Science of the Total Environment 407 (2009) 5085–5095

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Science of the Total Environment

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The effect of temperature inversions on ground-level nitrogen dioxide (NO2) and fineparticulate matter (PM2.5) using temperature profiles from the Atmospheric InfraredSounder (AIRS)

Julie Wallace ⁎, Pavlos KanaroglouCentre for Spatial Analysis, School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada

⁎ Corresponding author: School of Geography anUniversity 1280 Main Street West, Hamilton, Ontario,525 9140x28613; fax: +1 905 546-0463.

E-mail addresses: wallaju@mcmaster.ca (J. Wallace),(P. Kanaroglou).

0048-9697/$ – see front matter © 2009 Elsevier B.V. Adoi:10.1016/j.scitotenv.2009.05.050

a b s t r a c t

a r t i c l e i n f o

Article history:Received 20 November 2008Received in revised form 26 May 2009Accepted 27 May 2009Available online 21 June 2009

Keywords:AIRSTemperature inversionsNitrogen dioxideParticulate matter

We investigate the effects of temperature inversions on the levels of nitrogen dioxide (NO2) and fineparticulate matter (PM2.5) in the atmosphere over the Hamilton Census Metropolitan Area and environs inOntario, Canada, for the period 2003 to 2007. Vertical temperature profiles extracted from data acquired bythe Atmospheric Infrared Sounder (AIRS) were used to determine the occurrences of daytime and nighttimetemperature inversions over the region. NO2 and PM2.5 data were obtained from three in situ air qualitymonitoring stations located in the study area. The results indicate increases of 49% and 54% in NO2 and PM2.5respectively, during nighttime inversion episodes. Daytime inversions resulted in an 11% increase in NO2

but a 14% decrease in PM2.5. Decreases occurred predominantly in the summer. We discuss these results andpossible explanations for the reduced PM2.5 concentrations on inversion days. Weekday and seasonalanalysis, with associated meteorological parameters are also discussed.

© 2009 Elsevier B.V. All rights reserved.

1. Introduction

In polluted environments such as urban centres, dispersion anddilution of pollution through vertical mixing and horizontal transport areessential in reducing air pollution concentrations. Temperature inversionslead to stable atmospheric conditions which constrain vertical airflow,trapping pollutants below the inversion cap, and increasing concentra-tions in the inhaled air (Oke,1987). Some of theworld'smost devastatingair pollution episodes occurred during periods of temperature inversions(Holzworth, 1972; Laskin, 2006; Malek et al., 2004). The pollutants ofinterest in this study are nitrogen dioxide (NO2) and fine particulatematter (PM2.5). Epidemiological studieshavedemonstrated that both aredetrimental to human health, sometimes leading to premature death(Gauderman et al., 2005; Pope et al., 2006; Næss et al., 2007).

Historically, inversions have been quantified using ground-basedmeteorological systems, such as radiosondes, located at widelydispersed upper air sounding stations (WMO, 2008). However, a newgeneration of satellite sensors provides regional scale atmospheric datawith unprecedented global coverage, and high spatial and temporalresolution (NASA, 2008).Weuse temperature profiles derived fromdatacaptured by the Atmospheric Infrared Sounder (AIRS) to identify

d Earth Sciences McMasterCanada L8S4L8. Tel.: +1 905

pavlos@mcmaster.ca

ll rights reserved.

episodes of temperature inversions in the study area. AIRS is one ofthe six instruments onboard theAqua satellite (NASA, 2008)whichhas asun-synchronous orbit, covering the Earth twice daily with equatorialcrossings at 1:30 am and 1:30 pm (Aumann et al., 2003). Daytime andnighttime profiles are therefore available daily.

We investigate the effects of temperature inversions on the levelsof NO2 and PM2.5 over the Hamilton CensusMetropolitan Area (CMA)and surrounding regions, for 2003–2007. Differences in pollutionlevels during daytime and nighttime inversion events were investi-gated. Weekday and seasonal changes in NO2 and PM2.5, and theimpact of wind speed and wind direction are discussed. The Analysisof Variance (ANOVA) statistical method (Turner and Thayer, 2001)was used to test whether the means of the inversion day NO2 andPM2.5 were significantly different from the means on normal days.The F-statistic, defined as the ratio of the variance between groups tothe variance within a group, was used to determine whether thedifferences in the mean values were statistically significant at the 5%significance level.

2. Study area

The study area is centered on the Hamilton CMA (population700,000) and includes the Town of Oakville (population 166,000) andsmall rural communities. The area is defined by 43.0o–43.5o N, 79.5o–80.25oW (Fig.1). Hamilton, the site of themajor industrial polluters inthe study area, is a mid-sized city through which major trans-Ontarioand Ontario–USA highways are routed. In 2005–2006, Hamilton

Fig. 1. Study area with major roads and highways, indicating annual NO2 emissions from industry sources.

5086 J. Wallace, P. Kanaroglou / Science of the Total Environment 407 (2009) 5085–5095

industries emitted 10,278 tonnes of NO2 with an additional 68 tonnesfrom Oakville (Fig. 1). Total PM2.5 from Hamilton sources was1589 tonnes, with 8 tonnes from Oakville (NPRI, 2007).

Both NO2 and PM2.5 data were obtained from three in situcontinuous ambient air quality monitors (CAAQM) that are main-tained by the Ontario Ministry of the Environment (MOE) (MOE,2008). Both pollutants exhibit strong diurnal cycles when averaged forall CAAQMs (Fig. 2). Maxima are attained at 8:00 am, coinciding withmorning peak hour traffic, and at 8:00 pm, approximately 2 h after theevening traffic peak hour. While the diurnal cycle of NO2 due tophotolytic reactions, and the supporting role of traffic emissions, iswell known (Crutzen, 1979; Sillman,1999) a diurnal cycle for PM2.5 isnoted in some studies (Pernigotti et al., 2007) but not in others(Hazenkamp-von Arx et al., 2004; Tanner et al., 2005). In thesestudies, the common factor in the diurnal cycle for PM2.5 appears tobe high traffic volume. Nighttime levels of both pollutants areelevated, decreasing gradually to a minimum at approximately4:00 am, after which levels climb to the morning maximum (Fig. 2).The diurnal changes are reflected in the results which follow.

The Niagara Escarpment is a limestone cuesta which divides thearea into a lower zone along the shoreline of Lake Ontario, and anupper plateau. This feature contributes to the frequency of advectivetemperature inversions, as easterly lake breezes flow under warmland air from the plateau. Elevation in the study area ranges from 74mat lake level to 240 m on the plateau.

Prevailing mid-latitude westerly winds are dominant and winddirections throughout the year are typically apportioned as follows:SW – 50%, NE and NW – 20% each and SE – 10% (EC, 2009).

Fig. 2. Diurnal distribution of NO2 and PM2.5 over a 24-h period based on averages fromair quality monitors in the study area for the period 2003–2007.

3. Data and methods

3.1. AIRS data

AIRS measures thermal radiance in the spectral ranges 3.74–4.61 µm, 6.20–8.22 µm, and 8.80–15.4 µm, with 2378 spectralchannels. Horizontal resolution at nadir for infrared channels is13.5 km, with a 1-km vertical resolution. Vertical temperatureretrieval methodology is described by Susskind et al. (2003). Briefly,AIRS determines temperature profiles using CO2 absorption lines inthe infrared spectral region to sense temperatures at various depths ofthe atmosphere. Temperature accuracy is 1K RMS or better in 1 kmvertical layers in the troposphere, over land (Susskind et al., 2003;Divakarla et al., 2006).

AIRS Level 3, version 5 daily ascending (daytime) and descending(nighttime) temperature profile data were acquired using the GES-DISC Interactive Online Visualization And aNalysis Infrastructure(GIOVANNI), part of NASA's Goddard Earth Sciences (GES) Data andInformation Services Center (DISC) (Giovanni, 2009). The GIOVANNIdata are binned in a 1°×1° grid. Evaluation of AIRS surface air tem-perature with Environment Canada data from the Hamilton Interna-tional Airport meteorological station, produced a very good fit (R2

coefficient 0.99) (Wallace and Wallace, 2008).AIRS temperature profiles for pressure levels 1000 hPa to 700 hPa

were obtained for the period 2003–2007. An increase in temperaturefrom pressure level 1000 hPa to the next level at 925 hPa was used toidentify inversions closest to the surface. The 925 hPa pressure leveltranslates to an average altitude of 780 m at the nearest WMOradiosonde station, located 100 km SE of Hamilton, in Buffalo, NY(NOAA, 2008) based on upper air soundings for 2003–2006.

3.2. Ground data

Of the three CAAQM stations used in the study, two are locatedclose to the industrial and downtown core of the city of Hamilton(Fig. 1), where industry and heavy traffic are juxtaposed. The third islocated in the Town of Oakville, in which traffic is the major source ofNO2. To best capture the conditions existing during the AIRSoverpass at 1:30 am/pm, CAAQM data for 0200 h and 1400 h wereobtained for 2003–2007. Wind direction, wind speed, temperatureand humidity were acquired from the Environment Canada meteor-ological station located at Hamilton International Airport (EC, 2008),for 0200 h and 1400 h, as well.

Table 1Descriptive statistics for NO2, PM2.5 and surface meteorology at 0200 h and 1400 h.

0200 h – AIRS AM crossing 1400 h – AIRS PM crossing

Minimum Maximum Mean Std. deviation Minimum Maximum Mean Std. deviation

Normal N=1000 Normal N=1120

Average NO2 (ppb) 0.0 55.3 14.3 8.3 1.0 54.8 11.5 6.3Average PM 2.5 (µg/m3) 0.0 47.7 6.8 7.0 0.0 51.3 8.4 8.4Wind speed (km/h) 0.0 52.0 12.7 8.4 0.0 74.0 19.0 9.7Temperature (°C) −27.0 26.6 5.5 10.4 −18.5 34.1 12.7 12.2Relative humidity (%) 33.0 100.0 84.8 10.6 21.0 100.0 60.6 16.1

Inversion N=436 Inversion N=330

Average NO2 (ppb) 2.3 62.5 21.4 11.8 2.2 40.7 12.7 7.0Average PM 2.5 (µg/m3) 0.0 55.7 10.5 9.2 0.0 46.3 7.2 7.3Wind speed (km/h) 0.0 39.0 10.7 7.6 0.0 54.0 20.3 10.3Temperature (°C) −20.0 23.8 6.8 8.3 −15.9 33.5 9.2 11.4Relative humidity (%) 40.0 100.0 80.2 13.2 20.0 100.0 68.9 18.8

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A database including NO2, PM2.5, AIRS daytime and nighttimeinversions and normal days, and associated meteorology, was created.The daily data were also tagged with the appropriate month and day ofthe week. Wind rose diagrams were created to show pollutionconcentrations with various wind directions at 2:00 am/pm.

The best data available for validation of the AIRS temperatureprofiles were obtained from a 91-m meteorological tower (MT) inHamilton (Fig. 1). The tower is maintained by the MOE, in conjunctionwith Rotek Environmental Inc., a local environmental company. TheMT is located in the lower city, in close proximity to the majorindustrial zone and below the Niagara Escarpment, both of whichcontribute to localized inversions (Rouse et al., 1973). The MT datawere available for 2005–2007 and temperature differences betweenthe 10 m and 91 m heights at 0200 h and 1400 h, were used todetermine the occurrence of a normal or inversion configuration.Because of the differences in the heights at whichMTand AIRS verticaltemperatures are determined, it is expected that inconsistenciesbetween the two profiles will arise.

4. Results

4.1. Temperature inversions

Of the 1826 days in the study period, 1436 and 1450 valid AIRSprofiles were available at 0200 h and 1400 h, respectively. Of these,436 inversions were identified at 0200 h and 330 at 1400 h (Table 1).Most nocturnal inversions developed in the spring (41%), with fall,winter and summer frequencies of 20%, 20% and 18% respectively(Fig. 3). Daytime inversions were most common in the winter (33%),

Fig. 3. Diurnal and seasonal frequency of temperature inversions based on AIRS data for2003–2007. Winter defined as December, Janaury, and February.

with 24%, 23% and 19% in the spring, fall and summer respectively.Overall, 20% of the AIRS dataweremissing. Seventy-three occurrencesof inversions (10%) were identified during both morning and after-noon crossings of the satellite on the same day. These were likelyprolonged inversions which persisted throughout the day. Most ofthese (42%) occurred in the spring. The strength of AIRS inversionsaveraged 2.8 °C in the daytime and 2.4 °C at night.

4.1.1. Validation of AIRS inversionsThe MT data for 2005–2007, provided 1087 data days with 583

inversions at 0200 h and 177 inversions at 1400 h. Inversions at 0200 hwere most frequent in the spring and summer – 32% and 30%respectively – with 19% and 20% in the fall and winter. At 1400 h,inversions were most common in the spring (34%), followed bywinter, summer and fall with 31%, 23% and 13% respectively. Thesepatterns roughly parallel those identified by AIRS. The strength of theMT inversions averaged 0.9 °C during the daytime and 1.6 °C at night.

For the purposes of validation, AIRS data for 2005–2007 wereselected for comparisonwithMTdata. Bothdatabaseswere compared totestwhether the occurrence of an inversion identifiedbyAIRS on a givenAM or PM overpass coincided with one identified by the MT at theequivalent 0200 h and 1400 h. On average, 67% of AIRS nighttimeinversions coincided with inversions identified by the MT at 0200 h,with up to 95% correspondence in spring (Table 2). However, just 20% ofAIRS daytime inversions coincided with inversions identified by theMTat 1400h (Table 3). This latter result is likely due to a higher frequencyoflocalized daytime surface inversions attributable to the effect of theindustry, lake breezes and the Niagara Escarpment (Rouse et al., 1973).

4.2. NO2 and PM2.5

Using AIRS temperature profiles, CAAQM data for the period 2003–2007 indicate that meanNO2 increased from 14.3 ppb to 21.4 ppb (49%)during night inversions and from 11.5 ppb to 12.5 ppb (11%) during thedaytime inversions (Table 1).

PM2.5 increased from 6.8 to 10.5 µg/m3 at night (54%) anddecreased from 8.4 to 7.2 µg/m3 during the daytime (14%) (Table 1).Wind speed was lower on inversion nights (10.7 km/h compared to12.7 km/h on normal nights) but marginally higher on inversion days(20.3 km/h compared to 19.0 km/h on normal days). Temperatureand humidity follow inverse patterns with higher temperature duringnight inversions (Table 1) as rapid cooling of the land keeps the airabove the surface warm when compared to the normal scenarios.Lower temperatures occur during daytime inversions, since themechanism of advective inversions prevail and cool lake breezespush onshore beneath warm air from the Escarpment. In addition, asdiscussed below, some of the AIRS daytime inversions are likely

Table 2Monthly counts and co-occurrence of inversions identified by AIRS AM overpass and the Hamilton meteorological tower at 0200 h.

Month Count of met. towerinversions

Count of AIRSinversions

Co-occurrence ofboth

% AIRSco-occurrence

Count of met. towernormals

Count of AIRSnormals

Co-occurrence ofboth

% AIRSco-occurrence

J 64 15 11 73 24 55 13 24F 37 13 9 69 47 53 31 58M 52 24 17 71 41 52 27 52A 56 39 37 95 34 28 21 75M 76 40 36 90 17 39 9 23J 59 29 25 86 31 45 22 49J 57 15 12 80 35 60 24 40A 55 11 9 82 38 64 29 45S 51 17 12 71 39 54 25 46O 38 21 13 62 54 53 33 62N 25 21 5 24 65 49 35 71D 13 15 1 7 79 59 47 80

20% of the AIRS data were missing.

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elevated, and a normal temperature profile would exist near thesurface.

4.2.1. Weekend and seasonal effectThe number of inversion and normal days and nights available for

monthly and seasonal analysis for 2003–2007 is presented in Table 4.Weekday analysis of NO2 during the daytime (Fig. 4A) distinctlydisplays the “weekend effect” (Murphy et al., 2007), a phenomenon ofdecreased pollution levels on weekends, in response to decreasedtraffic volumes. This effect is observed in daytime NO2 during bothnormal and inversion scenarios. Inversion day increases ranged from8% to 17% (Table 5). ANOVA reveals that NO2 means during daytimeinversion and normal configurations are not statistically different onany day (Table 5).

Weekly nighttime data (Fig. 4B), indicate that the weekend effectis present but is much less distinct. Instead, NO2 appears toaccumulate during the weeknights, steadily increasing from Sundaysto Fridays and gradually diminishing over the weekend. Thepercentage increases during nighttime inversions range from 28% to74% and were significantly higher than the daytime increases.Nighttime means were also higher than daytime means (Table 5).This reflects the stronger impact of inversions at night, whennighttime chemistry is different, the mixing height is lower and theair is more stable, compounding the effect of the increased nighttimeconcentrations as observed in the diurnal cycle (Fig. 2). ANOVAindicates that the differences between normal and inversion NO2 atnight are statistically significant for every night of the week (Table 5).

Monthly distribution of NO2 indicates that daytime inversionvalues in the late fall, winter and early spring were higher than onnormal days (Fig. 4C). During the summer, however, inversion NO2

was actually lower than levels on normal days. Seasonal analysis(Table 6) show a 9% decrease in NO2 on summer inversion days and 0%

Table 3Monthly counts and co-occurrence of inversions identified by AIRS PM overpass and the Ha

Month Count of met.tower inversions

Count of AIRSinversions

Co-occurrenceof both

% AIRSco-occurrence

J 39 26 7 27F 10 21 4 19M 13 27 6 22A 17 20 5 25M 30 25 10 40J 18 17 3 18J 11 22 4 18A 11 16 0 0S 5 21 0 0O 9 15 1 7N 9 21 1 5D 5 8 1 13

21% of AIRS data are missing.

change over the fall (October and November increases were counter-balanced by a September low). Nighttime winter and summerincreases were 30% and 31% respectively with 50% and 75% increasesin the fall and spring (Table 6, Fig. 4D). ANOVA indicates statisticallysignificant differences between normal and inversion means duringthe daytime, forwinter only. During nighttimeepisodes, ANOVA showsstatistically significant differences in NO2 for all seasons (Table 6).The peak increases in February during both day and night inversions(Fig. 4C, D) are likely due to residential and commercial heating in thecold mid-latitude climate of Hamilton.

Daytime PM2.5 plots revealed an unexpected result, even as theweekend effect is observed. Daytime inversion values were, in general,lower than normal daytime values (Fig. 5A), with an average decreaseof 13%. A small 3% increase is observed onWednesdays but otherwise,decreases ranged from 7% to 25% (Table 5). The weekend effect issubtle, but present. Nighttime PM2.5, however, reveals the anticipatedincreases during night inversion episodes (Fig. 5B). The weekendeffect is not evident at night. The average increase on inversion nightswas 55% with values ranging from 27% to 73%. ANOVA indicates thatdaytime inversion values are not significantly different from normalmeans for any day. At night, the differences are significant for all days,except Mondays (Table 5).

Monthly analyses of daytime inversion PM2.5 indicate that valueswere lower than on normal days formost of the year – spring, summerand early fall (Fig. 5C). Decreases ranged from 5% in the spring to 25%in the summer (Table 6). Winter daytime inversion values increasedby32%, however, with the largest increase in February, likely dueto residential and commercial heating. During nighttime inversions,PM2.5 was higher than on normal nights, for all months (Fig. 5D).Increases were lowest in the summer and ranged from 25% in summerto 106% in spring. ANOVA indicates that differences in seasonal PM2.5during daytime inversions were significantly different from normal

milton meteorological tower at 1400 h.

Count of met.tower normals

Count of AIRS normals Co-occurrenceof both

% AIRSco-occurrence

30 49 36 6145 49 49 9249 53 57 9245 51 52 8837 55 44 6751 63 62 8153 59 69 9052 60 64 8753 58 72 9152 57 57 9145 49 50 9287 59 58 98

Table 4Number of AIRS daytime and nighttime inversion events during 2003–2007 forweekday and monthly analyses.

1400 h 0200 h

Normal Inversion Normal Inversion

DaySu 169 51 138 71M 154 51 145 68Tu 157 44 136 65W 162 43 147 54Th 158 36 133 70F 166 47 148 54Sa 154 58 153 54

MonthsJ 90 34 93 25F 82 33 89 24M 83 43 79 40A 87 27 51 65M 89 33 65 61J 110 19 70 52J 103 25 93 30A 108 18 111 21S 106 21 91 29O 98 22 91 33N 77 22 70 31D 87 33 97 25

5089J. Wallace, P. Kanaroglou / Science of the Total Environment 407 (2009) 5085–5095

days only in winter (Table 6) but on inversion nights, the increaseswere statistically significant for all seasons.

The unexpected lower inversions values for NO2 during thesummer daytime and more particularly, for PM2.5 during the daytimefor both theweekday andmonthly analysis, prompted similar analysesusing the MT inversion data (Figs. 6 and 7). The NO2 patterns ofdistribution over time, based on MT data, were similar to the AIRSpatterns, with the exception that NO2 values were consistently higherduring inversions (Fig. 6). AIRS inversion NO2 values were higher fornighttime inversions (21.1 ppb compared to 19.1 ppb for the MT) butlower for daytime inversion (12.6 ppb compared to 15.6 ppb for MT).

Fig. 4. Comparison of NO2 data from ground monitors for

PM2.5 weekday inversion values were consistently higher thannormal values, contrary to the results from AIRS. For daytime monthlyanalysis, inversion PM2.5 was also consistently higher than normalvalues, though significant dips were observed in April, July andAugust, months which exhibit lower PM2.5 with the AIRS data. Atnight, however, PM2.5 was lower on MT summer inversion nightsthan on normal nights, contrary to the AIRS results which exhibitedthis phenomenon for summer daytime inversions. However, thispresents another situation of lower PM2.5 during inversion scenarios.As observed with the NO2 values, AIRS nighttime inversion PM2.5 washigher than the MT data (10.5 compared to 8.8 µg/m3) but lowerduring daytime inversions (7.2 compared to 11.5 µg/m3). Long-rangetransport is likely a major factor in the nighttime increases, asdiscussed below.

From these analyses for both NO2 and PM2.5, it is clear that thediurnal and seasonal changes, particularly in the summer, influencethe behavior of pollutants under inversion scenarios.

4.3. Meteorology

Table 7 displays the frequency of wind directions on normal andinversion days and nights. The prevailing SW winds were dominant,followed by NE winds on inversion days and nights. NW winds weresignificantly less frequent on inversion nights and appear to be replacedby calm situations, which are defined by a wind speed of 0 km/h andtherefore, no wind direction. Wind speed was considerably higherduring the daytime and in fact, was slightly higher on inversion days.Differences in wind speed during inversion and normal configurationswere significant only on spring nights (Table 6). Calm conditionsoccurred most often on inversion nights (10% frequency), as expected.Lowest wind speeds (other than calm conditions) were associated withSE winds, which were also the least frequent.

Differences inwind speed during inversion and normal configurationswere significant only on spring nights (Table 6). Relative humidity wasstatistically different in all seasons, day and night except for summer daysand winter nights (Table 6). Temperature means were statistically dif-ferent during spring and summer only. Seasonal inversion NO2 increased

AIRS day and night, normal and inversion scenarios.

Table 5Average NO2 and PM2.5 at 1400 h and 0200 h during weekday normal and inversion configurations, with ANOVA results (5% significance level) for comparison of means.

NO2 (ppb) PM2.5 (µg/m3)

Weekday Normal NO2 Inversion NO2 % increase F p Normal PM2.5 Inversion PM2.5 % increase F p

1400 h

Sunday 8.00 8.67 8% 0.5 0.47 7.58 5.93 −22% 2.0 0.16Monday 12.43 13.61 9% 0.9 0.35 8.54 6.43 −25% 2.4 0.12Tuesday 12.89 14.90 16% 2.5 0.11 8.68 7.50 −14% 0.8 0.36Wednesday 13.02 14.28 10% 2.6 0.11 9.45 9.73 3% 0.0 0.87Thursday 12.44 14.38 16% 2.8 0.10 8.85 7.68 −13% 0.5 0.47Friday 12.21 13.81 13% 1.7 0.20 8.48 7.38 −13% 0.8 0.39Saturday 8.60 10.07 17% 1.9 0.17 7.11 6.61 −7% 0.2 0.67

0200 h

Sunday 12.03 16.94 41% 17.3 0.000 6.36 10.19 60% 14.0 0.000Monday 12.62 18.03 43% 18.9 0.000 6.30 8.01 27% 3.5 0.062Tuesday 13.75 20.54 49% 28.1 0.000 6.52 10.97 68% 17.3 0.000Wednesday 16.21 20.69 28% 9.7 0.002 7.65 12.04 57% 11.1 0.001Thursday 14.96 22.89 53% 34.0 0.000 7.07 11.10 57% 9.4 0.003Friday 15.11 25.69 70% 37.8 0.000 6.54 11.33 73% 12.2 0.001Saturday 14.33 24.93 74% 44.4 0.000 7.25 10.44 44% 7.0 0.009

5090 J. Wallace, P. Kanaroglou / Science of the Total Environment 407 (2009) 5085–5095

with humidity during the daytime but decreased at night, and decreasedwith increasing temperaturebothdayandnight (Fig.10). PM2.5decreasedwith increasing humidity during the daytime. The nighttime relationshipwas lesswell-definedbut PM2.5 increasedwith temperaturebothdayandnight (Fig. 10).

Table 8 displays NO2 and PM2.5 associated with various winddirections for both day and night scenarios. During the daytime, calmsituations were associated with a 41% decrease in NO2. However, as only11datadayswere available (6normal and5 inversions), these resultsmaynot be reliable. Otherwise, increases in NO2 were observed and rangedfrom 7% with NE winds to 29% with SE winds. At night, NO2 increasesranged from 29% with SE winds to 56% with NE and NW winds. Calmsituations produce a 46% increase in NO2. ANOVA indicates statisticallysignificant daytime differences in NO2 only with SEwinds, but significantdifferences for all wind directions at night.

Daytime PM2.5 displayed inversion decreases for all wind directionsincluding calm situations. However, PM2.5 increased with all winddirections at nights, with increases ranging from 28% with NE winds to

Table 6Average NO2 and PM2.5 at 1400 h and 0200 h during seasonal normal and inversion config

Season Variables 1400 h

Normal Inversion % inc

Spring N= 258 103Average NO2 (ppb) 12.3 13.5 10Average PM 2.5 (µg/m3) 8 7.6 −5Wind speed (km/h) 19.2 23.3 21Temperature (°C) 10.4 8.4 −19Relative humidity (%) 56 67.1 20

Summer N= 321 62Average NO2 (ppb) 10 9.1 −9Average PM 2.5 (µg/m3) 12.5 9.4 −25Wind speed (km/h) 16.4 17 4Temperature (°C) 25 23.8 −5Relative humidity (%) 55.7 55.8 0

Fall N= 281 65Average NO2 (ppb) 11.1 11.1 0Average PM 2.5 (µg/m3) 7.4 6.1 −18Wind speed (km/h) 19.1 17 −11Temperature (°C) 15.1 13.3 −12Relative humidity (%) 62.1 68.9 11

Winter N= 258 100Average NO2 (ppb) 13.2 15.1 14Average PM 2.5 (µg/m3) 4.7 6.2 32Wind speed (km/h) 22 21.3 −3Temperature (°C) −3.1 −1.8 −42Relative humidity (%) 69.4 78.8 14

67% and 69% with SW and NW winds respectively. Calm scenariosproduced a 33% increase. ANOVA indicates that the changes betweennormal and inversions situations were not significant during the daytimefor any wind direction but were significant for NE, NW and SWwinds atnight (Table 8).

4.3.1. Rose diagramsThese changes are presented graphically with wind rose diagrams

for AIRS daytime and nighttime normal and inversion episodes (Figs. 8and 9). Calm scenarios are not represented. Wind directions werebinned in 10-degree sectors with the length of each sector indicatingthe percentage of data values that were included in each bin. Eachsector was further classified by NO2 or PM2.5 levels, indicated by acolour legend. The length of each colored section indicates thepercentage of NO2 or PM2.5 values within that bin. Correspondingrose diagramswere prepared for theMT data, using the samepollutantscale, and displayed as insetswithin the related AIRS diagram. Thiswasdone to facilitate easy qualitative comparisons.

urations, with ANOVA results (5% significance level) for comparison of means.

0200 h

F Sig. Normal Inversion % inc F Sig.

195 1652.8 0.096 14.2 24.9 75 85.4 0.0000.2 0.691 4.8 9.9 106 53.1 0.000

13.1 0.000 14.1 10.4 −26 19 0.0004.3 0.040 1.9 5.3 179 23.5 0.000

29.1 0.000 81.9 73.9 −10 32.1 0.000274 103

1.9 0.167 12.4 16.3 31 23 0.0005.5 0.020 10 12.5 25 5.2 0.0230.2 0.634 9.2 8.2 −11 2.3 0.1304.1 0.043 16.8 15.4 −8 11.7 0.0010 0.970 85.5 82.3 −4 8.4 0.004

252 930 0.994 13.7 20.5 50 49.6 0.0001.4 0.235 6.7 11.4 70 23.3 0.0002.9 0.089 11.6 10.2 −12 2.6 0.1072.8 0.098 8.2 8.6 5 0.4 0.5479.2 0.003 88.8 86.4 −3 4 0.047

278 744.4 0.036 16.9 21.9 30 12.3 0.0015.7 0.018 5.3 8.1 53 18.7 0.0000.3 0.611 16.1 15.7 −2 0.1 0.7193.5 0.061 −5.6 −4.3 −23 2.1 0.149

33 0.000 82.3 83.5 1 0.7 0.397

Fig. 5. Comparison of PM2.5 data from ground monitors for AIRS day and night, normal and inversion scenarios.

5091J. Wallace, P. Kanaroglou / Science of the Total Environment 407 (2009) 5085–5095

Fig. 8 demonstrates that ahighproportionofhighNO2valuesoccurredwithwinds fromthe SSE through to theWon inversiondays. On inversionnights,NO2 increases areobserved inall directionsbutgreatestprevalenceis associated with NE winds. This is true for both AIRS andMT data. Fig. 9displays high PM2.5 concentrations from the S to SW on normal daysfor both AIRS and MT data. However, while AIRS shows lower inversionday concentrations from these directions, the MT data exhibit increasedPM2.5 from the SSE to the SW and to a lesser extent from the NE.The source of this elevated PM2.5 on inversion days is likely to be very

Fig. 6. Comparison of NO2 data from ground monitors for day and night, normal a

localized, and may partially account for the higher inversion PM2.5 seenin the MT daytime graphs (Fig. 7A), but not in the AIRS daytime graphs(Fig. 5A). Both datasets display high PM2.5 on inversion nights from theSW, and secondarily, from the NE.

5. Discussion

We have observed and quantified the changes in levels of NO2 andPM2.5duringday/nightnormaland temperature inversionconfigurations,

nd inversion scenarios determined from the Hamilton meteorological tower.

Fig. 7. Comparison of PM2.5 data from ground monitors for day and night, normal and inversion scenarios determined from the Hamilton meteorological tower.

5092 J. Wallace, P. Kanaroglou / Science of the Total Environment 407 (2009) 5085–5095

for the 5-year period from2003 to 2007. Theweekend effectwas observedwith NO2 on both normal and inversion days. This effect was less distinctwith PM2.5 and was not observed at night for either pollutant. Highestinversion increases occurred during nighttime episodes for both pollu-tants. This was evident in both weekday and seasonal analyses. The highlevels of NO2 and the accompanying large inversion increases at nightmaybe explained, in part, by differences in daytime and nighttime chemistry.NO2 has a relatively short lifetime, and gas-phase reactions involving NO2

occur rapidly, usually within minutes or hours. During the daytime, in thepresence of sunlight, NO2 is consumed in photochemical reactions thatform ground-level ozone (O3), peroxyacetyl nitrates (CH3CO–OO–NO2),and nitric acid (HNO3), and rapid dry deposition of HNO3 (Brook et al.,1996). The cycle of photochemical reactions involving NO2 helps tomaintain relatively low levels during the daytime. At night, in the absenceof sunlight, photolysis doesnot occur.While total traffic volume is reduced,

Table 7Frequency of wind directions and associated wind speed at 1400 h and 0200 h for AIRSnormal and inversion configurations.

Wind direction Normal Inversion

1400 h

Wind frequency(%)

Wind speed(km/h)

Wind frequency(%)

Wind speed(km/h)

Calm 0.5 0 1.5 0NE 23.5 18 27.9 19NW 23.1 19 20.6 20SE 7.9 12 5.2 14SW 44.9 21 44.8 23

0200 h

Calm 6.6 0 9.7 0NE 23.2 14 29.7 13NW 20.8 11 13.6 9SE 4.3 10 7.4 8SW 45.1 14 39.8 13

industry sources are maintained throughout the night. In addition, heavyduty trucks, which often preferentially travel at night, ply themajor trans-USA–Canada highways which traverse the study area. Hence, sources ofthe primary pollutant, nitric oxide (NO) persist, but photolytic reactionsthat transformthe speciesdonot.NOwill reactwithO3 to formNO2,whichwill further reactwithO3 to formNO3.NO2will be regeneratedby reactionsbetween NO and NO3, and the NO2+NO3 reaction maintains dynamicequilibrium with N2O5 (Hobbs, 2000). NO2 loss rates are lower thandaytime losses, and levels are therefore higher at night. Nighttimetemperature inversions lead to pollution buildup which may alsoencourage increased reaction rates.

Based on wind rose diagrams, NE and SW winds are factors inenhanced NO2 on inversion nights (Fig. 8). The prevailing SW windsbring pollutants from industrial US states during both day and night.NE winds are lake breezes which facilitate inversion developmentover the study area. They also carry traffic pollutants from the majorhighways located to the N and E of the air quality monitors used in thisstudy, further enhancing inversion NO2 at night.

The increases in PM2.5 at night reflect, in part, a change in thenocturnal gas to particle conversion process. Both NO2 and sulphurdioxide (SO2)maycontribute to the increasedPM2.5 at night (Wanget al.,2009). The formation of particulate ammonium nitrate (NH4NO3), themost abundant of the particulate nitrates, is favoured when conditions ofhigh relative humidity and low temperature prevail (Stockwell et al.,2000). We observe high concentrations of PM2.5 coincident with highhumidityon summerand fall nights (Table6 andFig.10). SO2will produceparticulate sulphates (Clement and Ford, 1999) but is dependent on thepresence of clouds or fog. Thus, on cloudless nights, which are typicalduring inversion development, particulate sulphates are likely to be alimited contributor (Liu et al., 2002). Particulate sulphates and nitratesfrom long-range transport couldbe significant, however (Yapet al., 2005).

The cause of lower inversion day PM2.5 when using the AIRS data isless obvious, particularly since this deviates from the observation madewith theMTdata. SincebothAIRS andMTdatasetsuse the samepollutiondata, the differences must lie in the inversions that are identified by each

Table 8NO2 and PM2.5 for various wind scenarios at 1400 h and 0200 h, during normal and inversion configurations.

NO2 (ppb) PM2.5 (µg/m3)

1400 h

Wind direction Normal Inversion % increase F Sig Normal Inversion % increase F Sig

Calm 24.4 14.5 −41 2.6 0.141 11.9 9.7 −18 0.3 0.601NE 12.2 13.0 7 1.0 0.307 7.3 6.9 −5 0.3 0.566NW 8.8 10.2 16 3.4 0.067 5.6 4.0 −29 3.6 0.058SE 14.5 18.7 29 4.5 0.037 12.3 11.9 −3 0 0.850SW 11.5 12.5 9 2.0 0.162 9.6 8.3 −14 2.4 0.123

0200 h

Calm 17.1 25 46 15.6 b .001 7.8 10.4 33 3.3 0.072NE 15.1 23.5 56 61.3 b .001 6.1 7.8 28 8.0 0.005NW 12.4 19.3 56 22.5 b .001 4.8 8.1 69 13.1 b .001SE 21 27.1 29 5.9 0.017 10.3 14.5 41 3.6 0.061SW 13.3 18 35 42.4 b .001 7.6 12.7 67 41 b .001

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instrument, particularly in the daytime. Nighttime inversions areprimarily radiation inversions caused by rapid cooling of the land.These inversions are near surface. Therefore, the inversions identifiedduring the night by both MT and AIRS instruments are likely to be thesame near-surface inversions and would account for the high rate ofcoincident nighttime inversions – up to 95% as previously noted.However, just 20% of AIRS daytime inversions were coincident with MTinversions. It is proposed that this may be due to higher incidence of

Fig. 8. Wind rose diagrams classified by NO2 using AIRS day and night, normal and inversiometeorological tower data for 2005–2007. (For interpretation of the references to colour in

elevated inversions on summer days. The vertical resolution of AIRS is1 km and therefore AIRS would not distinguish an inversion at 100 mfrom one at 700 m. The MT records data near the surface – up to 91 m.Hence, an inversionwith a base at 700 mwould not be identified by theMT. Thebaseof the elevated inversionwould serveas theupperboundaryof the mixing layer. AIRS would identify an inversion, but beneath thebase of that inversion, the normal venting of pollution would occur, theMT would identify a normal configuration, and ground monitors would

n scenarios, for 2003–2007. Insets show the equivalent rose diagrams for the Hamiltonthis figure legend, the reader is referred to the web version of this article.)

Fig. 9.Wind rose diagrams classified by PM2.5 using AIRS day and night, normal and inversion scenarios, for 2003–2007. Insets show the equivalent rose diagrams for the Hamiltonmeteorological tower data for 2005–2007. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 10. Relationship between inversion levels of NO2 and PM2.5, with relative humidityand temperature during AIRS day and night inversion episodes.

5094 J. Wallace, P. Kanaroglou / Science of the Total Environment 407 (2009) 5085–5095

not identify elevated pollution. Because reactions involving NO2 occurrapidly, buildup of NO2 below the inversion base but elevated above thesurface, is identified by AIRS. Reactions involving PM2.5 however, aremuch slower and hence, the elevated AIRS inversion may not detecthigher PM2.5.

The question remains, however, why inversion PM2.5 levelswhich arelower than normal day PM2.5 are associated with AIRS daytimeinversions. This phenomenon is most pronounced in the summer. Theheight of the boundary layer increaseswith temperature and is highest onsummer days (Simpson et al., 2007). During this time, the boundary layeris least stable andmostwell-mixed.With the increasedmixing height andhence the increased volume of air available for mixing, the effect of thedaytime temperature inversion in summer has less impact than at othertimeswhen themixing layer is low, such asduring adaytime fall orwinterinversion, or with nighttime inversions. If the inversion is elevated, as isproposed for the AIRS daytime data, it may be that ventilation below theelevated inversion raises the PM2.5 particles toward the base of theinversion to forman elevated aerosol layer. The surface values recorded bythe CAAQMs are lower than they would be if the elevated inversions didnot exist. This would also account for the slightly lower inversion NO2

observed on summer days.The phenomenon of lower PM2.5 during inversion episodes is also

observed with the MT data in the summer, but at night (Fig. 7). Thesewouldbeassociatedwith surface radiation inversions, and lower inversionvaluesmay reflect the influenceof local landbreezes at night.With limitedvertical ventilation on inversion nights, the land breezes may serve to

carry particulates over the lake, reducing the levels at the locations of themonitors.

Wind rose diagrams (Fig. 9) suggest the prevailing SWwinds whichfacilitate long-range transport of pollutants, play amajor role in elevatedPM2.5. Yap et al. (2005) indicate that neighbouring industrial U.S. statescontribute 50% or more to elevated levels of PM2.5 in southern andcentral Ontario fromMay to September. Fig. 9 indicates that long-rangetransport from the SW was a factor in elevated PM2.5 on normal daysobservedbyAIRS. TheMT,however, identifiedelevatedPM2.5 fromtheSand SSW. Thismay be from local sources, since theMT is located in closeproximity to the major industrial area in Hamilton (Fig. 1). At night,

5095J. Wallace, P. Kanaroglou / Science of the Total Environment 407 (2009) 5085–5095

both AIRS and the MT identify SW winds associated with significantlyelevated PM2.5, further emphasizing the influence of long-rangetransport of PM2.5. The strong impact of NE winds identified withNO2 is not observed with PM2.5, suggesting that traffic impacts NO2

more adversely, because of the rapid reaction rates.Errors in the AIRS data arise due to the heterogeneity of land surface,

cloud cover, and temperature retrieval methods as well as the verticalresolution of the temperature profiles (Divakarla et al., 2006). In addition,20% of the data for the study periodweremissing. Nonetheless, 5 years ofdata have been used in these analyses and it is anticipated that thenumber of data points used (Table 4) was sufficient for reliable results.

6. Conclusions

The unique contributions of this study include the use of AIRS data toidentifydaytimeandnighttime inversions for thepurposeof quantifyingthe inversion effects on NO2 and PM2.5, over an extended period. Theresults indicate that nighttime inversions were associated with thehighest pollution level increases – 49% and 54% for NO2 and PM2.5,respectively. Daytime inversions resulted in an 11% increase in NO2 but a14% decrease in PM2.5. Seasonal patterns of increases in both NO2 andPM2.5 on inversion days and nights are evident, with the highestincreases in late winter–spring and in the fall. The weekend effect wasdistinguishedwithNO2 onboth inversion andnormal days, identifying adecrease in NO2 on weekends, indicative of decreased traffic volume.The analyses produced inversion day PM2.5 levels which were lowerthan levels onnormal days. This phenomenonwasmost dominant in thesummer and contradicts the generally accepted premise of consistentlyhigher pollution levels during temperature inversions. We propose thatelevated inversions, increased mixing heights, slow PM2.5 reactionrates, and anelevated aerosol layermayaccount for thedecreasedPM2.5levels associated with AIRS summer daytime inversions.

Overall, the study affirms empirically the magnifying effects oftemperature inversions on the levels of NO2 and PM2.5 in the studyarea. In this highly polluted environment in which temperatureinversions occur with high frequency, the results warrant more in-depth studies to better understand the phenomenon of temperatureinversions, and to further explore the findings of lower pollutionlevels during some inversion scenarios. The results will also contributeto the literature probing associations between population exposureand related health impacts.

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