Redundancy Analysis for the Mexico City Air Monitoring
Network: The Case of SO2
Paper # 1172
Monica Jaimes, Roberto Muñoz, Cristina Ortuño, Armando Retama, Rafael Ramos, Victor H.
Paramo
Secretaría del Medio Ambiente, Gobierno del Distrito Federal, Agricultura No. 21, Del. Miguel
Hidalgo, 11800, Mexico, D. F.
ABSTRACT
The Mexico City ambient air monitoring network (Red Automática de Monitoreo Atmosférico RAMA) is one of the largest air monitoring networks in Latin America. Currently, RAMA comprises
36 remote automatic stations distributed in the Mexico City Metropolitan Area (MCMA). From these,
26 remote stations are set for the continuous determination of sulfur dioxide (SO2), among other criteria
pollutants and meteorological parameters.
RAMA is a surveillance type monitoring network for the determination of hourly concentrations of
criteria pollutants in order to observe the compliance of the national health standards related to air
quality. For the purpose of timely public information dissemination, data generated in the remote
stations is employed in the determination of the air quality index. It is recognized that the monitoring
network uses a larger number of monitors than it is required. This translates in an extra expense for the
operation and maintenance of such equipment.
In this paper, data from the 26 original network SO2 monitors was employed in order to determine
redundancy in the measurements of this pollutant. Three complementary methods were applied for the
identification of site redundancy in the urban atmospheric monitoring network: the nominative method
proposed by Hwang and Chan (1997), cluster analysis, and descriptive statistics.
Site redundancy will be used to re-design the SO2 RAMA network with an optimized representative
sub-network that will warranty the adequate surveillance of the air quality in relation to this pollutant.
The immediate application of the results will be an important economical saving for the Mexico City
Government by reducing the costs of the network operation.
INTRODUCTION
Surveillance air quality monitoring has the purpose to determine dangerous pollutant concentrations for
the protection of public health. Additionally, air monitoring is the mean by which the effectiveness of
the air quality improvement programs can be assessed. (Haas 1992; Langstaff J. et al,1987 ;TrujilloVentura et al, 1991; WHO, 1980).
For these purposes, a representative and effective air monitoring network is required for the continuous
surveillance of an urban area. Pollutant measurements and source characterization assist in the
definition of the specific problems that must be addressed for the abatement of the air pollution (Noll et
al, 1983; Miller and Sager, 1994).
In the early 1980s the MCMA presented very high concentrations of SO2. As consequence, 26 monitors
were integrated in the RAMA for the continuous determination of this pollutant (SEDUE, 1987;
Martínez and Romieu, 1997). The original distribution of the remote stations is shown in Map 1.
The industrialized part of the city in the North houses 15, SO2 monitors; other 4 are located in the
commercial and residential Central zone, and the remaining 7 are in the residential South side of the
city.
Map 1. Original SO2 Monitoring Network (26 stations) in Mexico City.
At present, actual concentrations of SO2, very seldom exceed the corresponding health standard (130
ppb as a 24-hours average), due to the effectiveness of several actions contained in the Programs for the
Improvement of the Air Quality in the MCMA. For example, since 1992 heavy oil is not consumed in
the city; large industries have converted processes to natural gas; the maximum content of sulfur in
diesel has been lowered to 0.05% by volume (GDF, 2002).
Hourly SO2 concentrations have been reduced 78% in the period 1990 – 1995. Thereafter, the field
measurements of this pollutant show a gradual-descending trend. These changes suggest that the
implemented actions have succeeded in controlling the SO2 emissions. However, at the same time, it
has been found that some monitoring stations present redundancy since their measurements are similar
(GDF, 2002).
The purpose of this paper is to identify the presence of redundancy in the SO2 measurements conducted
in the MCMA air monitoring network and to design a sub-network with representative monitoring sites
that warranty the warranty the adequate surveillance of the air quality in relation to this pollutant.
METHODS
Redundancy Methods
The redundancy analysis was conducted using three different methods: Hwang & Chan, Descriptive
Statistics, and Cluster Analysis; each one assesses a particular aspect of the SO2 behavior.
Hwang & Chan Method (1997): This method uses a backward approach to eliminate remote
stations, one by one, until a representative sub-network is obtained in terms of similarity
(similar arithmetic average change), equivalence (number of days in a year in which the
difference of the average concentrations are within a selected range), and spatial variation
(relative error of the variance of the sub-network in relation to the original network within a
selected range) (Hwang and Chan, 1997).
Descriptive Statistics: This method analyzes the median and interquartile range for both the
original network and the sub-network. Redundancy is determined by grouping monitoring
stations with similar values. The interpretation of the analysis results and selection of redundant
stations requires experience from the analyst.
Multivariate Analysis (Clusters Analysis): This method identifies and groups the monitoring
stations that register concentrations of similar magnitudes (Canberra metrics), or stations with
similar trends (correlation metrics) regardless the magnitudes. The final selection of redundant
stations requires experience from the analyst. (Kachigan S., 1991; Flynn C., 2000
Air Quality Data for Redundancy Methods
SO2 data used in for the redundancy approach were the 1-hour averages, 24-hours mobile averages and
annual averages from the 26 remote monitoring stations during the period 1998 – 2000. Only data that
fulfills the completeness criterion of 75% of valid data in a year is considered in the analysis.
Sub-network proposal
For the integration of the sub-network monitoring stations three criteria were taken into consideration:
 Results from the redundancy analysis
 Monitoring stations performance (data completeness  75%)
 Case episodes registered by station and boundary representativeness
Sub-network assessment
Sub-network selection is assessed against the original network by comparisons of the
corresponding air pollution index, trends analysis and spatial distribution analysis.
Air Quality Index Analysis:
The Air Quality Metropolitan Index (Indice Metropolitano de la Calidad del Aire - IMECA) is
determined on an hourly basis for the criteria pollutants in a similar fashion as the former US Pollution
Standard Index. For the purpose of the IMECA dissemination, the MCMA is divided in the 5 zones
shown in Map 1. The IMECA for a zone and hour is the highest of the IMECAs in that zone and hour
for all the criteria pollutants.
A representative SO2 sub-network must replicate the IMECA generated by the original network.
The analysis conducted compared the IMECA generated with the original network and the sub-network
for the period 1998 – 2002.
Trend Analysis: This method identifies the existence of trends in the pollutant concentrations in
bothe the original and the sub-network and determines the percentage of change through time. The subnetwork must show the same historic behavior than the original network. The annual average is
obtained and its trend is determined using the Mann-Kendall method (Gilbert, 1992).
Spatial Representativeness: The analysis determines the spatial distribution of the pollutant
concentrations and its relation with the emission sources. Therefore, the sub-network’s spatial pattern
must show a similar pattern as the original network. Spatial representativeness was conducted in the
annual average for the period 1998 – 2002.
RESULTS
SO2 Monitoring Sub-network Proposal
1. The redundancy analysis determines as non-redundant the following 12 stations: LAG, HAN,
MER, LLA, XAL, ATI, EAC, TAC, TLA, VAL, CES and TAX.
Additional considerations
2. PED station must be part of the sub-network since it is the only one in the SW zone.
3. Stations SAG, LVI and TLI will be part of the sub-network since they reported frequent episodes in
the North of the MCMA.
4. TAH station will be part of the sub-network since it represents boundary and transport conditions.
Therefore, a sub-network of 17 SO2 monitoring stations was integrated. The North part of the city
continues to house the larger number of analyzers since the higher concentrations and number of
episodes occur in this part of the city. Map 2 shows the distribution of stations in the sub-network.
Map 2. SO2 sub-network in the MCMA
Monitoring sub-network assessment
Air Quality Index Analysis: The following results were obtained by calculating the IMECA for the subnetwork and compare it with the IMECA of the original network: Central zone loses 1% of the IMECA
information. The Southwest zone loses 20% of the information because it only houses one monitoring
station (PED). To overcome this, PLA station in the Southwest can be incorporated. However, it is
important to note that this part of the city has registered the lowest SO2 concentrations and has not
presented excedances to the corresponding health standard. In the other zones of the city there are no
lose of representativeness in the generation of the IMECA, therefore the proposed sub-network
preserves the importance of the source emissions in the industrialized North part of the city.
Table 2. Percentage of hours per year that a sub-network station determined
the highest pollution index IMECA
Central Zone
Northeast Zone
Northwest Zone
Southeast
SW
Year
HAN
LAG
MER
LVI
LLA
SAG
XAL
ATI
EAC
TAC
TLA
TLI
VAL
CES
TAH
TAX
PED
1998
38
34
28
6
30
16
48
13
9
17
28
22
11
43
6
51
100
1999
7
33
60
18
25
10
47
5
7
24
18
22
24
37
19
44
100
2000
37
19
43
12
18
7
63
4
6
33
40
1
16
57
15
28
97
2001
45
37
18
9
4
22
65
11
3
24
54
8
0.2
37
4
59
88
2002
20
42
38
28
8
5
60
9
14
13
42
16
6
50
12
38
95
Total hours per station
12,866
14,506
16,382
6,351
7,466
5,212
24,795
3,642
3,327
9,725
15,898
6,112
5,120
19,663
4,987
19,174
42,068
% of information lost
1
0
0
0
20
Trends Analysis: SO2 has shown a decreasing trend in the years of study 1986 – 2002. The slope and
percentage of decrement of the sub-network and the original network are very similar (see Table 3).
This implies that the SO2 sub-network will continue to characterize the historic trends in comparison
with the original network.
Table 3. Mann-Kendall Test for 1986 a 2002.
Original
Sub-network
Network
Trend
Slope
Percent of decrement (1986 – 2002)
Yes
-0.00232
96%
yes
-0.00248
99%
Spatial Representativeness: A spatial distribution of the SO2 averages was conducted for the original
network and the sub-network in the period 1998-2002. Maps 3a and 3b show the comparison for the
case year 2002. It can be noticed that there are not noticeable changes in the spatial distribution and
that the zones with high or low concentrations are clearly identified in both cases. This means that the
proposed sub-network will continue to characterize the spatial distribution of the pollutant with respect
to the original network. Same conclusions were obtained comparing the other case years.
.
Map 3. Spatial representativeness of the SO2 annual concentrations for the year 2002
a) Original Network (26 remote stations)
b) Proposed sub-network (17 remote stations)
CONCLUSIONS
The redundancy analysis using three complementary methods and other criteria determined the
possibility to define a representative sub-network of 17 remote stations, in which:
1. The SO2 pollution index IMECA will continue being registered in the same fashion by the
representative monitoring sub-network.
2. SO2 trends will continue to be characterized.
3. Spatial representativeness will continue to characterize the influence of emission sources and
transport processes.
The proposed 17 remote stations sub-network will continue to represent the SO2 behavior as the
original network. Important cost savings in operation and maintenance are expected when continue the
surveillance of the air quality in the MCMA with the sub-network.
AKNOWLEDGMENTS
Authors wish to thanks Guadalupe Granados, Rocío Carmona, Samuel López, Vicente Pérez, Antonio
Valdés, Eduardo Preciado, Laura Ocampo, Felipe Rosales, Angel Sánchez and Marco Morales for their
assistance in the development the this case study.
REFERENCES
DDF, “Programa para Mejorar la Calidad del Aire en el Valle de México 1995-2000”. Gobierno del
Distrito Federal. 1996.
Flynn Colleen, T., Multivariate Statistics, http://trochim.human.cornell.edu/tutorial/flynn/multivar.htm,
(accessed March 2000).
GDF, “Informe del Estado de la Calidad del Aire y Tendencias 2001". Gobierno del Distrito Federal.
2002.
Gilbert, R.O., “Statistical Methods for Environmental Pollution Monitoring”, Van Nostrand Reinhold.
1987.
Haas, Timothy C., “Redesigning Continental-Scale Monitoring Networks”, Atmospheric Environment,
Vol. 26A(18): 3323-3333. 1992
Hwang J.S. and Chan Ch.Ch., “Redundant Measurements of Urban Air Monitoring Networks in Air
Quality Reporting”, Journal Air & Waste Management Association, 47:614-619, 1997.
Kachigan S. K., “Multivariate Statistical Analysis”, 2ª. Edition, Radius Press, New York, 1991.
Langstaff J., Seigneur C., Liu M.K., Behar J., and McElroy J.L., “Design of an Optimum Air
Monitoring Network for Exposure Assessments”. Atmospheric Environment Vol. 21 (6): 1393-1410,
1987.
Martínez A. P. y Romieu I., “Introducción al Monitoreo Atmosférico”, OPS, GTZ y DDF, México,
1997.
Miller, A. Y Sager, T. “Site redundancy in Urban Ozone Monitoring”, Journal Air & Waste
Management Association, 44:1097-1102, 1994.
Noll, K. Y Mitsutomi, S., “Design Methodology for Optimum Dosage Air Monitoring Site Selection”,
Atmospheric Environment, 17(12):2583-2590, 1983.
SEDUE-FUNDACIÓNFR IEDICH EBERT, (1987). "Primer Seminario Internacional sobre
administración de la Calidad del Aire", Metepec, Puebla, 2 al 6 de noviembre.
S-Plus, Hand Book, 2000.
Statistica, HandBook, StatSoft, 1994.
Trujillo-Ventura, A. and Ellis J. H. ,“Multiobjetive Air Pollution Monitoring Network Design”,
Atmospheric Environment, Vol. 25A(2): 469-479, 1991.
U. S. Environmental Protection Agency. (1998). “Quality Assurance Handbook for Air Pollution
Measurement Systems. Volume II: Part 1. Office of Air Quality Planning and Standards”. Research
Triangle Park, NC 27711. EPA-454/R-98-044. August 1998.
World Healt Organization, “Analyzing and Interpreting Air Monitoring Data”, WHO Offset
Publication No. 51, Geneva, 1980.
KEY WORDS
Mexico City Metropolitan Area
Monitoring Network Redesign
Redundancy
Sulfur dioxide
Sub-network