APPENDIX B.3.
AUTOCORRELATION ANALYSIS
EVALUATION OF THE
MONITORING NETWORK OF
AIR QUALITY IN BAJA CALIFORNIA
APPENDIX B.3
APPENDIX B.3.
AUTOCORRELATION ANALYSIS
This section shows the results of the Autocorrelation analysis to the records of concentrations in the
BC monitoring network for the years 2005, 2006 and 2007. No subsequent years have been included
because they present a great lack of information.
The analyses were performed with the autocorrelation function with the objective of finding
repetitive patterns in the concentrations obtained from monitoring stations. This function represents
a cross correlation function for each series of pollutant concentrations with itself.
For this analysis the averages of each pollutant concentration is used, within its own season, T1, T2,
T3 and T4, previously defined. The autocorrelation function is used for up to 8 "Lags", which
compares the differences between the concentrations of each season, being the Lag 1 a comparison
of each consecutive season during the three years, the Lag 2 shows the comparison each 2 seasons,
so to Lag 8. As shown in the analysis, a significant Lag is the Lag 4, which compares the same season
each year, i.e. T1 in 2005 with T1 in 2006 and 2007, etc.
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APPENDIX B.3
B.3.1 ROSARITO MONITORING STATION
Figure B.3.1 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2, CO and PM10, respectively. In practically all pollutants, a strong positive component in
the Lag number 4 can be seen; this means a persistent behavior of each pollutant in the same season
each year.
Remembering that there are four defined seasons per year, the graphs show that there is a tendency
to remain with a similar magnitude of concentrations in the same season each year, this is known as
seasonality. Looking at Lag 2 with a significant negative magnitude, we conclude that there are
significant changes in the concentrations of pollutants every two seasons. PM10 has a different
behavior, because although there is a positive component at Lag 4, this is not very large and is similar
to the Lag 3, we can assume that there are similar behaviors every 3 and every 4 seasons, but this
consideration is not conclusive because PM10 shows some lack of data in 2007.
FIGURE B.3.1 AUTOCORRELATION FUNCTION FOR ROSARITO STATION.
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APPENDIX B.3
B.3.2 PLAYAS MONITORING STATION
Figure B.3.2 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2 and CO, respectively. In practically all pollutants, a strong positive component in the Lag
number 4 can be seen; this means a persistent behavior of each pollutant in the same season each
year.
Remembering that there are four defined seasons per year, the graphs show that there is a tendency
to remain with a similar magnitude of concentrations in the same season each year, this is known as
seasonality. Looking at Lag 2 with a significant negative magnitude, we conclude that there are
significant changes in the concentrations of pollutants every two seasons.
FIGURE B.3.2 AUTOCORRELATION FUNCTION FOR PLAYAS STATION.
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APPENDIX B.3
B.3.3 LA MESA MONITORING STATION
Figure B.3.3 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2, CO, PM10 and SO2, respectively. In practically all pollutants, a strong positive component
in the Lag number 4 can be seen; this means a persistent behavior of each pollutant in the same
season each year.
Remembering that there are four defined seasons per year, the graphs show that there is a tendency
to remain with a similar magnitude of concentrations in the same season each year, this is known as
seasonality. Looking at Lag 2 with a significant negative magnitude, we conclude that there are
significant changes in the concentrations of pollutants every two seasons. PM10 and SO2 have a
different behavior, in PM10 autocorrelation values are relatively small and do not allow us to ensure
a behavioral trend, this coupled with the fact that there is a significant amount of missing data in the
three years, which may not allow us to conclude properly. In SO2 the correlation coefficient data are
also small, the important positive value in Lag 1 indicates that all seasons would show a certain
similarity to each other, but not too strong to suggest that there are no changes throughout the year;
by the size of the correlation coefficients, we are closer to say that there is no link between all
seasons.
FIGURE B.3.3 AUTOCORRELATION FUNCTION FOR LA MESA STATION.
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APPENDIX B.3
B.3.4 ITT MONITORING STATION
Figure B.3.4 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2 and CO, respectively. In practically all pollutants, a strong positive component in the Lag
number 4 can be seen; this means a persistent behavior of each pollutant in the same season each
year.
Remembering that there are four defined seasons per year, the graphs show that there is a tendency
to remain with a similar magnitude of concentrations in the same season each year, this is known as
seasonality. Looking at Lag 2 with a significant negative magnitude, we conclude that there are
significant changes in the concentrations of pollutants every two seasons.
FIGURE B.3.4 AUTOCORRELATION FUNCTION FOR ITT STATION.
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APPENDIX B.3
B.3.5 TECATE MONITORING STATION
Figure B.3.5 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2 and CO, respectively. In practically all pollutants, the correlation coefficients are relatively
small and with positive and negative random distributions, so it is not easy to conclude the existence
of trends or seasonality.
In O3, there is a small trend, since the correlation coefficients start mostly positive decreasing and
end negative approaching to zero, this means a reduction in the concentrations magnitude from one
year to another. For the rest of the concentrations, it can be concluded that there is not a strong
relationship between the different seasons, almost all of them behave randomly, with a certain
similarity every two seasons, seeing the Lag 2; and every two years, given the behavior of the Lag
number 8.
FIGURE B.3.5 AUTOCORRELATION FUNCTION FOR TECATE STATION.
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APPENDIX B.3
B.3.6 ITM MONITORING STATION
Figure B.3.6 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2 and CO, respectively. In practically all pollutants, a strong positive component in the Lag
number 4 can be seen; this means a persistent behavior of each pollutant in the same season each
year.
Remembering that there are four defined seasons per year, the graphs show that there is a tendency
to remain with a similar magnitude of concentrations in the same season each year, this is known as
seasonality. Looking at Lag 2 with a significant negative magnitude, we conclude that there are
significant changes in the concentrations of pollutants every two seasons.
FIGURE B.3.6 AUTOCORRELATION FUNCTION FOR ITM STATION.
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APPENDIX B.3
B.3.7 COBACH MONITORING STATION
Figure B.3.7 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2 and CO, respectively. In practically all pollutants, a strong positive component in the Lag
number 4 can be seen; this means a persistent behavior of each pollutant in the same season each
year.
Remembering that there are four defined seasons per year, the graphs show that there is a tendency
to remain with a similar magnitude of concentrations in the same season each year, this is known as
seasonality. Looking at Lag 2 with a significant negative magnitude, we conclude that there are
significant changes in the concentrations of pollutants every two seasons.
FIGURE B.3.7 AUTOCORRELATION FUNCTION FOR COBACH STATION.
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APPENDIX B.3
B.3.8 ESTACIÓN DE MONITOREO UABC
Figure B.3.8 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2, CO, PM10, PM2.5 and SO2. In practically all pollutants we can see a strong positive
component in Lag number 4, this means the existence of a persistent behavior of each pollutant in
the same season each year.
Remembering that there are four defined seasons per year, the graphs show that there is a tendency
to remain with a similar magnitude of concentrations in the same season each year, this is known as
seasonality. Looking at Lag 2 with a significant negative magnitude, we conclude that there are
significant changes in the concentrations of pollutants every two seasons. SO2 has a different
behavior, although there is a positive component in Lag 4, this is not very large and is similar to Lag
1, while the Lag 2 shows a significant negative value, we cannot conclude that there is a clear trend of
behavior, but some random behavior between seasons.
FIGURE B.3.8 AUTOCORRELATION FUNCTION FOR UABC STATION.
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APPENDIX B.3
B.3.9 ESTACIÓN DE MONITOREO CAMPESTRE
Figure B.3.9 shows the results of applying the autocorrelation function for the pollutants O3, NOx,
NO, NO2 and CO, respectively. There is a lack of data for all pollutants in some seasons, making it
difficult to draw conclusions on behavior.
Ozone is the pollutant that has more complete data, it is clearly seen in the typical seasonal behavior
of the other monitoring stations, that is, each year presents a similar behavior in each season, which
can be concluded seen the size of Lag 4. In the rest of the pollutants, the analysis does not reach the
Lag 8, because of the lack of data in certain seasons; T2, T3 and T4 in 2007 are missing for all
pollutants except O3 which does have data on T3. Additionally there are no data on T1 and T2 for
any of the nitrogen oxides, yet, with the data it can be seen with a relatively high correlation
coefficient, compared to the others, a seasonal behavior that occurs every 4 seasons, as in previous
cases. Looking at Lag 2 with a significant negative magnitude, we conclude that there are significant
changes in the concentrations of pollutants every two seasons.
FIGURE B.3.9 AUTOCORRELATION FUNCTION FOR CAMPESTRE STATION.
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APPENDIX B.3
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