CHAPTER 4: RESULTS AND DISCUSSION
4.6. Correlations between AOD and PM 10 Concentrations
4.6.1. Correlations between AOD and Hourly Averaged PM 10 Concentrations
The differences observed in terms of AODaqua.dt-PM10 correlations and AODterra.dt-PM10
correlations were attributed to the differences between the AOD retrieved from sensors
-0.02 0 0.02 0.04 0.06 0.08 0.1 0.12
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
AOD (unitless)
Time in months AODaqua.dt AODterra.dt
88
onboard different satellites and the PM10 measured at different sites. AODaqua.dt observed in this study over the Malmesbury AQM station were higher than AODterra.dt over the Malmesbury AQM station. Similarly other locations have observed the same difference between AODaqua.dt and AODterra.dt (Koejoelmiere et al. 2006 and Ichoku et al. 2005). The causes of the differences between AOD observations are varied but mainly attributed to differences due to the MODIS sensors being mounted on different satellites (Yoon et al.
2012). One such example of differences between satellites are the different times at which AOD is remotely sensed.
The hourly PM10 concentrations at Malmesbury and George stations for the period of AQUA satellite overpass were generally lower than the PM10 concentrations during the period of TERRA satellite overpass. The time of TERRA satellite overpass was generally coincident just after the morning diurnal peak in PM10 concentration, while the time of AQUA satellite overpass was coincident to the period of lowest diurnal PM10
concentrations. This was contrasting to AOD comparisons with AOD from the AQUA satellite higher than the AOD from the TERRA satellite. This suggests that the AOD was not a good indicator of hourly PM10 concentration as it was not sensitive to the diurnal differences of the PM10 concentrations. Ichoku et al. (2005) highlighted that AOD from the AQUA satellite were higher than the AOD from the TERRA satellite. This is further supported by a finding in Ma et al. (2013). This is likely due to the aerosols in the afternoon experiencing greater dispersal to the upper atmosphere enhanced by convective mixing which would indicate a higher AOD relative to aerosols being trapped nearer the surface in the morning.
The observed correlations (R) over the Malmesbury AQM station for hourly PM10 was 0.35 (p < 0.0005) for AODaqua.dt (Figure 25) and 0.33 (p < 0.0005) for AODaqua.db (Figure 26), indicating weak but significant correlations. There was no significant linear correlation between AODterra.dt and hourly log10PM10 (p = 0.315) (Figure 27). The findings indicated that the AOD which used the dark target algorithm were slightly better correlated to hourly PM10 concentrations than the AOD retrieved using the deep blue algorithm over the Malmesbury AQM station.
89
Figure 25: Linear correlation between one hourly PM10 and AODaqua.dt for the Malmesbury AQM station.
Figure 26: Linear correlation between one hourly log10 transformed PM10 and log10 transformed AODaqua.db for the Malmesbury AQM station.
Figure 27: Linear correlation between one hourly log10 transformed PM10 and AODterra.dt for the Malmesbury AQM station.
0 10 20 30 40 50 60 70 80 90
-0.1 0.0 0.1 0.2 0.3 0.4
PM10mass concentration (µg/m3)
AODaqua.dt(unitless)
PM10=14.305+73.336(AODaqua.dt) R=0.354
n=134
0 0.5 1 1.5 2 2.5
-2 -1.5 -1 -0.5 0 0.5
log10PM10mass concentration (µg/m3)
log10AODaqua.db(unitless) log10PM10=1.499+0.351(log10AODaqua.db) R=0.333
n=116
0 0.5 1 1.5 2 2.5
-0.1 0 0.1 0.2 0.3 0.4
log10PM10mass concentration (µg/m3)
AODterra.dt(unitless)
90
The observed correlations for hourly PM10 over the George AQM station, was 0.32 (p = 0.001) for AODaqua.dt (Figure 28) which was lower than the correlation over the Malmesbury AQM station. There was no significant linear correlation between AODterra.dt
and hourly log10PM10 (p = 0.653) (Figure 29) over the George AQM station. The AOD- PM10 correlation over the George and Malmesbury stations were lower to those observed over Switzerland in 2008 by Emili et al. (2010). Over Switzerland, greater correlations were observed for hourly PM10 which were 0.34 - 0.52 for AODSEVIRI and 0.42- 0.46 for AODMODIS which varied due to differences in topography (Emili et al. 2010). In the case of Emili et al. (2010), the AOD–PM10 correlation benefited from the inclusion of the MLH into the linear PM10 model. The AOD-PM10 correlations determined in Emili et al. (2010) included the measurements from a number of AQM stations and thus comparatively differ to the correlations determined in this study over individual AQM stations.
Figure 28: Linear correlation between one hourly PM10 and AODaqua.dt for George AQM station.
Figure 29: Linear correlation between one hourly log transformed PM10 and AODterra.dt for George AQM station.
0 20 40 60 80 100
-0.1 0 0.1 0.2 0.3
PM10mass concentration (µg/m3)
AODaqua.dt (unitless)
PM10=22.770+81.747(AODaqua.dt) R=0.319 n=108
0 0.5 1 1.5 2 2.5
-0.1 0 0.1 0.2 0.3
log10PM10 mass concentration (µg/m3)
AODterra.dt(unitless)
91
In this study the use of the cloud mask did not substantially change the correlation, for example over the Malmesbury AQM station the use of cloud screening slightly decreased the correlation from 0.37 (Table 9) to 0.35 when cloud free AOD data were used. This suggests that the level of cloudiness within AOD pixels are not adequately determined by the MODIS algorithm. Similarly a previous study found that the use of cloud screening did not improve the AOD-PM10 correlation over Thailand (Sukitpaneenit and Oanh, 2013).
Table 9: Correlation between AOD and one hourly PM10 over Malmesbury and George before cloud screening.
Satellite AOD
Correlation over Malmesbury Correlation over George Correlation
before cloud screening
n p value
Correlation before cloud
screening, (significance)
n P value
AODaqua.dt 0.371 137 p < 0.0005 0.362 86c 0.001
AODaqua.db 0.344a 118 p < 0.0005 Not applicable Not applicable
Not applicable
AODterra.dt 0.081b 132 P = 0.353 0.083 148d 0.313
a: log10PM10, log10AOD;
b: log10PM10
c: log10PM10, log10AOD d: log10PM10
Over Thailand, for all the AQM stations used in the study the correlations for AODterra – PM10 (0.34) and AODaqua-PM10 (0.34) were similar (Zeeshan and Oanh, 2014) to the correlations observed over the Malmesbury and George AQM stations for AODaqua.dt. However when the AODaqua.dt-PM10 correlation was up scaled by obtaining mean AOD and PM10 from Malmesbury and George data on the same days, the correlation for AODaqua.dt-PM10 was greater (0.48) and was similar to those determined by Emili et al.
(2010). Zeeshan and Oanh, 2014 apportioned the AOD-PM10 correlations at different sites using meteorological effects by cluster analysis. The clustering approach used was not intended to improve the AOD-PM10 correlation but rather to improve its use for air quality analysis.
In this study, it was found that that the daily mean PM10 concentrations at the George and Malmesbury AQM stations were weakly correlated. In the up scaled analysis it was determined that a better AODaqua.dt-PM10 correlation was determined than what was determined individually over the Malmesbury and George AQM stations. However it was also determined that the AODaqua.dt over the stations and the PM10 measured at the stations
92
were different. Thus the upscaled AOD-PM10 correlation was not applicable for air quality studies in this case.
A filtering procedure was applied to determine the AOD-PM10 correlations for high and low AODs and for high and low PM10 concentrations over Malmesbury and George. An interesting finding from this analysis using hourly averaged PM10 data was the determination of a significant AODaqua.dt-PM10 correlation with low PM10 concentrations over the Malmesbury AQM station while a significant correlation was determined for AODaqua.dt-PM10 at high PM10 concentrations over the George AQM station.
For the previous case studies in Europe and in Asia, it was observed that the correlations between 0.4 and 0.5 were more abundant than correlations between 0.7 and 0.9 (Koelemeijer et al. 2006; Emili et al. 2010; Sukitpaneenit and Oanh, 2013; Wu et al.
2012). This suggests that the AOD-PM10 relationship itself can be used as an indicator of PM10 pollution distribution within a region which may not require very strong AOD-PM10
correlations such as the case of Mahmud (2013) rather than as predictive proxy indicator for PM10 as an air quality pollutant over region such as the case of Emili et al. (2010). The correlations determined over the Malmesbury and George AQM stations were weak. Thus it is suggested that further work is required to further explore methods to optimise the hourly AOD-PM10.