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Artículo científico / Research paper=

A= nálisis espacial de PM₁₀ en el aire y su composición de metales c= on relación a factores ambientales alrededor de centros de educaci&oacu= te;n preescolar en Cuenca=

Spatial analysis of PM10 in the air and its metals composition with relation to environmental factors surrounding preschool educational centers in Cuenca

<= span lang=3DES-EC style=3D'font-size:11.0pt;line-height:107%;mso-ansi-language:E= S-EC'>Ronny Zegarra¹, Susana Andrade¹, Mayra Parra¹, Danilo Mejía², Claudia Rodas³

^1 =
;Departamento= de Biociencias, Universidad de Cuenca, Av. 12 de Abril, Cuenca, Ecuador.<= /o:p>

^2 =
;Facultad de = Ciencias Químicas, Centro de Estudios Ambientales (CEA), Universidad de Cuenc= a, Av. 12 de Abril, Cuenca, Ecuador.

^3 =
;Facultad de = Medicina, Universidad del Azuay Av. 24 de Mayo, Cuenca, Ecuador.

Autor para la corresponde= ncia. ronny.zegarra @ucuenca.edu.= ec

Fecha de recepción: 6 de enero de 2020 - Fecha de aceptación: 29 de abril de 2020

RESUMEN

La contaminación del aire por Material Particulado (PM), como consecuen= cia del aumento del tráfico, es de gran preocupación para la salud pública en las zonas urbanas. El PM llama mucho la atención, primero porque, debido a su tamaño micrométrico, las partículas pueden penetrar en los pulmones e impedir el intercambio = de gases, y en segundo lugar debido a su composición química, incluida la presencia de metales pesados, que pueden dañar los siste= mas biológicos. A pesar de sus efectos perjudiciales para toda la población, los niños son más vulnerables debido a su organismo en desarrollo. Se sabe que las concentraciones de PM pueden verse influenciadas por las características de la calle, la meteorología y la presencia de vegetación circundante, que constituyen factores ambientales relacionados con la generación, dispersión y deposición de PM. Debido a la necesidad de reduc= ir la exposición de la población a este tipo de contaminación, el presente estudio analizó la correlaci&oacut= e;n de los factores ambientales de generación, dispersión y deposición en función de la información secundaria disponible con la concentración de PM₁₀ y metales (plomo, cadmio, cobre, y zinc) en muestras de aire recolectadas en 21 centros de educación inicial en la ciudad de Cuenca. Los resultados obtenidos indican bajas relaciones entre contaminantes y factores ambientales, la presencia de alta variabilidad en las concentraciones de contaminantes y niveles de PM₁₀ que a menudo exceden los límites establec= idos por la Organización Mundial de la Salud y la legislación nacional.

Palabras clave: Material particulado, metales pesados, efectos en la salud, factores ambientales, Cuenca.

ABSTRACT<= /o:p>

Particulate matter (PM) air polluti= on, as a consequence of increasing traffic, is of high concern for public healt= h in urban areas. The PM draws major attention, first because by its micrometric sizes can particles penetrate the lungs and hinder the gas exchange, and secondly due to its chemical composition including the presence of heavy metals, which can damage the biological systems. Despite its detrimental effects for the entire population, kids are more vulnerable because of its developing stage. It is known that PM concentrations can be influenced by street characteristics, meteorology, and the presence of surrounding vegetation, which constitute environmental factors related to the generatio= n, dispersion, and deposition of PM. Due to the necessity of reducing populati= on exposure to this type of pollution, the present study analyzed the correlat= ion of environmental factors of generation, dispersion and deposition based on = the available secondary information with the concentration of PM₁₀, = and metals (lead, cadmium, copper, and zinc) in air samples collected at 21 ini= tial education centers in the city of Cuenca. The obtained results indicate low relationships between pollutants and environmental factors, the presence of high variability in pollutant concentrations, and PM₁₀ levels of= ten exceeding the limits established by the World Health Organization and natio= nal law.

Keywords: Particulate matter, heavy metals, health effects, environmental factors, Cuenca.

1. INTRODUCCIÓN

Las elevadas concentraciones de material particulado (PM) en las áreas urbanas causadas por el incremento del tráfico vehicular, urbanizació= n e industrialización constituyen un problema ambiental de riesgo para la salud pública. La atención sobre este contaminante se debe a = sus

fracciones de PM₁₀ y PM2.5, que hacen referencia a partículas con diámetros iguales y menores a 10 y 2.5 µm, respectivamente. Estas partículas tienen una alta capacida= d de entrada al aparato respiratorio y circulatorio, además de contener e= n su superficie diferentes elementos orgánicos e inorgánicos, incl= uyendo metales pesados (Li, Qian, & Wang, 201= 3). Los metales pueden ingresar a los pulmones por inhalación del PM, ll= egar hasta el punto de intercambio del oxígeno, viajar a través de= la sangre y ser absorbidos hacia los riñones, hígado y otros tej= idos como el cerebro y corazón (Flora, F= lora, & Saxena, 2006; Fortoul et al., 2015). Las elevadas concentraciones de metales en partículas finas est&aacu= te;n asociadas a marcadores de enfermedades cardiovasculares como: altos niveles= de inflamación y reducidos niveles de células endoteliales progenitoras, indicando una reducción en la capacidad de reparación endotelial (Niu et al= ., 2013). Los efectos respiratorios generados por el estrés oxidativo de los metales sobre las células pulmonares resultan en inflamación y daño de los tejidos (Cakmak et al., 2014). Su llegada al hígado por el torrente sanguíneo altera el parénquima hepático, causando hepatitis crónica, fibrosis, cirrosis, fallo del hígado y mue= rte (Laing et al., 2010; Tarantino, 2013). Además, los metales generan reacciones autoinmunes y promueven la carcinogénesis por medio del daño al ADN y activación = de diferentes vías de respuesta que conllevan a la progresión de tumores (Fortoul et al., 2015).

La exposición a = las elevadas concentraciones del PM y su contenido de metales está relacionada con la presencia factores ambientales que aportan a la generación, dispersión y deposición de dicho contamina= nte. En las áreas urbanas, las emisiones del tráfico vehicular representan la principal fuente de generación de PM, especialmente de aquellos vehículos que usan combustible diésel, cuyas partículas finas y ultrafinas son de mayor toxicidad por su alto contenido de metales (Mohankumar & Senthilkumar, 2017). Sin embargo, estos también provienen de otras fuentes como son: el desgaste de neumáticos, frenos y vías (L= enschow, 2001; World Health Organization, 2013). Datos del inventario de emisiones atmosféricas nacional del Reino Unido indican que las partículas del uso de frenos, neumáticos y superficie de las vías constituyen el 60% y 73% (en masa), respectivamente, de las emisiones de PM₁₀ y PM_2.5provenientes del transporte= en ese país (Monks et al., 2013= ). De esta manera, se ha determinado que cerca del 75 al 90% de los metales encontrados en las fracciones de PM₁₀ corresponden a plomo (Pb), cadmio (Cd), cobre (Cu) y zinc (Zn) (Mohan= raj, Azeez, & Priscilla, 2004).

El Pb y Cd presentan una alta toxicidad incluso a bajos niveles y afectan directamente al sistema nervioso central produciendo efectos neurotóxicos y desordenes neurodegenerativos, incluyendo Parkinson y Alzheimer (Jiang, Yao, Zhu, Wang, & Ji, 2007; Rehman, Fatima, Waheed, & Akash, 2018). La exposició= ;n aguda y crónica al Pb y Cd causa daños severos al aparato respiratorio por la alteración de las membranas mucosas y la generación de edema pulmonar, congestión capilar, ampliación del intersticio pulmonar y áreas de atelectasia (Fortoul et al., 2015). Por otro l= ado, metales como el cobre y zinc son metales traza que actúan en funcion= es catalíticas, estructurales y reguladoras de importancia para los procesos biológicos (Bost et al<= /i>., 2016; Roohani, Hurrell, Kelishadi, & Schulin, 2013). El exceso d= e Zn puede promover síndromes metabólicos como: sobrepeso, hipertensión y dislipidemia en ratas Wistar (Singh, 2012). Altas concentraciones de Cu conducen a la generación de especies oxidativas altamente reactivas, modulando la proliferación de las células cancerígenas= y el crecimiento de tumores, (Fortoul et = al., 2015). Además, su biodisponibilidad facilita la producci&oacu= te;n de ATP, como energía para la rápida proliferación de l= as células cancerosas (Ishida, Andreux, Poitry-Yamate, Auwerx, & Hanahan, 2013).

La cercanía a vías de alto tráfico representa un factor de mayor exposición (U.S. Environmental Protection Agency, 2001). Karner, Eisinger, & Niemeier (2010), indican que las altas concentracion= es de los contaminantes atmosféricos presentan una disminución gradual desde el borde de la vía hasta en 570 metros, con una reducción del 50% en los primeros 150 m. Kliengchuay, Meeyai, Worakhunpiset, & Tantrakarnapa (2018) indican que la velocidad del viento, precipitación, temperatura del aire, temperatu= ra y humedad del suelo, son factores meteorológicos que están correlacionados negativamente con la presencia de PM. La presencia de vegetación es otro factor que incide tanto en la deposición c= omo dispersión del contaminante. Esto debido a que afecta los flujos de = aire en los cañones urbanos con influencia en el transporte y diluci&oacu= te;n de los contaminantes (Janhäll, 2015)<= /span>. La vegetación es considerada una herramienta eficiente en la mitigación de la contaminación del aire debido a la mayor probabilidad de deposición de partículas en su superficie, en comparación con las superficies lisas urbanas (Roupsard et al., 2013). Hoy en día, existe una fácil identificación de la presencia y características= de la vegetación por medio de índices basados en informaci&oacut= e;n satelital, como el Índice de Vegetación de Diferencia Normali= zada, que ha sido relacionado con la provisión de una mejor calidad del ambiente (Buyadi, Mohd, & Misni, 2015)= .

En la ciudad de Cuenca, se ha determinado que el tráfico vehicular constituye la principal fuente de PM. Astudillo-Alemán et al. (2015) indica que el PM₁₀ de la zona urbana= de Cuenca en Ecuador, tiene como parte de su composición: aniones (, = NO<= m:ctrlPr>3<= m:ctrlPr>-<= m:ctrlPr>, ) y metales pesados (Cr, Fe, Ni, Zn, Cu y Mn), los cuales ya han sido asociados con daño genotóx= ico de tipo I en células epiteliales alveolares a 24 horas de exposición. = Si bien la afecci&oacut= e;n por metales tiene efectos negativos en toda la población, estos difi= eren correspondiente a la edad de la población expuesta, existiendo una m= ayor frecuencia de casos de asma, reducción del desarrollo pulmonar, rini= tis alérgica e infecciones respiratorias en niños (Fortoul et al., 2015). Esto se de= be a que este grupo se encuentra en una etapa de rápido crecimiento y sus sistemas de detoxificación, inmunitario y termorreguladores est&aacu= te;n en desarrollo (Perera, 2018). Además de que, por su crecimiento, los niños tienen una mayor frecuencia respiratoria respecto a los adultos y requieren de tres a cuatro veces más la cantidad de comida por peso corporal, presentando mayor exposición a los contaminantes en el aire y comida (World Health Organization, 2006).

Relacionar las interacciones del ambiente con las concentraciones de= PM y su contenido de metales es de importancia para medir el impacto de su exposición en futuros análisis de riesgo de la salud humana.<= /span> De esta manera, el presente estudio tiene por objetivo analizar la relación de las concentraciones de metales pesados en el aire en centros de educación preescolar con: i) las variables meteorológicas presentes durante el monitoreo:

precipitación, temperatura, humedad relativa y radiación solar global y difusa; ii) entorno de monitoreo y iii) información secundaria relacionada a factores ambientales de generación (tipo de vía, material de calzada, capacidad vehicular), dispersión (ancho de la vía) y deposición = de material particulado sobre la vegetación (Norma= lized Difference Vegetation Index, NDVI) en una ciudad andina.<= /p>

2.            MATERIALES Y MÉTODOS

El presente estudio es de tipo exploratorio transversal y utiliza datos de material particulado (PM₁₀) y met= ales pesados (Pb, Cd, Cu y Zn) en el aire de centros educativos preescolares, monitoreados como parte del proyecto CEDIA-CEPRA de la Universidad del Azua= y, Universidad Indoamérica y Universidad de Cuenca (Rodas Espinoza et al., 2017), con título: “Análisis de metales pesados en PM₁₀ y su relación con enfermedades alérgicas en niños de 3 a 5 años en centros de educación inicial de las áreas urba= na y periurbana de Cuenca”.

2.1.        Área de estudio

El área de estudio comprende la zona urbana= y periurbana del cantón Cuenca, con una superficie aproximada de 73 km= ², la cual representa 2.34% de la municipalidad y alberga una población cercana a 600,000 habitantes. La ciudad está localizada en la provin= cia del Azuay, al centro-sur del Ecuador con latitud 2°53´12” S= ur y longitud 79°09´ Este. El área urbana es ubicada en un valle del callejón interandino a 2530 metros sobre el nivel del mar, y presenta una clasificación climática Semihúmedo Mesotérmico con temperaturas desde los 18 a 22°C (Ilustre Municipalidad de Cuenca, 2015). = Los registros más bajos de temperatura se presentan en los meses de juni= o a septiembre, mientras que los más calientes entre los meses de diciem= bre a febrero. Pese a no presentar variaciones significativas a lo largo del año, la amplitud térmica diaria puede ser de hasta 9°C (Cordero & Guillén, 2013). Se presenta una humedad relativa de 65% en promedio anual, sin variaciones considerables a lo largo del año. La nubosidad sobre el área = de estudio es elevada, siendo esta entre seis y siete oct= as, con mayor presencia entre febrero y mayo. Sin embargo, la radiación solar varía desde los 3.92 a 5.06 KWh m^-2 con los valores más altos en noviembre y diciembre, mientras que los más bajo= s se presentan en junio (Cordero & Guillén, 2013).

La movilidad en la ciudad de Cuenca está dominada por el modo motorizado (69%), siendo los viajes dentro de la ciuda= d en vehículos privados y transporte público el 32% y 31%, respectivamente. Los viajes desde la periferia con mayor circulación= en las vías externas cuentan un promedio de 80,000 viajes en vehí= ;culo privado y 26,000 pasajeros movilizados por transporte público hacia = el área urbana (GAD Municipal, 2014).

2.2.        Puntos de monitoreo de material particulado y muestreo de metales pesad= os

Se seleccionaron 21 centros de educación preescolar como puntos de monitoreo, a partir un total de 141 distribuidos = en la ciudad de Cuenca y su periferia. La ubicación geográfica de estos puntos, tras una selección aleatoria dentro del límite = de la ciudad y considerando una distribución ecuánime en base a = su tipología de financiamiento (público o particular), se indica= n en la Figura 1.

=

Se obtuvieron concentraciones de los metales pesad= os (Pb, Cd, Cu y Zn) en el aire a través del análisis de una mue= stra de material particulado PM₁₀, que comprende partículas de tamaño iguales y menores a 10 µm, obtenida de un monitoreo pre= vio en cada punto. Dicho monitoreo fue de 24 horas y u= na sola vez en cada punto durante el periodo febrero - abril 2018. El equi= po de monitoreo (Partisol i2000) fue ubicado en las áreas de juego de los niños, algunas de las cuales se encontr= aban cubiertas parcial o totalmente, con el fin de determinar la exposició= ;n de los preescolares a la contaminación en su entorno de mayor activi= dad. El análisis de composición de los metales pesados fue realiza= do por el laboratorio certificado de la Facultad de Ciencia y Tecnologí= a de la Universidad del Azuay, utilizando el método de voltametría (Farghaly & Ghandour, 2005), con límites de detección de 5.20, 5.36, 5.07 y 4.20 pg/g para los metales Pb, Cd, Cu y Zn, respectivament= e.

2.3.        Análisis de concentraciones de metales con variables meteorológicas, entorno de monitoreo y factores ambientales

Todos los procesos de análisis espacial relacionados a factores ambientales fueron realizados utilizando el softwar= e de acceso abierto QGIS v3.4 Madeira.

2.4.        Determinación de variables meteorológicas y entorno de monitoreo

Se determinaron los valores de las variables meteorológicas: precipitación, humedad relativa, temperatura y radiación solar global y difusa, correspondientes al día de monitoreo en cada punto mediante una interpolación de = tipo distancia inversa ponderada (Inverse Distance = Weighting, IDW) (Wong, Yuan, & Perlin,= 2004), utilizando los datos de sumatoria para la precipitación y promedio p= ara el resto de variables. Estos datos fueron registrados por las estaciones UP= S y Yanuncay, de la red de monitoreo de la Universidad Politécnica Salesiana. De forma adicional, el equipo Partisol i2000 registró también la temperatura obtenida en el punto ex= acto de monitoreo durante las 24 horas. El entorno de monitoreo hace referencia = a la cobertura presente en el espacio de monitoreo, debido a que este se realizó en las áreas de juego de los niños. De esta manera, este se clasificó en abierto, abierto con barreras fí= sicas, abierto con barreras vegetales y cerrado.

2.5.        Determinación de factores ambientales de generación, dispersión y deposición de metales pesados en el aire

Se determinó el número de segmentos y pixeles correspondientes a cada categoría establecida en tres capas de información geográfica correspondiente a vías, transporte público,= e índice de vegetación de diferencia normalizada (NDVI), asocia= dos a factores ambientales de generación, dispersión y filtración de material particulado. La Tabla 1 indica el núme= ro de segmentos extraídos para cada categoría en una circunferen= cia de 150 m alrededor de los puntos de monitoreo (buffer) debido a su rango de influencia en las concentraciones de PM. El número de líneas = de buses está dado por la sumatoria de las líneas, mientras que = el NDVI, corresponde al número de pixeles por categoría dentro d= el buffer indicado. Los rangos de valores para las categorías numéricas fueron determinados considerando el método de ruptu= ras naturales (Jenks, 1967).=

La información sobre vías y transporte público fue proporcionada por la Dirección Municipal de Tránsito y corresponde a un registro vial y de fiscalización de transporte público utilizados en el plan de movilidad de la ciudad para el año 2015. El NDVI, como indicador de la presencia de vegetació= ;n en el área urbana, se obtuvo tras la conversión y reemplazo de las bandas de infrarrojo cercano (Near InfraRED, NIR) y rojo (Red, R) de una imagen satelital Landsat 8 en la ecuación (1) (Landsat 8 Data Users Handbook, 201= 8). La imagen satelital corresponde a la fecha 20/9/2017, sin presencia de nubosidad sobre el área de estudio.

=

NDVI =3D NIR – R/NIR + R

(1)

Posteriorme= nte se realizó una base de datos, utilizando el software Microsoft Excel, con los resultados del monitoreo: concentración de PM₁₀ (µg/m³); concentraciones metales pesados (pg/g); entorno de monitoreo (1. Abierto, 2. Abierto con barrera física, 3. Abierto con barrera vegetal y 4. Cerrado); condiciones meteorológica= s: precipitación total (mm), temperatura del aire (°C), humedad relativa (%) y radiación solar global y difusa (W/m²); factores ambientales de generación: tipo de vía, material de calzada, capacidad vehicular y número de líneas de transporte público; factores ambientales de dispersión: ancho de la vía; y factores ambientales de filtración: NDVI.

2.6.        Análisis estadístico

Se realizó un análisis de las variables con herramientas de estadística descriptiva, incluyendo pruebas de normalidad gráficas y estadísticas, utilizando el test Shapiro-Wilk (α=3D0,05). También se determinó el coeficiente de correlación entre las concentraciones de material particulado y meta= les pesados como variables dependientes de las condiciones de monitoreo y facto= res ambientales. Todos los análisis se realizaron utilizando el software estadístico R 3.2.2 y su interfaz R Studio 0.99.484.

3.      =       RESULTADOS Y DISCUSIÓN

3.1.      = Resultados del monitoreo de material particulado PM₁₀ y meta= les pesados

Las concentraciones de material particulado (PM₁₀) y metales pesados: Pb, Cd, Cu y Zn presentaron una alta variabilidad entre los puntos y días de monitoreo. Esto se puede observar al comparar las medidas de centralidad y dispersión de los resultados (Tabla 2), los cuales presentan una diferencia considerable entre la media y mediana de los datos, indicando una distribución no normal de los contaminantes. Esta condición se confirmó tras obtener probabilidades menores a 0= .01 en el test Shapiro-Wilk, aceptando la hipótesis nula de no normalida= d.

Las concentraciones de PM₁₀presentan un amplio rango con una mayor frecuencia (57%) de puntos de monitoreo sobre los valores de 50 µm/m³ en 24 horas, límite establecido= por la Organización Mundial de la Salud (OMS) para este contaminante; registrándose también valores sobre el límite nacional establecido en el texto

=

= =

=

unificado de legislación ambiental secundar= ia del Ecuador (TULSMA) (100 µg/m³ en 24 horas). Esta condición es puesta en consideración por otros estudios que indican el exceso de las concentraciones del PM₁₀ y PM sedimenta= ble en la ciudad (Astudillo-Alemán e= t al., 2015; Mejia, Zegarra, Astudillo, & Moscoso, 2018). Por otro lado, los metales pesados presentaron una tendencia a concentraciones bajas, pero= con la ocurrencia de valores elevados en algunos puntos de monitores. Dichos metales han sido asociados con el incremento de especies reactivas de oxígeno, y con daño genotóxico de tipo I en cél= ulas epiteliales alveolares en 24 horas de exposición (Astudillo-Alemán et al., 2015). La distribución espacial de las concentraciones de metales, obtenidas en cada punto de monitoreo, se indican en la Figura 2. Los metales presentan u= na circunferencia en aumento para cada metal, considerando una escala de color= de acuerdo a su rango de concentraciones determinado.

Las concentraciones de los metales analizados en el estudio no pudieron ser comparadas con los límites permisibles establecidos para aire ambiente debido a la diferencia en los tiempos de monitoreo. Dichos límites son: 0.15 µg/m³ promedio en 3 meses para Pb; 2 a 15 ng/m³ para Cd; 1 mg/m^{3<=
/sup>
en 8 horas para Cu; y 0.02 a 0.16 μg/m³ para Zn (Agency for Toxic Substances & Disesase Regi=
stry,
2017). Sin embargo, las concentraciones de los metales expresadas en=
ppm
o pg/g pueden ser transformadas a µg/m3} al multiplicarlas por las respectivas concentraciones de PM₁₀ob= tenidas en cada punto y sus debidas transformaciones de masa. De esta manera las concentraciones máximas de Pb (6.13e^-10 µg/m^{3<=
/sup>),
Cd (3.14e^-10 µg/m³), Cu (1.06e^-9 &mi=
cro;g/m³)
y Zn (1.99e^-10 µg/m³) se encontraron por debajo=
de
los límites establecidos, considerando además que estas
corresponden a un monitoreo de 24 horas.}

=

3.2.      = Variables meteorológicas y de entorno durante el periodo de monitoreo

Las variables meteorológicas de humedad relativa, temperatura in situ y ambiente, y radiación solar global y difusa presentaron uniformidad durante los distintos días de monitoreo, sin embargo, la precipitación presentó una alta variabilidad al comparar los valores de media y mediana (Tabla 3), cuya condición de normalidad f= ue rechazada al obtener valores de probabilidad menores a 0.001 en el test Shapiro-Wilk (α=3D0.05).

Los entornos de monitoreo abierto y abierto = con presencia de barreras físicas fueron predominantes en este estudio c= on un 42 y 36%, respectivamente. Mientras que el 16% presentaron barreras vegetales y el restante con un entorno cerrado por completo.

3.3.      = Relación entre las concentraciones de contaminantes con las variables meteorológicas y entorno de monitoreo

Los resultados de las correlaciones en= tre los contaminantes atmosféricos PM₁₀ y metales pesados con= las variables meteorológicas y entorno de monitoreo se pueden observar e= n la Tabla 4. En este estudio, la variable de precipitación present&oacut= e; un mayor número de correlaciones positivas con todos los contaminant= es, a excepción del Zn; siendo la más representativa con el Pb. Si bien esto indica un incremento proporcional entre la variable y las concentraciones de los contaminantes, estudios previos han señalado = que la precipitación actúa como un factor de deposición, debido a que arrastra los contaminantes suspendidos y los deposita en el su= elo (Donahue, 2018). Por otro lado, aunque la temperatura in situ (T*) y la temperatura ambiente presentaron una correlación elevada, estas fueron variables con los contaminantes, indicando coeficientes mayores con los datos in situ como se observa en el = caso del PM₁₀. El aumento de la temperatura se ha relacionado con la capacidad de reacción de las emisiones vehiculares, generació= n de partículas secundarias y un incremento en las concentraciones de PM = en el aire ambiente (Kalisa, Fadlallah, Amani, Nahayo, & Habiyaremye, 2018). La humedad relativa, temperatura ambiente y ambos tipos de radiación solar no presentaron un comportamiento definido con las concentraciones de los contaminantes, así las correlaciones más elevadas corresponden a T* - Cd, P - Pb, HR - Cu y RSG - Pb.

Con relación al entorno de monitoreo, se observó que el PM₁₀ fue el contaminante que presentó una mayor correlación de tipo inverso con esta condición. Esto se puede atribuir a la presencia de barreras físicas o vegetales circundante al área de monitoreo, las cua= les pueden disminuir las concentraciones del PM por deposición o dispersión de las partículas (Janhäll, 2015). De esta manera, todos los puntos con entornos de monitoreo cerrados y con presencia de vegetación en su entorno de monitoreo presentaron valores de PM₁₀ por debajo de los 50 µg/m^=
3, presentándose resultados similares en el 63 y 38% de los puntos con barreras físicas y abiertos, respectivamente. Así tambi&eacut= e;n, se observan correlaciones negativas entre el entorno de monitoreo y las variables de temperatura ambiente y humedad relativa, indicando que el incremento de barreras, especialmente vegetación tiene influencia so= bre dichas variables. Se ha indicado que la presencia de áreas verdes ej= erce una reducción de la temperatura y humedad relativa hasta en 250 m, v= ariando por la tipología de infraestructura verde (Martini et al., 2018).

&n= bsp;

= .

Se analizaron también las relaciones entre los contaminantes y entre las variables meteorológi= cas. De entre los contaminantes analizados, los coeficientes más elevados= se presentaron entre Cd - Cu, Cu - Zn, Cd - Zn y Cu - PM₁₀. Dichas relaciones pueden deberse a que estos metales se encuentran en los material= es de frenos y neumáticos de los vehículos y son emitidos por su= uso (Penkała, Ogrodnik, & Rogula-Kozl= owska, 2018), estimándose una contribución de 47% y 21%, respectivamente, a las concentraciones de Cu y Zn en el PM₁₀ (Monks et al., 2013). Las relacion= es Cd - Cu y Cd - Zn se atribuyen al uso del combustible diésel, el cual presenta un factor de emisión de 0.75 µg/km; mientras que el desgaste de frenos y neumáticos representan las mayores fuentes emis= oras de Cu (383 µg/km) y Zn (79.5 µg/km), respectivamente. Relaciones similares han sido encontradas en otros estudios (Kim et al., 2010). La falta de relación entre los metales en general con el PM₁₀se debe a que las concentraciones= de los metales en el PM dependen de la naturaleza del ambiente analizado, más no de la concentración del PM (Popoola, Adebanjo, & Adeoye, 2018).

Por último, se presentaron correlacio= nes elevadas entre la precipitación con la humedad relativa y un comport= amiento inverso con la temperatura in situ, temperatura ambiente y ambos tipos de radiación solar. Esto se debe a la capacidad de saturación del aire que al alcanzar mayor HR genera precipitación, la cual disminuy= e la temperatura del aire. Las relaciones directas entre la RSG y RSD se debe a = que la segunda es la energía reflejada de la RSG, la cual tiene una influencia positiva en la temperatura y negativa con la humedad relativa.

3.4.      = Factores ambientales de generación, dispersión y deposición de material particulado con contenido de metales pesados<= /span>

La información secundaria sobre vías presentó un total de 13,251 segmentos, los cuales están clasificados en calles (57%), calles de retorno (20%), avenidas (9%) y pasajes (3%), además de un 11% no clasificado. Aproximadamente del 70 al 80% del total de segmentos carecieron de información detal= lada sobre diferentes características de las vías, incluyendo materiales de la calzada, ancho de las mismas y su capacidad vehicular; sie= ndo las avenidas y una minoría de calles localizadas principalmente en el centro de la ciudad las que presentan mayor detalle de dichas características. De esta manera, el 66, 91 y 99% de los segmentos de calles, calles de retorno y pasajes, respectivamente, no presentaron inform= ación sobre los materiales de calzada; el 79, 98 y100% de los mismos segmentos sin información sobre su capacidad vehicular; y de igual manera con el 8= 1 y 100% de calles y segmentos de retorno - pasajes en relación a las dimensiones de calzada. Además, con los datos disponibles, se pudo identificar que las avenidas están mayormente compuestas por asfalto (51%) y hormigón (46%), presentando el 50% de sus segmentos en el ra= ngo de mayor capacidad vehicular (5,701 - 13,664) y mayor amplitud de calzada (26.01 m - 50 m). Mientras que las calles presentan hormigón (18%), asfalto (9%) y adoquín (5%) como materiales de calzada; ademá= s de un 11 % con capacidades vehiculares bajas (1 - 2,725) y 18% con anchos de calzada reducidos (0.01 m - 16.5 m).

3.5.=         Relación entre= concentraciones de contaminantes con factores ambientales

Las correlaciones obtenidas entre las concentraciones de los contaminantes con = la información secundaria de tipos de vía, material de calzada y capacidad vehicular como factores ambientales de generación se prese= ntan en la Tabla 5. Los contaminantes que presentaron un mayor número de correlaciones positivas con las tipologías de vía, materiales= de calzada y capacidades vehiculares (de mayor a menor) son, según orden descendente, el PM₁₀, Pb, Zn, Cd y Cu.

Tras analiz= ar las relaciones de las concentraciones de los contaminantes con las características viales, se identificó que las concentraciones= de PM₁₀ están ligadas a todos los segmentos viales y con una mayor correlación con la capacidad vehicular más alta, indica= ndo que el alto tráfico vehicular es un factor de mayor exposició= n a este contaminante. Si bien el PM₁₀ presentó una mayor correlación con la categoría de vía pasaje, que repres= enta apenas el 3% del total de segmentos, también presentó correlaciones positivas con todos los tipos de segmentos. Además de = su alta relación con los materiales de vía no definidos que se encuentran en el 69% de los segmentos de la información disponible. = Si bien las concentraciones de Cd, Cu y Zn difirieron en sus relaciones con los tipos de segmentos y materiales de calzada, estos también compartier= on una relación positiva con los segmentos de mayor capacidad vehicular. Por otro lado, el Pb presenta una mayor relación con los segmentos de calle ubicados en el centro de la ciudad, dadas las elevadas correlaciones positivas con la categoría calle, adoquín como material de calzada y las capacidades vehiculares intermedias y bajas, propias de estos segmentos. El Pb está relacionado con el uso de frenos y neumáticos para mantener las bajas velocidades promedio de circulación en calles (50 km/h) en comparación con las avenid= as (90 km/h). Además de que las bajas velocidades de circulación generan una mayor cantidad de contaminantes, especialmente en áreas = de alto tráfico vehicular (Tang, McNab= ola, Misstear, Pilla, & Alam, 2019). Finalmente, el PM₁₀ y= Zn fueron los únicos contaminantes que presentaron una correlació= ;n positiva con el número de líneas de transporte público alrededor de los puntos de monitoreo.

Las correlaciones entre los tipos de segmentos con los materiales de calzada y = las capacidades vehiculares remarcan la composición de avenidas por asfa= lto y hormigón, además de indicar su relación con capacida= des vehiculares elevadas (C4). Por otro lado, los segmentos categorizados como calles indicaron una alta relación con las capacidades vehiculares intermedias (C3) y nulas o faltas de información(C1). Además,= si bien se presentó una alta correlación de los segmentos de cal= le con el lastre, este material compone apenas el 2% de los segmentos; mientras que su segunda correlación más elevada que corresponde con los materiales no definidos representa la relación con el 69% de los segmentos totales. De esta manera, las calles pese a ser el 58% del total de segmentos, presentan una alta falta de información sobre material de calzada (66%) y capacidades vehiculares (79%), al igual que con el resto de segmentos.

Los resulta= dos de las correlaciones entre los contaminantes atmosféricos y los fact= ores ambientales de dispersión y deposición se observan en la Tabl= a 6. Con respecto a los factores ambientales de dispersión; los segmentos= sin información, indicados con un ancho de calzada de cero metros (AC1), representaron el 79% de los

= =

=

segmentos alrededor de los puntos de monitoreo seguidos por el 14%, 5% y 2% de los segmentos con anchos de calzada AC2, AC3 y AC4, respectivamente. Mientras q= ue el NDVI como factor de filtración tuvo una mayor presencia promedio = de suelo desnudo o sin vegetación (NDVI 1 - 49%), seguido por su combinación con vegetación (NDVI 2 - 47%), y vegetación abundante (NDVI 3 - 4%) de un total de 80 pixeles de 30 m² cada = uno alrededor de los puntos de monitoreo.

El ancho de la calzada no pudo ser identific= ado como factor de dispersión de los contaminantes atmosféricos debido a sus elevadas correlaciones positivas con las categorías más elevadas (AC4) y negativas de menor magnitud con su inmediata inferior (AC3). En el caso del PM₁₀, el ancho de la calzada indicaría que el segmento presenta mayor espacio de alberge para vehículos, mas no una distancia de la vía al punto de monitor= eo donde las concentraciones pueden dispersarse. Mientras que, para el Pb, se presentaron correlaciones negativas con las dos categorías superiores del ancho de calzada, sugiriendo una mayor dispersión con la categoría AC3. Sin embargo, debemos recordar que este contaminante f= ue ligado a las calles del centro de la ciudad, las cuales presentan anchos de= la vía menores a 16 m.

El NDVI ha sido utilizado como un índ= ice de vegetación de fácil obtención a partir de imágenes satelitales. Si bien la presencia de vegetación abundante (NDVI 3) está ligada con mejoras de la calidad del aire (Yitshak-Sade, Kloog, & Novack 2017),= en el presente estudio las correlaciones negativas de las concentraciones de los contaminantes con este rango de NDVI fueron variadas. El PM₁₀ presentó una relación positiva con el NDVI, indicando un incremento de las concentraciones con la presencia de vegetación; ca= so contrario a lo indicado para el Pb, Cu y Zn.

La principal limitación de este estudio fue el corto periodo de monitoreo en cada punto, la cual sumada las diferencias en las condiciones meteorológicas y del entorno durante = el mismo y entre puntos de monitoreo resultan en alta variabilidad. Adem&aacut= e;s se presentaron limitaciones relacionadas al análisis con informaci&oacu= te;n secundaria por la falta de detalle de características viales en los segmentos, los cuales han sido ampliamente recomendados a actualizaci&oacut= e;n por su representatividad en estimaciones de emisiones vehiculares (EMOV EP, 2014).

4.      = CONCLUSIONES

El presente estudio analiza el uso de información secundaria de meteorología, características viales y del entorno en la ciudad de Cuenca, asociadas a factores ambiental= es de generación, dispersión y deposición del material particulado respirable (PM₁₀) y su contenido de metales pesados (Pb, Cd, Cu y Zn). Esclarece la situació= ;n actual sobre la contaminación del aire en la ciudad de Cuenca con especial énfasis en la exposición de los niños, como población de mayor vulnerabilidad por sus sistemas biológicos= en desarrollo. De esta manera, el estudio determinó una alta variabilid= ad en la exposición a dichos contaminantes presentes en el aire ambient= e de centros de educación inicial en la ciudad de Cuenca con concentracio= nes de PM₁₀por enc= ima de los límites permisibles establecidos por la OMS (50 µg/m3)y legislación ambiental ecuatoriana (TULSMA) (100 µg/m3). Por otro lado, las concentraciones de metales pesados obtenidas no fueron comparables con los límites establecidos por la Agencia para Substan= cias Tóxicas y Registro de Enfermedades debido a la necesidad de periodos= de monitoreo más específicos, distintos al monitoreo de 24 horas continúas utilizado para el PM₁₀.

Las concentraciones de los contaminantes atmosféricos presentar= on relaciones poco significativas con las variables meteorológicas dura= nte el estudio, siendo el entorno de monitoreo la variable de mayor influencia = en las concentraciones del PM₁₀. Se identificaron leves patrones de exposición con base a la información vial secundaria. El PM10 presentó una relación positiva con todos los tipos de segment= os y materiales de calzada, pero una mayor relación con los segmentos de mayor capacidad vehicular. El Pb presentó mayores relaciones con las categorías de tipos de segmentos: calle, material de calzada: adoquín y capacidades vehiculares: medio-altas (2,659 - 5,415); las cuales corresponden a las calles ubicadas en el centro de la ciudad. La exposición a los metales Cd, Cu y Zn presentó variaciones de acuerdo a los tipos de vía y materiales de calzada debido a que estos están relacionados mayormente con el estilo de conducción (us= o de frenos y neumáticos) indicando una relación común con = las capacidades vehiculares elevadas. Finalmente, no se determinó una relación significativa entre el número de líneas de transporte público con la exposición a los contaminantes atmosféricos en este

estudio. Es necesario establecer monitoreos ambientales con técnicas estandarizadas, registros contin= uos por periodos más prolongados y considerando la estacionalidad para establecer relaciones más confiables entre las variables analizadas = y la exposición a contaminantes ambientales en la ciudad de Cuenca.<= /o:p>

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MASKANA, Vol. 11, No. 1, 57-68, 2020

https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/= 3036

doi: = 10.18537/= mskn.11.01.06

© Author(s) 2= 020. CC Attribution 4.0 License.

  Pu= blicado por DIUC - Dirección de Investigación de la Universidad de Cuenca       = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp; 59

MASKANA, Vol. 8, No. = 2, 2017

MASKANA, Vol. 11, No. 1, 57-68, 2020

https://publicaciones.ucuenca.edu.ec/ojs/index.php/maskana/article/view/= 3036

doi: = 10.18537/= mskn.11.01.06

© Author(s) 2= 020. CC Attribution 4.0 License.

  Pu= blicado por DIUC - Dirección de Investigación de la Universidad de Cuenca       = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp; 59

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, = No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        62

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, = No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        62

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, = No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        62

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, = No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        62

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, = No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;               = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;               = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;               = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;               = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;               = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;       &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        63

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

10.18537/mskn.11.01.06        =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             <= /span>64

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

10.18537/mskn.11.01.06        =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             <= /span>64

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

10.18537/mskn.11.01.06        =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             <= /span>64

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

10.18537/mskn.11.01.06        =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             <= /span>64

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

10.18537/mskn.11.01.06        =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             <= /span>64

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

10.18537/mskn.11.01.06        =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             <= /span>64

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

10.18537/mskn.11.01.06        =             &nb= sp;            =             =             &nb= sp;            =             &nb= sp;            =             &nb= sp;               =             &nb= sp;            =             &nb= sp;            =             &nb= sp;               =             &nb= sp;            =             &nb= sp;            =             &nb= sp;               =             &nb= sp;            =             &nb= sp;            =             &nb= sp;               =             &nb= sp;            =             &nb= sp;            =             &nb= sp;               =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            =             &nb= sp;            = 65

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        68

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        68

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        68

MASKANA, Vol. 8, No. = 2, 2017

R. Zegarra et al: Análisis espacial de PM₁₀ en el air= e y su composición de metales

MASKANA, Vol. 11, No. 1, 57-68, 2020

doi: 10.18537/= mskn.11.01.06        = ;            &n= bsp;            = ;    &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;            &n= bsp;            = ;        68

MASKANA, Vol. 8, No. = 2, 2017

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