-
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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iii
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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iv
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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v
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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vi
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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viii
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 4-2 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 2 (observation period 1998
to 2007).
......................................................................
41
Table 4-3 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 4 (observation period 1998
to 2007).
......................................................................
42
Table 4-4 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 5 (observation period 1998
to 2007).
......................................................................
44
Table 4-5 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 6 (observation period 1998
to 2007).
......................................................................
45
Table 4-6 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 7 (observation period 1998
to 2007).
......................................................................
49
Table 4-7 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 8 (observation period 1999
to 2007).
......................................................................
52
Table 4-8 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 9 (observation period 2001
to 2007).
......................................................................
54
Table 4-9 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 10 (observation period
2001 to 2007).
....................................................................
55
Table 4-10 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 11 (observation period
2001 to 2007).
....................................................................
57
Table 4-11 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 12 (observation period
2004 to 2007).
....................................................................
59
Table 4-12 Summary of trend for hourly average percentile
concentrations for air pollutants at AMS 13 (observation period
2003 to 2007).
....................................................................
62
Table 5-1 Summary of trends for hourly average concentrations of
air pollutants in WBEA zone. .... 66
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x
LIST OF FIGURES
Figure 1-1 Map of northeastern Alberta showing the location of
the WBEA air monitoring stations.
...............................................................................................................................
3
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xi
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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xii
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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2
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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3
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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4
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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5
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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6
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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10
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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20
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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21
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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24
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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25
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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28
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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29
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