Wood Buffalo Environmental Association Ambient Air Quality Data … · 2012. 10. 16. · Department...

Department of Public Health Sciences

Wood Buffalo Environmental Association

Ambient Air Quality Data Summary and Trend AnalysisPart I Main ReportforWood Buffalo Environmental AssociationFort McMurray, Alberta

W.B. Kindzierski,1 PhD, P.Eng.P. Chelme-Ayala,2 PhD

M. Gamal El-Din,2 PhD, P.Eng.

1Department of Public Health Sciences, School of Public Health2 Department of Civil and Environmental Engineering

University of Alberta, Edmonton, Alberta

December 2009

This study was sponsored by the Wood Buffalo Environmental Association (WBEA), Fort McMurray, Alberta (www.wbea.org). The content and opinions expressed by the author(s) in this report do not necessarily reflect the views of the WBEA or of the WBEA membership.

Department of Public Health Sciences

Wood Buffalo Environmental Association

Ambient Air Quality Data Summary and Trend AnalysisPart I Main ReportforWood Buffalo Environmental AssociationFort McMurray, Alberta

W.B. Kindzierski,1 PhD, P.Eng.P. Chelme-Ayala,2 PhD

M. Gamal El-Din,2 PhD, P.Eng.

1Department of Public Health Sciences, School of Public Health2 Department of Civil and Environmental Engineering

University of Alberta, Edmonton, Alberta

December 2009

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EXECUTIVE SUMMARY

The Wood Buffalo Environmental Association (WBEA), Fort McMurray, Alberta requested that an analysis of air quality monitoring data be undertaken for the Regional Municipality of Wood Buffalo to assist stakeholders and interested parties in understanding the state of and trends in regional air quality. This report presents results of an investigation of short-term behaviors and long-term trends in continuously-measured ambient air quality data for the WBEA.

Daily and monthly (seasonal) behaviors and long-term trends in historical data for a number of air pollutants were investigated over the period 1998 to 2007. This period of time represented the most complete set of air quality data that was available in which to perform the investigation. Air pollutants included oxides of nitrogen, sulphur dioxide, particulate matter with aerodynamic diameter less than or equal to 2.5 µm (PM2.5), ground level ozone, total hydrocarbon, total reduced sulphur or hydrogen sulphide, and carbon monoxide. An objective of the study was to establish whether, and the extent to which, concentrations of air pollutants have changed over this time period in relation to industrial and community development.

Percentiles values taken from a cumulative frequency distribution of data can be more representative than general average values. Values representing 50th, 65th, 80th, 90th, 95th, and 98th percentile concentrations were identified from frequency distributions for each year and used for trend analysis. One definition of a percentile for a distribution of values is that it is the percentage of values that are smaller than the value at that percentile.

For example, if the 50th percentile 1-hour concentration for ozone is 20 ppb during a year, 50% of the 1-hour concentrations are smaller than 20 ppb and 50% are larger. For a 98th percentile 1-hour concentration of 40 ppb during the year, 98% of the 1-hour concentrations are smaller than 40 ppb and only 2% are larger. A 50th percentile concentration is a typical concentration experienced on any given day. A 98th percentile concentration is a high-end value, or something that – on average – occurs much less frequently or not at all on any given day.

Table ES-1 summarizes trends for hourly average concentrations of air pollutants at WBEA monitoring stations. The record for three monitoring stations (AMS 3 – Lower Camp; AMS 14 – Anzac; and AMS 15 – CNRL Horizon) was less than four years. This period is considered too short to offer a meaningful understanding about concentration trends. Therefore results for these stations are not shown in Table ES-1.

Results indicated statistically significant increasing hourly concentrations for oxides of nitrogen (including nitric oxide and nitrogen dioxide) at the Fort McMurray Patricia McInnes station and the Fort McKay station.

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Table ES-1 Summary of trends for hourly average concentrations of air pollutants in WBEA airshed.

Station Number Pollutant Observation Period Trend

1 Comment

AMS 1 (Fort McKay)

Nitric Oxide (NO) Jan 1998 to Dec 2007 ▲ small increase at highest percentile level Nitrogen Dioxide (NO2) Jan 1998 to Dec 2007 ▲ increase Oxides of Nitrogen (NOx) Jan 1998 to Dec 2007 ▲ increase Sulphur Dioxide (SO2) Jan 1998 to Dec 2007 ▬ no change Particulate Matter (PM2.5) Jan 1998 to Dec 2007 ▼ decrease Ground Level Ozone (O3) Jan 1998 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 1998 to Dec 2007 ▬ no change Total Reduced Sulphur (TRS) Jan 1998 to Dec 2007 ▲ small increase

AMS 2 (Mildred Lake)

Sulphur Dioxide (SO2) Jan 1998 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 1998 to Dec 2007 ▼ decrease at high percentile levels only Hydrogen Sulphide (H2S) Jan 1998 to Dec 2007 ▲ increase

AMS 4 (Buffalo Viewpoint)

Sulphur Dioxide (SO2) Jan 1998 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 1998 to Dec 2007 ▼ decrease at high percentile levels only Hydrogen Sulphide (H2S) Jan 1998 to Dec 2007 ▬ no change

AMS 5 (Mannix)

Sulphur Dioxide (SO2) Jan 1998 to Dec 2007 ▲ increase at high percentile levels only Total Hydrocarbon (THC) Jan 1998 to Dec 2007 ▬ no change Hydrogen Sulphide (H2S) Jan 1998 to Dec 2007 ▬ step increase observed after 2005

Nitric Oxide (NO) Jan 1998 to Dec 2007 ▲ increase

AMS 6 (Patricia McInnes)

Nitrogen Dioxide (NO2) Jan 1998 to Dec 2007 ▲ increase Oxides of Nitrogen (NOx) Jan 1998 to Dec 2007 ▲ increase Sulphur Dioxide (SO2) Jan 1998 to Dec 2007 ▬ no change Particulate Matter (PM2.5) Jan 1998 to Dec 2007 ▼ decrease Ground Level Ozone (O3) Jan 1998 to Dec 2007 ▼ decrease at low percentile levels only Total Hydrocarbon (THC) Jan 1998 to Dec 2007 ▬ no change Total Reduced Sulphur (TRS) Jan 1998 to Dec 2007 ▬ no change

AMS 7 (Athabasca Valley)

Nitric Oxide (NO) Jan 1998 to Dec 2007 ▬ no change Nitrogen Dioxide (NO2) Jan 1998 to Dec 2007 ▬ no change Oxides of Nitrogen (NOx) Jan 1998 to Dec 2007 ▬ no change Sulphur Dioxide (SO2) Jan 1998 to Dec 2007 ▬ no change Carbon monoxide (CO) Jan 1998 to Dec 2007 ▼ small decrease Particulate Matter (PM2.5) Jan 1998 to Dec 2007 ▼ decrease Ground Level Ozone (O3) Jan 1998 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 1998 to Dec 2007 ▬ no change Total Reduced Sulphur (TRS) Jan 1998 to Dec 2007 ▼ small decrease

AMS 8 (Fort Chipewyan)

Nitric Oxide (NO) Jan 1999 to Dec 2007 ▬ no change Nitrogen Dioxide (NO2) Jan 1999 to Dec 2007 ▬ no change Oxides of Nitrogen (NOx) Jan 1999 to Dec 2007 ▬ no change Sulphur Dioxide (SO2) Jan 1999 to Dec 2007 ▬ no change Particulate Matter (PM2.5) Jan 1999 to Dec 2007 ▼ decrease Ground Level Ozone (O3) Jan 1999 to Dec 2007 ▬ no change

1 Direction of trend: ▬ no change; ▲ increasing; ▼ decreasing

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Table ES-1 Summary of trends for hourly average concentrations of air pollutants in WBEA zone (con’t).

Station Number Pollutant Observation Period Trend

1 Comment

AMS 9 (Barge Landing)

Total Hydrocarbon (THC) Jan 2001 to Dec 2007 ▲ small increase at low percentile levels only Total Reduced Sulphur (TRS)

Jan 2001 to Dec 2007 ▲ small increase at high percentile levels only AMS 10 (Albian Mine Site)

Nitric Oxide (NO) Jan 2001 to Dec 2007 ▬ no change Nitrogen Dioxide (NO2) Jan 2001 to Dec 2007 ▬ no change Oxides of Nitrogen (NOx) Jan 2001 to Dec 2007 ▬ no change Sulphur Dioxide (SO2) Jan 2001 to Dec 2007 ▬ no change Particulate Matter (PM2.5) Jan 2001 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 2001 to Dec 2007 ▲ small increase

AMS 11 (Lower Camp)

Sulphur Dioxide (SO2) Jan 2001 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 2001 to Dec 2007 ▬ no change

Hydrogen Sulphide (H2S) Jan 2001 to Dec 2007 ▲ small increase at highest percentile level only, step increase observed 2005 to 2007

AMS 12 (Millenium Mine)

Nitric Oxide (NO) Jan 2004 to Dec 2007 ▲ increase Nitrogen Dioxide (NO2) Jan 2004 to Dec 2007 ▲ increase Oxides of Nitrogen (NOx) Jan 2004 to Dec 2007 ▲ increase Sulphur Dioxide (SO2) Jan 2004 to Dec 2007 ▬ no change Particulate Matter (PM2.5) Jan 2004 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 2004 to Dec 2007 ▲ increase at high percentile levels only Total Reduced Sulphur (TRS)

Jan 2005 to Dec 2007 period too short to judge trend

AMS 13 (Syncrude UE1)

Nitric Oxide (NO) Jan 2003 to Dec 2007 ▬ no change Nitrogen Dioxide (NO2) Jan 2003 to Dec 2007 ▬ no change Oxides of Nitrogen (NOx) Jan 2003 to Dec 2007 ▬ no change Sulphur Dioxide (SO2) Jan 2003 to Dec 2007 ▬ no change Particulate Matter (PM2.5) Jan 2003 to Dec 2007 ▬ no change Ground Level Ozone (O3) Jan 2003 to Dec 2007 ▬ no change Total Hydrocarbon (THC) Jan 2003 to Dec 2007 ▬ no change Total Reduced Sulphur (TRS)

Jan 2003 to Dec 2007 ▲ small increase

1 Direction of trend: ▬ no change; ▲ increasing; ▼ decreasing

In addition, decreasing hourly concentrations were observed for PM2.5 at all of the community air monitoring stations (Fort McMurray, Fort McKay, and Fort Chipewyan). Results for other air pollutant at other stations were mixed; but in most cases indicated negligible or small amounts of change.

Air quality at the Fort Chipewyan monitoring station was quite unique and separate from air quality observed at the other monitoring stations. It is apparent that the Fort Chipewyan monitoring station is located far enough away from sources and activities that it is only slightly influenced by the regional development and activity that is influencing, to varying degrees, many of the other monitoring stations in the airshed.

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Only a few of the air pollutant datasets analyzed showed statistically significant change – e.g., oxides of nitrogen concentrations at Fort McKay and Fort McMurray, and PM2.5 concentrations at all of the community air monitoring stations. In most cases it was difficult to show change, or change that was large enough to be statistically significant. Given the limits posed by accuracy of monitors for measuring low levels of air pollutants and noise inherent in environmental monitoring data, longer time periods (i.e., longer than the 5- to 10-year periods that were available here) are needed to reliably detect change. In general, what was observed in this analysis was positive as it is apparent that changes to regional air quality in the WBEA airshed – where observed – were either negligible or small for most of the air pollutants at most of the stations.

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TABLES OF CONTENTS TABLES OF CONTENTS ......................................................................................................................... vii CHAPTER 1. INTRODUCTION ............................................................................................................ 1

1.1 Background ................................................................................................................................... 1 1.2 Objective of Study ........................................................................................................................ 1 1.3 Report Organization ...................................................................................................................... 2 1.4 Study Area and Air Monitoring Stations ...................................................................................... 2

CHAPTER 2. CHARACTERISTICS OF AIR POLLUTANTS .............................................................. 9

2.1 Oxides of Nitrogen (NO and NO2) ................................................................................................ 9 2.2 Sulphur Dioxide (SO2) ................................................................................................................ 11 2.3 Carbon Monoxide (CO) .............................................................................................................. 12 2.4 Particulate Matter (PM2.5) ........................................................................................................... 13 2.5 Ground Level Ozone (O3) ........................................................................................................... 15 2.6 Total Hydrocarbon (THC) .......................................................................................................... 16 2.7 Total Reduced Sulphur (TRS) and Hydrogen Sulphide (H2S) .................................................... 17

CHAPTER 3. METHODOLOGY .......................................................................................................... 21

3.1 Monitoring Data .......................................................................................................................... 21 3.2 Data Management and Screening ............................................................................................... 21 3.3 Types of Trends and Behaviors Examined ................................................................................. 23 3.4 Methods for Statistical Analysis of Trend Data .......................................................................... 25

3.4.1 Background .................................................................................................................... 25 3.4.2 Trend Analysis Approach .............................................................................................. 26 3.4.3 Linear Regression .......................................................................................................... 29 3.4.4 Hypothesis Testing ........................................................................................................ 30 3.5.5 Interpretation of Trends ................................................................................................. 33

CHAPTER 4. RESULTS AND DISCUSSION ..................................................................................... 35

4.1 AMS 1 (Fort McKay) .................................................................................................................. 36 4.2 AMS 2 (Mildred Lake) ............................................................................................................... 40 4.3 AMS 3 (Lower Camp) ................................................................................................................ 42 4.4 AMS 4 (Buffalo Viewpoint) ....................................................................................................... 42 4.5 AMS 5 (Mannix) ......................................................................................................................... 43 4.6 AMS 6 (Patricia McInnes) .......................................................................................................... 44 4.7 AMS 7 (Athabasca Valley) ......................................................................................................... 48

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4.8 AMS 8 (Fort Chipewyan) ........................................................................................................... 51 4.9 AMS 9 (Barge Landing) ............................................................................................................. 53 4.10 AMS 10 (Albian Mine North) ..................................................................................................... 54 4.11 AMS 11 (Lower Camp B) ........................................................................................................... 57 4.12 AMS 12 (Millennium Mine Site) ................................................................................................ 58 4.13 AMS 13 (Syncrude UE-1) ........................................................................................................... 60

CHAPTER 5. FINDINGS ...................................................................................................................... 65

5.1 Oxides of Nitrogen ...................................................................................................................... 65 5.2 Sulphur Dioxide .......................................................................................................................... 68 5.3 Particulate Matter (PM2.5) ........................................................................................................... 68 5.4 Ground Level Ozone ................................................................................................................... 69 5.5 Total Hydrocarbon ...................................................................................................................... 70 5.6 Total Reduced Sulphur and Hydrogen Sulphide ......................................................................... 71 5.7 Carbon Monoxide ....................................................................................................................... 72 5.8 Closing Remarks ......................................................................................................................... 72

REFERENCES ......................................................................................................................................... 75 LIST OF APPENDICES ............................................................................................................................. 81

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LIST OF TABLES Table 1-1 WBEA ambient air monitoring stations and their location in northeastern Alberta. ............. 4

Table 2-1 Examples of air quality objectives and standards. ............................................................... 19

Table 3-1 WBEA stations, measurement instruments, and observation periods for trend analysis. .... 22

Table 4-1 Summary of trend for hourly average percentile concentrations for air pollutants at AMS 1 (observation period 1998 to 2007). ...................................................................... 37








Table 4-9 Summary of trend for hourly average percentile concentrations for air pollutants at AMS 10 (observation period 2001 to 2007). .................................................................... 55




Table 5-1 Summary of trends for hourly average concentrations of air pollutants in WBEA zone. .... 66

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LIST OF FIGURES

Figure 1-1 Map of northeastern Alberta showing the location of the WBEA air monitoring stations. ............................................................................................................................... 3

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LIST OF ABBREVIATIONS

AENV Alberta Environment

AMS air monitoring station

EPA Environmental Protection Agency

CASA Clean Air Strategic Alliance

CCME Canadian Council of Ministers of the Environment

CDC Centers for Disease Control

CH3SH methyl mercaptan

C2H6S dimethyl sulphide

C2H6S2 dimethyl disulphide

CH4 methane

CO carbon monoxide

COS carbonyl sulphide

CS2 carbon disulphide

CWS Canada-wide Standard

GVRD Greater Vancouver Regional District

HNO3 nitric acid

H2S hydrogen sulphide

H2SO4 sulphuric acid

MSE mean square error

MSR mean square regression

NAS National Academy of Sciences

NESCAUM Northeast States for Coordinated Air Use Management

NSTC National Science and Technology Council

NMHCs nonmethane hydrocarbons

NO nitric oxide

NO2 nitrogen dioxide

NOx oxides of nitrogen

O3 ozone

OME Ontario Ministry of the Environment

PAHs polycyclic aromatic hydrocarbons

PM2.5 particulate matter with aerodynamic diameter ≤2.5 µm

PM10 particulate matter with aerodynamic diameter ≤10 µm

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SO2 sulphur dioxide

SO3 sulphur trioxide

SSE sum of square error

SST sum of square total

SSR sum of square regression

SSX standard error of the estimates

TEOM Tapered Element Oscillating Microbalance

THC total hydrocarbon

TRS total reduced sulphur

VOC volatile organic compound

UV ultra-violet

WBEA Wood Buffalo Environmental Association

WHO World Health Organization

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CHAPTER 1. INTRODUCTION

1.1 Background Air pollutants are often unevenly dispersed in the environment. In many cases, areas

with higher concentrations are near emission sources. Transport and dispersion of air pollutants in the atmosphere are influenced by numerous complex factors – proximity of emission sources, local meteorology, and local topography – being the primary factors. All of these can influence ambient air quality.

Air monitoring is one technique used to measure and assess the status of ambient air quality. In an area with multiple emission sources, short-term variability of emissions in both time and space – as well as variations in winds, temperature, precipitation, and atmospheric circulation patterns – produce a complex varying pollution concentration field in the atmosphere (WHO, 1999). Monitoring results only represent only the point and time where and when the sample was taken or the measurement was made.

Long-term changes in air quality can be masked by these hour-to-hour, day-to-day, season-to-season, and year-to-year variations in atmospheric dispersion conditions that in turn affect transport and deposition of pollution. In order to see the larger picture – beyond the short-term variations – it is important to monitor for long periods of time using consistent procedures and quality assurance practices to observe possible long-term and important changes. However, only evaluating and reporting results of continuous air monitoring data overlooks the important aspect of detecting changes in air quality over time (i.e., trends) (Bower 1997).

It is of great interest to know whether changes in air quality have occurred over time where continuous air monitoring is conducted (US EPA, 1999). Detection of long-term temporal trends is useful as comparison of changes in ambient air concentrations with changes in emissions from community and industrial development can provide a better understanding of how these development activities actually influence air quality.

1.2 Objective of Study The Wood Buffalo Environmental Association (WBEA), Fort McMurray, Alberta

requested that an analysis of air quality data be undertaken for the Regional Municipality of Wood Buffalo to assist stakeholders and interested parties in understanding regional air quality and trends in regional air quality. This report presents results of an investigation of short-term behaviors and trends in continuously-measured ambient air quality data for the WBEA. Daily, monthly (seasonal), and annual trends in historical data for a number of air pollutants were investigated over the period 1998 to 2007. This period of time represented the most complete set of air quality data that was available in which to perform the investigation. An objective of the

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study was to establish whether, and the extent to which, concentrations of air pollutants have changed over this time period in relation to industrial and community development.

1.3 Report Organization

Because of the large amounts of air quality data evaluated, this report was separated into three parts: Part I (the main body whose contents are described further below); and Parts II and III (which contain evaluation results for continuous air pollutants from each of the air monitoring stations in the WBEA zone).

The Part I report is organized into the following sections: The remainder of Chapter 1 describes the study area (WBEA zone) and air

monitoring stations (AMSs) in the zone. Chapter 2 includes a brief discussion of characteristic of air pollutants, their important

sources, how they are monitored, and ambient air guidelines and objectives. Chapter 3 describes the approach used to retrieve air monitoring data from WBEA

and how it was organized for further analysis. The statistical methods used for analyzing long term trends are also presented.

Chapter 4 presents a summary of the trend analysis for each of the air monitoring stations and explanations of what is likely influencing air quality.

Chapter 5 presents the findings of the study. Chapter 6 list scientific references. Parts II and III are the Appendices presenting detailed results for the air monitoring

stations. Part II contains results for AMS 1 to AMS 6; Part III presents results for AMS 7 to AMS 13. No trend analysis was performed on air monitoring data for AMS 14 and 15 because the period of record for these stations was too short (i.e., less than 4 years) to offer any meaningful indication of trends.

An important point to note about results presented in the Appendices is that the scale used for all graphical results was the same in order to allow the reader to make comparisons of results among stations. A result of this is that some graphs show small concentrations and other graphs show much larger concentrations because, in simple terms, that is what they are.

1.4 Study Area and Air Monitoring Stations Networks that monitor levels of pollutants in the atmosphere provide important

information regarding the current status of the composition, and how levels, of pollutants differ over time and space (NSTC, 1993). WBEA was established as an Air Quality Task Force in 1985 to address environmental concerns raised by the Fort McKay First Nations (WBEA, 2009).

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Today, WBEA conducts air, terrestrial, and human monitoring programs in the Regional Municipality of Wood Buffalo. Currently WBEA collects information from 15 air monitoring stations located in northeastern Alberta between Anzac and Fort Chipewyan (Figure 1-1). Figure 1-1. Map of northeastern Alberta showing the location of the WBEA air monitoring stations.

Table 1-1 lists the number and name of each WBEA air monitoring station, and the

community and/or industry facility located in closest proximity to the station. Background information about each station is described below. Additional information about each station is provide on the WBEA website (www.wbea.org).

AMS 10

AMS 8

AMS 15

AMS 1

AMS 13

AMS 2

AMS 4

AMS 5

AMS 6

Fort Chipewyan

AMS 9

AMS 3

AMS 11

AMS 12

AMS 7

AMS 14

Fort McKay

Fort McMurray

WBEA Monitoring Stations AMS 1: Fort McKay AMS 2: Mildred Lake AMS 3: Lower Camp AMS 4: Buffalo Viewpoint AMS 5: Mannix AMS 6: Patricia McInnes AMS 7: Athabasca Valley AMS 8: Fort Chipewyan AMS 9: Barge Landing AMS 10: Albian Mine North AMS 11: Lower Camp B AMS 12: Millennium Mine Site AMS 13: Syncrude UE-1 AMS 14: Anzac AMS 15: CNRL Horizon

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Table 1-1 WBEA ambient air monitoring stations and their location in northeastern Alberta.

Station Number Station Name

Community or Industry Facility Location Proximity Latitude Longitude

AMS 1 Fort McKay Fort McKay 57°11’20.9”N 111°38’25.9”W AMS 2 Mildred Lake Syncrude near airstrip 57°02’59.7”N 111°33’51.1”W AMS 3 Lower Camp Syncrude Canada Ltd. 57°01’54.9”N 111°30’23.8”W AMS 4 Buffalo Viewpoint Syncrude Canada Ltd. – South Mine 56°59’48.29”N 111°35’33.2”W AMS 5 Mannix Suncor near main entrance 56°58’07.8”N 111°28’55.2”W AMS 6 Patricia McInnes Fort McMurray 56°45’08.3”N 111°28’34.1”W AMS 7 Athabasca Valley Fort McMurray 56°43’58.0”N 111°23’24.6”W AMS 8 Fort Chipewyan Fort Chipewyan 58°42’30.1”N 111°10’35”W AMS 9 Barge Landing Albian Sands Mine South 57°11.892’N 111°35.976’W AMS 10 Albian Mine North Albian Sands Mine North 57°16.852’N 111°31.539’W AMS 11 Lower Camp B Syncrude Canada Ltd. – Lower camp 57°01.611’N 111°30.049’W AMS 12 Millennium Mine Site Suncor Steepbank Mine North 56°53’20.6”N 111°22’59.2”W AMS 13 Syncrude UE-1 Between Syncrude and Fort McKay 57°08’57.05”N 111°38’32.82”W AMS 14 Anzac Hamlet of Anzac 56°26.934’N 111°02.283’W AMS 15 CNRL Horizon Canadian Natural Resources Limited

Horizon 57°18’13.4”N 111°44’21.8”W

Fort McKay (AMS 1) The WBEA Fort McKay station is located near the northwest corner of the Fort McKay

Water Treatment Plant. This station was built in the fall of 1997 and replaced a nearby station operated by Alberta Environment. The station contains instruments that continuously measure nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), sulphur dioxide (SO2), particulate matter (PM2.5), ground level ozone (O3), total hydrocarbon (THC), total reduced sulphur (TRS), ammonia (NH3), global radiation, leaf wetness, humidity, wind speed and direction, and ambient temperature at a height of 2 m and 10 m. Additional instruments are used to take non-continuous (integrated) measurements of PM10, volatile organic compounds (VOCs), chemicals in precipitation, semi-volatile organic compounds, and monthly SO2, NO2, O3, nitrous and nitric acid (HNO2 and HNO3), and ammonia (NH3) measurements using passive samplers.

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Mildred Lake (AMS 2) The WBEA Mildred Lake station is located at the Syncrude Canada Ltd. airstrip. This

station was originally part of Syncrude's air monitoring network. Air monitoring data from this station dates back to December 1997. The station contains instruments that continuously measure SO2, THC, hydrogen sulphide (H2S), wind speed and direction, and temperature.

Lower Camp Met Tower (AMS 3)

The WBEA Lower Camp Met Tower station was originally built as part of an air monitoring network operated by Suncor Energy Ltd. The station contained instruments that continuously measured SO2, THC, and H2S. Air monitoring data from this station were recorded from December 1997 to October 2000. Because of the presence of an unsuitable microclimate (climate difference within area compared to broader area) at this site, the continuous analyzers were subsequently moved to WBEA AMS 11 located nearby (WBEA, 2009). Currently at AMS 3, temperature, horizontal wind speed and direction, and vertical wind speed are continuously monitored at heights of 20, 45, 100, and 167 m.

Buffalo Viewpoint (AMS 4)

The WBEA Buffalo Viewpoint station is located at the south end of Syncrude Canada Ltd.’s South Mine and was built originally as part of the Syncrude air monitoring network. This station contains instruments that continuously measure SO2, THC, H2S, wind speed and direction, and temperature.

Mannix (AMS 5)

The WBEA Mannix station is located near Suncor Energy’s main plant entrance and was originally part of the air monitoring network operated by Suncor. The station contains instruments that continuously measure SO2, THC, and H2S. It also contains meteorological instruments that measure temperature, horizontal wind speed and direction, and vertical wind speed at heights of 2 (temperature only), 20, 45, and 75 m.

Patricia McInnes (AMS 6)

The WBEA Patricia McInnes station is situated on the west edge of Fort McMurray's Timberlea subdivision. This station was built in 1997 and air monitoring data have been recorded at the site since that time. The station contains instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, O3, THC, TRS, ammonia (NH3), wind speed and direction, and temperature. Additional instruments are used to take non-continuous (integrated) measurements of PM10, VOCs, chemical in precipitation, semi-volatile organic compounds, and monthly SO2,

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NO2, O3, nitrous and nitric acid (HNO2 and HNO3), and ammonia (NH3) measurements using passive samplers.

Athabasca Valley (AMS 7)

The WBEA Athabasca Valley station is located in Fort McMurray (adjacent to the Athabasca River just off the road to McDonald Island). This station was initially built and operated by Alberta Environment. WBEA assumed responsibility for operating the station in the fall of 1997. The station contains instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, O3, THC, TRS, CO, wind speed and direction at 10 m, and temperature at 2 m. Additional instruments are used to take non-continuous (integrated) measurements of PM10, VOCs, and semi-volatile organic compounds.

Fort Chipewyan (AMS 8)

The WBEA Fort Chipewyan station overlooks Lake Athabasca on the outskirts of Fort Chipewyan, Alberta. This station began operating during the summer of 1998. The station contains instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, O3, wind speed and direction, temperature, global radiation, leaf wetness, and relative humidity.

Barge Landing (AMS 9)

The WBEA Barge Landing station is located adjacent to Barge Road off of Highway 63, north of Fort McKay and east of the Athabasca River. The station was constructed by Shell Albian Sands and donated to WBEA in 2001. The station contains instruments that continuously measure TRS, THC, wind speed and direction, and temperature. Additional instruments are used to take non-continuous (integrated) measurements of VOCs.

Albian Mine Site (AMS 10)

The WBEA Albian Mine North station is located on the Shell Albian Sands site. The station was built as part of the Albian Sands monitoring program and was donated to WBEA in 2001. The station contained instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, THC, wind speed and direction, and temperature. Additional instruments were used to take non-continuous (integrated) measurements of PM10 and PM2.5. This station was decommissioned on February 4, 2009 due to mining activities. A new station (AMS 16, Albian Muskeg River) began operation on February 10, 2009.

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Lower Camp (AMS 11) The WBEA Lower Camp station (AMS 11) began operation in 2000 after relocation of

monitoring instrumentation from AMS 3. This station is located in the lower camp of Syncrude Canada Ltd. The station contains instruments that continuously measure SO2, THC, H2S, wind speed and direction, and temperature.

Millennium (AMS 12)

The WBEA Millennium station was originally situated at the south end of the Suncor Energy Mine. It was moved to the east side of the Athabasca as part of Suncor Energy’s Millenium project. The station contains instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, THC, TRS, wind speed and direction, and temperature. Additional instruments are used to take non-continuous (integrated) measurements of VOCs and PM10.

Syncrude UE-1 (AMS 13)

The WBEA Syncrude UE-1 station is situated between the community of Fort McKay and the Syncrude Canada Ltd. mine site. The station contains instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, O3, THC, TRS, wind speed and direction, and temperature. Additional instruments are used to take non-continuous (integrated) measurements of VOCs and PM10.

Anzac (AMS 14)

The WBEA Anzac Station is located approximately 35 km southeast of Fort McMurray on the northern edge of the hamlet of Anzac. The station was established in connection to the Nexen and OPTI Canada Ltd. Long Lake project. The station contains instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, O3, THC, TRS, wind speed and direction, and temperature.

CNRL Horizon (AMS 15)

The WBEA CNRL Horizon station is located in the regional Municipality of Wood Buffalo, 75 km Northwest of Fort McMurray, and west of Fort McKay. The station was established in connection to the CNRL Horizon project. The station contains instruments that continuously measure NO, NO2, NOx, SO2, PM2.5, THC, TRS, wind speed and direction, and temperature. Additional instruments are used to take non-continuous (integrated) measurements of VOCs and PM10.

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CHAPTER 2. CHARACTERISTICS OF AIR POLLUTANTS

Air pollutants are all very different in terms of chemical composition, reaction properties, emission sources, and fate and transport in the environment. Seven ambient air pollutant types were analyzed in this study based upon available data from WBEA:

oxides of nitrogen (NOx) consisting of nitric oxide (NO) and nitrogen dioxide (NO2) sulphur dioxide (SO2) carbon monoxide (CO) particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) ground level ozone (O3) total hydrocarbon (THC) total reduced sulphur (TRS) and hydrogen sulphide (H2S)

Characteristics, monitoring techniques, and examples of ambient air criteria used by various jurisdictions that regulate these pollutants are briefly discussed below.

2.1 Oxides of Nitrogen (NO and NO2)

Characteristics. Oxides of nitrogen (NOx) is a generic term used to represent a group of reactive gases containing nitrogen and oxygen – mostly in the form of nitric oxide (NO) and nitrogen dioxide (NO2). The concentration of NOx is calculated from the addition of NO and NO2 concentrations. High temperature combustion of hydrocarbon fuel sources – such as gasoline, coal, and oil – with air produce NO and smaller quantities of NO2 from reactions between the oxygen and nitrogen present in the combustion air. Most of the NO in ambient air rapidly turns into NO2.

Almost every combustion source will emit NO and produce NO2 (including processes associated with the extraction, upgrading, and refining of bitumen; motor vehicles; commercial and residential furnaces; gas stoves; heaters; etc.). Automobile emissions are the largest single source of air pollutants in urban areas (Moeller, 2004) and often account for more than 50% of man-caused NO and NO2 emissions in urban areas.

NO2 along with NO, volatile organic compounds that are anthropogenic (man-made) and biogenic hydrocarbons (from vegetation), and carbon monoxide are precursors in the formation of ground-level ozone (O3) and photochemical smog (US EPA, 2008a). NO2 also reacts with O3 and various free radicals in the gas phase and on surfaces in multiphase processes to form oxidation products in the atmosphere. These products include inorganic and organic species. Inorganic reaction products include nitrous acid (HNO2), nitric acid (HNO3), and other

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inorganic species. Organic reaction products include nitrosamines and nitro-polycyclic aromatic hydrocarbons (nitro-PAHs).

The concentrations and atmospheric lifetimes of these oxidation products vary widely in space and time. The timescale for reactions of NOx to form products like HNO3 typically ranges from a few hours during summer to about a day during winter (US EPA, 2008a). As a result, morning rush hour emissions of NOx from motor vehicles in a city can be converted almost completely to products including HNO3 by late afternoon during warm, sunny conditions.

Sources of NOx are distributed with height; some are at or near ground level (e.g., motor vehicle exhaust) and others are aloft (e.g., industrial stacks). Because the time required for mixing emissions down to the surface is similar to or longer than the time for oxidation of NOx, emissions of NOx from elevated sources tend to be transformed to products including HNO3 before they reach the surface (US EPA, 2008a). Ultimately, oxidized N compounds are lost from the atmosphere by deposition to the earth’s surface.

Monitoring. One method of measuring oxides of nitrogen continuously in Alberta is by the principle of chemiluminescence (CASA, 2006). An air sample is divided into two pathways in this method: one to measure NO levels, and the other to measure total NOx. In the first pathway, the sample goes directly to the analysis chamber where the sample is mixed with O3 producing light. The amount of light detected is proportional to the NO concentration and is a measurement of NO in air. In the second pathway, a catalytic converter transforms all NO2 in the sample air into NO, and then the sample goes on to the analysis chamber. This measurement is the expressed as NOx.

Ambient air criteria. In Alberta, air quality guidelines are established to define desired environmental quality that will protect public health and ecosystems and are based on an evaluation of scientific, social, technical and economic factors. Alberta objectives for nitrogen dioxide, the major component of nitrogen oxides in the ambient atmosphere, are (AENV, 2008):

212 ppb (400 µg/m3) as a 1-hour average concentration 106 ppb (200 µg/m3) as a 24-hour average concentration 32 ppb (60 µg/m3) as an annual average concentration

Canada has National Ambient Air Quality Objectives (NAAQOs) for nitrogen dioxide (CCME, 2009):

213 ppb (400 µg/m3) as a 1-hour average maximum acceptable level and 532 ppb (1000 µg/m3) as 1-hour annual average maximum tolerable level

106 ppb (200 µg/m3) as a 24-hour average maximum acceptable level and 160 ppb (300 µg/m3) as 24-hour annual average maximum tolerable level

32 ppb (60 µg/m3) as an annual average maximum desirable level and 53 ppb (100 µg/m3) as an annual average maximum acceptable level

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The World Health Organization (WHO) guidelines for nitrogen dioxide in the ambient atmosphere are (WHO, 2005):

106 ppb (200 µg/m3) as a 1-hour average concentration 21 ppb (40 µg/m3) as an annual average concentration

The United States Environmental Protection Agency has an annual average air quality standard for nitrogen dioxide (US EPA, 2009a) and is currently proposing a 1 hour average value:

53 ppb (100 µg/m3) as an annual average concentration

2.2 Sulphur Dioxide (SO2)

Characteristics. High temperature combustion of hydrocarbon fuel sources – such as coal and oil – can produce sulphur dioxide (SO2) and sulphur trioxide (SO3) from the oxidation of any sulphur in these fuels. Emissions of these sulphur compounds are associated with industrial operations (e.g., combustion processes associated with the extraction, upgrading, and refining of bitumen, electricity and steam generation, etc.) and contribute to the majority of SO2 emissions from man’s activities. Transportation-related sources are estimated to contribute small amounts of sulphur emissions to the atmosphere. All of these are sources in the WBEA airshed.

SO3 emitted to the atmosphere reacts rapidly with moisture to form sulphuric acid (H2SO4), which condenses onto existing particles (when particle loadings are high) or acts as a nucleus to form new particles (under low particle loading conditions) (US EPA, 2008b). SO2 can react with oxidants and moisture in the atmosphere to form H2SO4. H2SO4 contributes to acidity of clouds, fog, and rainwater.

Monitoring. SO2 is monitored continuously in Alberta by pulsed fluorescence (CASA, 2006). An air sample is drawn through a chamber where it is irradiated with pulses of ultra-violet light. Any SO2 in the sample is excited to a higher energy level and upon returning to its original state, light or fluorescence is released. The amount of fluorescence that is measured is proportional to the SO2 concentration.

Ambient air criteria. Alberta Environment has adopted Environment Canada’s maximum desirable levels for sulphur dioxide as Alberta Ambient Air Quality Objectives (AAAQO) (AENV, 2008):

172 ppb (450 µg/m3) as a 1-hour average concentration 57 ppb (150 µg/m3) as a 24-hour average concentration 11 ppb (30 µg/m3) as an annual average concentration Canada has National Ambient Air Quality Objectives (NAAQOs) for sulphur dioxide

(CCME, 2009):

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172 ppb (450 µg/m3) as a 1-hour average maximum desirable level and 334 ppb (900 µg/m3) as 1-hour annual average maximum acceptable level

57 ppb (150 µg/m3) as a 24-hour average maximum desirable level, 115 ppb (300 µg/m3) as a 24-hour average maximum acceptable level and 306 (800 µg/m3) as 24-hour annual average maximum tolerable level

11 ppb (30 µg/m3) as an annual average maximum desirable level and 23 ppb (60µg/m3) as an annual average maximum acceptable level

The World Health Organization (WHO) guidelines for sulphur dioxide in the ambient atmosphere are (WHO, 2005):

191 ppb (500 µg/m3) as a 10 minute average concentration 7.6 ppb (20 µg/m3) as a 24-hour average concentration

The United States Environmental Protection Agency has air quality standards for sulphur dioxide (US EPA, 2009a):

140 ppb (366 µg/m3) as a 24-hour average concentration 30 ppb (79 µg/m3) as an annual average concentration

2.3 Carbon Monoxide (CO)

Characteristics. Carbon monoxide (CO) is formed primarily by incomplete combustion of carbon-containing fuels and photochemical reactions in the atmosphere. By far the most important source of CO emissions to the atmosphere are from transportation. For example, in the U.S. as much as 67% of all CO emissions came from on-road vehicle exhaust in 2006, the most recent year in which inventory data are available (US EPA, 2009b). Combustion processes associated with the extraction, upgrading, and refining of bitumen are also sources in the WBEA airshed. At times forest fires can be an important natural source of CO.

Monitoring. Carbon monoxide is monitored continuously in Alberta either by nondispersive infrared photometry or gas filter correlation (CASA, 2006). The non dispersive infrared photometry process is based upon absorption of infrared light by CO. Gas filter correlation operates on the same principle as non-dispersive infrared photometry but is more specific to CO by eliminating water vapour, CO2, and other interferences.

Ambient air criteria. Objectives for CO are based on prevention of adverse human health effects. Alberta has adopted Environment Canada's most stringent ambient air quality objective for CO –maximum permissible concentration:

13 ppm (15 mg/m3) as a 1-hour average concentration 5 ppm (6 µg/m3) as an 8-hour average concentration

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Canada has National Ambient Air Quality Objectives (NAAQOs) for carbon monoxide (CCME, 2009):

13 ppm (15 mg/m3) as a 1-hour average maximum desirable level and 31 ppm (35 mg/m3) as 1-hour annual average maximum acceptable level

5 ppm (6 mg/m3) as an 8-hour average maximum desirable level, 13 ppm (15 mg/m3) as an 8-hour average maximum acceptable level and 17 ppm (20 mg/m3) as an 8-hour annual average maximum tolerable level

The World Health Organization (WHO) guidelines for carbon monoxide in the ambient atmosphere are (WHO, 2000):

87 ppm (100 mg/m3) as a 15 minute average concentration 52 ppm (60 mg/m3) as a 30 minute average concentration 26 ppm (30 mg/m3) as a 1-hour average concentration 9 ppb (10 mg/m3) as an 8-hour average concentration

The United States Environmental Protection Agency has air quality standards for carbon monoxide (US EPA, 2009a):

35 ppm (40 mg/m3) as a 1-hour average concentration 9 ppm (10 mg/m3) as an 8-hour average concentration

2.4 Particulate Matter (PM2.5)

Characteristics. Particulate matter (PM) is a general term used to describe mixtures of solid particles and liquid droplets (except for pure water) that are very small in size – microscopic – and found in the air. These mixtures include larger particles called coarse particles and smaller particles called fine particles. Coarse particles have diameters greater than 2.5 μm and less than10 μm; while fine particles (PM2.5) have diameters less than 2.5 μm (Health Canada, 1999). PM10 refers to all particles that have diameters less than 10 μm.

Course particles are mainly produced by abrasion at the earth’s surface (e.g., silt particles) or by suspension of biological material composed of microorganisms (e.g., bacteria, viruses, fungal spores, pollens) and fragments of living things (e.g., plant and insect debris). The makeup of fine PM tends to be dominated by particles that form during combustion of material that has volatilized in combustion chambers and then re-condenses before emission to the atmosphere (US EPA, 2004) or after emission to the atmosphere.

PM mixtures have a wide variety of sources in the environment (US EPA, 2004). Anthropogenic (man-made) sources can include:

stationary sources (e.g., fuel combustion from residential space heating and cooking; industrial boilers)

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mobile or transportation-related sources (e.g., direct emissions from highway vehicles and non road sources as well as fugitive dust from paved and unpaved roads

In addition, combustion processes associated with the extraction, upgrading, and refining of bitumen can contribute to emissions of PM mixtures to the atmosphere in the WBEA airshed. Biomass burning (e.g., forest fires, wood burned for fuel, and burning of vegetation cleared from land) also emits PM mixtures and other environmentally significant compounds (e.g., carbon monoxide, gaseous elemental mercury).

Monitoring. Particulate matter (PM10 and PM2.5) is monitored on a continuous (hourly) basis in Alberta using the Tapered Element Oscillating Microbalance (TEOM) (CASA, 2006). The TEOM draws an air sample through an inlet stream that aerodynamically separates particles of a specified diameter (e.g., 2.5 or 10 µm). The air then passes through a filter that is attached to a tapered element in a mass transducer. This element vibrates at its natural frequency. As particles are deposited onto the filter the oscillating frequency changes in proportion to the amount of mass deposited.

Ambient air criteria. A Canada-wide Standard (CWS) benchmark concentration for PM2.5 is set at 30 µg/m3 as a 24-hour average concentration (Health Canada, 1999). Alberta has adopted the Canada-wide Standard as a 24-hour Air Quality Objective (AENV, 2008). In addition, Alberta has adopted a 1-hour guideline value of 80 µg/m3. This guideline is based on a statistical equivalent of the 24-hour Canada Wide Standard (CWS) and is not used for compliance purposes.

The World Health Organization (WHO) guidelines for PM2.5 and PM10 in the ambient atmosphere are (WHO, 2005):

PM2.5 - 25 µg/m3 as a 24-hour average concentration (99th %ile during a year) PM10 - 50 µg/m3 as a 24-hour average concentration (99th %ile during a year)

The United States Environmental Protection Agency (USEPA) has PM2.5 and PM10 air quality standards (US EPA, 2009a) and is currently proposing a 1 hour average value:

PM2.5 - 35 µg/m3 as a 24-hour average concentration (3-year average of the 98th percentile of 24-hour concentrations) and 15 µg/m3 as an annual average concentration (3 year average)

PM10 - 150 µg/m3 as a 24-hour average concentration (not to be exceeded more than once over a 3 year averaging period)

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2.5 Ground Level Ozone (O3)

Characteristics. In the case of ground level ozone (O3), what is measured at the surface is not always sufficient in understanding factors affecting the ozone levels because chemical composition of the surface layer can be significantly affected by mixing from above (Zhang and Rao, 1999). Ground level O3 can originate in a number of important ways:

brought down to the earth’s surface from the tropospheric reservoir by daily (diurnal) mixing of the atmospheric boundary layer

photochemical production Role of mixing of the atmospheric boundary layer – The presence of ground level O3 at

the surface is strongly influenced by the daily development and dissipation of turbulent mixing within the atmospheric boundary layer. When depth of the boundary layer increases during mid-morning hours, O3 suspended aloft is mixed downward to the earth’s surface and surface concentrations increase (Singh et al., 1978; Taylor and Hanson, 1992; NESCAUM, 1993; Lovett, 1994; Zhang and Rao, 1999; Aneja et al., 2000; Steinbacher et al., 2004). Once atmospheric boundary layer mixing ceases during late evening and night hours, surface concentrations decrease due to scavenging by chemical species such as nitric oxide (NO). Stratospheric ozone can be a major source of ozone supply to the tropospheric reservoir and is frequently associated with tropospheric folding and subsequent higher regional ground level ozone observed during the spring.

Role of photochemical production – In urban areas, and downwind areas influenced by urban air masses, photochemically produced ground level O3 and other oxidants form by atmospheric reactions involving two main classes of chemical precursors – volatile organic compounds (VOCs) and oxides of nitrogen (NOx) (US EPA, 2006). VOCs refer to all carbon containing gas-phase compounds in the atmosphere, both biogenic (emitted from vegetation) and anthropogenic (man-made) in origin.

Photochemically-produced O3 takes some time to occur. Maximum O3 concentrations from photochemical reactions usually occur 4 to 6 hours after maximum emissions of chemical precursors, and under conditions of light winds, usually downwind of urban areas (US EPA, 1998; Chu, 1995). Weather patterns and meteorological conditions play a major role in establishing conditions conducive to photochemical O3 formation and accumulation, and in terminating episodes of high O3 concentrations (NAS, 1991). Episodes of high O3 concentrations from photochemical production are associated with slow-moving, high-pressure weather systems.

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Away from areas affected by urban emissions, ground level O3 is driven by the atmospheric boundary layer mixing effect in the spring and early summer (March thru June) period (Singh et al., 1978). In the summer (June thru August) period, ozone levels can be enhanced by photochemical reactions. Biogenic VOCs can contribute to summertime photochemical O3 production close to urban areas in the presence of anthropogenic NOx and under favourable meteorological conditions (NAS, 1991).

Monitoring. An ultra-violet (UV) light process is used as the method to continuously monitor O3 at monitoring stations in Alberta (CASA, 2006). Sampled air is exposed to UV light, which is absorbed by O3. The amount of UV light absorbed is proportional to the amount of O3 in air; that is, the more UV light is absorbed, the greater the amount of O3 in a sample.

Ambient air criteria. Alberta has adopted a 1-hour Ambient Air Objective of 82 ppb (160 µg/m3) based on prevention of adverse effects to human health (AENV, 2008). A Canada-wide Standard (CWS) benchmark concentration for O3 is set by the Canadian Council of Ministers of the Environment at 65 ppb (128 µg/m3) as an 8-hour average concentration (CCME, 2006). Alberta also uses this standard. Achievement is based on the 4th highest measurement annually, averaged over 3 consecutive years.

The World Health Organization (WHO) guideline for ozone in the ambient atmosphere is (WHO, 2005):

51 ppb (100 µg/m3) as a maximum daily 8 hour average The United States Environmental Protection Agency (US EPA) has ozone air quality standards US EPA, 2009a):

120 ppb (230 µg/m3) as a 1-hour average concentration (99th %ile during a year) 75 ppb (144 µg/m3 as an annual average concentration (3 year average) of 4th highest

value each year)

2.6 Total Hydrocarbon (THC)

Characteristics. Total hydrocarbon (THC) refers to a range of volatile chemicals that contain carbon and hydrogen atoms. Major forms of total hydrocarbon in ambient air are aromatic hydrocarbons (i.e., containing one or more benzene rings) and aliphatic hydrocarbons (i.e., no carbon atoms joined to form a benzene ring). Methane (CH4) constitutes by far the largest form (by mass) of total hydrocarbon in ambient air. Other common hydrocarbons include ethane, propane, butane, ethylene, benzene, toluene, and ethylbenzenes.

THC is produced both from natural (biogenic) and anthropogenic sources. Trees and plants are the major sources of natural hydrocarbons. Transportation, industrial processes (e.g., those processes associated with the extraction, upgrading, and refining of bitumen), and

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evaporation of gasoline represent anthropogenic sources in the WBEA airshed. When ambient air monitoring of THC is undertaken, it is normally used as a surrogate measurement to indicate the presence of atmospheric emissions from industrial activities.

Monitoring. Hydrocarbons are monitored continuously by hydrogen flame ionization (CASA, 2006). Carbon-hydrogen bonds break when burned creating ions that conduct an electric current. This current is then measured by an electrometer to give a signal proportional to the number of ions.

Ambient air criteria. Alberta does not have ambient air objectives for total hydrocarbon nor does the WHO, US EPA, and Canadian Federal Government. Many hydrocarbons, such as CH4, are emitted by natural sources. Normal background THC concentrations recorded in rural Alberta range from 1.5 to 2 ppm (CASA 2006). Background hydrocarbons are primarily composed of CH4 with a small contribution from nonmethane hydrocarbons (NMHCs) – about 0.2 ppm.

2.7 Total Reduced Sulphur (TRS) and Hydrogen Sulphide (H2S)

Characteristics. Total reduced sulphur (TRS) compounds include hydrogen sulphide (H2S), methyl mercaptan (CH3SH), dimethyl sulphide (C2H6S), dimethyl disulphide (C2H6S2), carbon disulphide (CS2), and carbonyl sulphide (COS) and other organic compounds containing sulphur in a reduced state. In general, H2S, CH3SH, C2H6S, and C2H6S2 are the reduced sulphur species most often emitted from industrial processes (OME, 2007; AENV, 2004). H2S is known for its characteristic rotten egg smell; while that other reduced sulphur compounds also have similar odorous properties at low concentrations.

Examples of anthropogenic sources of reduced sulphur compounds in Alberta reported by Alberta Environment (AENV, 2004) are Kraft pulp mills, natural gas wells, processing of natural gas and crude oil at upstream stages and downstream refining, specific manufacturing processes (e.g., smelting of non-ferrous ores, steel mills), intensive livestock operations, and sewage treatment facilities. Important anthropogenic sources of reduced sulphur compounds in the WBEA airshed include processes associated with the extraction, upgrading, and refining of bitumen. Natural sources include biogenic emissions related to either aerobic metabolism or anaerobic decomposition of organic residues (NRCC, 1977 as cited in AENV, 2004). Natural sources of TRS include biomass burning, and soils, oceans, marshes, and tidal flats where biological activity takes place.

Monitoring. H2S is measured continuously by pulsed fluorescence (same principle as for SO2). Initially, all SO2 in air is scrubbed out so that it does not interfere with H2S. H2S is then converted to SO2. The air is then drawn through a chamber where it is irradiated with

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pulses of ultra-violet light. SO2 is excited to a higher energy level and upon returning to its original state, light or fluorescence is released. The amount of fluorescence measured is proportional to the amount of SO2 converted from H2S (or TRS). Analysis of TRS works on the same principle as H2S. The only difference is that conversion of reduced sulphur compounds to SO2 occurs at a much higher temperature. Therefore there is a more complete conversion of sulphur compounds to SO2.

Ambient air criteria. Alberta Environment has adopted Air Quality Objectives for H2S based on odour (AENV, 2008):

10 ppb (14 µg/m3) as a 1-hour average concentration 3 ppb (4 µg/m3) as a 24-hour average concentration

The main component of TRS is considered to be H2S and it is often compared to H2S objectives. The World Health Organization (WHO) guideline for H2S in the ambient atmosphere based on odour is (WHO, 2000):

5 ppb (7 µg/m3) as a 30 minute average The Canada and the United States Environmental Protection Agency (US EPA) do not have ambient air quality limits for H2S or TRS.

Table 2-1 summarizes air quality objectives and standards for the air pollutants described above. The WBEA measures ambient air quality at a number of continuous monitoring stations located in proximity to industrial and municipal developments using well established monitoring methods. Ambient air quality criteria have been established by many organizations/jurisdictions for most of the substances being measured by WBEA. Differences in the emphasis given to protection of health and environment, weighting of air pollutant exposure-response relationships, averaging times, current ambient air quality, and economic impacts of achieving a standard result in a variety of air quality limits established in practice for substances shown in Table 2-1. Alberta Environment’s criteria apply in a regulatory context.

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Table 2-1 Examples of air quality objectives and standards.

Parameter Averaging Time

Alberta Ambient Air Quality Objective1

(AAAQO)

Canada-Wide Standard2 (CWS)

Canadian Federal Objectives and Guidelines3 (NAAQOs)

World Health Organization (WHO)4

United States Environmental Protection Agency (US EPA)5

NO2 1 hour 400 g/m3 (212 ppb)

- 400 µg/m3 (212 ppb) - MAL 1000 µg/m3 (532 ppb) - MTL

200 µg/m3 (106 ppb)

-

24 hour 200 g/m3 (106 ppb)

- 200 µg/m3 (106 ppb) - MAL 300 µg/m3 (160 ppb) - MTL

- -

annual 60 g/m3 (32 ppb)

- 60 µg/m3 (32 ppb) - MDL 100 µg/m3 (53 ppb)- MAL

40 µg/m3 (21 ppb)

100 µg/m3 (53 ppb)

SO2 1 hour 450 g/m3 (172 ppb)

- 450 g/m3 (172 ppb) - MDL 900 g/m3 (334 ppb) - MAL

500 µg/m3 (191 ppb) – 10 min. ave.

-

24 hour 150 g/m3 (57 ppb)

- 150 g/m3 (57 ppb) - MDL 301g/m3 (115 ppb) - MAL 801g/m3 (306 ppb) - MTL

20 µg/m3 (7.6 ppb)

366 µg/m3 (140 ppb)

annual 30 g/m3 (11 ppb)

- 30 g/m3 (11 ppb) - MDL 60 g/m3 (23 ppb) - MAL

30 ppb (79 µg/m3)

CO 1 hour 15 mg/m3 (13 ppm)

15 mg/m3 (13 ppm) - MDL 35 mg/m3 (31 ppm) - MAL

30 mg/m3 (26 ppm)

40 mg/m3 (35 ppm)

8 hour 6 mg/m3 (5 ppm)

6 mg/m3 (5 ppm) - MDL 15 mg/m3 (13 ppm) - MAL 19.5 mg/m3 (17 ppm) - MTL

10 mg/m3 (9 ppm)

10 mg/m3 (9 ppm)

NOTE: - not applicable 1 AENV (2008). 2 CCME (2006). 3 MDL – maximum desirable level, MAL – maximum acceptable level, MTL – maximum tolerable level

(CCME, 2009). 4 WHO (2000), WHO (2005). 5 http://www.epa.gov/air/criteria.html, US EPA, Washington, DC (last visited 6 July 2009).

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Table 2-1 Examples of air quality objectives and standards (con’t).

Parameter Averaging Time

Alberta Ambient Air Quality Objective1

(AAAQO)

Canada-Wide Standard2 (CWS)

Canadian Federal Objectives and Guidelines3 (NAAQOs)

World Health Organization (WHO)4

United States Environmental Protection Agency (US EPA)5

PM2.5 1 hour 80 g/m3 - 24-hour

average 30 g/m3

based on 98th %ile value over 3 years

- 25 µg/m3 (99th %ile in a year)

35 µg/m3(3 year average of 98th %ile of 24-hour concentrations)

annual - - - - 15 µg/m3 (3 year average)

PM10 24 hour - - - 25 µg/m3 (99th %ile in a year)

150 µg/m3 (not to be exceeded more than once over 3 years)

O3 1 hour 160 g/m3 (82 ppb)

- - - 235 µg/m3 (120 ppb) (99th %ile in a year)

8 hour average

- 128 g/m3 (65 ppb) based on 4th highest value over 3 years

- 100 µg/m3 (51 ppb)

147 µg/m3 (75 ppb) 3 year average of 4th highest value each year

H2S 1 hour 14 g/m3

(10 ppb) - - 7 µg/m3

(5 ppb) as a 30 minute average

-

24 hour 4 g/m3 (3 ppb)

- - - -

NOTE: - not applicable 1 AENV (2008). 2 CCME (2006). 3 MDL – maximum desirable level, MAL – maximum acceptable level, MTL – maximum tolerable level

(CCME, 2009). 4 WHO (2000), WHO (2005). 5 http://www.epa.gov/air/criteria.html, US EPA, Washington, DC (last visited 6 July 2009).

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CHAPTER 3. METHODOLOGY

3.1 Monitoring Data

There are 15 continuous air monitoring stations operated by the Wood Buffalo Environmental Association. Monitoring data from AMS 14 and AMS 15 were not considered in this review because the period of record for these station was insufficient (i.e., less than four years). The main air pollutants analyzed in the region include:

NO, NO2, and NOx SO2 PM2.5 O3 THC TRS or H2S CO

Not all of these air pollutants are measured at every monitoring station. A list of air pollutants, current monitoring equipment, and length of monitoring records for each station are given in Table 3-1. A 10-year monitoring record was available at most stations. Of the stations included in this review AMS 12 (Millennium Mine Site) had shortest monitoring record (i.e., only four year –January 2004 to December 2007).

3.2 Data Management and Screening

Hourly concentration data were obtained in electronic format from WBEA and imported into MS Excel®. These electronic data were obtained in temporal order of year, month, day, and hour. A cut-off criterion of 80% completeness was used as an initial screening step to establish whether to include an annual dataset in trend analysis. This criterion represents ~7,000 hourly values for an annual dataset and was judged more than adequate for purposes of this study; in addition it is a criterion similar to that used by others (e.g., 85% used by Jo et al., 2000). All annual datasets reported here met this criterion, except for the following:

% completeness for PM2.5 at AMS 1 was 76% for the 1998 dataset % completeness for PM2.5 at AMS 6 was 76% for the 1998 dataset % completeness for CO at AMS 7 was 62% for the 2002 dataset % completeness for PM2.5 at AMS 7 was 78% for the 1998 dataset % completeness for SO2 at AMS 8 was 68% for the 1999 dataset

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Table 3-1 WBEA stations, measurement instruments, and observation periods for trend analysis.

Station Number Parameter Instrumentation Observation period

AMS 1 Nitric Oxide (NO) TECO 42C Jan 1998 to Dec 2007 (Fort Nitrogen Dioxide (NO2) TECO 42C Jan 1998 to Dec 2007 McKay) Oxides of Nitrogen (NOX) TECO 42C Jan 1998 to Dec 2007 Sulphur Dioxide (SO2) TECO 43A Jan 1998 to Dec 2007 Particulate Matter (PM2.5) TEOM Jan 1998 to Dec 2007 Ozone (O3) TEI 49C Jan 1998 to Dec 2007 Total Hydrocarbon (THC) TEI 51LT Jan 1998 to Dec 2007 Total Reduced Sulphur (TRS) TEI 43C Jan 1998 to Dec 2007 AMS 2 Sulphur Dioxide (SO2) TECO 43A Jan 1998 to Dec 2007 (Mildred Total Hydrocarbon (THC) Rosemount 400A Jan 1998 to Dec 2007 Lake) Hydrogen Sulphide (H2S) TEI 45C Jan 1998 to Dec 2007 AMS 3 Sulphur Dioxide (SO2) TECO 43A Jan 1998 to Oct 2000 (Lower Total Hydrocarbon (THC) Rosemount 400A Jan 1998 to Oct 2000 Camp) Hydrogen Sulphide (H2S) TEI 45C Jan 1998 to Oct 2000 AMS 4 Sulphur Dioxide (SO2) API 100A Jan 1998 to Dec 2007 (Buffalo Total Hydrocarbon (THC) TECO 51LT Jan 1998 to Dec 2007 Viewpoint) Hydrogen Sulphide (H2S) API 101A Jan 1998 to Dec 2007 AMS 5 Sulphur Dioxide (SO2) TEI 43C Jan 1998 to Dec 2007 (Mannix) Total Hydrocarbon (THC) TECO 51LT Jan 1998 to Dec 2007 Hydrogen Sulphide (H2S) API 102A Jan 1998 to Dec 2007 AMS 6 Nitric Oxide (NO) API 200A Jan 1998 to Dec 2007 (Patricia Nitrogen Dioxide (NO2) API 200A Jan 1998 to Dec 2007 McInnes) Oxides of Nitrogen (NOX) API 200A Jan 1998 to Dec 2007 Sulphur Dioxide (SO2) TECO 43A Jan 1998 to Dec 2007 Particulate Matter (PM2.5) TEOM Jan 1998 to Dec 2007 Ozone (O3) TEI 49C Jan 1998 to Dec 2007 Total Hydrocarbon (THC) TEI 51C Jan 1998 to Dec 2007 Total Reduced Sulphur (TRS) TEI 43C Jan 1998 to Dec 2007 AMS 7 Nitric Oxide (NO) TEI 42C Jan 1998 to Dec 2007 (Athabasca Nitrogen Dioxide (NO2) TEI 42C Jan 1998 to Dec 2007 Valley) Oxides of Nitrogen (NOX) TEI 42C Jan 1998 to Dec 2007 Sulphur Dioxide (SO2) TEI 43C Jan 1998 to Dec 2007 Carbon monoxide (CO) TECO 48A Jan 1998 to Dec 2007 Particulate Matter (PM2.5) TEOM Jan 1998 to Dec 2007 Ozone (O3) TEI 49C Jan 1998 to Dec 2007 Total Hydrocarbon (THC) Rosemount 400A Jan 1998 to Dec 2007 Total Reduced Sulphur (TRS) TEI 43C Jan 1998 to Dec 2007 AMS 8 Nitric Oxide (NO) TEI 42CTL Jan 1999 to Dec 2007 (Fort Nitrogen Dioxide (NO2) TEI 42CTL Jan 1999 to Dec 2007 Chipewyan) Oxides of Nitrogen (NOX) TEI 42CTL Jan 1999 to Dec 2007 Sulphur Dioxide (SO2) TEI 43C Jan 1999 to Dec 2007 Particulate Matter (PM2.5) TEOM Jan 1999 to Dec 2007 Ozone (O3) TEI 49C Jan 1999 to Dec 2007 AMS 9 Total Hydrocarbon (THC) Rosemount 400A Jan 2001 to Dec 2007 (Barge Landing)

Total Reduced Sulphur (TRS) API 102A Jan 2001 to Dec 2007

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Table 3-1. WBEA stations, measurement instrument, and observation period for trend analysis (cont.).

Station Number Parameter Instrumentation Observation period

AMS 10 Nitric Oxide (NO) API 200A Jan 2001 to Dec 2007 (Albian Nitrogen Dioxide (NO2) API 200A Jan 2001 to Dec 2007 Mine Site) Oxides of Nitrogen (NOX) API 200A Jan 2001 to Dec 2007 Sulphur Dioxide (SO2) API 100A Jan 2001 to Dec 2007 Particulate Matter (PM2.5) TEOM Jan 2001 to Dec 2007 Total Hydrocarbon (THC) Rosemount 400A Jan 2001 to Dec 2007 AMS 11 Sulphur Dioxide (SO2) TECO 43A Jan 2001 to Dec 2007 (Lower Total Hydrocarbon (THC) Rosemount 400A Jan 2001 to Dec 2007 Camp) Hydrogen Sulphide (H2S) TEI 43C Jan 2001 to Dec 2007 AMS 12 Nitric Oxide (NO) API 200A Jan 2004 to Dec 2007 (Millenium Nitrogen Dioxide (NO2) API 200A Jan 2004 to Dec 2007 Mine) Oxides of Nitrogen (NOX) API 200A Jan 2004 to Dec 2007 Sulphur Dioxide (SO2) API 100A Jan 2004 to Dec 2007 Particulate Matter (PM2.5) TEOM Jan 2004 to Dec 2007 Total Hydrocarbon (THC) Rosemount 400A Jan 2004 to Dec 2007 Total Reduced Sulphur (TRS) TEI 43C Jan 2005 to Dec 2007 AMS 13 Nitric Oxide (NO) TEI 42C Jan 2003 to Dec 2007(Syncrude Nitrogen Dioxide (NO2) TEI 42C Jan 2003 to Dec 2007 UE1) Oxides of Nitrogen (NOX) TEI 42C Jan 2003 to Dec 2007 Sulphur Dioxide (SO2) API 102A Jan 2003 to Dec 2007 Particulate Matter (PM2.5) TEOM Jan 2003 to Dec 2007 Ozone (O3) TEI 49C Jan 2003 to Dec 2007 Total Hydrocarbon (THC) Rosemount 400A Jan 2003 to Dec 2007 Total Reduced Sulphur (TRS) TECO 43A Jan 2003 to Dec 2007

The median concentration (50th percentile) was used for representing the central value for

an annual dataset for each pollutant. As most environmental data distributions are usually skewed to the right (i.e., most data values are low and only a few values are high), the arithmetic mean would be biased by high concentrations (Gilbert, 1997; US EPA, 2002). Next, a visual examination of the hourly datasets in MS Excel® was carried out to identify whether any abnormal values (e.g., negative values, etc.) existed; these data were removed. If an hourly value was missing from a dataset, that specific hour was not included in subsequent trend analysis.

3.3 Types of Trends and Behaviors Examined

Trends and behaviors in the datasets for each air pollutant and period of record were examined in a number of ways to assist in understanding what might be influencing air quality and for subsequent statistical trend analysis of long term data. These were:

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Diurnal (daily) concentration behavior for a pollutant. These represented plots of the hourly concentrations “for a specific hour” averaged over the period of record. For example, the average O3 concentration at AMS 1 for the 8th hour of the day was calculated as the average of all 8th-hour readings for the period of record (i.e., 1998 to 2007), regardless of day of week or season. These patterns aid in understanding short-term cyclical behavior in environmental data (i.e., over the course of a day).

Day of week concentration behavior for a pollutant. These represented plots of 24-hour average concentrations “for a specific day” averaged over the period of record. For example, the average O3 concentration at AMS 1 for a Monday was calculated as the average of all 24-hour Monday readings for the period of record (i.e., 1998 to 2007), regardless of day of week or season. These patterns aid in understanding cyclical behavior in environmental data over the course of a week.

Monthly average concentration behavior for a pollutant. These represented plots of average concentrations for each month for each year of record. These patterns aid in understanding average seasonal (month-to-month) behavior in environmental data.

Monthly maximum 1-hour concentration behavior for a pollutant. These represented plots of the maximum 1-hour concentration for each month for each year of record. These patterns also aid in understanding seasonal (month-to-month) behavior in environmental data.

Cumulative frequency distribution behavior for a pollutant. This represented plots of the cumulative distribution of hourly values for a pollutant during a year. These patterns aid in understanding how frequent certain hourly concentrations were for a pollutant during a year. For example, if the 50th percent concentration frequency for O3 during a year was 20 ppb; hourly concentrations were ≤20 ppb for 50% of the time in that year.

Long term benchmark concentration trends for a pollutant. Various benchmarks representing 50th, 65th, 80th, 90th, 95th, and 98th percentile concentrations for a year were identified from frequency distributions and were plotted for visual display and then used for statistical detection of long term trends (described further in Section 3.4). These values were taken from cumulative frequency distributions for each year. Colls (1997) indicated that these types of measure are more representative for portraying the distribution characteristics of a population of environmental data than general average values.

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3.4 Methods for Statistical Analysis of Trend Data

3.4.1 Background

People’s awareness of air quality is generally only related to times where and when poor conditions (i.e., maximum concentrations) exit. It was not an objective of this study to consider trends for these infrequent events. In the case of concentration maxima, these conditions tend to be associated with rare meteorology (e.g., weather inversion) or rare emission (e.g., upset, start up) events. In areas where air quality is good most of the time – i.e., where only a very low frequency of occurrence of concentration maxima occur – the ability to detect trends (change) over time in these maxima is difficult. In addition, just analyzing concentration maxima does not detect possible gradual changes in environmental data.

Trend detection is considered a key aspect of understanding the state of air quality based on past data (Blanchard, 1999; Klemm and Lange, 1999). The general approach for detecting air quality trends is to begin with valid data, and then:

Select response variable (metrics) – such as means, medians, maximums, minimums, selected percentiles, etc.

Select appropriate time periods to investigate (e.g., season, episode, annual, etc.). Apply statistical methods for detecting trends. Evaluate the trends for direction, rate of change, statistical significance, etc. The two basic types of trends that can be statistically analyzed are step and monotonic

trends (Kundzewicz and Robson, 2004; Hirsch et al., 1991). Step trends include either a sudden increase or decrease in concentrations resulting from a sudden change in emissions. With respect to point source emissions (e.g., industrial point sources), an example of a step trend might be related to a change (e.g., increase) in emissions due to an industrial project startup and a corresponding direct source-to-receptor relationship between the emission source and a nearby ambient air monitoring station.

If the monitor is located further away from the new source and/or the source-to-receptor relationship is weaker (i.e., due to influences of randomness of meteorological processes affecting dilution in the atmosphere), what is observed by the monitor will tend to be moderated and more gradual. Monotonic trends are generally gradual changes that are either increasing or decreasing with no reversal of direction. An example might be in an urban area where gradual increases in ambient concentrations occur from growing numbers of automobiles on roadways in proximity of a nearby ambient air monitoring station.

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3.4.2 Trend Analysis Method

Statistical methods for trend analysis of environmental data can be classified into two categories – parametric tests (e.g., linear regression) and nonparametric tests (e.g., Mann-Kendal). Parametric tests are simple and straightforward; however they require making assumptions about normality of data and homogeneity of variance of data. At the very least the Central Limit Theorem should apply – i.e., sample sizes should be sufficiently large (usually greater than 30) to lead to approximate normality and variances of the different samples should be approximately equal.

Nonparametric tests are used as alternatives to parametric tests when the assumptions for parametric tests cannot be met. Although these methods make fewer assumptions regarding the distribution of data, they have lower power than parametric methods. In addition, they still require “similar” data distributions and are not robust to homogeneity of variance of data (Day and Quinn, 1989; Fowler, 1990; White and Bennetts, 1996).

Time series linear regression – a parametric method – was chosen for this study due to its simplicity and straightforwardness of interpretation. Environmental data – such as air quality data – are time series data that have sequential correlating relationships (autocorrelation) and are non-normally distributed (Ott, 1995). Because of this autocorrelation, an annual distribution of ambient air quality data often appears as right-skewed and averages computed from these correlated time series will not obey the Central Limit Theorem. That is to say, these data will not converge to a normal distribution as rapidly as the Central Limit Theorem predicts.

Despite this, several explanations support use of parametric testing methods for trend detection of air quality datasets used in this study:

The methods (e.g., simple linear regression) are simple and straightforward to use. Considering what ambient air monitoring data represent, the air pollutants of interest

in this study are predominantly anthropogenic (man-made) and emitted into the atmosphere at relatively high concentrations (e.g., SO2, NO2), and they are diluted by meteorological processes and these processes occur with considerable randomness.

Parametric tests tend to be more powerful than nonparametric tests and they have an ability to quantify the magnitude of a trend (McLeod et al., 1991).

Effects of seasonality and autocorrelation data can be ignored where only selective values from a cumulative frequency distribution curve – percentiles – are subsequently used for testing trends.

Parametric approach for regression testing – The trend analysis method used here consisted of time series linear regression using various percentiles of a distribution of 1-hour concentrations for a pollutant during a year.

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A definition of a percentile for a given distribution of values is that it is the “percentage of values that are smaller than the value at that percentile.” For example, if the 50th percentile 1-hour concentration for ozone is 20 ppb in a calendar year, 50% of the hourly concentrations are smaller than or equal to 20 ppb and 50% are larger. For a 98th percentile hourly concentration of 40 ppb, 98% of the hourly concentrations are smaller than or equal to 40 ppb and only 2% are larger during the year.

The 50th percentile (or median) concentration represents a central tendency for an actual distribution. The lower percentile concentrations (e.g., the 50th and 65th percentile concentrations) represent more typical hourly concentrations experienced on any given day. A 98th percentile concentration represents an upper bound for the distribution. The higher percentile concentrations (e.g. the 95th and 98th percentile concentrations) represent hourly concentrations that, on average, occur much less frequently or not at all on any given day.

Cumulative frequency distributions were prepared for an annual (yearly) dataset for each air pollutant. Hourly concentration values for a year were first sorted in ascending order and the whole range of the dataset was divided into sub-ranges. The concentration values falling in each sub-range was identified and transformed into a percentile of the total number of concentration values (i.e., frequency expressed as percentiles of the annual dataset). Benchmarks representing 50th, 65th, 80th, 90th, 95th, and 98th percentile concentrations for a year were identified as response variables from these frequency distributions and used for parametric trend analysis.

Requirements for use of parametric approach – Time series trend analysis of environmental data – parametric or nonparametric – must address issues of autocorrelation, seasonality, and nonnormality (US EPA, 2000; Weatherhead et al., 1998; Ott, 1995):

Autocorrelation – Autocorrelation refers to the existing tendency of similar characteristics or mutual bias for data of neighboring observations in time or space. Many environmental datasets are observed as temporal and/or spatial sequences. Datasets from hourly air concentration values for a year are time series data that have sequential correlating relationship and are usually not normally distributed. Because of autocorrelation of these data, the distribution of hourly ambient air quality data often appears skewed to the right (i.e., most data values are low and only a few values are high) (Ott, 1995).

On the other hand, data extracted from a frequency distribution and/or a cumulative frequency distribution of raw data are recognized as being more representative than general average values (Colls, 1997). Further, since only detectable values from the cumulative frequency distribution curve for a year are considered for trend analysis, these values are free from bias related to autocorrelation. The reason is that missing

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concentration values (e.g., due to instrument malfunctions, nondetectable data, etc.) – which are represented in the lower tail of a distribution curve – are treated as unknowns, but their percentile values are accounted for in the analysis but not used. Similarly, extreme values (outliers) which tend to skew the data to the right are accounted for in the analysis but not used.

Seasonality – Seasonality refers to patterns in data that depend on or are controlled by season of the year. Analysts should ensure that time plots of datasets show no cyclical (e.g., seasonally) patterns, outlier tests show no extreme data values, and data validation reports that indicate nearly all measurements are above detection limits (US EPA, 2000). The last point is a bit hard to satisfy as virtually all annual ambient air datasets have measurements that are below detection limits. Thus it is not possible to meet this requirement regardless of the statistical method – parametric or nonparametric – used.

The cyclical nature of air quality datasets is related to effect of season which occurs within a calendar year. However, response variables used for trend analysis in this study (hourly concentration percentiles) are drawn from cumulative frequency distributions for each year. The so-called “cyclical effect of season” is the same for each annual cumulative frequency distribution from which each response variable was drawn. Thus the absence of making specific adjustments for the effect of season is not considered important regardless of the statistical method – parametric or nonparametric – used.

Nonnormality –Air quality data are generally not normally distributed (i.e., they are nonnormal) (Ott, 1995). Hess et al. (2001) reviewed, evaluated, and compared statistical methods for trend analysis of nonnormal environmental data. They compared the ability of seven parametric and nonparametric regression methods using yearly average values.

Hess et al. (2001) showed that although an assumption of independence and normality might not be met by linear trend (parametric) analysis of environmental data, this assumption should more likely be satisfied if a single statistic – such as an annual average or other metric from a calendar year – is used as the response variable in the regression analysis. This is the approach used in this study – i.e., response variables employed for trend analysis are single values whose concentrations are ≥50th percentiles and ≤98th percentiles for each calendar year.

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3.4.3 Linear Regression

Linear regression may be suitable for situations where a set of time series data suggest a simple linear increasing or decreasing

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