Atmospheric Environment 38 (2004) 1437–1445

ARTICLE IN PRESS

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doi:10.1016/j.at

Atmospheric PCBs and organochlorine pesticides inBirmingham, UK: concentrations, sources, temporal

and seasonal trends

Stuart Harrad*, Hongjun Mao

Division of Environmental Health and Risk Management, Public Health Building, School of Geography,

Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK

Received 8 September 2003; received in revised form 27 November 2003; accepted 1 December 2003

Abstract

Concentrations of individual PCBs and DDT, DDE, a- and g-HCH were recorded in 62 air samples of 24 h duration

taken every 1–2 weeks at an urban location in Birmingham, UK between April 1999 and July 2000. Concentrations of

PCBs 31/28, 52, 49, 47, 105, 149, 153, 138/164, 174, and 180 were significantly lower ðpo0:05Þ than those recorded at

the same site in 1997–1998. While DDT concentrations and DDT:DDE ratios were much lower than those recorded in

southern England in 1992–1993; no such decline was observed in concentrations of a- and g-HCH, or the a:g-HCH

ratio. These data are consistent with declining European usage of DDT, but continuing UK use of g-HCH, and

overseas use and subsequent atmospheric transport of ‘‘technical’’ HCH. g-HCH concentrations displayed two non-

temperature dependent peaks in spring and late summer/early autumn, consistent with agricultural use patterns.

Multiple linear regression analysis was used to elucidate the relative influence of temperature, wind direction and a

variety of other meteorological variables on atmospheric concentrations of PCBs. When all samples were considered,

concentrations of most PCB congeners were influenced by a combination of reciprocal temperature, wind direction, and

wind speed. Plotting the ratio of the Beta weightings for the regression coefficients for reciprocal temperature and sine

(or cosine) of wind direction against chlorine number, revealed a general increase in the relative influence of

temperature compared to wind direction with increasing chlorine number. However, when the 31 samples for which the

wind speed o4.4 m s�1 were analysed; only temperature and atmospheric relative humidity were influential for most

congeners. This absence of influence of wind direction under relatively calm atmospheric conditions, suggests that it is

medium-to-long range transport rather than local sources that exerts the greatest influence on PCB concentrations at

our site.

r 2003 Elsevier Ltd. All rights reserved.

Keywords: PCBs; Organochlorine pesticides; Temporal trends; Sources; Concentrations

1. Introduction

Although direct human exposure via inhalation of

outdoor air is not a significant exposure pathway, the

atmosphere represents the primary vector by which

polychlorinated biphenyls (PCBs) and organochlorine

ing author. Tel.: 44-121-414-7298; fax: +44-

ess: s.j.harrad@bham.ac.uk (S. Harrad).

e front matter r 2003 Elsevier Ltd. All rights reserve

mosenv.2003.12.002

pesticides (OCPs) enter the grass–cattle–human food

chain, which is responsible for a significant proportion

of human exposure. It is, therefore, important to

monitor and improve understanding of the factors that

influence atmospheric concentrations of such contami-

nants. There is also considerable interest in monitoring

the temporal trend in PCB concentrations in response to

the imposition of restrictions in their manufacture and

use. Most recently, atmospheric concentrations of PCBs

at a number of UK locations (but not including

d.

ARTICLE IN PRESSS. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–14451438

Birmingham, the UK’s second largest city) were

evaluated and a temporal decline reported with half-

lives for individual PCBs of 2–6 years (Sweetman and

Jones, 2000).

In comparison to PCBs, little is known about recent

levels of UK atmospheric contamination with organo-

chlorine pesticides (OCPs), specifically a-HCH, g-HCH

(lindane), plus DDT and its degradation product DDE.

These compounds are not currently routinely monitored

in the UK, largely because their use is either banned

(DDT and ‘‘technical’’ HCH of which a-HCH was the

major component), or restricted (g-HCH). Despite this,

knowledge of their atmospheric concentrations is needed

in order to evaluate the impact of the use restrictions, as

part of the UK’s commitments under the recently agreed

UNEP POPs protocol, and to assess to what extent their

continuing use (either illicit or licit) in the UK or

overseas impacts on present-day contamination.

This study reports concentrations of individual PCB

congeners and DDT, DDE, g-HCH, and a-HCH in

samples of air collected over 62 separate 24 h periods

between April 1999 and July 2000. In this paper, we

compare these data with similar studies elsewhere,

examine them for any temporal and seasonal trends,

investigate the influence of various meteorological

parameters on concentrations, and make inferences

regarding the relative influence of short and medium-

to-long range atmospheric transport.

2. Experimental section

2.1. Sampling location

Samples were taken every 1–2 weeks between April

1999 and July 2000 at the Elms Road Observatory Site

(EROS) on the campus of Birmingham University,

about 3 km southwest of the city center of Birmingham,

UK. Birmingham is the major city within the West

Midlands conurbation, the second largest urban center

in the UK with a population of ca. 2.5 million. Sampling

equipment was located at ground level ca. 20 m from the

nearest building, well clear of any building air outfalls.

EROS is identical to the site for which PCB concentra-

tions have been reported for the period July 1997–July

1998 (Currado and Harrad, 2000).

2.2. Air sampling

Our sampling procedures for determining PCBs in air

have been reported previously (Currado and Harrad,

2000). In summary, samples were taken using a

Graseby–Andersen Hi–Vol sampler fitted with a total

suspended particulate (TSP) inlet modified to hold a

glass-fibre filter (GFF, 0.6 mm pore size, Whatman) and

a pre-cleaned polyurethane foam (PUF) plug (827 cm3

volume). Sampling was conducted for 24 h at an

accurately measured flow-rate of ca. 0.7 m3 min�1

yielding sample volumes of ca. 1000 m3.

2.3. Sample purification and analysis

GFFs and PUFs were analysed separately to yield

concentrations in both particulate and vapour phases.

Analyses were conducted using well-validated, contain-

ment enrichment, GC/MS procedures, based on those

reported elsewhere for PCBs (Ayris et al., 1997; Currado

and Harrad, 2000). A brief summary is given here

however. Samples were Soxhlet extracted for 16 h with

dichloromethane, prior to washing with concentrated

sulphuric acid, lipid removal via liquid–liquid partitioning

between dimethyl sulfoxide, hexane and water, elution

through a 1 g florisil column (pre-activated for 24 h at

400�C) with hexane:diethyl ether (4:1 v/v; 15 ml), and

concentration to 25ml of nonane containing PCB # 157 as

a recovery determination standard. One microlitre of the

final extract was injected onto a Fisons’ MD-800 GC/MS

system fitted with a 60 m HP5 column (0.2 mm id, 0.2mm

film thickness). Both injector and interface temperatures

were 250�C, while the oven temperature program was:

140�C for 2 min, 5�C/min to 200�C and held for 2 min,

then 2�C/min to 280�C and held for 5 min. The mass

spectrometer was operated in EI+ SIM mode; m=z

values monitored were: 181 and 183 (both HCHs), 235

and 237 (DDT), 247 and 249 (13C12-DDT) and 316 and

318 for DDE. For PCBs, m=z values monitored were as

previously reported (Ayris et al., 1997).

2.4. Quality control and quality assurance

Mean recoveries of quantitation standards added to

PUFs and GFFs after sampling but prior to extraction to

check analyte losses during analysis (13C12-DDT and

PCB congeners 34, 62, 119, 131, and 173—internal

standards (ISs)) ranged between 50% and 80% for all

samples. Mean recoveries of the quantitation standards

added to GFFs prior to sampling to check analyte losses

due to both sampling and analysis (PCBs congeners 19

and 147—sampling evaluation standards, SESs) were

82% and 84%. Concentrations were not corrected for

SES recoveries. The limits of detection for individual

congeners (typically 0.1 pg m�3) were essentially defined

by the levels detected in method blanks. In all, 9 method

blanks were conducted, with mean PCB concentrations in

blanks no greater than 7% of those in samples. All

samples were corrected for the mean blank concentra-

tions. Method accuracy and precision were evaluated by

replicate ðn ¼ 5Þ analysis of NIST SRM 1941a (Organics

in Marine Sediment). Precision for individual target

compounds was typically ca. 10% and better than 20% in

all cases. The accuracy of our method (and comparability

with data reported previously from our laboratory that

ARTICLE IN PRESSS. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–1445 1439

obtained similar data for the same SRM (Currado and

Harrad, 2000) was demonstrated by the good agreement

with certified values for the SRM. Note thatP

PCB

refers to the sum of all trichlorinated through hepta-

chlorinated PCB congeners detected in a sample.

2.5. Determination of meteorological parameters

Automatic monitoring of air temperature (t), relative

humidity (RH), rainfall (RF), wind speed (WS), and

wind direction (WD) was conducted during all sampling

events. These data were measured by a meteorological

station situated 600 m from our sampling site. The

station supplied 3-h average values derived from

measurements made at 20 s intervals for t and RH and

15 min intervals for RF, WS, and WD.

2.6. Statistical analyses

All statistical analyses were conducted using SPSS for

Windows version 10.0.

3. Results and discussion

3.1. Temporal trend in atmospheric concentration of

PCBs in Birmingham

Table 1 summarises the concentrations (sum of both

vapour and particle phases) of individual PCBs and

OCPs detected in all samples in this study. We have

previously compared PCB atmospheric concentrations

Table 1

Summary of atmospheric concentrations (sum of vapour and particle

PCB/OCP Average Geometric mean sn�1

18 25 23 14

31/28 19 17 10

32/16 13 12 7.2

26 3.3 2.9 1.9

33 13 11 7.4

22 7.5 6.7 4.3

52 22 18.3 15

49 6.7 5.7 4.4

47 2.7 2.3 1.8

44 9.1 7.6 6.3

42 2.5 2.1 1.7

41/64 9.6 8.3 6.1

74 6.6 4.7 7.0

70/76 11 9.1 8.5

56 4.8 3.9 3.4

101 15 13 11.2

99 3.2 2.6 2.5

118 10 8.1 11.3

105 1.6 1.2 1.6

95 13 11 10

at this site with those at other locations in Europe and

North America (Currado and Harrad, 2000), and the

data reported here confirm concentrations at our site to

be at the lower end of those reported for urban areas.

While discernible differences in average concentrations

are evident between those detected in this campaign and

at the same location over the period 7/97–7/98 (Currado

and Harrad, 2000), the present study includes more

samples taken during the spring period April–June

inclusive. Given the well-established seasonal variation

in atmospheric PCB concentrations at this site and

others (Currado and Harrad, 2000; Wania et al., 1998),

Table 2 compares our data for July 1999 to July 2000

(average temperature for all sampling events =

10.574.5�C) with that for July 1997 to July 1998

(average temperature for all sampling events =

10.874.7�C). Given the distribution of the concentra-

tion data we used the Mann–Whitney U-test—a non-

parametric equivalent of the t-test. This statistical

comparison reveals there to be a statistically significant

decline ðpo0:05Þ in atmospheric concentrations of PCBs

31/28, 52, 49, 47, 105, 149, 153, 138/164, 174, and 180.

We are continuing monitoring to elucidate whether these

are genuine temporal trends or merely a reflection of

normal year-on-year variation.

3.2. Spatial and temporal trends in atmospheric

concentrations of OCPs

With respect to OCPs, Table 3 compares concentra-

tions in this study with those previously reported

elsewhere. In general, concentrations in this study are

phase) of PCBs and selected OCPs (pg m�3) in this study

PCB/OCP Average Geometric mean sn�1

91 1.6 1.3 1.3

84/92 1.8 1.5 1.5

97 2.6 2.0 2.2

87 4.4 3.6 3.8

111 1.2 0.93 1.3

110 10 8.2 9.2

153 3.5 3.0 2.2

138 3.5 2.9 2.5

148 2.0 1.7 1.4

151 1.8 1.5 1.1

149 4.5 3.7 3.4

132 2.1 1.5 2.3

180 1.0 0.90 0.59

177 0.4 0.36 0.29

190/170 0.3 0.29 0.21PPCB 252 218 156

a-HCH 30 27 15

g-HCH 453 332 363

DDE 8.4 7.1 4.7

DDT 3.1 2.7 1.8

ARTICLE IN PRESS

Table 2

Comparison of PCB concentrations recorded at EROS between

July 1999 and July 2000 with those between July 1997 and July

1998 (Currado and Harrad, 2000)

Congenera Average(sn�1)—1999–2000

Average(sn�1)—1997–1998

p

18 25.1 (14.5) 30 (24.3) >0.0532/16 18.4 (10.4) 14 (9.5) >0.0531/28 13.1 (7.4) 30 (20.5) o0.001

33 12.5 (7.5) 14 (9.0) >0.0522 7.3 (4.3) 7.4 (5.2) >0.0552 20.3 (13.9) 27 (16.6) 0.028

49 6.4 (4.3) 9.5 (6.1) 0.006

47 2.6 (1.9) 4.1 (3.2) 0.021

44 8.4 (5.9) 9.3 (5.6) >0.0542 2.3 (1.6) 2.4 (1.7) >0.0541/64 9.0 (5.7) 8.7 (5.2) >0.0574 5.1 (4.4) 3.8 (2.3) >0.0570/76 10.2 (7.7) 11 (6.5) >0.0556 4.4 (3.2) 4.1 (2.0) >0.0595 12.9 (10.2) 11 (7.9) >0.0591 1.5 (1.2) 1.4 (0.9) >0.0584/92 1.7 (1.4) 1.7 (1.1) >0.0590/101 14.4 (10.8) 15 (10.6) >0.0599/113 3.0 (2.4) 3.7 (2.7) >0.0597 2.3 (2.2) 2.5 (1.8) >0.0587 4.0 (3.7) 4.3 (3.2) >0.05111 1.0 (1.0) 1.1 (0.7) >0.05110 9.5 (9.0) 11 (8.4) >0.05118 9.7 (11.5) 6.9 (6.0) >0.05105 1.4 (1.7) 2.3 (2.0) 0.026

148 1.9 (1.4) 2.2 (1.4) >0.05151 1.6 (1.1) 2.1 (1.3) >0.05149 4.0 (3.0) 6.1 (3.7) 0.005

153 3.3 (2.2) 5.8 (3.7) o0.001

132 2.1 (2.5) 2.0 (1.4) >0.05138/164 3.2 (2.5) 5.4 (3.0) o0.001

174 0.4 (0.5) 0.8 (0.7) 0.05

177 0.4 (0.3) 0.5 (0.4) >0.05180 1.0 (0.5) 2.0 (1.5) o0.001P

PCB 227 (145.3) 264 (163.9) >0.05

a Data for congeners for which a statistically significant

decline was observed between 1997–1998 and 1999–2000 are

emboldened.

Table 3

Average atmospheric concentrations of (pg m�3) and ratios of selected

studies

Location a-HCH g-HCH a:g-HC

Birmingham, UK (this study) 29 449 0.064

Stoke Ferry, UKa 64 940 0.068

Hazelrigg, UKa 30 220 0.136

Southern Englandb 39 408 0.096

Southern Norwayc 66 48 1.38

Thames Valley, UKb 29 140 0.207

Paris, Franced NA 100–6000 0.02–0

a Peters et al. (1999).b Turnbull (1996).c Haugen et al. (1998).d Granier and Chevreuil (1997).e NA: No data available.

S. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–14451440

reasonably consistent with those recorded in Hazelrigg,

in north-west England (Peters et al., 1999). Our

detection of DDT (not detected at Hazelrigg) is

interesting, as it suggests either some continued—

illicit-use of DDT within the UK, or long-range atmo-

spheric transport. However, the 5-fold decline in the

concentration of DDT compared to that recorded in

1992–93 in Southern England (ca. 150 km from our site)

(Turnbull, 1996), is—along with the decline in the

DDT:DDE ratio (an indication of the ‘‘age’’ of the

DDT)—a clear indication of the decline in DDT usage

in Europe. In contrast, the average g-HCH concentra-

tion in this study is similar to that reported in 1992–93 in

Southern England. A similar absence of temporal

change was reported for Paris between 1986 and 1990

(Granier and Chevreuil, 1997). Furthermore, the a:g-HCH ratio is comparably low in both this study and

that in the 1992–93 study. While the excess of g-HCH is

to be expected, given the continuing use in the UK of

‘‘pure’’ g-HCH (lindane) rather than technical HCH (a

mix of largely a- and some g-HCH), the absence of any

appreciable temporal change in both parameters sug-

gests no decline in use over the last decade. The lower

g-HCH concentration and higher a:g-HCH ratio at the

Hazelrigg site appear consistent with a lower rate of

lindane application in that location compared to the

Birmingham area.

3.3. Seasonal variation in g-HCH concentrations

Fig. 1 shows the average concentration of g-HCH in

April to May 1999 and 2000, plus August to October

1999 inclusive to exceed that recorded at other times

(i.e. June–July 1999 and 2000, plus November 1999 to

March 2000). While use of the Mann–Whitney U-test

reveals no significant difference in temperatures re-

corded during the 2 periods, there is a statistically

significant ðpo0:001Þ difference in concentrations. This

organochlorine pesticides reported in this and other European

H DDE DDT DDT:DDE Sampling period

8.4 3.1 0.37 1999–2000

97 o1 o0.01 1997–1998

4.0 o1 o0.25 1997–1998

14 17 1.2 1992–1993

NAe NA NA 1991–1995

NA 29 NA 1987–1990

.11 NA NA NA 1986–1990

ARTICLE IN PRESS

659

295

0

200

400

600

800

1000

1200

1

Con

cent

rati

on (

pg m

-3) 04-05/99-00 + 08-10/99

06-07/99-00 + 11/99-03/00

Fig. 1. Comparison of average 7sn�1 concentrations of g-HCH in April to May and August to October with that

recorded at other times.

Table 4

Regression parameters of PCBs and selected pesticides for plots

of ln P versus reciprocal temperature for all samples

PCB/OCP m1

(slope)

b

(y-intercept)

P value DHSA

(kJ mol�1)

18 �2830 �19.24 0.02 23.5

31/28 �4986 �11.97 0.001 41.5

32/16 �3332 �18.12 0.01 27.7

26 �5456 �12.08 0.001 45.4

33 �5108 �11.93 0.001 42.5

22 �5019 �12.78 0.001 41.7

52 �7148 �4.41 0.001 59.4

49 �5732 �10.56 0.001 47.7

47 �6114 �10.25 0.02 50.8

44 �7081 �5.52 0.001 58.9

42 �7626 �4.87 0.001 63.4

41/64 �6475 �7.61 0.001 53.8

70/76 �8523 �0.30 0.001 70.9

56 �8656 �0.69 0.001 72.0

101 �8481 �0.24 0.001 70.5

99 �8573 �1.51 0.001 71.3

118 �10 289 5.56 0.001 85.5

105 �11 765 8.75 0.001 97.8

95 �7233 �4.73 0.001 60.1

91 �7249 �6.82 0.001 60.3

84/92 �8183 �3.44 0.001 68.0

97 �8766 �1.09 0.001 72.9

87 �8812 �0.38 0.001 73.3

111 �9151 �0.52 0.001 76.1

110 �9582 3.16 0.001 79.7

153 �9269 0.87 0.001 77.1

138 �8155 �3.17 0.001 67.8

148 �7407 �6.14 0.001 61.6

151 �7661 �5.38 0.001 63.7

149 �5655 �11.58 0.001 47.0

132 �8160 �3.66 0.001 67.8

180 �10 005 1.80 0.001 83.2

177 �11 930 7.80 0.001 99.2PPCB �6696 �3.61 0.001 55.7

a-HCH �4298 �14.01 0.001 35.7

g-HCH �9239 5.89 0.001 76.8

DDE �5017 �12.95 0.001 41.7

DDT �6435 �9.16 0.001 53.5

S. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–1445 1441

is presumably attributable to seasonal variations in the

use of g-HCH. Similar observations have been made in

both the UK and France (Peters et al., 1999; Granier

and Chevreuil, 1997).

3.4. Influence of temperature on atmospheric

concentrations

Thermodynamically, the vapour-phase behaviour of

PCBs and OCPs can be described in terms of the

Clausius–Clapeyron equation.

ln P ¼DHv

R

� �1

T

� �þ const; ð1Þ

where P is the partial pressure (atm), T is the

temperature (K), DHv the heat of vapourization

(kJ mol�1), and R the gas constant. Note that in line

with the approach of Simcik et al. (1999), we refer to

DHV as the enthalpy of surface-air exchange (DHSA).

Hence, regression of ln P against 1=T ; should be linear

with negative slope m1, and intercept b1.

ln P ¼ ðm1Þ1

T

� �þ b1: ð2Þ

Partial pressures (p) of individual PCBs and OCPs

were calculated for each sample from gas phase

concentrations using the ideal gas law (forP

PCB, an

average molecular mass of 326.4 was assumed). Natural

logarithms of these partial pressures were plotted

against reciprocal mean temperature for each sampling

event. The slopes ðm1Þ; intercepts ðbÞ; statistical

significance values ðpÞ; and enthalpies of surface:air

exchange (DHSA) (Simcik et al., 1999) derived from

these plots are included as Table 4. For each individual

PCB studied, the temperature-dependence of vapour-

phase concentrations was significant at at least

the 98% level, with the significance level exceeding

99% for most congeners. ForP

PCB, a- and g-HCH,

temperature dependence was significant at the 99.9%

level, and the m1 values (�6696, �4298, and –9239 K

forP

PCB, a- and g-HCH, respectively) were within

the range reported elsewhere (Wania et al., 1998).

In particular, the slope forP

PCB is almost identical

to the value of –6323 reported at the same site in 1997–

1998 (Currado and Harrad, 2000). Furthermore, the

steeper slopes for g-HCH compared to a-HCH are

consistent with other studies (Wania et al., 1998;

Cortes et al., 1998), and according to the hypothesis of

Wania et al. (1998) are an indication of long-range

transport being the principal source of a-HCH, with

more localised sources driving concentrations of

g-HCH. This is consistent with the continued UK use

of g- but not a-HCH.

ARTICLE IN PRESSS. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–14451442

3.5. Seasonal variations in temperature dependence of

atmospheric concentrations

Temperature may not always be significantly corre-

lated with airborne concentrations. Although the

temperature dependence of PCB 53 and g-HCH at

Egbert, Ontario was strong when temperatures ranged

between 5�C and 25�C, this relationship broke down

(i.e. very low slopes or insignificant p values) when the

temperature fell below 5�C (Hoff et al., 1992). In order

to investigate further for the influence of temperature in

this respect, we split our data into two groups, one

consisting of those with temperature above 10�C and

one below 10�C (see Table 5). In essence, our data show

a similar pattern to that for Egbert (Hoff et al., 1992)—

Table 5

The influence of temperature on the temperature dependence of PCB

PCB/OCP T>10�C ðn ¼ 38Þ

m1 b p DHSA (kJ mol�

18 �3293 �17.65 >0.1

31/28 �5806 �9.11 o0.1 48

32/16 �5161 �11.75 o0.1 43

26 �5858 �10.68 o0.1 49

33 �5849 �9.34 o0.1 49

22 �6733 �6.81 o0.05 56

52 �7086 �4.61 o0.05 59

49 �6012 �9.58 o0.1 50

44 �7799 �3.00 o0.05 65

42 �8687 �1.16 o0.01 72

41/64 �6896 �6.14 o0.05 57

70/76 �7403 �4.18 o0.05 62

56 �7436 �4.93 o0.05 62

101 �8430 �0.40 o0.02 70

99 �8366 �2.21 o0.02 70

118 �10229 5.37 o0.02 85

105 �8537 �2.45 o0.05 71

95 �9718 3.94 o0.01 81

91 �8930 �0.94 o0.05 74

84/92 �8584 �2.02 o0.05 71

97 �8760 �1.08 o0.05 73

87 �8970 0.20 o0.02 75

111 �7827 �5.10 o0.1 65

110 �9231 1.96 o0.02 77

153 �10863 5.79 o0.00 89

138 �8015 �3.65 >0.1

148 �11483 8.05 o0.01 95

151 �10429 5.29 o0.01 89

149 �10901 6.66 o0.1 91

132 �10895 5.89 o0.1 91

180 �9851 1.25 o0.01 82

177 �12915 11.20 o0.01 107PPCB �7300 �1.49 o0.05 61

a-HCH �1951 �22.18 >0.1

g-HCH �1038 �22.67 >0.1

DDE �1212 �26.24 >0.1

DDT �4173 �17.07 >0.1

i.e. a break down in temperature dependence below

10�C (a similar lack of temperature dependence below

5�C was observed at Egbert). This is due to the influence

of temperature becoming less important as temperature

falls. Interestingly, the opposite observation was made

for our target OCPs—i.e. although airborne concentra-

tions were temperature dependent when temperatures

were below 10�C, they were not when temperatures were

above 10�C. It is thought that this may be due to

seasonal use pattern of OCPs, either in the UK (e.g.

lindane) or elsewhere (with subsequent atmospheric

transport). As pesticide applications occur during the

warmer growing season, the effect of temperature-

dependent volatilisation from surfaces would be less

marked during these periods.

s and OCPs

T o10�C ðn ¼ 24Þ

1) m1 b p DHSA (kJ mol�1)

�4424 �13.52 >0.1

�4399 �14.07 >0.1

�3215 �18.52 >0.1

�5835 �10.71 >0.1

�3408 �18.03 >0.1

�3549 �18.04 >0.1

�5090 �11.80 >0.1

�4676 �14.35 >0.1

�4581 �14.49 >0.1

�5241 �13.42 o0.1 44

�5782 �10.09 o0.05 48

�6433 �7.82 o0.1 53

�7586 �4.55 o0.05 63

�5106 �12.36 o0.1 42

�4668 �15.54 >0.1

�6425 �8.32 o0.1 53

�7148 �7.87 >0.1

�3588 �17.80 >0.1

�2699 �23.14 >0.1

�4841 �15.43 >0.1

�4477 �16.49 >0.1

�4375 �16.31 >0.1

�5037 �15.31 o0.1 42

�5810 �10.39 o0.1 48

�8593 �1.54 o0.00 71

�7039 �7.17 o0.02 59

�5241 �13.88 >0.1

�7639 �5.42 o0.05 64

�6404 �8.84 o0.05 53

�3909 �18.89 >0.1

�12271 9.94 o0.00 102

�15428 20.37 o0.01 128

�4773 �10.51 o0.1 40

�4439 �13.53 o0.05 37

�14314 24.03 o0.01 119

�12797 14.94 o0.00 106

�11686 9.67 o0.01 97

ARTICLE IN PRESS

Table 6

Beta weightings for correlation between concentrations of PCBs

and OCPs and meteorological parameters for all samples

ðn ¼ 62Þ

PCB/OCP 1/T sin WD cos WD WS RH

18 �0.515 0.317

31/28 �0.540 0.333 �0.290

32/16 �0.380 0.324 �0.368 0.243

26 �0.528 0.198 �0.332

33 �0.514 0.195 �0.336

22 �0.566 0.316 �0.286 0.205

52 �0.585 0.253 �0.190 �0.364

49 �0.511 0.304 �0.371

47 �0.618 0.274 0.246

44 �0.614 0.292 �0.323

42 �0.622 0.199 �0.361

41/64 �0.581 0.255 �0.365

70/76 �0.635 0.210 �0.320

56 �0.606 0.182 �0.381

101 �0.722 0.244 �0.204

99 �0.636 0.175 �0.179 �0.309

118 �0.591 �0.314

105 �0.580 �0.348

95 �0.728 0.336

91 �0.720 0.345

84/92 �0.759 0.299

97 �0.677 0.234 �0.218

87 �0.802 0.275

111 �0.534 �0.326

110 �0.595 �0.194 �0.320

153 �0.866 0.201

138 �0.670 0.247

148 �0.595 �0.278

151 �0.649 �0.306

149 �0.484

132 �0.594 0.288

180 �0.738 �0.202

177 �0.723

190/170 �0.695PPCB �0.619 0.292 �0.289

a-HCH �0.547

g-HCH �0.704 0.294

DDE �0.493 �0.317 0.385

DDT �0.583 0.334

S. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–1445 1443

3.6. Influence of wind direction on atmospheric

concentrations

In addition to reciprocal temperature, the potential

influence on atmospheric PCB levels of WS, WD, RH,

and RF was investigated. To do so, we introduced each

into the Clausius–Clapeyron equation to yield four new

equations (Currado and Harrad, 2000).

ln P ¼ ðm1Þ1

T

� �þ m2 ln WS þ b; ð3Þ

ln P ¼ ðm1Þ1

T

� �þ m3 sin WD þ m4 cos WD þ b; ð4Þ

ln P ¼ ðm1Þ1

T

� �þ m5 RH þ b; ð5Þ

ln P ¼ ðm1Þ1

T

� �þ m6 RF þ b; ð6Þ

Note that wind direction is expressed in degrees relative

to true north (0�), and average WD for a given sampling

event was calculated via use of trigonometric relations to

calculate the direction of the sum of individual wind

vectors.

Multiple regression analysis of the relationship

between ln P of each individual PCB andP

PCB and

each of these meteorological parameters was conducted

for all samples. The beta weightings for the regression

coefficients for each individual compound are listed in

Table 6—these are a measure of the relative influence of

each parameter correlated with concentration. In addi-

tion to the statistically significant negative linear

relationship with reciprocal temperature that was

observed for all target compounds except for PCB

#18; concentrations of many PCBs were also negatively

correlated with wind speed, and positively correlated

with sin WD. Negative correlation with wind speed

indicates the expected diluting effect of atmospheric

turbulence on pollutant concentrations. More interest-

ing is the positive correlation with sin WD. Positive sine

values are associated with averaged wind directions

from the sector 0�–90�–180�, and indicate that concen-

trations are higher in samples during which the

predominant wind direction was from the east. For a

few compounds, a negative correlation with cos WD was

detected. Where this was detected in the absence of any

correlation with sin WD, higher concentrations are

associated with winds from the south; while for PCB #s

52 and 99—where correlations with both sin and cos

WD were detected—higher concentrations are asso-

ciated with winds from the southeast. Most puzzling is

the observation of a positive correlation between RH

and concentrations of DDE, DDT, and 5 individual

PCBs. It is possible that this may be an indication

of the influence of the so-called ‘‘wicking’’ effect

enhancing evaporation of PCBs from soils under more

humid conditions (Eduljee, 1987). No statistically

significant relationship was detected between concentra-

tion and RF.

Fig. 2 plots the ratio of the beta weightings for the

regression coefficients for reciprocal temperature and

sine (or cosine) of wind direction against chlorine

number (for congeners correlated with both sin and

cos WD, the highest beta weighting was used). It reveals

a general increase in the relative influence of temperature

compared to wind direction with increasing chlorine

number. Note that for congeners where no relationship

ARTICLE IN PRESS

13 4 5 6 7

1.5

2

2.5

3

3.5

4

4.5

5

5.5

Chlorine Number

[β(1

/T)/

β(W

D)]

Fig. 2. Relationship between chlorine number and the modulus

of the ratio of beta weightings for 1/T to those for sin (or cos)

WD.

Table 7

Beta weightings for correlation between concentrations of PCBs

and OCPs and meteorological parameters for samples where

averaged wind speed o4.4 m s�1 ðn ¼ 31Þ

PCB/OCP 1/T sin WD cos WD WS RH

18 �0.367 0.442

31/28 �0.574 0.384

32/16 �0.342 0.556

26 �0.332 �0.392

33 �0.540 0.427

22 �0.573 0.476

52 �0.720 0.332

49 �0.582 0.437

47 �0.560 0.410

44 �0.699 0.365

42 �0.565 �0.324

41/64 �0.571 �0.300 0.293

70/76 �0.579 �0.307

56 �0.548 �0.372

101 �0.780 0.272

99 �0.768 0.253

118 �0.704

105 �0.689

95 �0.700 0.338

91 �0.660 0.311

84 �0.632

97 �0.659

87 �0.700

111 �0.625

110 �0.723

153 �0.758

138 �0.447

148 �0.696 0.294

151 �0.750 0.303

149 �0.487

132 �0.671 0.349

180 �0.693

177 �0.687

190/170 �0.687PPCB �0.671 0.379

a-HCH �0.481

g-HCH �0.529

S. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–14451444

with sin or cos WD was detected, a default beta

weighting of 0.15 (i.e. just below the minimum value

detected) was assumed for the calculation of ratios.

In an effort to further interpret the relative influence

of different meteorological parameters on concentra-

tions at our site, we decided to factor out the influence of

high wind speed. To do so, we considered only those

31 samples for which the wind speed o4.4 m s�1.

Multiple regression analysis of this reduced data

set revealed only temperature and atmospheric relative

humidity to be influential for most congeners (Table 7).

The fact that wind direction exerts a significant influence

on concentrations when all samples are considered, but

that this influence disappears under relatively calm

atmospheric conditions when local source inputs would

be expected to predominate, suggests that it is

medium-to-long range transport from the east of the

UK and continental Europe, rather than local sources

that exerts the greatest influence on PCB concentrations

at our site.

DDE �0.551 �0.315 �0.326

DDT �0.308

4. Conclusions

This study shows that atmospheric concentrations of

PCBs 31/28, 52, 49, 47, 105, 149, 153, 138/164, 174, and

180 recorded at an urban location in Birmingham, UK

between April 1999 and July 2000, were significantly

lower ðpo0:05Þ than those recorded at the same site in

1997–1998. This study’s evidence that concentrations of

DDT and DDT;DDE ratios but not those of g-HCH or

a:g-HCH ratios are declining, is consistent with declin-

ing European usage of DDT, but continuing UK use of

g-HCH, and overseas use and subsequent atmospheric

transport of ‘‘technical’’ HCH. a-HCH concentrations

displayed two non-temperature dependent peaks in

spring and late summer/early autumn, consistent with

agricultural use patterns. Multiple linear regression

analysis of all samples revealed concentrations of most

PCB congeners to be influenced by a combination of

reciprocal temperature, wind direction, and wind speed.

However, when samples for which the wind speed

o4.4 m s�1 were analysed; only temperature and atmo-

spheric relative humidity were influential for most

congeners. This absence of influence of wind direction

under relatively calm atmospheric conditions, implies

that medium-to-long range transport rather than local

sources exerts the greatest influence on PCB concentra-

tions at our site.

ARTICLE IN PRESSS. Harrad, H. Mao / Atmospheric Environment 38 (2004) 1437–1445 1445

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