Revista Mexicana de Ingeniería Biomédica · INGENIERÍA BIOMÉDICA Vol. XXVI, Núm. 1 Marzo 2005 pp 22 - 36 Correlation Study of the Association of PM10 with the Main Respiratory

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Artículo:

Correlation Study of the Association ofPM10 with the Main RespiratoryDiseases in the Populations of Mexicali,Baja California and Imperial County,California

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ARTÍCULO DE INVESTIGACIÓN ORIGINALSOMIB

REVISTA MEXICANA DEINGENIERÍA BIOMÉDICA

Vol. XXVI, Núm. 1Marzo 2005

pp 22 - 36

Correlation Study of the Association of PM10

with the Main Respiratory Diseases in the

Populations of Mexicali, Baja California and

Imperial County, California

Marco Antonio ReynaCarranza,* Margarito Quintero

Núñez,* Kimberly Collins**

* Instituto de Ingeniería de laUniversidad Autónoma de BajaCalifornia.Mexicali, Baja California, México.

** California Center for Border andRegional Economic Studies(CCBRES), San Diego StateUniversity-I.V. Campus,Calexico, CA

Correspondence:Marco Antonio Reyna Carranza.Blvd. Benito Juárez y Calle de la Normal,Unidad Universitaria s/n, Col.Insurgentes-Este, C.P. 21280, Mexicali,Baja California, México,E-mail: [email protected],Fax:+52 686 566-4150.PO BOX Address: PMB 10241, 317Heffernan Ave. Calexico, CA. 92231-2739

ABSTRACT

Mexicali is the third most contaminated city in Mexico and thesecond city in the country with the highest level of particles smallerthan 10 micrometers in diameter (PM10). It continuously fails tomeet annual air quality standards (50 µg/m3). Mexicali ’s U.S. neigh-bor, the Imperial County, has the same geographical character-istics, and is in non-attainment for PM10, ozone, and carbon mon-oxide. One health condition associated with high PM10 levels isasthma and both Imperial Valley and Mexicali have the highestchild asthma indices for their respective states of California andBaja California. PM10 is primarily formed naturally, from dust andpollen, but anthropogenic sources are gaining importance dueto an accelerated growth in this bi-national basin. Thermoelec-tric plants recently constructed are an example of this acceler-ated growth and their construction prompts bi-national concernin a basin with an already deteriorated air quality. There are alimited number of published documents focusing on Mexicali ’sand Imperial County ’s health or environment. Only a few articlescontain data on PM10 levels or respiratory illnesses (includingasthma), and even less jointly report on health conditions andthe environment. This lack of information has produced flawedand unproductive environmental policy in the U.S.-Mexican bor-der region. A model based on the Poisson regression was used tostudy the impact PM10 may have had on primary respiratory dis-eases given the temperature and relative humidity in the Imperi-al Valley-Mexicali basin. The years 1997 to 2000 were measuredin series by seasons. Air quality data, specifically PM10 data, weresupplied by a regional network of air monitoring stations in theImperial Valley and Mexicali. Health data, including asthma,acute respiratory infections, bronchitis, and pneumonia were ob-tained from Baja California public health centers and the Cali-fornia Office of Statewide Health Planning and Development,Healthcare Quality and Analysis Division.

Key Words:

PM10, Poisson, Mexicali-Imperial airshed, respiratory diseases, asthma,thermoelectric plants, air pollution.

23Reyna MA et al. Association of PM10 with the Main Respiratory Diseases in Mexicali and Imperial County

edigraphic.comINTRODUCTION

The first evidence suggesting that air pollution wasrelated to illness and death was recorded in En-gland in 1880. It was in this year that a large, dark

cloud appeared in the London sky and subsequent-ly nearly 2,200 people died. Almost three-quartersof a century later, a similar situation occurred inthe same city, in which 4,000 people died. Thisevent is well known as the London Fog of 19521.

RESUMEN

Mexicali es la tercera ciudad más contaminada de la RepúblicaMexicana en términos generales y la segunda más contaminadapor micropartículas respirables menores a los diez micrómetrosde diámetro (PM10). Los estándares de protección al ambiente(50 µg/m3), son rebasados de manera continua durante práctica-mente todo el año. La ciudad de Mexicali es vecina del CondadoImperial en los Estados Unidos de Norteamérica, y por lo tanto ade-más de compartir las mismas condiciones geográficas, tambiéncomparten los mismos problemas de contaminación existentesen la cuenca atmosférica binacional. Los principales contaminan-tes que no cumplen con los estándares de calidad del aire son:PM10, Ozono (O

3), y Monóxido de Carbono (CO). Una de las prin-

cipales enfermedades respiratorias asociadas a los altos nivelesde PM10 es el asma. Mexicali y el Valle Imperial presentan losmayores índices de niños asmáticos dentro de sus respectivos es-tados (i.e., Baja California y California respectivamente). En estaregión fronteriza, el PM10 tiene su origen principalmente por fuen-tes naturales, tales como polvo y polen, aunque las fuentes antro-pogénicas han ido en aumento dado el crecimiento tan acelera-do de la cuenca binacional. Las plantas termoeléctr icasrecientemente construidas son un ejemplo de este crecimientoacelerado y su operación genera preocupación en la poblaciónbinacional por las posibles consecuencias que esto conllevará enla ya de por sí deteriorada calidad del aire de la cuenca atmos-férica fronteriza. Existen algunos documentos publicados en la li-teratura que tocan temas sobre salud o sobre contaminación deMexicali o del Condado de Imperial, pero un número muy reduci-do de estos documentos reportan datos y mucho menos tratanproblemas de contaminación y efectos en la salud de manerabinacional. Esta carencia de información se traduce en diseñode políticas y lineamientos muy pobres en materia de salud y con-taminación ambiental sobre esta tan descuidada zona fronterizaentre EUA y México. Se propone un modelo basado en la regre-sión de Poisson para estudiar el posible impacto que el PM10 pu-diera tener sobre las principales enfermedades respiratorias, da-das las condiciones de temperatura y humedad relativa de lacuenca atmosférica entre Mexicali e Imperial. Fueron analizadasseries de tiempo para los años de 1997 al 2000. Las series de tiem-po de la calidad del aire, específicamente de PM10, fueron pro-porcionadas por una red de estaciones de monitoreo ambientalestablecida en Mexicali e Imperial. Los datos de salud, los cualesincluyeron: asma, infecciones respiratorias agudas, bronquitis yneumonía se obtuvieron de los centros de salud públicos de Mexi-cali y de la California Office of Statewide Health Planning and

Development, Healthcare Quality and Analysis Division.

Palabras clave:

PM10, Poisson, cuenca atmosférica Mexicali-Imperial, enfermedadesrespiratorias, asma, plantas termoeléctricas, contaminación del aire.


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eral plants. The finest particles, those that mea-sure less than 2.5 microns in diameter, comemainly from the emissions from fossil fuel com-bustion motors (such as those found in automo-biles) and from nitrates and sulphates in the formof aerosols.

In order to prevent the harmful impacts of sus-pended particles on the health of the commu-nity, there are regulations that establish maxi-mum permissible amounts in the concentrationof particles in the air. Both the Mexican and U.S.regulatory standards for PM10 should not exceed150 µg/m3 (micrograms per cubic meter) for the24 hour average and 50 µg/m3 for the annualaverage.

BACKGROUND

The municipality of Mexicali, the capital of thestate of Baja California, Mexico, is located inthe farthest northwest state of the countr y. Itshares the border with the Imperial County, CAin the United States. Imperial County is alsoknown as the Imperial Valley. Mexicali and Im-perial Valley share the same physical, geograph-ical, and meteorological conditions. These com-mon characteristics provide a situation in whichcontamination or pollution is shared through abinational airshed and watershed. For our pur-poses here, we will analyze the impacts of airpollution on the airshed. As seen in Table 1 bothMexicali and the Imperial Valley is non-attain-ment for PM10.

After these and numerous other comparableevents took place, scientists became more in-volved in the study of air pollution and its effectson health. In the twenty-first century, the relation-ship between breathable suspended particles andmortality and morbidity has been further elucidat-ed. The issue of environmental health has beengaining importance as a subject of profound in-terest throughout the world2-8.

The most serious threats to the respiratory sys-tem are particulates that are suspended in theatmosphere for long periods of time at the heightat which most people breathe. Perhaps the mostdangerous of these materials is Total SuspendedParticles (TSP). The particles in this classificationthat have the greatest impact on the respiratorysystem range from 10 microns in diameter (PM10)to smaller than 2.5 microns in diameter (PM2.5).The particles that measure between five and 10microns do not reach the deepest tissues of therespiratory system because they are caught bymucous in the nose, larynx, pharynx, and tra-chea. Even though these particles are caughtbefore reaching the depths of the respiratorysystem, they can still harmfully affect the bodyby inducing sneezing. If the sneezing becomessevere, it could lead to chronic illnesses suchas otitis depending on the composition of theparticles.

The smaller the diameter of the particle, themore harmful it becomes since these smaller par-ticles are able to penetrate deeper areas of therespiratory track containing the internal tissues. Twoof these areas, the bronchus and the alveolus, areparticularly dangerous as reactions may take placethat could bring about serious complications. Theseinclude: premature death, grave respiratory symp-toms, irritation of the eyes, ears and nose, an in-creased risk of developing lung cancer, moreacute asthma cases, and a worsening of cardio-vascular diseases9.

The suspended particles originate in either nat-ural or anthropogenic sources. When they mixwith the air, they become more complex. Forlarger particles with diameters between 2.5 and10 microns, the principal sources of anthropo-genic origination are factory smoke (such ascement kilns), dust from lime and other miner-als, agricultural practices such as emissions fromburning of agricultural residues and pesticide use,and dust from construction sites and unpavedstreets. The majority of the naturally producedparticles in this group come from pollen of sev-

Table 1. Border Counties and Municipalitiesthat do not meet U.S. and Mexico nationalair quality standards.

U.S. Counties PM10 SO2

CO O3

El Paso, TX X X XDoña Ana, NM X XImperial, CA X X XSan Diego, CA X XDouglas, AZ X XNogales, AZ X

Mex. MunicipalitiesTijuana, BC XMexicali, BC X X XSan Luis Río Colorado, SON XNogales, SON XAgua Prieta, SON X XCiudad Juárez, CHIH X X X


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Air pollution has been blamed for the high ratesof asthma, bronchitis, pneumonia, and allergies inthis region, especially among children between theages of 1-14 years10,11. In fact, the rates of respira-tory diseases have been worsening in both valleysas the concentrations of suspended micro parti-cles continue to increase12. The Secretariat of theEnvironment and Natural Resources (SEMARNAT,formally known as SEMARNAP (Secretaría de Me-dio Ambiente, Recursos Naturales y Pesca)) in itssecond report classified Mexicali´s air quality asseriously dangerous (see Figure 1). Mexicali is thesecond city in the country after Mexico City thatcontinuously exceeds the permitted annual aver-ages of PM10 (150 µg/m3)13. Additionally, the twonewly constructed thermoelectric plants in Mexi-cali, La Rosita and La Termoeléctrica Mexicali, havecreated apprehensions that the air quality in thetwo valleys will diminish once the plants begin op-erations14,15.

Interesting data on concentrations of variouspollutants and respiratory diseases are found inMexicali ’s emissions inventory16, and in a pre-sentation by IseSalud11, at the Forum on Envi-ronmental Health at the California-Baja Califor-nia Border that was held in Mexicali in Apri l200217. The graphs of these reports show a sys-tematic increase in respiratory diseases from1998 to 2001.

In 1991, an article published in the Journal ofEnvironmental Research18 detailed the results ofan analysis on the dust from Mexicali and from aseries of experiments on rats to determine the de-gree of toxicity of the inorganic particles. This anal-ysis found that samples of Mexicali dust are a mix-ture of 75% potassium aluminum silicates (illite)and 20% silica. The rats exposed to this dust de-veloped a multifocal interstitial lung disease as-

sociated with deposits of the aluminum silicates.Mexicali dust induced biological activities andlung changes similar to those of asbestos and sil-ica, suggesting that this material could be an eti-ologic agent of pulmonary fibrosis in exposed in-dividuals.

In 1997, the U.S. Environmental ProtectionAgency, Region IX conducted a study to deter-mine more precisely the sources and types ofmicro fractionated materials suspended in theair in the Imperial-Mexicali valleys19. The samplesdetermined that annual averages for PM10 wereexceeded on both sides of the border. On aver-age, the greatest component of PM10 was geo-logical material (50-60 percent). Soot was sec-ond, comprising approximately 25 percent oftotal PM10. Sulfates, nitrates, ammonium, alumi-num, silica, titanium, calcium, and iron were oth-er components, comprising between one andfour percent of total PM10.

The California Center for Border and RegionalEconomic Studies (CCBRES) located at San DiegoState University-Imperial Valley Campus reportedthat the PM10 levels in Imperial Valley were 80,414tons per year and 71,323 tons per year for the cityof Mexicali, according to the California Air Resourc-es Board20. It should be noted that the data forMexicali is somewhat misleading as it only accountsfor the urban areas and does not include data forthe whole valley.

The problem of air contamination in both val-leys has created an alarming reaction with in-creasing cases of asthma and cardio respirato-r y i l lnesses. These health condit ions are soalarming that the former president of Mexico,Ernesto Zedillo, initiated the Program to Improvethe Quality of the Air of Mexicali 2000-200521.The primary goal of this program intends is to

Figure 1. Average annual PM10 levels inmajor Mexican cities, 1997.

160140120100

80604020

0

µg

/m3

México Guadalajara Monterrey Cd. Juárez Tijuana Mexicali


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sustraídode-m.e.d.i.g.r.a.p.h.i.ccihpargidemedodabor

findings were that Imperial County had the high-est rate for asthma hospitalizations for childrenunder 14 years in the entire state of California.Additionally, from 1983 to 1994, there was a 59percent increase in the hospitalization rates inImperial County, compared to a reduction ofnine percent in hospitalizations in San DiegoCounty.

Each of the above studies sets the base of ref-erence for the correlation between PM10 and res-piratory illnesses in the Imperial-Mexicali valley. It isrecommended though those additional bination-al studies are conducted to further understand therelationships between respiratory illnesses and airpollution.

MATERIALS AND METHODS

In order to organize the data used in this study, aclinical and environmental database for Mexi-

decrease the amount of PM10 in the region by30 percent through the implementation of sev-eral programs. These include: inspection and vig-ilant enforcement of industrial and service es-tablishments; vehicle inspections and sanctionson high polluters; urban transportation manage-ment; reforestation; increase the number ofpaved streets; landuse planning; and promo-tion of an environmental education program.The environmental education program is linkedwith binational border agreements and designedto encourage civic participation in environmen-tal matters. To date, implementation of the pro-gram has been slow as financial support was notincluded.

Finally, an important study that quantifies thenumber of asthma cases in the Imperial Valleywas conducted by English et al. in 199822. Thisstudy did a comparative analysis of asthma ratesin Imperial and San Diego counties. A few of the

Figure 2. Hospitalizations from 1997 to 2000 in Mexicali for: (a) Asthma, (b) Pneumo-nia, (c) Acute Respiratory Infections (ARI); Atmospheric variables: (d) temperature, (e)relative humidity (f) PM-10 (thick dotted line indicates the 50 µg/m3 annual air qualitystandard, and the solid thick line indicates the 150 µg/m3 standard for a 24 hourperiod).

Total morbidity of asthma300

200

100

01997 1998 1999 2000

(a)

Total morbidity of pneumonia150

100

50

01997 1998 1999 2000

(b)

Total morbidity of ARI10 000

5 000

01997 1998 1999 2000

(c)

Temp (°C)40

30

20

101997 1998 1999 2000

(d)

Rh (°%)100

50

01997 1998 1999 2000

(e)

PM10 (µg/m )3

400350300250200150100

500

1997 1998 1999 2000

(F)

Years


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cali and the Imperial Valley was generated. Thetime series included weekly data for the years1997 to 2000. The original air pollution series wasin 24 hour intervals but only weekly hospitaliza-tion data were available. For Mexicali, the clini-cal database contained time series data of asth-ma, pneumonia and acute respiratory illnesses(ARI). These data were collected from the clini-cal files supplied by Official Public Health Cen-ters in Mexicali. For the Imperial Valley, the clini-cal database included t ime ser ies data ofasthma, bronchitis, and pneumonia. This datawas supplied by the State of California Health andHuman Services Agency, Office of StatewideHealth Planning and Development23.

The environmental database included thesame time series for temperature, relative hu-midity, and PM10. All of the environmental datawas supplied by the California Air ResourcesBoard (CARB)24, which manages six air monitor-ing stations in the city of Mexicali and eight sta-tions in Imperial Valley. Both the environmentaltime series and clinical data vary between Mex-icali and the Imperial Valley as seen in Figures 2and 3.

The collection of the health data in the regionwas the most complex and difficult task in thisproject. In Mexicali, the data was available but notdigitally. Therefore, all of the data had to be input-ted into the database. Furthermore, the respirato-

Figure 3. Hospitalizations from 1997 to 2000 in Imperial Valley for: (a) asthma, (b) pneu-monia, (c) bronchitis; Atmospheric variables for: (d) temperature, (e) relative humidity(f) PM-10 (thick dotted line shows the 50 µg/m3 annual air quality standard, and thesolid thick line shows the 150 µg/m3 standard for a 24 hour period).

Total morbidity of asthma20

15

10

5

01997 1998 1999 2000

(a)

1997 1998 1999 2000

1997 1998 1999 2000

1997 1998 1999 20001997 1998 1999 2000

1997 1998 1999 2000

Total morbidity of pneumonia

Total morbidity of bronchitis

Temp (°C)

Rh (°%)

PM10 (µg/m )3

Years

(b)

(c)

(e)

(F)

(d)

40

30

20

10

15

10

5

0

80

60

40

20

0

30

20

10

0

400

300

200

100

0


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ry diagnoses by doctors in Mexico are grouped intoone category called acute respiratory illnesses. Thisincludes acute bronchitis, acute bronchiolitis, acuterinofaringitis, acute faringitis due to other specificorganisms, acute infections of the superior respira-tory pathways, and acute amigdalitis due to otherspecific organisms. This diagnosis process made itdifficult to differentiate between respiratory illness-es in Mexicali.

In the Imperial Valley, the data was limited byonly including hospitalizations in the region. Clear-ly, these data do not collect all of the cases in thearea. Though it is not fully understood, it is estimat-ed that many individuals in the region either self-medicate or visit doctors in Mexicali for monetaryor cultural reasons. It could therefore be assumed

that the number of asthma cases in the region ismuch higher than reported in the data collected.These factors (along with other possible factors)show that this analysis is limited in developing anabsolute understanding of the impacts of PM10 onrespiratory illnesses.

RESULTS

ANALYSIS BY YEARS

The PM10 annual standard of 50 µg/m3 was ex-ceeded every year in Mexicali but only in 2000in the Imperial Valley (see Figure 2f and 3f). Theannual average of 50 µg/m3 was exceededeach year under study in both valleys, with the

Figure 4. Annual PM10 Averages for Mexicali (a) and Imperial County (b).

101.2 92

112.3131.7

2000199919981997Time in years

140

120

100

80

60

40

20

0

µg/m

3

(a)100

80

60

40

20

0

(b)

2000199919981997Time in years

60.6 49.8 64

93.7

µg/m

3

Figure 5. Number of Weeks that Exceeded the 150 µg/m3 Air Quality Standards in Mexico and the United States andthe PM10 averages for those weeks in the year. (a) Mexicali (b) Imperial.

250

200

150

100

50

0

(a)

µg/m3

3

2000199919981997

Time in years

245.8

8

173.6

12

199.9

16

201.2

2000199919981997

Time in years

µg/m3

300

250

200

150

100

50

0

(b)

290.7

0 00 00 0 3


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Imperial Valley and Mexicali for the years studied.This data makes it possible to determine the yearswith the highest and lowest hospitalization rates,to establish a visual association between the at-mospheric and meteorological variables, and tocreate a correlation between the Imperial andMexicali valleys. For example, it was possible todetermine that on average Mexicali had morecases of asthma than bronchitis. Furthermore, ifindices were calculated as a function of the pop-ulation, Mexicali has a higher index of asthma thanImperial. Imperial and Mexicali have the same in-dices in the case of pneumonia. For the case ofbronchitis it is not possible to compare indices ofImperial with indices of Mexicali because bron-chitis is an illness that is included in the ARI groupin Mexicali.

exception of 1998 in which the level in ImperialValley reached the maximum permissible level(see Figure 4).

Figure 5 displays the number of weeks that thePM10 standard was exceeded for the 24 hournorm of 150 µg/m3 and provides the averagePM10 levels for each week of the exceedances.In Mexicali (see Figure 5(a)), the PM10 standardwas exceeded more than 3 weeks in 1997, andcontinued to rise each year thereafter. In Imperi-al County, the PM10 24 hour standard was ex-ceeded for only three weeks in the year 2000 (seeFigure 5(b)).

Figure 6 provides the number of hospitalizationsfor each selected pathology under study by yearin Mexicali and Imperial Valley. Figure 7 gives thetotal hospitalizations for each pathology in both

Figure 6. (a) Number of hospitalizations due to asthma, pneumonia and ARI in Mexicali, 1997 to 2000. (b) Numberof hospitalizations from asthma, pneumonia, and bronchitis in Imperial County, 1997 to 2000.

Figure 7. Total of hospitalizations occurs during 1997 to 2000 in (a) Mexicali and (b) Imperial.

17,643 6,400

705,222

Asthma Pneumonia ARI

800 000700 000600 000500 000400 000300 000200 000100 000

0

(a)

Hospitalizationsnumber

1 400

1 200

1 000

800

600

400

200

0

(b)

Hospitalizationsnumber 1,184

Asthma Pneumonia Bronchitis

1,223

989

Hospitalizations

number

188,443

4,458

200 000

180 000

160 000

140 000

120 000

100 000

80 000

60 000

40 000

20 000

0

a)

175,676180,532

160,571

1,922 1,452

4,2014,2551,524

4,729

2000199919981997

1,502

Asthma Pneumonia ARI Asthma Pneumonia Bronchitis

Hospitalizations

number

400

350

300

250

200

150

100

50

02000199919981997

271

369

308

253

194

339

303

274 274

298

213

300

b)


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SEASONAL ANALYSIS

Table 2 displays the average atmospheric vari-ables by season for the respective years ana-lyzed. Table 3 provides the hospitalization ratesfor asthma and pneumonia in both areas alongwith bronchitis in the Imperial Valley and acuterespiratory illness in Mexicali by season of yearsunder analysis.

These tables make it possible to determinewhich season of the year had the highest hospi-talization rate and also what effect different at-mospheric variables had on those hospitaliza-tions. Fewer hospitalizations occurred during thesummer season, in which the highest tempera-ture and the lowest PM10 value were recorded.It is important to note that the average atmo-spheric-meteorological variables and clinical

Table 2. Averaged Temperature, Relative Humidity, and Particulate Matter Smaller than 10 microns by season of theyear, 1997 to 2000.

Mexicali Atmospheric VariablesSeason Average Temperature Average Relative Humidity Average PM10

1997 1998 1999 2000 1997 1998 1999 2000 1997 1998 1999 2000

Winter 9.57* 13.77 14.78 14.99 27.25* 55.65 41.66 44.35 57.39* 92.82 148.21 139.88Spring 24.81 20.87 20.73 23.50 30.03 39.28 36.41 33.41 98.51 67.44 79.59 121.43Summer 32.21 32.98 31.22 32.11 30.30 38.87 36.85 38.28 90.50 71.39 74.82 103.77Fall 24.23 23.52 25.92 22.77 41.51 37.93 35.91 42.11 121.46 131.1 136.95 149.05

Imperial County Atmospheric VariablesSeason Average Temperature Average Relative Humidity Average PM10

1997 1998 1999 2000 1997 1998 1999 2000 1997 1998 1999 2000

Winter 12.11* 17.82 18.64 19.64 43.69* 88.9 65.05 49.8 33.64* 56.38 85.25 110.2Spring 30.4 25.55 26.05 29.17 56.09 68.85 43.07 38.96 75.87 67.79 73.21 132.03Summer 41.49 41.38 40.04 41.38 58.96 66.99 39.51 40.97 86.95 60.37 89.38 134.61Fall 32.73 32.37 35.53 31.22 73.81 67.46 42.31 51.59 95.4 73.09 76.85 105.72

*Winter 1997 data is only for 2 months as the monitoring stations began operation in January 1997.

Table 3. Number of Hospitalizations for Asthma, Pneumonia, Acute Respiratory Illnesses/Bronchitis by Season, 1997to 2000.

MexicaliSeason Asthma Pneumonia Acute Respiratory Illness

1997 1998 1999 2000 1997 1998 1999 2000 1997 1998 1999 2000

Winter 712* 1,262 1,165 1,376 417* 590 793 1,019 43,113* 59,391 62,863 67,820Spring 1,005 1,223 1,120 901 481 482 406 225 40,355 40,721 40,242 37,207Summer 646 700 629 702 126 232 154 131 24,913 25,855 23,630 27,076Fall 1,815 1,233 1,361 1,290 243 280 278 293 50,590 45,818 41,760 46,643

Imperial ValleySeason Asthma Pneumonia Bronchitis

1997 1998 1999 2000 1997 1998 1999 2000 1997 1998 1999 2000

Winter 56* 76 130 101 82* 164 135 210 79* 88 100 106Spring 61 83 51 51 106 61 74 56 85 54 74 56Summer 19 32 46 35 26 19 18 22 34 24 29 39Fall 83 109 112 87 85 43 46 34 64 45 52 40

*For the 1997 winter season, data from December 1996 is not available as data series began in January 1997.


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Figure 8. Dispersion graphs depicting the association between atmo-spheric variables with clinical variables in Mexicali (a) and Imperial (b).

Temp Asthmavs

(a)

r = -0.48300

200

100

00 20 40 0 20 40 0 20 40

0 50 1000 50 1000 50 100

r = 0.11 r = 0.23 r = 0.30

r = -0.79

Temp Pneumoniavs Temps ARIvs

Rh Asthmavs Rh Pneumoniavs Rh ARIvs

PM10 Asthmavs PM10 Pneumoniavs PM10 ARIvs

0 200 4000 200 4000 200 400

300

200

100

0

300

200

100

0

150

100

50

0

150

100

50

0

150

100

50

0

10 000

5 000

0

10 000

5 000

0

10 000

5 000

0

r = 0.35 r = 0.15 r = 0.39

r = -0.66

Temp. Asthmavs

(b)

r = -0.5320

15

10

5

010 20 40

0 50 1000 50 1000 50 100

r = -0.69

Temp. Pneumoniavs Temp. Bronchitisvs

Rh Asthmavs Rh Pneumoniavs Rh Bronchitisvs

PM10 Asthmavs PM10 Pneumoniavs PM10 Bronchitisvs

0 200 4000 200 4000 200 400

20

15

10

5

0

20

15

10

5

0

20

15

10

5

0

20

15

10

5

0

30

20

10

0

30

20

10

0

30

20

10

0

r = -0.57

30

r = 0.11 r = 0.12

r = -0.1r = -0.1

10 20 4030 10 20 4030

20

15

10

5

0

r = 0.24

r = 0.045


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Table 4. R2 for Mexicali for asthma, pneumonia, and acuterespiratory diseases each correlated with temperature,relative humidity and PM10.

Asthma Model R2

Temp, RH, PM10 0.80Temp, PM10 0.75Temp, RH 0.65RH, PM10 0.43

Pneumonia Model R2


ARI Model R2


Table 5. R2 for Imperial Valley for asthma, pneumonia,and bronchitis each correlated with temperature, rela-tive humidity, and PM10.

Asthma Model R2


Pneumonia Model R2


Bronchitis Model R2


variables that correspond to winter 1997 consistof data for the months of January and Februaryas the monitoring stations only began to oper-ate adequately in January 1997. This explains whythe average values for those dates are lower incomparison with later years.

Figure 8 is the dispersion graphs and r valueof the simple regression analysis of the atmo-spheric and clinical variables. Note that when the

dispersion graphs show a linear tendency behav-ior, the r values are larger. An example of thisphenomenon is the correlation of temperaturewith each illness. Another interesting observationis that the weakest correlation is seen betweenrelative humidity and asthma with a value of 0.11,followed by PM10 and pneumonia with a valueof 0.15, and finally, relative humidity and pneu-monia with 0.23.

Table 6. Estimated percentage of hospitalizations per year in Mexicali calculated from the models for: (a) Asthma,(b) Pneumonia, and (c) Acute Respiratory Infections (ARI).

(a) Asthma Model Year

1997 1998 1999 2000 1997-2000Model (T, RH, PM10) 4,089 4,084 4,282 4,326 16,781†Expected 15.4% 14.4% 17.7% 19.9% 16.9%(PM10 = 0) Less Less Less Less Less

(b) Pneumonia Model Year

1997 1998 1999 2000 1997-2000Model (T, RH, PM10) 1,389 1,465 1,438 1,393 5,685†Expected (PM10 = 0) 2.3% 2.1% 2.7% 3.2% 2.6%

Less Less Less Less Less

(c) ARI Model Year

1997 1998 1999 2000 1997-2000Model (T, RH, PM10) 166,141 171,047 174,971 177,727 689,888†Expected (PM10 = 0) 14.5% 13.7% 16.9% 19% 16.12%


†Expected Hospitalizations for each model assume the value for PM10 is equal to zero µg/m3 of air.


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The weekly number of illnesses (i.e., asthma, ARI,pneumonia and bronchitis), can be considered asa Poisson random variable, due to this is a discretevariable that takes only positive integer values andits range should not be very large.

Poisson regressions that combined the temper-ature, relative humidity, and PM10 variables wereutilized to obtain different models for asthma, pneu-monia, and ARI in Mexicali. For the Imperial Valley,the same models were used but bronchitis wasanalyzed instead of ARI.

The R2 value for the predictions obtainedthrough multiple regression analysis for both val-leys are seen in Tables 4 and 5. The highest R2

value is found in the model that included all threevariables—temperature, relative humidity, andPM10. The R2 value in the correlation of asthmawith temperature, relative humidity, and PM10 forMexicali was 0.80. In the Imperial Valley for thesame correlation, it was 0.70. In the asthma mod-el, the R2 value remained high with the removalof the relative humidity variable—indicating thatrelative humidity is not an important variable inthe analysis of respiratory illnesses and PM10 lev-els. On the other hand, PM10 and temperaturehave stronger connections to the levels of asth-ma in the region as seen in R2 values of 0.65 and0.43 for Mexicali. In Imperial Valley, the R2 was 0.55in the correlation of temperature and relative hu-midity with asthma and 0.31 for the correlation of

PM10, relative humidity, and asthma. The other R2

values for pneumonia, ARI, and bronchitis are seenin Tables 4 and 5.

With these Poisson regression models, a num-ber of experiments were conducted to determinethe number of hospitalizations that would occurfor asthma, ARI, and pneumonia in Mexicali if thelevels of PM10 were zero during the same timeperiod. Similar calculations were also done forthe Imperial Valley. The results of these calcula-tions are provided in Tables 6 and 7 for Mexicaliand Imperial Valley, respectively. As seen in Ta-ble 6, in 1997 the number of asthma cases wouldhave been 15.4 percent less in Mexicali if thePM10 levels had been zero. In the Imperial Val-ley, as seen in Table 7, the number of asthmahospitalizations in 1997 would have been 4.16percent less if the PM10 values were zero. Thedifferences in percentages between the Imperi-al Valley and Mexicali models show possible com-plications with the data.

DISCUSSION AND CONCLUSIONS

Through the exploratory analysis of the time seriesof the atmospheric variables (temperature, rela-tive humidity, and PM10) and the clinical variables(asthma, pneumonia and bronchitis/acute respi-ratory infections (ARI)), it was possible to determinethat the average annual air quality standard of 50

Table 7. Expected number of hospitalizations in Imperial: (a) asthma model, (b) pneumonia model, and (c) bronchi-tis model.

(a) Asthma Model Year

1997 1998 1999 2000 1997-2000Model (T, RH, PM10) 338 355 347 345 1,385†Expected (PM10 = 0) 4.16% 4.0% 4.6% 5.43% 4.5%


(b) Pneumonia Model Year



(c) Bronchitis Model Year



† Expected hospitalizations for each model assume the value for PM10 is equal to zero µg/m3 of air.


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µg/m3 for PM10 was exceeded during the four yearsstudied. It was also possible to locate the numberof weeks per year that exceeded the 150 µg/m3

standard for PM10. Furthermore, it was possible todetermine the number of hospitalizations for asth-ma, pneumonia, and bronchitis/acute respiratoryinfections that occur during the four seasons of theyear (fall, winter, spring, and summer).

With the initial analysis, it can be concludedthat temperature and PM10 levels have a great-er impact on the clinical variables than relativehumidity. However, such a conclusion cannot besupported quantitatively. For this reason, disper-sion graphs and correlation coefficients were ob-tained. Under the Colton lineament, it was de-termined that the degree of correlation amongvariables was highest for the temperature vari-able. The coefficient between temperature andARI was the strongest, with a value -0.79 (consid-ered excellent); this was followed by pneumo-nia, with a good correlation of -0.66; and finally,asthma, with a moderate correlation of -0.48.Relative humidity showed a relatively low corre-lation of 0.39 for ARI, and null correlations be-tween pneumonia and asthma of 0.23 and 0.11,respectively. The correlations of PM10 on asth-ma, pneumonia, and ARI/bronchitis were 0.35,0.15, and 0.39, respectively, indicating a mod-erate effect on asthma and ARI/bronchitis and anull effect on pneumonia.

With this new information, it was possible to de-termine:

• That temperature has an important impact onthe three pathologies studied

• That relative humidity does not have a linear af-fect on pneumonia, asthma, ARI in Mexicali andon bronchitis in Imperial

• That PM10 has an affect only on asthma andARI.

Determining the type of distribution for eachvariable provides insight into the type of modelor models that are helpful a regression analysis.In this case, as often happens with this type ofdata, the majority of temporary series that wereanalyzed followed a Poisson distribution, andtherefore, these were used to derive the modelsfor each illness.

In the case of asthma, the model lowered itsconfidence from 0.86 to 0.70 when relative hu-midity was not considered, from 0.86 percent to0.65 when PM10 was omitted, and from 0.86 to

0.43 when temperature was disregarded. Theseomissions show that temperature and PM10have a greater influence on asthma than rela-tive humidity.

For the pneumonia model, the R2 value stayednearly the same, 0.67 to 0.67 for Mexicali, and 0.68to 0.58 when relative humidity and then PM10 wereeliminated. However, it went down dramaticallyfrom 0.67 to 0.22 for Mexicali and from 0.68 to0.20 for Imperial when temperature was omitted.This demonstrates that pneumonia is more sensi-tive to temperature, and less sensitive to relativehumidity and PM10.

The ARI/bronchitis model maintained the R2 val-ue of 0.83 when relative humidity was omitted,and decreased slightly from 0.83 to 0.79 whenPM10 was omitted. When temperature was omit-ted in this model, the R2 value decreased from0.83 percent to 0.37 percent. This indicates thatARI/bronchitis is highly sensitive to temperature, lesssensitive to PM10, and has no relationship to rela-tive humidity.

Once these three models were derived, theywere used to determine the number of hospi-talizations from 1997 to 2000 that could havebeen avoided if the PM10 levels could havebeen zero. For the asthma model, it was foundthat there could have been 15.4 percent lesshospitalizations in 1997, 14.4 percent less in1998, 17.7 percent less in 1999, and 19.9 per-cent less in 2000. The pneumonia model con-cluded that there could have been 2.3 per-cent less hospitalizations in 1997, 2.1 percentless in 1998, 2.7 percent less in 1999, and 3.2percent less in 2000. In the ARI/bronchitis mod-el, there could have been 14.5 percent lesshospitalizations in 1997, 13.7 percent less in1998, 16.9 percent less in 1999, and 19 per-cent less in 2000.

When making an informed decision relatedto the environment or at least one with the low-est degree of error possible, the availability andreliability of information is critical. Intuition, forexample, that air pollution in a city causes ill-ness in people is not enough. It needs to bedemonstrated but this is not a trivial task as thereare many different aspects from anthropogen-ic to natural that enter the equation. Therefore,it is necessary to try and understand all of theaspects that are part of this equation and rankthem appropr iately. Clear ly, envi ronmentalconcerns are one of the most important ele-ments in the equation and need to be includ-


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ed in the decision making process. This projectprovides additional information about the envi-ronment in the Imperial-Mexicali valleys that canhelp decision makers in the region take actionsto improve the quality of air in the binationalairshed.

This work considered only PM10 as a factorof pollution, and temperature and relative hu-midity as meteorological variables. It studiedthe how meteoro log ica l va r iab les impactPM10. Also, it analyzed how PM10 influencesasthma, pneumonia, and ARI/bronchitis. The ex-periments demonstrated that temperature hasa greater impact on the respiratory diseasesstudied than relative humidity and PM10. Thisis shown in the fact that not all of the respira-tory diseases responded in the same level whencorrelated with PM10 and the meteorologicalvariables.

The Poisson regression models predicted thatasthma, pneumonia, and ARI are much moresensitive to temperature than to PM10, and havea very low relationship to relative humidity. Pneu-monia, for example, shows null sensitivity to PM10.On the other hand, asthma shows more sensitiv-ity to PM10 than ARI. The fact that pneumoniaand ARI/bronchitis are not as sensitive to PM10does not mean that other atmospheric pollut-ants like carbon monoxide (CO) or ozone (O

3)

are not harmful. In fact, the standards of theseelements are also frequently exceeded in Mexi-cali. The confidence percentage in the predic-tions could have been improved if these ele-ments were included when the models weredesigned.

These results were derived from the analysisof cl inical and atmospheric-meteorologicaldata done on a weekly basis. However, it is use-ful to conduct studies with more in-depth anal-ysis or for a shorter period of time, such as for a24 hour period or by geographical areas in thecities. These types of tests are being exploredand call for further work within this line of re-search.

ACKNOWLEDGMENTS

This research work has been partially sponsored bythe Universidad Autónoma de Baja California(UABC), Southwest Center for Environmental Re-search and Policy (SCERP) and by the FulbrightScholarship Board (FSB) from the Border Academ-ic Program, COMEXUS.

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Documents

Revista Mexicana de Ingeniería Biomédica · INGENIERÍA BIOMÉDICA Vol. XXVI, Núm. 1 Marzo 2005 pp 22 - 36 Correlation Study of the Association of PM10 with the Main Respiratory