak”, “moisture levels in the soil are relatively high” and “the potential for dust emission is low” are vague descriptions and are not quantified. They do not provide specific quantitative direction to PSG operating staff to denote at which point, and at which level of processing and shipping, stripping is allowable as an additional activity. Thus, RWDI argue that emissions from stripping of overburden are not required. However assessments are required to quantify the production levels that would allow overburden stripping and still keep air quality at appropriate levels.
120. Wood burning is associated with benzo(a)pyrene (BaP) air emissions as described in the report by the Great Lakes Regional Toxic Air Emissions Inventory Steering Committee “Assessment of Benzo(a)pyrene Air Emissions in the Great Lakes Region” http://www.glc.org/air/BapReport.pdf. (Appendix I).
121. A potential source of BaP emissions related to the proposed Henning Pit operations is the burning of trees/stumps at the site. While the wood burning activities are mentioned in the siteoperational plan, the emissions from this source are ignored in the RWDI report.
Excerpt from Preston’s SITE PLAN AMENDMENTS:
“PHASE 1: POLYGON 2A (SPRUCE AND WALNUT PLANTATION) WILL BE REMOVED PRIOR TO EXTRACTION AND THE TREES/STUMPS WILL EITHER BE BURNED ON-SITE (SUBJECT TO A MUNICIPAL BURN PERMIT) OR BURIED ON-SITE OUTSIDE THE EXTRACTION AREA.” (also Phases 2, 5, and 7)
122. In addition, the US EPA AP-42 (Chapter 2.5, Open Burning Appendix J) document contains information on emissions related to open burning sources. Such sources, in particular burning forest residue, may emit particulate matter, carbon monoxide, methane and non-methane organic compounds. The RWDI report does not mention any contribution from this type of emission.
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123. RWDI should have assessed these additional sources to determine whether their contribution to the CoC concentrations were important or not. By not assessing these sources, RWDI may have underestimated air quality levels of these CoCs in the surrounding neighbourhood.
3.3.1.4 Ranking of Diesel Combustion Emissions
124. Ten constituents of diesel combustion by-products (from diesel powered vehicles) were described in the RWDI report (AQA Table 4, p.12). To identify negligible contaminants RWDI used a ranking procedure, which combined the information for each contaminant with regard to its emission factor and air quality standard. The ranking was used to decide which contaminants are more significant than others. Of note is that the PM2.5 contaminant was ranked as No.6 by RWDI, assuming that the Lowest Standard or
AAQC was 25 g/m3 (Table 4). However, the lowest threshold value for PM2.5 is 8.8
g/m3 (Table 3, 3 years average), and when this value is used then this contaminant should be ranked higher, perhaps as high as number 2, which makes it a significant contaminant.
125. Thus, with this correct ranking, PM2.5 emissions from diesel combustion is more important than what was implied in the RWDI report
3.3.1.5. Site operational times
126. RWDI outlines in their report:“Trucking on approx. 190 operating days/year, 11 hours/day, 28.5 tonne average load = 13 inbound truck trips per hour during the operating period (26 truck pass-bys)” and “Material handling and processing rates based on 750,000 tonnes of annual production over 190 days/year and 11 hours/day (Table 2).”
127. A footnote on page 24 states “The pits were assumed to operate continuously from April through November” but there is no mention in the report that there will be no operations of any kind from December 1st to March 31st every year. This was the assumption made in the cumulative assessment. These operational specifications should be included in the Site Plan.
128. It should be noted that some of the proposed pit operations, especially those planned for Phase 3 of the extraction process, are located in the woodland lot marked EIS P1, or within the 5 m buffer region surrounding that area. The Site Plans indicate that “NO EXTRACTION OR ANY PIT RELATED ACTIVITY SHALL BE PERMITTED WITHIN EIS P3: ESPA 54 OR EIS P1: WESTERN WOODLOT (FRESH-MOIST SUGAR MAPLE HARDWOOD DECIDUOUS WOODLOT). IN ADDITION, NO STOCKPILING OF TOPSOIL OR OTHER MATERIAL WILL BE PERMITTED WITHIN THE 15.0 METRE SETBACK ADJACENT TO THE WESTERN WOODLOT.”
129. It is important that these specifications, in the AQA, are mirrored in the operational plans. Conversely, RWDI must be consistent with the operational plan, which they seem not to be.
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3.3.2 Emissions of CoCs from Nearby Non-subject Facilities
130. RWDI includes two adjacent non-subject sources of the same CoCs as emitted by the proposed Henning Pit. However, RWDI does not explain why only these two sources are included and the other gravel pits in the area are excluded.
131. There are numerous other sources of CoCs in the area, as exemplified in the Figure 1, shown earlier. Without inclusion of these other non-subject sources pre-existing background concentrations of CoCs may have been under-estimated, therefore under-estimating community-level exposures to CoCs.
132. Due to issues during my review, as explained earlier, there was insufficient time to review emissions calculations from the two non-subject sources. Some data was provided, on the operations of these two sites, on December 5th. This should have been provided to me at the beginning of this assessment (November 14th, 2013). Therefore, uncertainties in the assessment of these emissions were not yet analyzed. Also, it was unclear from the RWDI report exactly how estimated emissions for those two non-subject sources were derived and on what basis RWDI assumed that Lafarge and St. Mary’s had similar operational characteristics as Preston’s proposed pit.
133. RWDI have presented no evidence that they have considered vacant lots in the area that, as of right, have ability to establish a non-subject source that could contribute to increases in cumulative levels during the lifetime of the proposed Henning Pit. It adds further uncertainty to RWDI assessment of background levels of CoCs.
3.3.3 Emission Factor Variability
134. Emissions calculated using AP-42 methods carry a level of uncertainty due to the variability of emission levels from one operation to another. According to the US EPA, AP-42 emission estimates are based on averages, and do not represent maximal emissions. They will result in underestimates of emissions for roughly half of the facilities that use them for that purpose:
“In most cases, these factors are simply averages of all available data of acceptable quality, and are generally assumed to be representative of long-term averages for all facilities in the source category (i. e., a population average).” (AP-42, Introduction: Appendix K)
“Because emission factors essentially represent an average of a range of emission rates, approximately half of the subject sources will have emission rates greater than the emission factor and the other half will have emission rates less than the factor. As such, a permit limit using an AP-42 emission factor would result in half of the sources being in noncompliance.” (AP-42 Introduction, Appendix K)
135. Additional caution should be used when applying emission factors on a 24-hr assessment basis. Usually these factors are established for long-term averaging periods and may not represent a worst-case day. The US EPA says:
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“Estimates of short-term or peak (e. g., daily or hourly) emissions for specific sources are often needed for regulatory purposes. Using emission factors to estimate short-term emissions will add further uncertainty to the emission estimate. Short-term emissions from a single specific source often vary significantly with time (i. e., within-source variability) because of fluctuations in process operating conditions, control device operating conditions, raw materials, ambient conditions, and other such factors. Emission factors generally are developed to represent long-term average emissions, so testing is usually conducted at normal operating conditions. Parameters that can cause short-term fluctuations in emissions are generally avoided in testing and are not taken into account in test evaluation. Thus, using emission factors to estimate short-term emissions will cause even greater uncertainty. The AP-42 user should be aware of this limitation and should evaluate the possible effects on the particular application.” (AP-42 Introduction, Appendix K)
136. Indeed the US EPA commissioned a study to examine the degree of variability and found that, at the very least, maximal emissions can be two times higher than the averages represented by the AP42 EF (Emissions Factor Uncertainty Assessment, prepared by RTI International for the U.S. EPA, February 2007).
137. This emphasizes the need to provide worst-case input data into the emission factor calculations to ensure that worst-case emissions are derived.
3.3.4 Off-site Dispersion
138. The RWDI AQA states “As per Section 16 of O. Reg. 419/05, terrain information for the area surrounding the facility was obtained from the MOE Ontario Digital Elevation Model (DEM) Data web site (file # 040p08DEMw.dem).” This file contains terrain height data required by the AERMOD dispersion model to properly calculate dispersion of CoCs into the surrounding neighbourhood. However, the file type cited was not obtained from the MOE website as indicated. Upon further request, RWDI provided the correct files on November 28th, as follows: 0821_3.dem, 0821_4.dem, 0822_3.dem, 0822_4.dem.
139. It should be noted that the results of RWDI modelled assessment are sensitive to the exact location of all sources of air emissions (roadways, location of processing equipment, etc.) as depicted in their mapping. Any deviation from these exact locations, in the operations, will cause different air quality levels and therefore the results of this assessment would be invalid.
3.3.5 Estimation of Pre-existing Background Levels of CoCs
3.3.5.1 BaP Background Estimation
140. Benzo(a)pyrene (BaP) is a constituent of the exhaust byproducts of diesel combustion and also wood burning. Emissions of BaP will add to pre-existing levels of BaP in the surrounding community. PSG did not conduct a measurement program in the area, that
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would have provided site-specific data on background levels of BaP, and so RWDI examined measurement data from other sites as follows:
“In the case of BaP, the Kitchener monitoring station does not measure this contaminant. It is measured at only a small number of sites across Ontario. For BaP, table shows representative data from a rural site (Egbert, Ontario) and an urban site (Gage Research Institute, downtown Toronto), which bracket the
range of possible background conditions for BaP.”
141. In their subsequent calculation of cumulative concentrations, however, RWDI used the lowest value of the range presented, measurements from the Egbert (Ontario) measurement site, as described in their report:
“The values presented are based on using the low end of the range of possible background concentrations.” from Table 6. on Page 23
142. This is inconsistent with the way other CoCs are assessed and is not worst-case. For example, background concentrations for particulate matter (PM2.5, PM10 and silica) are chosen by RWDI as tending towards the highest (90th percentile) values. On the contrary, the BaP values were chosen as the lowest values. It is not clear why the BaP background concentrations were chosen in this manner but may lead to underestimation of background levels of BaP in the surrounding community. Preston should have initiated a measurement program to investigate local neighbourhood air quality some time ago, and used such data to inform this study.
143. Furthermore, to estimate background values for particulates (PM2.5, PM10 and TSP), RWDI scaled air quality levels from rural sites up by 40%, as described below:
“Data on particulate matter and silica have been scaled upward by 40% to represent levels near a busy rural roadway.” in Page 17.
144. Following this logic, given that most non-subject (off-site) sources of BaP are likely to be vehicular emissions, the BaP values used should have also been scaled up by 40% However, this was not done. For modelling purposes, RWDI considered that BaP and NOx were co-emitted from vehicle exhaust so it is clear that RWDI recognizes BaP as a vehicular emission. Therefore, to be consistent with their own methodology (which I do not necessarily agree with) RWDI should have scaled up the BaP background data to a 40% higher value.
145. By underestimating the background BaP values, the impacts on the surrounding community may be underestimated. An appropriate assessment of resultant BaP concentration in the should be reviewed by a health impact expert. Although I am not a health impact expert, I do know that BaP is known to be carcinogenic.
Health Canada, 1988, http://www.hc-sc.gc.ca/ewh-semt/pubs/water-eau/benzo_a_pyrene/index-eng.php
World Health Organization, http://monographs.iarc.fr/ENG/Monographs/vol100F/mono100F-14.pdf
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3.3.5.2 Use of Unverified Data for Background Assumptions
146. It would have been appropriate to measure background in the area, in particular at receptors, prior to this assessment. Instead, RWDI attempted to account for enhanced levels of certain vehicle-emitted CoCs near roadways. However, this accounting does not seem to based on widely recognized, used and vetted studies but instead on internal RWDI studies, as described in their report (AQA p. 17):
“Some of the residences that are in proximity to the proposed Henning Pit are located
along these roads. Dust levels, particularly respirable dust (PM2.5), at such locations tend to be higher than at locations with less traffic. This was verified in previous measurements by RWDI along a 2-lane rural highway in Southern Ontario (July through October, 2011). Overall measured levels of TSP and PM2.5 were 30 to 40% higher than
those on days when the highway was not upwind of the monitor.”
147. I have not had the time to review RWDI’s other measurement data to assess whether it is credible or applicable. Therefore, at this point, RWDI’s roadway enhancement factor remains uncertain.
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3.3.5.3 Scaling Factors for Particulate Data to Determine Background
148. RWDI used scaling factors to derive (by calculation) estimated background levels of PM10 and TSP (based upon measurements of the PM2.5 dust size fraction) as measurements of these larger size fractions were not conducted at the chosen sites. RWDI obtained these scaling factors from a study by Lall et al. (Atmos.Environ. 2004) (Appendix L), which represented measurements from Metropolitan locations in the US. However, there is a similar Canadian version of this study, which provides different scaling factors (Brook et al. J. Air & Waste Manage. Assoc., 1997) (Appendix M). The values derived in the Canadian study indicates:
“On average across all sites, PM2.5 accounted for 49% of the PM10, and PM10 accounted for 44% of the TSP.”
149. Therefore, the US version of ratios leads to an underestimate of background PM10 and TSP by 8% and 29%, respectively when compared to the Canadian-based study. This suggests another source of underestimation of community-level exposures to air emissions in this study.
3.3.5.4 Crystalline Silica Scaling Assumptions
150. As PSG did not initiate a measurement program to assess background airborne levels of crystalline silica in the local community, RWDI attempted to estimate silica concentration. Crystalline silica air concentrations were estimated as 6% of PM10 levels; this 6% value was based on studies conducted in Northeastern US (United States Environmental Protection Agency (1996). Ambient Levels and Noncancer Health effects of Inhaled Crystalline Silica and Amorphous Silica: Health Issue Assessment. EPA/600/R-95-115.) but requiring further explanation in an email from Mr. Lepage:
Regarding your question about background silica, we relied on Tables 3-7 and 3-10 of the EPA report, focussing northeastern locations: Akron, Boston, Buffalo, Cincinatti, Hartford, all of which had quartz percentages of 6% or less. (Email from Mr. Lepage dated 26-Nov-2013)
151. The measurements cited were obtained from urban areas, unlike the situation in North Dumfries, and without the concentration of gravel pits as exists in North Dumfries. It is uncertain that the proportion of silica in dust in those urban areas would fairly represent the portion of silica-laden dust in an area with a concentration of aggregate activities such as North Dumfries. This calculation, and subsequent assessment of cumulative crystalline silica levels, remains uncertain; therefore there remains no assurance that cumulative concentrations and exposure levels have been calculated in a worst-case manner.
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3.3.6 Resultant Air Quality Levels in the Surrounding Community
3.3.6.1 Presentation of Results
152. RWDI presented their estimates of community-level exposures to air emissions from the proposed Henning Pit in their Tables 9-10 (AQA p. 24-25). However, there seem to be a number of problems with the way the results are presented:
A. Table 9, row PM2.5 24 hour average values - there are a number of exceedances shown (column 5) and yet there is “NA” indicated in column 6, with no explanation.
B. Table 10, row PM2.5 24 hour – receptor RENTAL3 had a maximum value occurring during Phase 9 (as verified in the model run outputs provided to me by RWDI) not Phase 8 as indicated.
3.3.6.2 Discrete Receptors Considered
153. RWDI performed their cumulative assessment at 7 receptors, labeled CRAND 4, CRAND5, CRAND6, CRAND15, CHURCH2, RENTAL3 and PAULCAB14. There is no description in the report of these receptors but they would seem to be residences. I noticed additional receptors that would seem to be also residences which I show in Figure 2 below. These additional receptors I have added to the dispersion model, provided to me by RWDI, to ensure all possible receptors were captured in the assessment. I provide these additional results later in this WS. Production of air concentration results at these locations ensures that full data are available to human health impact experts to allow them to assess any potential human health impacts. These additional receptors I have added I have denoted as:
Receptor A: House West of St. Mary’s Receptor B: House on Preston property on the west side Receptor C: House West of Lafarge Receptor D: House Northeast of Preston property Receptor E: House Northeast of Preston property Receptor F: House East of Lafarge Receptor G: House on Preston property on the north side
154. I have also shown these additional receptors, along with RWDI original 7 receptors, in Figure 2, below.
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Figure 2: Particular Receptors, in the immediate area, identified by RWDI and Airzone
155. Additionally, no assessment of vacant lots of record that have, as of right, ability to build future sensitive receptors were included in the assessment.
156. The cumulative air quality assessment by RWDI was seemingly confined to human receptors, based upon what seem to be residences. However, ecological/environmental effects may be possible at other locations also. RWDI have not provided analysis that could be made available to ecological/environmental impacts assessment expert for their review as they have not provided results at other locations.
157. Accordingly, I have taken RWDI’s model for cumulative assessment and added additional receptors (“grid” of receptors) to provide estimated air quality levels at other locations, as determined by RWDI’s model. The receptor grid used in the model assessment is shown in Figure 3. Much of the grid of receptors was set-up outside of the combined property lines of Lafarge, St. Mary’s and the proposed Henning Pit assuming no “sensitive” receptors within each property. However, additional receptors were included inside of the property lines of the proposed Henning Pit, on the forested areas on the east and the west sides of the proposed Henning Pit, to provide information for those areas. These areas are shown on the Site Plans as protected areas and, therefore, I felt it was important to view the air emission exposure levels in those areas.
158. The figure (Figure 3) below represents Henning’s proposed operations during their “Phase 9” of aggregate extraction, and is based on the original information provided by RWDI describing different scenarios; subsequently, however, RWDI have provided
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altering information and have apparently revised those scenarios. As I was informed of these alterations on December 9th I have not been able to review or check them.
159. Figure 3 shows colour-shaded contours of equal airborne concentration of PM2.5 (varying from 0.2 – 17.5 µg m-3 of PM2.5 24-hour average basis) based upon data estimated at the receptor points and as estimated by RWDI’s model. Maximal values are shown for each receptor and the graphics package used interpolates and extrapolates contour lines based on RWDI’s model estimates at each receptor. The contours are provided for illustration of general patterns rather than numeric interpretation. Also shown are air emission sources, as provided by RWDI, all superimposed on satellite imagery of the area.
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Figure 3: Receptor grid (shown as green “cross” marks) added to RWDI’s model. Property boundaries are shown as red lines and the greyed areas show where no assessments were made. Illustrative air quality patterns results for PM2.5 are shown as produced by RWDI’s model and for the Central Processing Plant operation at Henning Pit Phase 9, but
do not include emissions from the two adjacent non-subject sources or regional background.
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160. Figure 3 above shows that the maximum level predicted by RWDI’s model, in the protected forest on the east side of the proposed Henning Pit, is 748% higher than values at any of the 7 receptors assessed by RWDI. The maximum value predicted by RWDI’s model at the property line is 161% higher than at the 7 receptors assessed by RWDI. Estimated levels for PM10 and TSP may be higher by approximately the same amount, although this requires further verification. These data, or a modified version thereof, should be provided to an ecological/environmental impacts expert for evaluation.
3.3.6.3 No Modelling of PM2.5 Annualized Levels
161. RWDI attempted to model annualized exposures of PM2.5 in the local community. According to O.Reg.419/05 s.17 this requires use of the annual averaging option within the approved model (AERMOD in this case):
162. To quote from O.Reg.419/05:
“Averaging periods
17. (1) If a provision of this Part refers to an approved dispersion model being used in connection with a standard that applies to a specified averaging period, the following rules apply for the purposes of this Part:
1. If an approved dispersion model was designed to be used for the specified averaging period, it shall be used as it was designed for that averaging period.
2. If an approved dispersion model was not designed to be used for the specified averaging period but was designed to be used for an averaging period shorter than the specified averaging period, the model may be used as it was designed for the shorter averaging period if the result produced by the model is adjusted in accordance with subsection (2).
3. If the specified averaging period is less than one hour and an approved dispersion model was designed to be used for a one hour period, the model may be used as it was designed for a one hour period if the result produced by the model is adjusted in accordance with subsection (2).
4. If the use of an approved dispersion model is not authorized or required by paragraph 1, 2 or 3, the model shall not be used. O. Reg. 419/05, s. 17 (1).”
(my own underlining).
163. Instead RWDI scaled annualized air concentrations of PM2.5 from 24-hr values as a “post-processing” calculation (after the model was run). The AERMOD model is designed to accommodate annual averaging (US EPA 2009 ADDENDUM USER'S GUIDE FOR THE AMS/EPA REGULATORY MODEL - AERMOD (EPA-454/B-03-001). Therefore I believe that RWDI have not carried-out the calculations following the method suggested in O.Reg.419/05. Results provided when using annual averaging within the AERMOD model may provide different results than using RWDI’s “scaling” method.
164. This means that the results presented by RWDI are uncertain but also that RWDI apparently did not follow the direction provided in the O.Reg.419/05 sections specified in the issues list.
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3.3.7 Standards and Criteria
165. In the RWDI report, no assessment was made of certain components of dust. Two constituents of dust were singled out in particular: Calcium Silicates and Calcium Aluminates were distinctly ignored and the reasoning provided was because they “do not have standards or criteria” (AQA, p. 9). It is normal practice under O.Reg.419/05 to nonetheless provide air quality assessment results to the Ministry of the Environment for further assessment, as explained in the following excerpt from the MOE document “Procedure for Preparing an Emission Summary and Dispersion Modelling Report” (Emission Summary and Dispersion Modelling Report aka “ESDM,” which is normally part of the ECA application package provided to the MOE):
“MOE POI Limits are available for approximately 350 contaminants used or produced by industry in Ontario as listed in the MOE publication, “Summary of Standards and Guidelines to Support Ontario Regulation 419: Air Pollution – Local Air Quality (including Schedule 6 of O. Reg. 419 on Upper Risk Thresholds)” (as amended).
However, there are many more compounds that meet the definition of a contaminant under the Ontario EPA than there are contaminants with MOE POI Limits. Persons preparing an ESDM report are accountable for the assessment of all contaminants that are discharged from the facility regardless of whether or not a MOE POI Limit is available. The MOE has published a “Jurisdictional Screening Level (JSL) List – A Screening Tool for Ontario Regulation 419: Air Pollution – Local Air Quality” (PIBs # 6547e) to assist in the assessment of contaminants with no MOE POI Limits. The ESDM report must provide an indication of the likelihood and nature of any adverse effect that may be caused by a contaminant with no MOE POI Limit.”
166. The Procedure document continues with a variety of suggestions of how a substance with no MOE POI Limit should be assessed.
167. RWDI should have enumerated the air quality levels of these two components, and provided them for further assessment, rather than ignore them simply because there is no MOE POI standard or guideline provided.
168. Table 3 (AQA, p. 10) of the report contains a compilation of standards and criteria chosen by RWDI. I reviewed the stated threshold values, and corresponding averaging periods, by comparison to the following documents:
• ONTARIO REGULATION 419/05; AIR POLLUTION — LOCAL AIR QUALITY; Consolidation Period: From February 1, 2013.
• SUMMARY of STANDARDS and GUIDELINES to support Ontario Regulation 419/05 - Air Pollution – Local Air Quality (including Schedule 6 of O. Reg. 419/05 on UPPER RISK THRESHOLDS); MOE PIBS # 6569e01
• ONTARIO’S AMBIENT AIR QUALITY CRITERIA; MOE PIBS # 6570e01 (Appendix H)
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• Guidance Document on Achievement Determination Canadian Ambient Air Quality Standards for Fine Particulate Matter and Ozone, Canadian Council of Ministers of the Environment, 2012 PN 1483.
169. One of the contaminants, in RWDI’s table, is listed as “Metal Oxides” in Tab.3; this is not mentioned directly in MOE’s document “SUMMARY of STANDARDS and GUIDELINES to support Ontario Regulation 419/05”. There are three chemicals in this document under Schedule 3 that fall into this category: Calcium Oxide, Ferric Oxide, and Magnesium Oxide. However, each chemical has its own 24-hour standard; for example,
ferric oxide has a 24-hour average standard of 25 g/m3 which is almost five times lower
than the 120 g/m3 threshold cited by RWDI. In Section 5.8 of the RWDI report (page 8), there is a narration about limestone composition, which mentions “Oxides of iron, magnesium, and aluminum”.
170. It appears that RWDI have singled out Magnesium Oxide to represent the whole category of oxides whereas other metal oxides have much lower threshold values. It appears that RWDI may be applying thresholds that are too high to certain metal oxides that may be emitted from the proposed pit and disperse into the surrounding community.
3.3.7.1 Treatment of Air Quality Threshold Exceedances
171. RWDI found, based on their analysis, that resultant cumulative levels exceeded quoted threshold values. However, RWDI dismiss those exceedances as insignificant by reference to a guide produced by the Ontario Ministry of Transportation (Ministry of Transportation, 2012). Here is an excerpt from the AQA (page 23):
“The results are interpreted based on the test in the MTO’s guidance for cumulative effects assessments of Provincial transportation projects, i.e., whether or not any contaminant exceeds its AAQC at a significant number of sensitive impact locations for a significant number of days per year. While the MTO does not define the term “significant”, the Canadian Ambient Air Quality Standards (CAAQS) give an indication of what is typically considered to be a “significant number of days per year”.”
172. The MTO guide is a guide on assessing transportation projects such as new highways and highway extensions. The MTO guide was not designed to apply to stationary sources such as the proposed Henning aggregate pit; thus the allowance for a “significant” number of exceedances, used for transportation projects, does not necessarily apply to stationary sources. The term “significant” is not defined in said guide.
173. Despite the questionable use of significance, RWDI then go on to attempt to interpret the term “significant” by reference to the metric for the CAAQS for PM2.5 (which is not referenced in the MTO guide), as outlined next.
174. The PM2.5 CAAQS compliance assessment is based on the 3-year average of the (annual) 98th percentile of the daily 24-hour concentrations. This metric applies only to the 24-hour average standard for the contaminant PM2.5 and not to any other contaminant of standard. Furthermore, the CAAQS are only applied as an air quality
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management tool rather than for the purposes of assessing air quality impacts. Thus this definition of significant is inappropriate even for PM2.5 24-hour assessments, as RWDI attempted to use, and is even more inappropriate for other contaminants and averaging periods.
175. According to the CAAQS compliance determination procedure for PM2.5, in any one year, the 98th percentile excludes the top 7 days per year of highest ranked concentration; this would seem to be the origin of RWDI’s assumption of an allowable level of exceedances of an air quality threshold, gathered from the following excerpt from the AQA (page 23):
“In the case of the CAAQS for PM2.5, the level of significance is defined as 2% of the days in a year (7 days/year).”
176. Thus it seems that the level of “significance” (i.e. the level at which exceedances were dismissed) was determined using the CAAQS for PM2.5.
177. RWDI then state:
“The table also shows, however, that the predicted frequency of exceeding the criteria is low. It occurs on less than 7 days/year at most locations, under the model assumption of all pits operating at capacity and dry conditions prevailing throughout the operating season (April through November).”
178. RWDI is again referring to “less than 7 days/year”, which was already presumably
accounted for in their compliance determination. In Tables 9 and 10, RWDI present “Maximum number of exceeding days per year;” one could presume this means the number of days exceeding the CAAQS threshold.
179. For the 24-hour PM2.5 standard, the compliance determination for PM2.5 levels already incorporates calculation of the 98th percentile. By then using that metric again, to determine “significant” exceedances, RWDI double-counts that “allowance.” In effect, this sets RWDI’s threshold at the 96th percentile not the 98th, as set out in the CAAQS and indicated in the AQA.
180. One step further, RWDI then state:
“For respirable particulate matter (PM2.5), which is the component of particulate matter of greatest potential concern to human health, the number of days per year above the MOE’s current AAQC is very small at all locations (less than 1 days/year).”
181. I do not understand why RWDI, at this stage, introduce the Ontario AAQC standard
when other references cite the Canadian Federal CAAQS standard.
182. It should also be noted that the threshold for PM2.5, based upon the Federal CAAQS, is set of air quality management and is not intended to be a threshold for health effects. The human health impacts of PM2.5 must be assessed by a health impacts expert.
183. Results of all other CoCs should also be supplied to a health impacts expert for further evaluation.
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3.3.7.2 Interpretation of Exceedances
184. RWDI (AQA p. 23) attributes the high level of exceedances, at receptors CRAND14 and PAULCAB15, to emissions from the Lafarge Pit to the south of the proposed Henning Pit site. These exceedances would seem to markedly violate RWDI’s own definition of a “significant” exceedance.
185. However, I have not been able to fully review emission estimates provided by RWDI for that site due to the issues mentioned with the review process. Also, it is not clear that RWDI consulted with Lafarge in the estimation of those emissions and that Lafarge was able to verify RWDI’s calculations and assessment.
186. Given that RWDI attribute such elevated concentration levels to Lafarge, I recommend that Lafarge be consulted to verify such claims by RWDI.
187. Figure 4 shows the same RWDI model simulation as in Figure 3, except that Lafarge and St. Mary’s contributions are also shown, as provided by RWDI. Lafarge’s emissions appear to have a much greater impact on the surroundings than the proposed Henning Pit. The calculations provided for Lafarge have not been assessed here due to the limited amount of time for this review.
188. Figure 4 represents Phase 9 of Henning’s proposed pit operations, based on the original information provided by RWDI describing different scenarios. I have not had time to re-review the (apparently) revised scenarios that RWDI informed me of on December 9th.
189. In summary, Lafarge should be given the opportunity to review the results presented here.
.
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Figure 4: Illustrative air quality patterns results for PM2.5 are shown as produced by RWDI’s model and for the Central Processing Plant operation at Henning Pit Phase 9, but does not include regional background. The greyed areas
show where no assessment was made.
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3.4 Re-analysis of RWDI’s Modelling Results
3.4.1 RWDI’s Results Re-examined
190. The following figure (Figure 5) displays the estimated PM2.5 levels in the surrounding community, caused by the proposed pit alone (i.e., not added to background, and not including Lafarge and St. Mary’s contributions and so therefore not cumulative). These values were obtained using methods that RWDI had used in their assessment based on data inputs provided by RWDI, although here a grid of additional receptors was used to provide air quality levels at locations that RWDI did not assess. This figure repeats figure 3 but no grid receptors are shown. The figure shows colour-shaded contours of equal airborne concentration of PM2.5 (varying from 0.2 – 17.5 µg m-3 of PM2.5 24-hour average basis) based upon data estimated at the receptor points and as estimated by RWDI’s model. Maximal values are shown for each receptor and the graphics package used in this examination interpolates and extrapolates contour lines based on RWDI’s model estimates at each receptor. The contours are provided for illustration of general patterns rather than numeric interpretation. Also shown are air emission sources, as provided by RWDI, all superimposed on satellite imagery of the area
191. Although these contours should not be interpreted numerically, they do illustrate the potential influence of the Henning Pit in the proposed area, extending out to the surrounding regions.
192. It also shows the fine dust fraction (PM2.5) predicted by RWDI’s model in the protected forest on the east side of the Henning property, as well as the protected forest on the west side of the proposed Henning Pit. Total suspended dust levels would be higher still. Information, such as this, should be provided to ecology experts to interpret impacts in areas such as these.
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Figure 5: Illustrative air quality patterns results for PM2.5 are shown as produced by RWDI’s model and for the Central Processing Plant operation at Henning Pit Phase 9, but do not include emissions from the two adjacent non-subject
sources or regional background. The greyed areas show where no assessments were made.
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3.4.2 RWDI’s Results Re-analyzed
193. As previously described, there were various assumptions and calculations in the air quality assessment that warranted further investigation. In the following sections of this witness statement I have conducted a re-analysis of the RWDI estimates to illustrate the impacts of these issues on the RWDI results.
194. The calculations that were investigated (as mentioned above) include: (i) the use of a more reasonably conservative estimate of road dustiness (silt level), (ii) testing a more conservative estimate of dust control efficiency due to watering (on unpaved roads) to a more conservative value of 58%, the lower value presented in the RWDI report, and, (iii) the reduction of estimated dust control efficiency due to watering (on unpaved roads) to a more conservative value of 25%, which is the actual minimum value from the literature from Rosbury (1985) (Appendix E; referenced by RWDI) for watering. These were sensitivity tests and not my opinion on how low watering control efficiencies can become.
195. The results of these alternative analyses can be seen in the Tables 3-6 below. However, the combined effect of these individual issues has not been investigated.
196. The results are provided for PM2.5 using the RWDI model provided. Results are displayed for scenario Central Processing at Phase 9 only, however I have noted that results are similar for the other phases of the proposed pit as tested by RWDI. These results represent the Phase 9 scenario, based on the original information provided by RWDI describing different scenarios (supplied November 14th, 2013) and not the revised scenarios that I was informed of on December 9th, as mentioned earlier.
197. The variations for PM2.5 will be similar for other dust size fractions (PM10 and TSP) and constituents of dust although further verification is required. PM2.5 is used as an example.
(i) The Use of a More Reasonable Conservative Estimate of Road Dust (Silt Level) versus RWDI’s Original Results
198. For reasons described above, I have substituted the use of a more reasonably conservative level of road dust in the RWDI model. The results are presented in the Tables 3-4. Both tables show the aggregate extraction phases at which the maximum occurs. This is based on the original information provided by RWDI describing different scenarios; I have not been able to review the revised scenarios I was informed about on December 9th.
199. Table 3 includes (RWDI’s interpretation of) cumulative air quality levels when the proposed aggregate pit emissions are added to background levels and the assumed activities at Lafarge and St. Mary’s. For illustration purposes for PM2.5, the background of 20.8 μg m-3 was applied following RWDI’s method. It was assumed that the same silt level occurred at Lafarge and St. Mary’s and not just for the proposed Henning Pit.
200. Table 4 includes the air quality levels from the proposed Henning Pit alone (i.e. no background added and no emissions from the two adjacent pits).
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201. When a more conservative estimate of road dustiness is used, PM2.5 levels increase. Levels at receptors examined are seen to increase by between 102 – 112% on a cumulative basis but by between 111 – 144% when Henning’s emissions are viewed alone.
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Table 3: A tabulation of the results of the RWDI model when silt content is altered to a more
conservative level of 16%. Proposed Henning Pit, Lafarge and St. Mary’s operations, as well as a
background of 20.8 μg m-3applied.
16% silt content Henning Phase Max. conc. % increase
PRIMARY PLANT related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 3 24.88 104%
AIRZONE ReceptorB Phase 3 27.49 105%
AIRZONE ReceptorC Phase 3 30.34 109%
AIRZONE ReceptorD Phase 3 23.49 102%
AIRZONE ReceptorE Phase 3 23.64 102%
AIRZONE ReceptorF Phase 3 23.80 104%
AIRZONE ReceptorG Phase 9 30.25 108%
RWDI CRAND4 Phase 3 26.76 106%
RWDI CRAND5 Phase 3 27.19 105%
RWDI CRAND6 Phase 3 25.11 104%
RWDI CRAND15 Phase 3 34.39 112%
RWDI CHURCH2 Phase 9 26.96 106%
RWDI RENTAL3 Phase 9 24.13 103%
RWDI PAULCAB14 Phase 3 35.50 111%
16% silt content Henning Phase Max. conc. % increase
AUXILIARY PLANT related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 6 23.97 103%
AIRZONE ReceptorB Phase 3 27.43 105%
AIRZONE ReceptorC Phase 3 30.32 109%
AIRZONE ReceptorD Phase 3 23.07 102%
AIRZONE ReceptorE Phase 3 23.07 102%
AIRZONE ReceptorF Phase 3 23.80 104%
AIRZONE ReceptorG Phase 9 28.86 104%
RWDI CRAND4 Phase 3 25.30 105%
RWDI CRAND5 Phase 9 25.91 103%
RWDI CRAND6 Phase 6 23.70 103%
RWDI CRAND15 Phase 3 34.34 112%
RWDI CHURCH2 Phase 8 25.70 105%
RWDI RENTAL3 Phase 3 23.41 103%
RWDI PAULCAB14 Phase 3 35.48 111%
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Table 4: A tabulation of the results of the RWDI model when silt content is altered to a more
conservative level of 16%. Proposed Henning Pit alone.
202. Figure 6 shows illustrative patterns when the above silt assumption is used.
203. Tables 3-4 and Figure 6 show that a more conservative silt assumption can increase airborne concentrations compared to RWDI’s assessment. This aspect of RWDI’s assessment may lead to underestimates of air emission exposure levels in the surrounding neighbourhood.
16% silt content Henning Phase Max. conc. % increase
PRIMARY PLANT related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 3 2.27 132%
AIRZONE ReceptorB Phase 3 5.13 121%
AIRZONE ReceptorC Phase 9 2.35 128%
AIRZONE ReceptorD Phase 3 1.19 126%
AIRZONE ReceptorE Phase 3 1.40 125%
AIRZONE ReceptorF Phase 3 1.27 130%
AIRZONE ReceptorG Phase 9 4.95 127%
RWDI CRAND4 Phase 3 2.74 126%
RWDI CRAND5 Phase 3 3.38 120%
RWDI CRAND6 Phase 3 2.49 123%
RWDI CRAND15 Phase 3 3.08 122%
RWDI CHURCH2 Phase 9 2.66 130%
RWDI RENTAL3 Phase 9 1.67 128%
RWDI PAULCAB14 Phase 3 3.32 122%
16% silt content Henning Phase Max. conc. % increase
AUXILIARY PLANT related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 6 1.36 144%
AIRZONE ReceptorB Phase 4 3.54 125%
AIRZONE ReceptorC Phase 4 1.11 134%
AIRZONE ReceptorD Phase 3 0.76 125%
AIRZONE ReceptorE Phase 3 0.82 131%
AIRZONE ReceptorF Phase 6 0.75 141%
AIRZONE ReceptorG Phase 9 5.26 111%
RWDI CRAND4 Phase 3 1.35 133%
RWDI CRAND5 Phase 9 2.58 119%
RWDI CRAND6 Phase 6 1.08 129%
RWDI CRAND15 Phase 3 1.71 125%
RWDI CHURCH2 Phase 8 1.87 124%
RWDI RENTAL3 Phase 8 1.07 124%
RWDI PAULCAB14 Phase 3 1.86 124%
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Figure 6: Illustrative air quality patterns results for PM2.5 are shown as produced by RWDI’s model and for the Central Processing Plant
operation at Henning Pit Phase 9, but do not include emissions from the two adjacent non-subject sources or regional background: for silt
content assumption of 16%. The greyed area shows where no assessment was made.
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(ii) The Use of a the lower limit (as presented by RWDI) of Road Dust Control Due to Watering on Unpaved Roads versus RWDI’s Original Results
204. For reasons described above, I have substituted the use of the more conservative dust control efficiency value for dust control by watering of unpaved roads. The 58% control efficiency modelled was the most conservative from the range of values provided by RWDI. The 25% control efficiency modelled was the most conservative from the range of values provided by Rosbury (1985; Appendix E) for watering every hour or more frequently. These altered control efficiencies were applied to Henning’s proposed operations but also the operations at Lafarge and St. Mary’s. The results of those calculations, on estimated dust levels in the surrounding community, are presented below in Table 5; Table 6 shows results using RWDI’s model for the proposed Henning Pit operations alone.
205. Both tables show the phases at which the maximum occurs and this is based on the original information provided by RWDI describing different scenarios and not the revised scenarios that I was informed about on December 9th.
206. From Table 5, it can be seen that, when RWDI’s model is used to assess cumulative levels (as defined by RWDI) PM2.5 levels increase by between 113 – 245%, and had higher increases for the 25% control efficiency compared to the 58% control efficiency.
207. From Table 6, it can be seen that, when RWDI’s model is used to assess Henning’s emissions alone PM2.5 levels increase by between 168 – 643%. A similar pattern of increases is expected for other size fractions of dust (PM10 and TSP) and other constituents of dust, although further verification is required. Results for PM2.5 are provided as an example.
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Table 5: Proposed Henning Pit, with emissions from Lafarge and St. Mary’s operations, as well as a background of 20.8 ug/m3 applied. This shows tests of more conservative
dust control efficiencies.
58% control efficiency 25% control efficiency
Henning Phase Max. conc. % increase Henning Phase Max. conc. % increase
PRIMARY PLANT related to max. (μg m-3) of original related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 9 29.63 124% Phase 9 35.51 148%
AIRZONE ReceptorB Phase 3 34.37 132% Phase 3 42.86 164%
AIRZONE ReceptorC Phase 3 43.87 158% Phase 3 60.77 219%
AIRZONE ReceptorD Phase 3 26.27 114% Phase 3 29.69 129%
AIRZONE ReceptorE Phase 3 26.48 115% Phase 3 29.99 130%
AIRZONE ReceptorF Phase 3 27.92 121% Phase 3 33.02 144%
AIRZONE ReceptorG Phase 9 41.98 150% Phase 9 56.46 202%
RWDI CRAND4 Phase 3 34.16 135% Phase 3 43.30 171%
RWDI CRAND5 Phase 3 34.43 134% Phase 3 43.37 168%
RWDI CRAND6 Phase 3 29.95 124% Phase 3 35.92 149%
RWDI CRAND15 Phase 3 52.77 171% Phase 3 75.46 245%
RWDI CHURCH2 Phase 9 34.14 134% Phase 9 43.01 168%
RWDI RENTAL3 Phase 9 27.94 119% Phase 9 32.64 140%
RWDI PAULCAB14 Phase 3 53.69 168% Phase 3 76.13 238%
58% control efficiency 25% control efficiency
Henning Phase Max. conc. % increase Henning Phase Max. conc. % increase
AUXILIARY PLANT related to max. (μg m-3) of original related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 6 28.02 120% Phase 6 33.03 142%
AIRZONE ReceptorB Phase 3 34.25 131% Phase 3 42.67 164%
AIRZONE ReceptorC Phase 3 43.82 158% Phase 3 60.69 219%
AIRZONE ReceptorD Phase 3 25.54 113% Phase 3 28.59 126%
AIRZONE ReceptorE Phase 3 25.51 113% Phase 3 28.51 126%
AIRZONE ReceptorF Phase 3 27.90 121% Phase 3 32.98 143%
AIRZONE ReceptorG Phase 3 36.63 131% Phase 3 47.95 172%
RWDI CRAND4 Phase 3 31.52 131% Phase 3 39.19 163%
RWDI CRAND5 Phase 3 30.62 122% Phase 3 37.77 151%
RWDI CRAND6 Phase 6 27.40 119% Phase 6 31.96 139%
RWDI CRAND15 Phase 3 52.69 171% Phase 3 75.34 245%
RWDI CHURCH2 Phase 6 31.55 128% Phase 6 38.90 158%
RWDI RENTAL3 Phase 3 26.61 117% Phase 3 30.59 134%
RWDI PAULCAB14 Phase 3 53.64 168% Phase 3 76.06 238%
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Table 6: Emissions from the proposed Henning Pit alone, with no cumulative influences and no background added. This shows tests of more conservative dust control
efficiencies.
208. Figure 7 shows illustrative patterns when the 25% control efficiency is applied.
58% control efficiency 25% control efficiency
Henning Phase Max. conc. % increase Henning Phase Max. conc. % increase
PRIMARY PLANT related to max. (μg m-3) of original related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 9 5.06 294% Phase 9 8.54 496%
AIRZONE ReceptorB Phase 3 9.87 234% Phase 3 15.73 372%
AIRZONE ReceptorC Phase 9 5.03 275% Phase 9 8.35 456%
AIRZONE ReceptorD Phase 3 2.47 263% Phase 3 4.05 431%
AIRZONE ReceptorE Phase 3 2.85 256% Phase 3 4.66 418%
AIRZONE ReceptorF Phase 3 2.77 282% Phase 3 4.61 469%
AIRZONE ReceptorG Phase 9 10.36 266% Phase 9 17.05 437%
RWDI CRAND4 Phase 3 5.63 258% Phase 3 9.20 422%
RWDI CRAND5 Phase 3 6.28 223% Phase 3 9.86 350%
RWDI CRAND6 Phase 3 4.87 240% Phase 3 7.81 385%
RWDI CRAND15 Phase 3 5.96 236% Phase 3 9.51 376%
RWDI CHURCH2 Phase 9 5.81 284% Phase 9 9.70 474%
RWDI RENTAL3 Phase 9 3.74 286% Phase 9 6.30 481%
RWDI PAULCAB14 Phase 3 6.41 235% Phase 3 10.21 374%
58% control efficiency 25% control efficiency
Henning Phase Max. conc. % increase Henning Phase Max. conc. % increase
AUXILIARY PLANT related to max. (μg m-3) of original related to max. (μg m-3) of original
AIRZONE ReceptorA Phase 6 3.46 367% Phase 6 6.05 643%
AIRZONE ReceptorB Phase 4 7.13 252% Phase 4 11.57 409%
AIRZONE ReceptorC Phase 3 2.72 329% Phase 3 4.71 570%
AIRZONE ReceptorD Phase 3 1.73 287% Phase 3 2.93 486%
AIRZONE ReceptorE Phase 3 1.87 299% Phase 3 3.18 508%
AIRZONE ReceptorF Phase 6 1.85 346% Phase 6 3.20 600%
AIRZONE ReceptorG Phase 9 7.95 168% Phase 9 11.26 238%
RWDI CRAND4 Phase 3 3.05 301% Phase 3 5.16 509%
RWDI CRAND5 Phase 9 4.70 217% Phase 9 7.32 338%
RWDI CRAND6 Phase 6 2.32 277% Phase 6 3.85 459%
RWDI CRAND15 Phase 3 3.44 251% Phase 3 5.57 406%
RWDI CHURCH2 Phase 3 3.88 258% Phase 3 6.72 446%
RWDI RENTAL3 Phase 3 2.46 286% Phase 3 4.27 496%
RWDI PAULCAB14 Phase 3 3.69 247% Phase 3 5.96 398%
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Figure 7: Illustrative air quality patterns results for PM2.5 are shown as produced by RWDI’s model and for the Central Processing Plant operation at Henning Pit Phase 9, but do not include emissions from the two adjacent non-subject
sources or regional background: for watering control efficiency of 25%. The greyed area shows where no assessment was made.
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209. This is a significant issue with the RWDI assessment as they have assumed a very high road dust control efficiency with no apparent guarantee they can maintain this continuously. This was a best–case assumption rather than a worst-case assumption. The consequences of taking a best-case control efficiency assumption would be to underestimate community-level exposures to dust emitted from the site, including PM2.5, and constituents of dust including crystalline silica.
3.5 Additional Issues with RWDI’s Analysis
210. The moisture content of storage piles proposed at the Henning Pit site can have a significant influence on dust emissions from those sources. The lower the moisture content, the dryer the storage pile and the more likely there will be dust emissions. Moisture content is described as a percentage by weight of the surface material and is used in determining the emissions of bulk material handling of storage piles.
211. The US EPA AP-42 document Chapter 13.2.4 Aggregate Handling and Storage Piles (Appendix N) presents typical values for moisture content for the stone quarrying and processing industry and shows them to be highly variable. For Stone Quarrying and Processing, the range provided is 0.3 to 1.1% and 0.46 to 5%.
212. The following explanation was provided in Appendix C (Bulk Material Handling) of the RWDI report:
“The US EPA's AP-42 does not report moisture content data for sand and gravel operations, but provides data for various limestone products, crushed limestone, sand, exposed ground, miscellaneous fill material and landfill soil cover. The data were based on a total of 20 measurement samples. The measured moisture contents ranged from 0.46% to 16%, with an overall average of 5.3%. RWDI previously collected 5 samples from stockpiles and 2 samples of raw material at two sand and gravel operations in Southern Ontario. Measurements were preformed during the summer time (early June and late August) For raw material from the face, the moisture content ranged from 5.1% to 6.1%. For the stockpiles, the moisture content ranged from 3.4 to 4.7%. Based on these data, the values of 2.1% for stockpiles and 4.8% for pit faces used here represent reasonable worst-case values.”
213. It is not clear where in the AP42 document’s the range of 0.46 – 16% originates.
214. Further, RWDI used their own data to provide a basis for their estimate of the moisture content to be used for the Henning Pit analysis. It is unknown if this data has been published and verified. I would need to review that data to ensure its credibility. I did not have the time to verify RWDI’s data, as indicated above, and therefore, their interpretation remains uncertain.
215. RWDI provides their own data for the moisture content of stockpiles as 3.4-4.7% and for pit faces as 5.1-6.1% and yet they use 2.1% and 4.8% in their emission calculations and so it is not clear how those final values were arrived at. No explanation was provided, but it is claimed this is worst case.
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216. According to the AP-42, the worst case assumption would have been 0.3% for stone quarrying and processing, an order of magnitude lower than the assumption provided by RWDI.
217. Overall, these issues point to uncertainties in the methods used by RWDI
3.6 Conclusions from the Review of RWDI’s Dust Assessment
218. Regarding the review process – I have not been able to complete my review due to reasons given earlier, primarily relating to errors and omission in the Air Quality Assessment.
219. Estimations of Air Emissions – there are a number of problems with RWDI’s calculations including (i) some sources of air emissions not assessed (for example, stripping of overburden and wood burning), (ii) no calculations were provided to verify RWDI’s claim of 90% road dust control efficiency for the proposed Pit, (iii) worst-case silt levels were not assumed thereby potentially underestimating road dust emissions, (iv) dust trackout on to paved roads was not assessed, (v) sources of asphalt dust from road coverings have not been included, (vi) no analysis was conducted of aggregate samples from the site that would have provided site-specific composition, and, (vii) estimates of aggregate composition were either missing or not based on worst-case. The sum total of these problems causes an underestimation in emissions of CoCs.
220. Estimates of Dispersion – RWDI did not use the AERMOD model to estimate annual levels of PM2.5. This is required by s.17 of O.Reg. 419/05, referred to in Issue 10 (a) (ii). This causes uncertainties in RWDI’s estimates of community-level exposures to PM2.5.
221. Estimates of Background Levels – (i) Preston did not conduct site-specific measurements of pre-existing background concentrations, (ii) when RWDI attempted, instead, to estimate background levels there were a number of problems with their estimates, (iii) the emissions of only two non-subject facilities were included in the background assessment, and (iv) not enough information was provided to assess the validity of the non-subject source emissions. The sum total of these problems may cause an under-estimation in community-level exposures to the CoCs.
222. Estimates of Cumulative Concentrations – (i) these estimates were not provided at all locations, as may be required by an ecological impacts expert for their review, (ii) some CoCs were not assessed or compared to appropriate threshold values, and (iii) results should be supplied to a human health impacts expert for review. The sum total of these problems is that insufficient information will be available to health and ecological impacts experts for review.
223. Interpretation of Results – RWDI (i) appealed to a “significance” concept that was not developed for stationary facilities such as gravel pits, to explain air quality exceedances of threshold values, (ii) attempted to define that significance by comparison to an inappropriate metric, and, (iii) attributed culpability to Lafarge’s operations for elevated dust levels at a receptor without clear basis (or, seemingly, Lafarge’s review). The sum total of these problems may cause an under-estimation in community-level exposures to the CoCs.
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PART IV: CONCLUSIONS AND SUMMARY OF EVIDENCE ON ISSUES
4.1 Conclusions
224. Based on my review I would conclude that:
225. I have not had the required material provided to me in a timely fashion, and due to errors discovered in the AQA during my review and subsequent revisions by RWDI, I cannot fully verify RWDI’s claims that cumulative concentrations of CoCs are at the levels they claim.
226. Of the material I have been able to review, there are significant issues with the air quality assessment that would lead me to conclude that emissions from the proposed Henning Pit may cause exposure levels, in the surrounding community, to exceed the values RWDI claims and perhaps significantly so.
227. RWDI has not provided sufficient or correct information to provide to health or ecological impacts experts to allow review of those impacts.
228. The study presented by RWDI requires considerable reworking. Site-specific background monitoring and aggregate sampling and analysis should be considered as part of that reworking; additional recommendations may emerge during further review.
229. RWDI state on page 23 in the final paragraph of their “Cumulative Concentrations at Sensitive Receptors” the following:
“The results for particulate matter presented in Tables 9 and 10 are generally consistent with RWDI’s experience with previous dispersion model assessments and monitoring campaigns at other sand and gravel operations in Southern Ontario, including ones that had other operating pits adjacent to them. These assessments have generally indicated the AAQC’s for some contaminants may occasionally be exceeded, but these occurrences are infrequent.”
230. I cannot verify RWDI’s claim about their experience at other operations. However, it is important to bear in mind that, if not located appropriately, gravel pits can be problematic from an air quality point-of-view.
231. About 40 km southwest of the proposed Henning Pit, measurements of dust from quarries in the vicinity of Ingersol were made for over a decade by the Ontario Ministry of the Environment. A few key points are summarized from this document as follows.
232. “A Summary of Air Monitoring in the Beachville Area” by Mike Parker, Gerald Diamond and Lee Orphan. MOE publication PIBS 4374e January 2003 (Appendix O). The Executive Summary states:
“In the Beachville area, the source of the main air quality concern particulate matter derives from the operation of local quarries and the manufacturing of cement and lime. Problems associated with high levels of particulate matter are: damage to agricultural crops, vegetation, homes; corrosion; reduced visibility and potential impacts on human health.”
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233. In the Conclusions and Observations section of this report:
“A summary' of air monitoring in the Beachville area using data summaries for HiVol samples taken during the period 1989-2000 and statistics based on one hour averages of real time (GRIMM) data for a 64 day period, during the summer of 2002 are presented.
• Particulate levels in the Beachville Area regularly exceed Ministry ambient air quality criterions (AAQC). • Particulate concentrations appear to be climbing at all of the Beachville HiVol stations. • Directional HiVols data, chemical analysis of particulate matter and pollution rose analysis of GRIMM data, lead to the conclusion that wind directions associated with quarry operations and the manufacturing of cement and lime, are associated with higher particulate levels than wind from other directions.
[…]
• Field staff have observed high dust concentrations on County Road 6 and on nearby vegetation and buildings. • Quarry operations and the manufacturing of cement and lime in the Beachville area contribute significantly to the elevated particulate levels that regularly exceed Ministry of the Environment annual and 24 hour AAQC.”
234. This clearly demonstrates that the MOE is aware and concerned about the high levels of particulate observed due to gravel pits.
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4.2 Summary of Evidence of Issues
235. I will now address the issues set out at the beginning of my witness statement:
ISSUE 6
Does the proposal meet the requirements of the Aggregate Resources Act and the Aggregate Resources of Ontario Provincial Standards? In particular: b. Has the proponent demonstrated that dust and air emissions will be mitigated on site? Should dust and air mitigation measures for each component of the operation be specified in more detail in the Site Plans? c. Should material specifications and the permitted volume of imported material for recycling be detailed in the Site Plans? d. Do the Site Plans adequately protect against continuation of the recycling use after the resource has been substantially depleted? e. Should the Site Plans specify what type or how much crushing is to take place at the pit face as opposed to the central processing facility? f. Should the specifications for inert fill to be used for rehabilitation and any testing and monitoring requirements be specified in the Site Plans? g. Has consideration been given to the compliance history of the proponent?
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Response by DiGiovanni
b. The proponent has indicated that they will attempt dust emission mitigation on-site. However the degree of mitigation is crucial and this seems to be uncertain and possibly under-estimated. There is insufficient information provided and so more detail is required in the Site Plans. c. Yes; especially limits on the content of the recycled materials must be specified in detail. This has not been adequately done in RWDI’s AQA. d. Cannot comment e. The exact operational scenario described in the AQA (if and when it is finally agreed and accepted) should be adhered to during actual operations. Deviations (for example, slightly different locations for roads or processing equipment) should not be allowed unless approved via an air quality re-assessment. f. Yes. Air emissions during rehabilitation have not been included in RWDI’s AQA and so inclusion of that assessment is a prerequisite to any specifications for inert fill. g. Cannot comment.
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ISSUE 10
Air and Dust
a. Whether dust and air emissions that may cause adverse effects have been appropriately assessed, having regard to:
i. Whether the appropriate operating scenarios, equipment locations and emissions rates were assessed in accordance with sections 10 and 11 of O. Reg. 419/05 and MOE Guidance;
ii. Whether all sources of emissions that may cause adverse effects and all airborne contaminants that can cause adverse effects were assessed, using the AERMOD dispersion model, in accordance with the dispersion modeling prescribed in Sections 6 through 17 of O. Reg. 419/05 and MOE dispersion modelling guidance;
iii. Whether sufficient evidence, i.e. appropriate data shown and references cited, to support all relevant assessment inputs, emissions rates and control efficiencies have been provided; and
iv. Whether all relevant outputs or predictions, including with respect to PM 2.5, to determine risk of adverse effects have been provided.
b. Does the proposed Henning Pit adhere to the PPS with respect to preventing adverse effects from dust and air emissions?
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Response by DiGiovanni
a. i. I cannot comment at this point on operating scenarios as RWDI recently altered their information on this and I have not had chance to re-review . As regards equipment locations, it is uncertain whether RWDI have chosen locations that lead to worst-case impacts as no analysis was provided that compared the locations used in the AQA versus other potential locations to clearly demonstrate that the chosen locations were the most conservative. A number of cases were found where emissions did not seem to be estimated as “is at least as high as the maximum emission rate that the source of contaminant is reasonably capable of for the relevant contaminant.” (O.Reg. 419/05, s.11(1)1.) ii. Not all sources of emissions were assessed by RWDI, as listed in my WS. Aspects of the dispersion modeling also did not seem to follow the specifications in the quoted sections of O.Reg. 419/05, e.g., not using AERMOD annual modelling for estimating annual PM2.5 levels, as required by OReg 419 s.17(1) 1. iii. Insufficient evidence has been provided as detailed in my WS. iv. No. RWDI did not assess all contaminants and all sources, as described in my WS. Further I would need to consult with health and ecological impact experts to confirm the exact form of the outputs and predictions they require to provide opinion on adverse effects. b. The RWDI report does not provide sufficient data, nor quality of data, that could be provided to health and ecological impact experts to provide opinion on adverse effects from air and dust emissions.
ISSUE 11
Does the proposed Henning Pit adhere to the PPS, and if required the Ministry of Natural Resources and Ministry of Environment Statements of Environmental Values with respect to preventing adverse effects and risk to public health from dust and air emissions, and if required have the cumulative effects of other pits and sources in the area been properly considered?
Response by DiGiovanni
The RWDI report does not provide sufficient data, nor quality of data, that could be provided to health and ecological impact experts to provide opinion on adverse effects from air and dust emissions. The cumulative effects of other pits and sources in the area would not seem to have been properly and fully considered.
ISSUE 12
Does the proposed Henning Pit prevent adverse effects from dust and air emissions and if not, what mitigation should be included?
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Response by DiGiovanni
The RWDI report does not provide sufficient data, nor quality of data, that could be provided to health and ecological impact experts to provide opinion on adverse effects from air and dust emissions. Opinion on mitigation is, at this point, premature.
ISSUE 13
Should the proposed Henning pit include conditions that monitor the dust and other relevant air emissions from the proposed operation?
Response by DiGiovanni
I have recommended that monitoring occur in the area to ascertain the current air quality; this can be used to inform this assessment. Should the pit be allowed to proceed, then air monitoring may be required; I leave exact specifications to a later date.
ISSUE 14
Are sensitive land uses protected from the proposed pit to prevent adverse effects from dust and air emissions?
Response by DiGiovanni
The RWDI report does not provide sufficient data, nor quality of data, that could be provided to health and ecological impact experts to provide opinion on adverse effects from air and dust emissions.